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Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models. J Clim 2013;26:7117–7135.\nBarpanda P, Shaw T. Using the moist static energy budget to understand storm-track shifts across a range of time scales. J Atmos Sci 2017;74:2427–2446.\nBender FA-M, Ramanathan V, Tselioudis G. Changes in extratropical storm track cloudiness 1983-2008: observational support for a poleward shift. Clim Dyn 2012;28:2037–2053.\nButler AH, Thompson DWJ, Heikes R. The steady-state atmospheric circulation response to climate change-like thermal forcings in a simple general circulation Model. J Clim 2010;23:3474–3496.\nButler AH, Thompson DWJ, Birner T. Isentropic slopes, downgradient eddy fluxes, and the extratropical atmospheric circulation response to tropical tropospheric heating. J Atmos Sci 2011;68:2292–2305.\nCeppi P, Hartmann DL. Connections between clouds, radiation, and midlatitude dynamics: a review. Curr Clim Chang Rep 2015;1:94–102.\nCeppi P, Hartmann DL. Clouds and the atmospheric circulation response to warming. J Clim 2016;29: 783–799.\nChang EKM, Guo Y, Xia X. 2012. CMIP5 multi-model ensemble projection of storm track change under global warming. J Geophys Res. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2012JD018578.\nChemke R, Polvani LM. Exploiting the abrupt 4xCO2 scenario to elucidate tropical expansion mechanisms. J Clim 2019;32:859–875.\nChen G, Lu J, Frierson DMW. Phase speed spectra and the latitude of surface westerlies: interannual variability and global warming trend. J Clim 2008;21:5942–5959.\nChen G, Lu J, Sun L. Delineating the eddy-zonal flow interaction in the atmospheric circulation response to climate forcing: uniform SST warming in an idealized aquaplanet model. J Atmos Sci 2013;70:2214–2233.\nCoumou D, Lehmann J, Beckmann J. The weakening summer circulation in the Northern Hemisphere mid-latitudes. Science 2015;348:324–327.\nCronin TW, Jansen MF. 2016. Analytic radiative-advective equilibrium as a model for high-latitude climate. Geophys. Res. Lett. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015GL067172.\nFrierson DMW. Midlatitude static stability in simple and comprehensive general circulation models. J Atmos Sci 2008;65:1049–1062.\nFu Q, Johanson CM, Wallace JM, Reichler T. Enhanced mid-latitude tropospheric warming in satellite measurements. Science 2006;312:1179.\nGertler CG, O’Gorman PA. 2019. Changing available energy for extratropical cyclones and associated convection in Northern Hemisphere summer. Proc. Nat. Acad. Sciences. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1812312116.\nGrise K, Polvani LM. Understanding the time scales of the tropospheric circulation response to abrupt CO2 forcing in the Southern Hemisphere: Seasonality and the role of the stratosphere. J Clim 2017;30:8497–8515.\nHall NJ, Hoskins BJ, Valdes PJ, Senior CA. Storm tracks in a high-resolution GCM with doubled carbon dioxide. Quart J Roy Met Soc 1994;120:1209–1230.\nHall A, Cox P, Huntingford C, Klein S. Progressing emergent constraints on future climate change. Nat Clim Chang 2019;9:269–278.\nHeld IM. Large-scale dynamics and global warming. Bull Amer Met Soc 1993;74:228–241.\nHeld IM. 2005. The gap between simulation and understanding in climate modeling. Bull. Amer. Met. Soc. https:\u002F\u002Fdoi.org\u002F10.1175\u002FBAMS-86-11-1609.\nHeld IM, Soden BJ. Robust responses of the hydrological cycle to global warming. J Clim 2006;19:5686–5699.\nHeld IM. 2015. Poleward atmospheric energy transport. https:\u002F\u002Fwww.gfdl.noaa.gov\u002Fblog\u002Fheld\u002F62-poleward-atmospheric-energy-transport.\nKaroly DJ, Hoskins BJ. Three-dimensional propagation of planetary waves. J Met Soc Jpn 1982;60:109–123.\nKidston J, Dean SM, Renwick JA, Vallis GK. 2010. A robust increase in the eddy length scale in the simulation of future climates Geophys. Res. Lett. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2009GL041615.\nKidston J, Vallis GK, Dean SM, Renwick JA. 2011. Can the increase in the eddy length scale under global warming cause the poleward shift of the jet streams. J Clim. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2010JCLI3738.1.\nKidston J, Vallis GK. 2012. The relationship between the speed and the latitude of an eddy-driven jet in a stirred barotropic model. J Atmos Sci. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-11-0300.1.\nKuo H-L. Forced and free meridional circulations in the atmosphere. J Meteorol 1956;13:561–568.\nKushner PJ, Held IM. A test, using atmospheric data of a method for estimating oceanic eddy diffusivity. Geophys Res Lett 1998;25:4213–4216.\nLee S, Feldstein SB. 2013. Detecting ozone- and greenhouse gas- driven wind trends with observational data. Science. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1225154.\nLi Y, Thompson DWJ, Bony S, Merlis TM. Thermodynamic control on the poleward shift of the extratropical jet in climate change simulations: the role of rising high clouds and their radiative effects. J Clim 2018; 32:917–934.\nLorenz DJ, DeWeaver ET. 2007. Tropopause height and zonal wind response to global warming in the IPCC scenario integrations. J Geophys Res. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2006JD008087.\nLorenz DJ. Understanding midlatitude jet variability and change using rossby wave chromatography: poleward-shifted jets in response to external forcing. J Atmos Sci 2014;71:2370–2389.\nLu J, Vecchi GA, Reichler T. 2007. Expansion of the Hadley cell under global warming. Geophys. Res Lett. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2006GL028443.\nLu J, Chen G, Frierson DMW. Response of the zonal mean atmospheric circulation to El Nino versus global warming. J Clim 2008;21:5835–5851.\nLu J, Sun L, Wu Y, Chen G. The role of subtropical irreversible PV mixing in the zonal mean circulation response to global warming-like thermal forcing. J Clim 2014;27:2297–2316.\nManabe S, Wetherald RT. The effects of doubling CO2 concentration in a general circulation model. J Atmos Sci 1975;32:3–15.\nMatsuno T. Vertical propagation of stationary planetary waves in winter Northern Hemisphere. J Atmos Sci 1970;27:871–883.\nMbengue C, Schneider T. Storm track shifts under climate change: what can be learned from large-scale dry dynamics. J Clim 2013;26:9923–9930.\nMbengue C, Schneider T. Storm-track shifts under climate change: toward a mechanistic understanding using baroclinic mean available potential energy. J Atmos Sci 2017;74:93–110.\nMbengue C, Schneider T. Linking Hadley circulation and storm tracks in a conceptual model of the atmospheric energy balance. J Atmos Sci 2018;75:841–856.\nMenzel ME, Waugh D, Grise K. 2019. Disconnect between Hadley cell and subtropical jet variability and response to increased CO2. Geophys. Res Lett. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2019GL083345.\nMuller CJ, Romps DM. Acceleration of tropical cyclogenesis by self-aggregation feedbacks. Proc Nat Acad Sci 2018;115:2930–2935.\nNakamura N, Zhu D. Finite-amplitude wave activity and diffusive flux of potential vorticity in eddy-mean flow interaction. J Atmos Sci 2010;67:2701–2716.\nNakamura N, Solomon A. Finite-amplitude wave activity and mean flow adjustments in the atmospheric general circulation. Part I: quasigeostrophic theory and analysis. J Atmos Sci 2010;67:3967–3983.\nO’Gorman PA, Schneider T. Energy of midlatitude transient eddies in idealized simulations of changed climates. J Clim 2008;21:5797–5806.\nO’Gorman PA. Understanding the varied response of the extratropical storm tracks to climate change. Proc Nat Acad Sci 2010;107:19176–19180.\nPfeffer RL. Wave-mean flow interactions in the atmosphere. J Atmos Sci 1981;38:1340–1359.\nRiviere G. A dynamical interpretation of the poleward shift of the jet streams in global warming scenarios. J Atmos Sci 2011;68:1253–1272.\nSchneider T. 2006. The general circulation of the atmosphere. Annu. Rev. Earth Planet. Sci. https:\u002F\u002Fdoi.org\u002F10.1146\u002Fannurev.earth.34.031405.125144.\nShaw T, Baldwin M, Barnes EA, Caballero R, Garfinkel CI, Hwang Y-T, Li C, O’Gorman PA, Riviere G, Simpson I, Voigt A. 2016. Storm track processes and the opposing influences of climate change. Nature Geoscience. https:\u002F\u002Fdoi.org\u002F10.1038\u002FNGEO2783.\nShaw T, Voigt A. 2016. What can moist thermodynamics tell us about circulation shifts in response to uniform warming? Geophys. Res Lett. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016GL068712.\nShaw T, Barpanda P, Donohoe A. A moist static energy framework for zonal-mean storm-track intensity. J Atmos Sci 2018;75:1979–1994.\nShaw T, Tan Z. 2018. Testing latitudinally dependent explanations of the circulation response to increased CO2 using aquaplanet models. Geophys. Res Lett. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2018GL078974.\nSigmond M, Siegmund PC, Manzini E, Kelder H. A simulation of the separate climate effects of middle-atmospheric and tropospheric CO2 doubling. J Clim 2004;17:2352–2367.\nSimpson I, Shaw T, Seager R. A diagnosis of the seasonally and longitudinally varying midlatitude circulation response to global warming. J Atmos Sci 2014;71:2489–2515.\nStaten PW, Lu J, Grise K, Davis SM, Birner T. Re-examining tropical expansion. Nat Clim Chang 2018;8:768–775.\nStevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S, et al. 2013. Atmospheric component of the MPI-M earth system model: ECHAM6. J. Adv. Mod. Earth Sys. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjame.20015.\nSun L, Chen G, Lu J. Sensitivities and mechanisms of the zonal mean atmospheric circulation response to tropical warming. J Atmos Sci 2013;70:2487–2504.\nTan Z, Lachmy O, Shaw T. The sensitivity of the jet stream response to climate change to radiative assumptions. J Adv Model Earth Sys 2019;11:1–23.\nTandon N, Gerber EP, Sobel AH, Polvani LM. Understanding hadley cell expansion versus contraction: Insights from simplified models and implications for recent observations. J Clim 2013;26:4304–4321.\nTrenberth KE, Stepaniak DP. Covariability of components of poleward atmospheric energy transports on seasonal and interannual timescales. J Clim 2003;16:3691–3705.\nVallis GK. Atmospheric and oceanic fluid dynamics. Cambridge: Cambridge University Press; 2006.\nVallis GK, Zurita-Gotor P, Cairns C, Kidston J. Response of the large-scale structure of the atmosphere to global warming. Quart J Roy Met Soc 2015;141:1479–1501.\nVoigt A, Shaw T. 2015. Circulation response to warming shaped by radiative changes of clouds and water vapour. Nature Geoscience. https:\u002F\u002Fdoi.org\u002F10.1038\u002FNGEO2345.\nVoigt A, Shaw T. Impact of regional atmospheric cloud radiative changes on shifts of the extratropical jet stream in response to global warming. J Clim 2016;29:8399–8421.\nWu Y, Seager R, Ting M, Naik N, Shaw T. Circulation response to an instantaneous doubling of carbon dioxide. Part I: model experiments and transient thermal response in the troposphere. J Clim 2012;25: 2862–2879.\nWu Y, Seager R, Shaw T, Ting M, Naik N. Atmospheric circulation response to an instantaneous doubling of carbon dioxide. part II: atmospheric transient adjustment and its dynamics. J Clim 2013;26:918–935.\nYin JH. 2005. A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res Lett. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2005GL023684.",{"EN":156},"State-of-the-art climate models predict the zonal mean mid-latitude circulation will undergo a poleward shift and seasonally and hemispherically dependent intensity changes in the future. Here I review the mechanisms put forward to explain the zonal mean mid-latitude circulation response to increased carbon dioxide (CO2) concentration. The mechanisms are grouped according to their thermodynamic starting point, which are thought to arise from processes independent of the zonal mean mid-latitude circulation response. There are 24 mechanisms and 8 thermodynamic starting points: (i) increased latent heat release aloft in the tropics, (ii) increased dry static stability and tropopause height outside the tropics, (iii) radiative cooling of the stratosphere, (iv) Hadley cell expansion, (v) increased specific humidity following the Clausius-Clapeyron relation, (vi) cloud radiative effect changes, (vii) turbulent surface heat flux changes, and (viii) decreased surface meridional temperature gradient. I argue progress can be made by testing the thermodynamic starting points. I review recent tests of the increased latent heat release aloft in the tropics starting point, i.e., prescribing diabatic perturbations, quantifying the transient response to an abrupt CO2 increase and imposing latitudinally dependent CO2 concentration. Finally, I provide a future outlook for improving our understanding of predicted changes in the zonal mean mid-latitude circulation.",{"EN":158},"Mechanisms of Future Predicted Changes in the Zonal Mean Mid-Latitude Circulation",{"VOID":160},"10.1007\u002Fs40641-019-00145-8","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40641-019-00145-8",[166],{"id":167,"sortIndex":19,"researcher":18,"roles":168,"affiliations":170,"properties":179},"792cfbb8-385c-4da0-9506-a015df676e67",[169],"AUTHOR",[171],{"id":18,"sortIndex":19,"affiliation":172,"properties":18},{"id":173,"createTime":174,"updateTime":174,"relativeEntities":175,"slug":18,"properties":176,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"5e0df3fa-1d04-4137-a1f3-e792d7b665fb","2024-02-21T01:30:53.462+00:00",[],{"title":177},{"VI":178},"Department of the Geophysical Sciences, The University of Chicago, Chicago, USA",{"title":180},{"VI":181},"Tiffany A. 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The use of cloud-resolving simulations of mesoscale convective systems to build a mesoscale parameterization scheme. J Atmos Sci 1998;55(12):2137–61.\nAndersen JA, Kuang Z. Moist static energy budget of mjo-like disturbances in the atmosphere of a zonally symmetric aquaplanet. J Climate 2012;25(8):2782–804.\nArakawa A. The cumulus parameterization problem: past, present, and future. J Clim 2004;17(13):2493–525. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2004)017\u003C2493:RATCPP>2.0.CO;2.\nArakawa A, Wu CM. A unified representation of deep moist convection in numerical modeling of the atmosphere. Part i. J Atmos Sci 2013;70(7):1977–92. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-12-0330.1.\nArakawa RA, Schubert WH. Interaction of a cumulus cloud ensemble with the large scale environment. Part I. J Atmos Sci 1974;31:674–701.\nBechtold P, Semane N, Lopez P, Chaboureau JP, Beljaars A, Bormann N. Representing equilibrium and nonequilibrium convection in large-scale models. J Atmos Sci 2014;71(2):734–53.\nBecker T, Bretherton CS, Hohenegger C, Stevens B. Estimating bulk entrainment with unaggregated and aggregated convection. Geophys Res Lett 2018;45(1):455–62.\nBenedict JJ, Randall DA. Observed characteristics of the mjo relative to maximum rainfall. J Atmos Sci 2007;64(7):2332– 54.\nBengtsson L, Steinheimer M, Bechtold P, Geleyn JF. A stochastic parametrization for deep convection using cellular automata. Q J Roy Meteorol Soc 2013;139(675):1533–43.\nBernstein DN, Neelin JD. Identifying sensitive ranges in global warming precipitation change dependence on convective parameters. Geophys Res Lett 2016;43:5841–50. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016GL069022.\nBhattacharya R, Bordoni S, Suselj K, Teixeira J. Parameterization interactions in global aquaplanet simulations. J Adv Modeling Earth Syst 2018;10:403–20. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017MS000991.\nBirch CE, Parker DJ, Marsham JH, Copsey D, Garcia-Carreras L. A seamless assessment of the role of convection in the water cycle of the west african monsoon. J Geophys Res: Atmos 2014;119(6):2890–912. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2013JD020887.\nBirch CE, Roberts MJ, Garcia-Carreras L, Ackerley D, Reeder MJ, Lock AP, Schiemann R. Sea-breeze dynamics and convection initiation: the influence of convective parameterization in weather and climate model biases. J Climate 2015;28(20):8093–108.\nBogenschutz PA, Krueger SK, Khairoutdinov M. Assumed probability density functions for shallow and deep convection. J Adv Model Earth Syst 2010;2:4.\nBöing SJ, Jonker HJ, Siebesma AP, Grabowski WW. Influence of the subcloud layer on the development of a deep convective ensemble. J Atmos Sci 2012;69(9):2682–98.\nBöing SJ, Jonker HJ, Nawara WA, Siebesma AP. On the deceiving aspects of mixing diagrams of deep cumulus convection. J Atmos Sci 2014;71(1):56–68.\nBony S, Dufresne JL. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys Res Lett 2005;32:20.\nBouniol D, Roca R, Fiolleau T, Poan DE. Macrophysical, microphysical, and radiative properties of tropical mesoscale convective systems over their life cycle. J Climate 2016;29(9):3353–71.\nCaldwell PM, Zelinka MD, Klein SA. Evaluating emergent constraints on equilibrium climate sensitivity. J Climate 2018;31 (10):3921–42.\nCesana G, Del Genio AD, Ackerman AS, Kelley M, Elsaesser G, Fridlind AM, Cheng Y, Yao MS. 2018. Evaluating models’ response of tropical low clouds to sst forcings using calipso observations. Atmos Chem Phys, submitted.\nChen B, Mapes BE. Effects of a simple convective organization scheme in a two-plume gcm. J Adv Model Earth Syst 2018;10(3):867–80.\nCheruy F, Campoy A, Dupont JC, Ducharne A, Hourdin F, Haeffelin M, Chiriaco M, Idelkadi A. Combined influence of atmospheric physics and soil hydrology on the simulated meteorology at the SIRTA atmospheric observatory. Clim Dyn 2013;40:2251–69. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-012-1469-y.\nColin M, Sherwood S, Geoffroy O, Bony S, Fuchs D. Identifying the sources of convective memory in cloud-resolving simulations. J Atmos Sci 2018;0(0):null. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-18-0036.1.\nCollis S, Protat A, May PT, Williams C. Statistics of storm updraft velocities from twp-ice including verification with profiling measurements. J Appl Meteorol Climatol 2013;52(8):1909–22.\nCouvreux F, Roehrig R, Rio C, Lefebvre MP, Caian M, Komori T, Derbyshire S, Guichard F, Favot F, D’Andrea F, et al. Representation of daytime moist convection over the semi-arid tropics by parametrizations used in climate and meteorological models. Q J Roy Meteorol Soc 2015;141(691): 2220–36.\nDai A. Precipitation characteristics in eighteen coupled climate models. J Climate 2006;19(18):4605–30.\nD’Andrea F, Gentine P, Betts AK, Lintner BR. Triggering deep convection with a probabilistic plume model. J Atmos Sci 2014;71:3881–901. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-13-0340.1.\nDavies L, Plant RS, Derbyshire SH. A simple model of convection with memory. J Geophys Res: Atmos 2009;114:D17.\nDavies L, Plant R, Derbyshire S. Departures from convective equilibrium with a rapidly varying surface forcing. Q J Roy Meteorol Soc 2013;139(676):1731–46.\nDel Genio A, Yao M. 1993. Efficient cumulus parameterization for long-term climate studies: the GISS scheme, pp 181–184.\nDel Genio AD, Wu J. The role of entrainment in the diurnal cycle of continental convection. J Climate 2010; 23(10):2722–38.\nDel Genio AD, Yao MS, Kovari W, Lo KK. A prognostic cloud water parameterization for global climate models. J Climate 1996;9(2):270–304.\nDel Genio AD, Kovari W, Yao MS, Jonas J. Cumulus microphysics and climate sensitivity. J Climate 2005;18(13):2376– 87.\nDel Genio AD, Chen Y, Kim D, Yao MS. The mjo transition from shallow to deep convection in cloudsat\u002Fcalipso data and giss gcm simulations. J Climate 2012;25(11):3755–70.\nDel Genio AD, Wu J, Wolf AB, Chen Y, Yao MS, Kim D. Constraints on cumulus parameterization from simulations of observed mjo events. J Climate 2015;28(16):6419–42.\nDeng Q, Khouider B, Majda AJ. The mjo in a coarse-resolution gcm with a stochastic multicloud parameterization. J Atmos Sci 2015;72(1):55–74.\nDiallo FB, Hourdin F, Rio C, Traore AK, Mellul L, Guichard F, Kergoat L. The surface energy budget computed at the grid-scale of a climate model challenged by station data in West Africa. J Adv Modeling Earth Syst 2017;9(7):2710–38. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017MS001081. https:\u002F\u002Fagupubs.onlinelibrary.wiley.com\u002Fdoi\u002Fabs\u002F10.1002\u002F2017MS001081.\nDixit V, Geoffroy O, Sherwood SC. Control of ITCZ width by low-level radiative heating from upper-level clouds in aquaplanet simulations. Geophys Res Lett 2018;45:5788–97. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2018GL078292.\nDonner LJ. A cumulus parameterization including mass fluxes, vertical momentum dynamics, and mesoscale effects. J Atmos Sci 1993;50(6):889–906. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0469(1993)050\u003C0889:ACPIMF>2.0.CO;2.\nDonner LJ, Phillips VT. Boundary layer control on convective available potential energy: implications for cumulus parameterization. J Geophys Res: Atmos 2003;108:D22. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2003JD003773.\nDonner LJ, Horowitz LW, Fiore AM, Seman CJ, Blake DR, Blake NJ. Transport of radon-222 and methyl iodide by deep convection in the gfdl global atmospheric model am2. J Geophys Res: Atmos 2007;112: D17. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2006JD007548.\nDonner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M, Golaz JC, Ginoux P, Lin SJ, Schwarzkopf MD, et al. The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component am3 of the gfdl global coupled model cm3. J Climate 2011; 24(13):3484–519.\nDonner LJ, O’Brien TA, Rieger D, Vogel B, Cooke WF. Are atmospheric updrafts a key to unlocking climate forcing and sensitivity? Atmos Chem Phys 2016;16(20):12,983–92.\nDorrestijn J, Crommelin DT, Siebesma AP, Jonker HJ. Stochastic parameterization of shallow cumulus convection estimated from high-resolution model data. Theor Comput Fluid Dyn 2013;27(1–2):133–48.\nDorrestijn J, Crommelin DT, Siebesma AP, Jonker HJ, Selten F. Stochastic convection parameterization with markov chains in an intermediate-complexity gcm. J Atmos Sci 2016;73(3):1367–82.\nDoswell I, Charles A, Brooks HE, Maddox RA. Flash flood forecasting: an ingredients-based methodology. Weather Forecast 1996;11(4):560–81.\nElsaesser GS, Del Genio AD, Jiang JH, van Lier-Walqui M. An improved convective ice parameterization for the nasa giss global climate model and impacts on cloud ice simulation. J Climate 2017;30(1): 317–36.\nEmanuel KA. A scheme for representing cumulus convection in large-scale models. J Atmos Sci 1991;48: 2313–35.\nEmanuel KA, Zivkovic-Rothman M. Development and evaluation of a convection scheme for use in climate models. J Atmos Sci 1999;56:1766–82. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0469(1999)056\u003C1766:DAEOAC>2.0.CO;2.\nFan J, Rosenfeld D, Zhang Y, Giangrande SE, Li Z, Machado LA, Martin ST, Yang Y, Wang J, Artaxo P, et al. Substantial convection and precipitation enhancements by ultrafine aerosol particles. Science 2018;359(6374):411–8.\nFeingold G. Modeling of the first indirect effect: analysis of measurement requirements. Geophys Res Lett 2003; 30:19. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2003GL017967.\nFeng Z, Hagos S, Rowe AK, Burleyson CD, Martini MN, de Szoeke SP. Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the amie\u002Fdynamo field campaign. J Adv Model Earth Syst 2015;7(2):357–81.\nFiolleau T, Roca R. An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite. IEEE Trans Geosci Remote Sens 2013;51(7):4302–15.\nFolkins I, Bernath P, Boone C, Donner LJ, Eldering A, Lesins G, Martin RV, Sinnhuber BM, Walker K. Testing convective parameterizations with tropical measurements of hno3, co, h2o, and o3: implications for the water vapor budget. J Geophys Res: Atmos 2006;111:D23. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2006JD007325.\nFu R, Del Genio AD, Rossow WB. Behavior of deep convective clouds in the tropical pacific deduced from isccp radiances. J Clim 1990;3(10):1129–52.\nGentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G. Could machine learning break the convection parameterization deadlock? Geophys Res Lett 2018;45(11):5742–51. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2018GL078202.\nGeoffroy O, Sherwood SC, Fuchs D. On the role of the stratiform cloud scheme in the inter-model spread of cloud feedback. J Adv Model Earth Syst 2017;9:423–37. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016MS000846.\nGiangrande SE, Collis S, Straka J, Protat A, Williams C, Krueger S. A summary of convective-core vertical velocity properties using arm uhf wind profilers in oklahoma. J Appl Meteorol Climatol 2013; 52(10):2278–95.\nGiangrande SE, Toto T, Jensen MP, Bartholomew MJ, Feng Z, Protat A, Williams CR, Schumacher C, Machado L. Convective cloud vertical velocity and mass-flux characteristics from radar wind profiler observations during goamazon2014\u002F5. J Geophys Res: Atmos 2016;121:21.\nGlenn IB, Krueger SK. Downdrafts in the near cloud environment of deep convective updrafts. J Adv Model Earth Syst 2014;6(1):1–8.\nGoswami B, Khouider B, Phani R, Mukhopadhyay P, Majda A. Implementation and calibration of a stochastic multicloud convective parameterization in the ncep c limate f orecast s ystem (cfsv2). J Adv Model Earth Syst 2017;9(3):1721–39.\nGoswami B, Khouider B, Phani R, Mukhopadhyay P, Majda A. Improving synoptic and intraseasonal variability in cfsv2 via stochastic representation of organized convection. Geophys Res Lett 2017;44(2): 1104–13.\nGrabowski WW. Towards global large eddy simulation: super-parameterization revisited. J Met Soc Japan 2016; 94(4):327–44. https:\u002F\u002Fdoi.org\u002F10.2151\u002Fjmsj.2016-017.\nGrandpeix J, Lafore J. A density current parameterization coupled with Emanuel’s convection scheme. Part I: the models. J Atmos Sci 2010;67:881–97. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2009JAS3044.1.\nGrandpeix J, Lafore J, Cheruy F. A density current parameterization coupled with Emanuel’s convection scheme. Part II: 1D simulations. J Atmos Sci 2010;67:898–922. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2009JAS3045.1.\nGrandpeix JY, Phillips V, Tailleux R. Improved mixing representation in Emanuel’s convection scheme. Q J R Meteorol Soc 2004;130:3207–22.\nGrant LD, Lane TP, van den Heever SC. 2018. The role of cold pools in tropical oceanic convective systems. Journal of the Atmospheric Sciences.\nGregory D, Rowntree PR. A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon Weather Rev 1990;118(7):1483–506. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0493(1990)118\u003C1483:AMFCSW>2.0.CO;2.\nGrell GA. Prognostic evaluation of assumptions used by cumulus parameterizations. Mon Weather Rev 1993;121 (3):764–87. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0493(1993)121\u003C0764:PEOAUB>2.0.CO;2.\nGuo H, Golaz JC, Donner LJ, Ginoux P, Hemler RS. Multivariate probability density functions with dynamics in the GFDL atmospheric general circulation model: global tests. J Climate 2014;27:2087–108. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJCLI-D-13-00347.1.\nGuo H, Golaz JC, Donner LJ, Wyman B, Zhao M, Ginoux P. CLUBB as a unified cloud parameterization: opportunities and challenges. Geophys Res Lett 2015;42:4540–47 . https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015GL063672.\nGuérémy JF. A continuous buoyancy based convection scheme: one- and three-dimensional validation. Tellus A 2011;63(4):687–706. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1600-0870.2011.00521.x.\nHagos S, Feng Z, Plant RS, Houze JRA, Xiao H. A stochastic framework for modeling the population dynamics of convective clouds. J Adv Model Earth Syst 2018;10(2):448–65.\nHannah WM. Entrainment versus dilution in tropical deep convection. J Atmos Sci 2017;74(11):3725–47.\nHarrop BE, Ma PL, Rasch PJ, Neale RB, Hannay C. The role of convective gustiness in reducing seasonal precipitation biases in the tropical west pacific. J Adv Modeling Earth Syst 2018;10:961–70. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017MS001157.\nHartmann DL, Larson K. An important constraint on tropical cloud-climate feedback. Geophys Res Lett 2002;29(20):12–1.\nHartmann DL, Gasparini B, Berry SE, Blossey PN. The life cycle and net radiative effect of tropical anvil clouds. J Adv Model Earth Syst 2018;10(12):3012–29.\nHeus T, Jonker HJ. Subsiding shells around shallow cumulus clouds. J Atmos Sci 2008;65(3):1003–18.\nHeymsfield AJ, Schmitt C, Bansemer A. Ice cloud particle size distributions and pressure-dependent terminal velocities from in situ observations at temperatures from 0 to- 86 c. J Atmos Sci 2013;70(12):4123–54.\nHirota N, Takayabu YN, Watanabe M, Kimoto M, Chikira M. Role of convective entrainment in spatial distributions of and temporal variations in precipitation over tropical oceans. J Climate 2014;27(23):8707–23.\nHohenegger C, Bretherton CS. Simulating deep convection with a shallow convection scheme. Atmos Chem Phys 2011;11(20):10,389–406.\nHolloway C, Woolnough S, Lister G. Precipitation distributions for explicit versus parametrized convection in a large-domain high-resolution tropical case study. Q J Roy Meteorol Soc 2012;138(668):1692–708.\nHolloway CE, Neelin JD. Moisture vertical structure, column water vapor, and tropical deep convection. J Atmos Sci 2009;66(6):1665–83.\nHolloway CE, Woolnough SJ, Lister GM. The effects of explicit versus parameterized convection on the mjo in a large-domain high-resolution tropical case study. Part i: Characterization of large-scale organization and propagation. J Atmos Sci 2013;70(5):1342–69.\nHolloway CE, Woolnough SJ, Lister GM. The effects of explicit versus parameterized convection on the mjo in a large-domain high-resolution tropical case study. Part ii: processes leading to differences in mjo development. J Atmos Sci 2015;72(7):2719–43.\nHourdin F, Couvreux F, Menut L. Parameterisation of the dry convective boundary layer based on a mass flux representation of thermals. J Atmos Sci 2002;59:1105–23.\nHourdin F, Grandpeix JY, Rio C, Bony S, Jam A, Cheruy F, Rochetin N, Fairhead L, Idelkadi A, Musat I, Dufresne JL, Lahellec A, Lefebvre MP, Roehrig R. LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection. Clim Dyn 2013;40:2193–222. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-012-1343-y.\nHourdin F, Gueye M, Diallo B, Dufresne JL, Escribano J, Menut L, Marticoréna B, Siour G, Guichard F. Parameterization of convective transport in the boundary layer and its impact on the representation of the diurnal cycle of wind and dust emissions. Atmos Chem Phys 2015;15:6775–88. https:\u002F\u002Fdoi.org\u002F10.5194\u002Facp-15-6775-2015.\nHourdin F, Gǎinusa-Bogdan A, Braconnot P, Dufresne JL, Traore AK, Rio C. Air moisture control on ocean surface temperature, hidden key to the warm bias enigma. Geophys Res Lett 2015;42:10. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015GL066764.\nHourdin F, Mauritsen T, Gettelman A, Golaz JC, Balaji V, Duan Q, Folini D, Ji D, Klocke D, Qian Y, Rauser F, Rio C, Tomassini L, Watanabe M, Williamson D. The art and science of climate model tuning. Bull Am Meteorol Soc 2017;98:589–602. https:\u002F\u002Fdoi.org\u002F10.1175\u002FBAMS-D-15-00135.1.\nHouston AL, Wilhelmson RB. The dependence of storm longevity on the pattern of deep convection initiation in a low-shear environment. Mon Weather Rev 2011;139(10):3125–38.\nHouze JRA. Mesoscale convective systems. Rev Geophys 2004;42:4.\nHouze RA, Churchill DD. Mesoscale organization and cloud microphysics in a bay of bengal depression. J Atmos Sci 1987;44(14):1845–68.\nHuang XY. The organization of moist convection by internal gravity waves. Tellus A: Dyn Meteorol Oceanogr 1990;42(2):270–85.\nJakob C. Accelerating progress in global atmospheric model development through improved parameterizations. Bull Am Meteorol Soc 2010;91(7):869–76. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2009BAMS2898.1.\nJam A, Hourdin F, Rio C, Couvreux F. Resolved versus parametrized boundary-layer plumes. Part iii: derivation of a statistical scheme for cumulus clouds. Boundary-layer Meteorol 2013;147(3):421–41.\nJeevanjee N, Romps DM. Effective buoyancy, inertial pressure, and the mechanical generation of boundary layer mass flux by cold pools. J Atmos Sci 2015;72(8):3199–13. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-14-0349.1.\nJohnson RH, Rickenbach TM, Rutledge SA, Ciesielski PE, Schubert WH. Trimodal characteristics of tropical convection. J Clim 1999; 12 (8): 2397–418. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1999)012\u003C2397:TCOTC>2.0.CO;2.\nJones TR, Randall DA. Quantifying the limits of convective parameterizations. J Geophys Res: Atmos 2011; 116:D8.\nKain JS, Fritsch JM. A one-dimensional entraining\u002Fdetraining plume model and its application in convective parameterization. J Atmos Sci 1990;47(23):2784–802. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0469(1990)047\u003C2784:AODEPM>2.0.CO;2.\nKay J, Wood R. 2008. Timescale analysis of aerosol sensitivity during homogeneous freezing and implications for upper tropospheric water vapor budgets. Geophys Res Lett, 35. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2007GL032628.\nKay JE, Wall C, Yettella V, Medeiros B, Hannay C, Caldwell P, Bitz C. Global climate impacts of fixing the southern ocean shortwave radiation bias in the community earth system model (cesm). J Climate 2016;29(12):4617–36.\nKeane R, Plant R. Large-scale length and time-scales for use with stochastic convective parametrization. Q J Roy Meteorol Soc 2012;138(666):1150–64.\nKeane RJ, Craig GC, Keil C, Zängl G. The plant–craig stochastic convection scheme in icon and its scale adaptivity. J Atmos Sci 2014;71(9):3404–15.\nKeane RJ, Plant RS, Tennant WJ. Evaluation of the plant–craig stochastic convection scheme (v2 0) in the ensemble forecasting system mogreps-r (24 km) based on the unified model (v7 3). Geoscie Model Develop 2016;9(5):1921–35.\nKhain A. Notes on state-of-the-art investigations of aerosol effects on precipitation: a critical review. Environ Res Lett 2009;4(1):015,004.\nKhairoutdinov M, Randall D. High-resolution simulation of shallow-to-deep convection transition over land. J Atmos Sci 2006;63(12):3421–36.\nKhouider B, Moncrieff MW. Organized convection parameterization for the itcz. J Atmos Sci 2015;72(8): 3073–96.\nKhouider B, Biello J, Majda AJ, et al. A stochastic multicloud model for tropical convection. Commun Math Sci 2010;8(1):187–216.\nKiehl J. On the observed near cancellation between longwave and shortwave cloud forcing in tropical regions. J Climate 1994;7(4):559–65.\nKim D, Sobel AH, Maloney ED, Frierson DM, Kang IS. A systematic relationship between intraseasonal variability and mean state bias in agcm simulations. J Climate 2011;24(21):5506–20.\nKim D, Ahn MS, Kang IS, Del Genio AD. Role of longwave cloud–radiation feedback in the simulation of the Madden–Julian oscillation. J Climate 2015;28(17):6979–94.\nKlein S, Hall A, Norris J, Pincus R. 2017. Low-cloud feedbacks from cloud-controlling factors: a review, vol 65.\nKlingaman NP, Jiang X, Xavier PK, Petch J, Waliser D, Woolnough SJ. Vertical structure and physical processes of the Madden-Julian oscillation: synthesis and summary. J Geophys Res: Atmos 2015;120(10): 4671–4689.\nKöhler M, Ahlgrimm M, Beljaars A. Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model. Q J R Meteorol Soc 2011;137:43–57. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fqj.713.\nKumar VV, Jakob C, Protat A, Williams CR, May PT. Mass-flux characteristics of tropical cumulus clouds from wind profiler observations at Darwin, Australia. J Atmos Sci 2015;72(5):1837–55.\nKuo YH, Schiro KA, Neelin JD. Convective transition statistics over tropical oceans for climate model diagnostics: Observational baseline. J Atmos Sci 2018;75(5):1553–70.\nKwon YC, Hong SY. A mass-flux cumulus parameterization scheme across gray-zone resolutions. Mon Weather Rev 2017; 145 (2): 583–98. https:\u002F\u002Fdoi.org\u002F10.1175\u002FMWR-D-16-0034.1.\nLabbouz L, Kipling Z, Stier P, Protat A. How well can we represent the spectrum of convective clouds in a climate model? Comparisons between internal parameterization variables and radar observations. J Atmos Sci 2018;75(5):1509–24.\nLane TP, Moncrieff MW. Characterization of momentum transport associated with organized moist convection and gravity waves. J Atmos Sci 2010;67(10):3208–25.\nLane TP, Moncrieff MW. Long-lived mesoscale systems in a low–convective inhibition environment. Part i: upshear propagation. J Atmos Sci 2015;72(11):4297–318.\nLang S, Tao WK. The next-generation goddard convective-stratiform heating algorithm: new tropical and warm season retrievals for gpm. J Clim 2018;31:5997–6026.\nLee SS, Donner LJ, Phillips VT. Impacts of aerosol chemical composition on microphysics and precipitation in deep convection. Atmos Res 2009;94(2):220–37. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atmosres.2009.05.015, http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS016980950900163X.\nLeMone MA, Pennell WT. The relationship of trade wind cumulus distribution to subcloud layer fluxes and structure. Mon Weather Rev 1976;104(5):524–39. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0493(1976)104\u003C0524:TROTWC>2.0.CO;2.\nLi JL, Waliser D, Stephens G, Lee S. Characterizing and understanding cloud ice and radiation budget biases in global climate models and reanalysis. Meteorol Monogr 2016;56:13–1.\nLocatelli R, Bousquet P, Hourdin F, Saunois M, Cozic A, Couvreux F, Grandpeix JY, Lefebvre MP, Rio C, Bergamaschi P, Chambers SD, Karstens U, Kazan V, van der Laan S, Meijer HAJ, Moncrieff J, Ramonet M, Scheeren HA, Schlosser C, Schmidt M, Vermeulen A, Williams AG. Atmospheric transport and chemistry of trace gases in LMDz5B: evaluation and implications for inverse modelling. Geosc Model Dev 2015;8:129–50. https:\u002F\u002Fdoi.org\u002F10.5194\u002Fgmd-8-129-2015.\nMapes B, Neale R. Parameterizing convective organization to escape the entrainment dilemma. J Adv Model Earth Syst 2011;3:2.\nMapes B, Tulich S, Lin J, Zuidema P. The mesoscale convection life cycle: building block or prototype for large-scale tropical waves? Dyn Atmos Oceans 2006;42(1–4):3–29.\nMasunaga H, Luo ZJ. Convective and large-scale mass flux profiles over tropical oceans determined from synergistic analysis of a suite of satellite observations. J Geophys Res: Atmos 2016;121(13):7958–74.\nMathon V, Laurent H, Lebel T. Mesoscale convective system rainfall in the Sahel. J Appl Meteorol 2002; 41(11):1081–92.\nMauritsen T, Stevens B. Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nat Geosci 2015;8(5):346.\nMcFiggans G, Artaxo P, Baltensperger U, Coe H, Facchini MC, Feingold G, Fuzzi S, Gysel M, Laaksonen A, Lohmann U, Mentel TF, Murphy DM, O’Dowd CD, Snider JR, Weingartner E. The effect of physical and chemical aerosol properties on warm cloud droplet activation. Atmos Chem Phys 2006;6(9):2593–649. https:\u002F\u002Fdoi.org\u002F10.5194\u002Facp-6-2593-2006. https:\u002F\u002Fwww.atmos-chem-phys.net\u002F6\u002F2593\u002F2006\u002F.\nMitchell D, Mishra S, Lawson R. Representing the ice fall speed in climate models: results from tropical composition, cloud and climate coupling (tc4) and the indirect and semi-direct aerosol campaign (isdac). J Geophys Res: Atmos 2011;116:D1.\nMiyamoto Y, Kajikawa Y, Yoshida R, Yamaura T, Yashiro H, Tomita H. Deep moist atmospheric convection in a subkilometer global simulation. Geophys Res Lett 2013;40(18):4922–26. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fgrl.50944.\nMoncrieff MW, Liu C. Convection initiation by density currents: role of convergence, shear, and dynamical organization. Mon Weather Rev 1999;127(10):2455–64.\nMoncrieff MW, Liu C, Bogenschutz P. Simulation, modeling, and dynamically based parameterization of organized tropical convection for global climate models. J Atmos Sci 2017;74(5):1363–80.\nMorita J, Takayabu YN, Shige S, Kodama Y. Analysis of rainfall characteristics of the Madden–Julian oscillation using trmm satellite data. Dyn Atmos Oceans 2006;42(1–4):107–26.\nMorrison H. Impacts of updraft size and dimensionality on the perturbation pressure and vertical velocity in cumulus convection. Part i: simple, generalized analytic solutions. J Atmos Sci 2016;73(4):1441–54.\nMorrison H. Impacts of updraft size and dimensionality on the perturbation pressure and vertical velocity in cumulus convection. Part ii: comparison of theoretical and numerical solutions and fully dynamical simulations. J Atmos Sci 2016;73(4):1455–80.\nMorrison H, Gettelman A. A new two-moment bulk stratiform cloud microphysics scheme in the community atmosphere model, version 3 (cam3). Part i: description and numerical tests. J Climate 2008;21(15):3642–59.\nMoseley C, Hohenegger C, Berg P, Haerter JO. Intensification of convective extremes driven by cloud–cloud interaction. Nat Geosci 2016;9(10):748.\nNesbitt SW, Zipser EJ. The diurnal cycle of rainfall and convective intensity according to three years of trmm measurements. J Climate 2003;16(10):1456–75.\nNie J, Kuang Z, Jacob DJ, Guo J. Representing effects of aqueous phase reactions in shallow cumuli in global models. J Geophys Res 2016;121:5769–87. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015JD024208.\nNitta T, Esbensen S. Heat and moisture budget analyses using bomex data. Mon Weather Rev 1974; 102(1):17–28. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0493(1974)102\u003C0017:HAMBAU>2.0.CO;2.\nO’Gorman PA, Dwyer JG. Using machine learning to parameterize moist convection: potential for modeling of climate, climate change, and extreme events. J Adv Model Earth Sys 2018;0:0. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2018MS001351.\nOng H, Wu CM, Kuo HC. Effects of artificial local compensation of convective mass flux in the cumulus parameterization. J Adv Model Earth Syst 2017;9(4):1811–27. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017MS000926.\nOrr A, Bechtold P, Scinocca J, Ern M, Janiskova M. Improved middle atmosphere climate and forecasts in the ecmwf model through a nonorographic gravity wave drag parameterization. J Climate 2010;23 (22):5905–26.\nOueslati B, Bellon G. The double itcz bias in cmip5 models: interaction between sst, large-scale circulation and precipitation. Climate Dynam 2015; 44: 585–607. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-015-2468-6.\nPan DM, Randall DD. A cumulus parameterization with a prognostic closure. Q J Roy Meteorol Soc 1998; 124(547):949–81.\nPark S. A unified convection scheme (UNICON). Part I: formulation. J Atmos Sci 2014;71:3902–30. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-13-0233.1.\nPark S. A unified convection scheme (UNICON). Part II: simulation. J Atmos Sci 2014;71:3931–73. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-13-0234.1.\nPerraud E, Couvreux F, Malardel S, Lac C, Masson V, Thouron O. Evaluation of statistical distributions for the parametrization of subgrid boundary-layer clouds. Boundary-layer Meteorol 2011;140(2):263–94.\nPeters JM. The impact of effective buoyancy and dynamic pressure forcing on vertical velocities within two-dimensional updrafts. J Atmos Sci 2016;73(11):4531–51. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-16-0016.1.\nPeters K, Jakob C, Davies L, Khouider B, Majda AJ. Stochastic behavior of tropical convection in observations and a multicloud model. J Atmos Sci 2013;70(11):3556–75.\nPeters K, Crueger T, Jakob C, Möbis B. Improved mjo-simulation in echam 6.3 by coupling a stochastic multicloud model to the convection scheme. J Adv Modeling Earth Syst 2017;9(1):193–219.\nPiriou JM, Redelsperger JL, Geleyn JF, Lafore JP, Guichard F. An approach for convective parameterization with memory: separating microphysics and transport in grid-scale equations. J Atmos Sci 2007;64 (11):4127–39.\nPlant R, Craig GC. A stochastic parameterization for deep convection based on equilibrium statistics. J Atmos Sci 2008;65(1):87–105.\nQin Y, Lin Y. Alleviated double itcz problem in the ncar cesm1: a new cloud scheme and the working mechanisms. J Adv Model Earth Syst 2018;10(9):2318–32.\nQin Y, Lin Y, Xu S, Ma HY, Xie S. A diagnostic pdf cloud scheme to improve subtropical low clouds in ncar community atmosphere model (cam 5). J Adv Model Earth Syst 2018;10(2):320–41.\nRandall D, Khairoutdinov M, Arakawa A, Grabowski W. Breaking the cloud parameterization deadlock. Bull Am Meteorol Soc 2003;84(11):1547–64. https:\u002F\u002Fdoi.org\u002F10.1175\u002FBAMS-84-11-1547.\nRaymond DJ. Convective processes and tropical atmospheric circulations. Q J Roy Meteorol Soc 1994;120 (520):1431–55.\nRaymond DJ, Blyth AM. A stochastic model for non precipitating cumulus clouds. J Atmos Sci 1986;43: 2708–18.\nRiette S, Lac C. A new framework to compare mass-flux schemes within the AROME numerical weather prediction model. Boundary-layer Meteorol 2016;160:269–7. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10546-016-0146-9.\nRiley EM, Mapes BE, Tulich SN. Clouds associated with the Madden–Julian oscillation: a new perspective from cloudsat. J Atmos Sci 2011;68(12):3032–51.\nRio C, Hourdin F, Grandpeix JY, Lafore JP. Shifting the diurnal cycle of parameterized deep convection over land. Geophys Res Lett 2009;36:7.\nRio C, Grandpeix JY, Hourdin F, Guichard F, Couvreux F, Lafore JP, Fridlind A, Mrowiec A, Roehrig R, Rochetin N, Lefebvre MP, Idelkadi A. Control of deep convection by sub-cloud lifting processes: the ALP closure in the LMDZ5B general circulation model. Clim Dyn 2013;40:2271–92 . https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-012-1506-x.\nRochetin N, Couvreux F, Grandpeix JY, Rio C. Deep convection triggering by boundary layer thermals. Part I: LES analysis and stochastic triggering formulation. J Atmos Sci 2014;71:496–514. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-12-0336.1.\nRochetin N, Grandpeix JY, Rio C, Couvreux F. Deep convection triggering by boundary layer thermals. Part II: stochastic triggering parameterization for the LMDZ GCM. J Atmos Sci 2014;71:515–38. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-12-0337.1.\nRoehrig R, Bouniol D, Guichard F, Hourdin F, Redelsperger JL. The present and future of the west african monsoon: a process-oriented assessment of cmip5 simulations along the amma transect. J Climate 2013; 26(17):6471–505.\nRomps DM. A direct measure of entrainment. J Atmos Sci 2010;67(6):1908–27.\nRomps DM. The stochastic parcel model: a deterministic parameterization of stochastically entraining convection. J Adv Model Earth Syst 2016;8(1):319–44.\nRomps DM, Kuang Z. Nature versus nurture in shallow convection. J Atmos Sci 2010;67(5):1655–66.\nRosenfeld D, Lohmann U, Raga GB, O’dowd CD, Kulmala M, Fuzzi S, Reissell A, Andreae MO. Flood or drought: how do aerosols affect precipitation? Science 2008;321(5894):1309–13.\nRossow WB, Tselioudis G, Polak A, Jakob C. Tropical climate described as a distribution of weather states indicated by distinct mesoscale cloud property mixtures. Geophys Res Lett 2005;32:21.\nRotunno R, Klemp JB, Weisman ML. A theory for strong, long-lived squall lines. J Atmos Sci 1988; 45(3):463–85.\nRozbicki JJ, Young GS, Qian L. Test of a convective wake parameterization in the single-column version of ccm3. Mon Weather Rev 1999;127(6):1347–61.\nSacks J, Welch WJ, Mitchell TJ, Wynn HP. Design and analysis of computer experiments. Statist Sci 1989;4(4):409–23. https:\u002F\u002Fdoi.org\u002F10.1214\u002Fss\u002F1177012413.\nSakradzija M, Seifert A, Dipankar A. A stochastic scale-aware parameterization of shallow cumulus convection across the convective gray zone. J Adv Model Earth Syst 2016;8(2):786–812.\nSassen K, Wang Z. Classifying clouds around the globe with the cloudsat radar: 1-year of results. Geophys Res Lett 2008;35:4.\nSchiro KA, Neelin JD. Tropical continental downdraft characteristics: mesoscale systems versus unorganized convection. Atmos Chem Phys 2018;18:1997–2010.\nSchlemmer L, Hohenegger C. The formation of wider and deeper clouds as a result of cold-pool dynamics. J Atmos Sci 2014;71(8):2842–58.\nSchmidt GA, Bader D, Donner LJ, Elsaesser GS, Golaz JC, Hannay C, Molod A, Neale RB, Saha S. Practice and philosophy of climate model tuning across six US modeling centers. Geosci Model Dev 2017;10:3207–23. https:\u002F\u002Fdoi.org\u002F10.5194\u002Fgmd-10-3207-2017.\nSchneider T, Lan S, Stuart A, Teixeira J. Earth system modeling 2.0: a blueprint for models that learn from observations and targeted high-resolution simulations. Geophys Res Lett 2017;44(24):12,396–417. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017GL076101.\nSchumacher C, Houze JRA. Stratiform rain in the tropics as seen by the trmm precipitation radar. J Climate 2003;16(11):1739–56.\nSchumacher C, Houze JRA. Stratiform precipitation production over sub-saharan africa and the tropical east atlantic as observed by trmm. Quarterly Journal of the Royal Meteorological Society: A journal of the Atmospheric Sciences, Applied Meteorology and Physical Oceanography 2006;132(620):2235–55.\nSchumacher C, Houze JRA, Kraucunas I. The tropical dynamical response to latent heating estimates derived from the trmm precipitation radar. J Atmos Sci 2004;61(12):1341–58.\nSherwood SC, Bony S, Dufresne JL. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 2014;505:37–42. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature12829.\nShige ea S. Spectral retrieval of latent heating profiles from trmm pr data. Part iv: comparisons of lookup tables from two- and three-dimensional simulations. J Clim 2009;22:5577–94.\nSimpson J, Wiggert V. Models of precipitating cumulus towers. Mon Wea Rev 1969;97(7):471–89.\nSlingo JM. The development and verification of a cloud prediction scheme for the ecmwf model. Q J Roy Meteorol Soc 1987;113(477):899–927. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fqj.49711347710.\nSong X, Zhang GJ, Li JL. Evaluation of microphysics parameterization for convective clouds in the ncar community atmosphere model cam5. J Climate 2012;25(24):8568–90.\nStorer RL, Griffin BM, Höft J, Weber JK, Raut E, Larson VE, Wang M, Rasch PJ. Parameterizing deep convection using the assumed probability density function method. Geosc Model Dev 2015;8: 1–19. https:\u002F\u002Fdoi.org\u002F10.5194\u002Fgmd-8-1-2015.\nStorer RL, Zhang GJ, Song X. Effects of convective microphysics parameterization on large-scale cloud hydrological cycle and radiative budget in tropical and midlatitude convective regions. J Climate 2015;28(23):9277–97.\nStratton R, Stirling A. Improving the diurnal cycle of convection in gcms. Q J Roy Meteorol Soc 2012; 138(666):1121–34.\nSušelj K, Teixeira J, Chung D. A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity\u002Fmass-flux parameterization. J Atmos Sci 2013;70(7):1929–53.\nTakahashi H, Luo ZJ, Stephens GL. Level of neutral buoyancy, deep convective outflow, and convective core: New perspectives based on 5 years of cloudsat data. J Geophys Res: Atmos 2017;122(5):2958–69.\nTalib J, Woolnough SJ, Klingaman NP, Holloway CE. The role of the cloud radiative effect in the sensitivity of the intertropical convergence zone to convective mixing. J Climate 2018;31:6821–38. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJCLI-D-17-0794.1.\nTan Z, Kaul CM, Pressel KG, Cohen Y, Schneider T, Teixeira J. An extended eddy-diffusivity mass-flux scheme for unified representation of subgrid-scale turbulence and convection. J Adv Model Earth Syst 2018;10(3):770–800.\nTao WK, Moncrieff MW. Multiscale cloud system modeling. Rev Geophys 2009;47:4. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2008RG000276.\nTiedtke M. A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Wea Rev 1989;117:1179–800.\nTiedtke M. Representation of clouds in large-scale models. Mon Weather Rev 1993;121(11):3040–61.\nTompkins AM. A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J Atmos Sci 2002;59(12):1917–42.\nVan Weverberg K, Morcrette CJ, Petch J, Klein SA, Ma HY, Zhang C, Xie S, Tang Q, Gustafson WI, Qian Y, Berg LK, Liu Y, Huang M, Ahlgrimm M, Forbes R, Bazile E, Roehrig R, Cole J, Merryfield W, Lee WS, Cheruy F, Mellul L, Wang YC, Johnson K, Thieman MM. Causes: attribution of surface radiation biases in nwp and climate models near the U.S. Southern great plains. J Geophys Res: Atmos 2018;123(7):3612–44. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017JD027188.\nVarble A. Erroneous attribution of deep convective invigoration to aerosol concentration. J Atmos Sci 2018; 75(4):1351–68.\nVentrice MJ, Thorncroft CD. The role of convectively coupled atmospheric kelvin waves on african easterly wave activity. Mon Weather Rev 2013;141(6):1910–24.\nVial J, Bony S, Stevens B, Vogel R. 2018. Mechanisms and model diversity of trade-wind shallow cumulus cloud feedbacks: a review, p 159. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-77273-8_8.\nVignon E, Hourdin F, Genthon C, Van de Wiel BJH, Gallée H, Madeleine JB, Beaumet J. Modeling the dynamics of the atmospheric boundary layer over the antarctic plateau with a general circulation model. J Adv Model Earth Syst 2018;10:98–125. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017MS001184.\nWang Y, Zhang GJ. Global climate impacts of stochastic deep convection parameterization in the ncar cam5. J Adv Model Earth Syst 2016;8(4):1641–56.\nWebb MJ, Lock AP, Bretherton CS, Bony S, Cole JNS, Idelkadi A, Kang SM, Koshiro T, Kawai H, Ogura T, Roehrig R, Shin Y, Mauritsen T, Sherwood SC, Vial J, Watanabe M, Woelfle MD, Zhao M. The impact of parametrized convection on cloud feedback. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2015;373(2054): 20140,414. https:\u002F\u002Fdoi.org\u002F10.1098\u002Frsta.2014.0414.\nWhite B, Gryspeerdt EG, Stier P, Morrison H, Thompson G, Kipling Z. Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects. Atmos Chem Phys 2017;17(1):12,145–75.\nWilliamson D, Blaker AT, Hampton C, Salter J. Identifying and removing structural biases in climate models with history matching. Clim Dyn 2015;45:1299–324. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-014-2378-z.\nWoelfle MD, Yu S, Bretherton CS, Pritchard MS. Sensitivity of coupled tropical pacific model biases to convective parameterization in CESM1. J Adv Model Earth Syst 2018;10:126–44. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017MS001176.\nWu CM, Arakawa A. A unified representation of deep moist convection in numerical modeling of the atmosphere. Part ii. J Atmos Sci 2014;71(6):2089–103. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-13-0382.1.\nWu X, Deng L, Song X, Zhang GJ. Coupling of convective momentum transport with convective heating in global climate simulations. J Atmos Sci 2007;64(4):1334–49. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS3894.1.\nXiang B, Zhao M, Held IM, Golaz JC. Predicting the severity of spurious “double ITCZ” problem in CMIP5 coupled models from AMIP simulations. Geophys Res Lett 2017;44:1520–27. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016GL071992.\nXu W, Rutledge SA. Morphology, intensity, and rainfall production of mjo convection: Observations from dynamo shipborne radar and trmm. J Atmos Sci 2015;72(2):623–40.\nYanai M, Esbensen S, Chu JH. Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J Atmos Sci 1973;30(4):611–27. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0469(1973)030\u003C0611:DOBPOT>2.0.CO;2.\nYano JI, Moncrieff MW. Numerical archetypal parameterization for mesoscale convective systems. J Atmos Sci 2016;73(7):2585–602.\nYano JI, Moncrieff MW. Convective organization in evolving large-scale forcing represented by a highly truncated numerical archetype. J Atmos Sci 2018;75(8):2827–47.\nYuter SE, Houze JRA. The natural variability of precipitating clouds over the western pacific warm pool. Q J Roy Meteorol Soc 1998;124(545):53–99.\nZelinka MD, Hartmann DL. The observed sensitivity of high clouds to mean surface temperature anomalies in the tropics. J Geophys Res: Atmos 2011;116:D23. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2011JD016459.\nZelinka MD, Zhou C, Klein SA. Insights from a refined decomposition of cloud feedbacks. Geophys Res Lett 2016;43(17):9259–69.\nZhang G, McFarlane NA. Sensitivity of climate simulations to the parameterization of cumulus convection in the canadian climate centre general circulation model. Atmos Ocean 1995;33(3):407–46. https:\u002F\u002Fdoi.org\u002F10.1080\u002F07055900.1995.9649539.\nZhang GJ. Convective quasi-equilibrium in midlatitude continental environment and its effect on convective parameterization. J Geophys Res: Atmos 2002;107(D14):ACL 12–1–16. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2001JD001005.\nZhang GJ. Convective quasi-equilibrium in the tropical western pacific: comparison with midlatitude continental environment. J Geophys Res: Atmos 2003;108:D19. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2003JD003520.\nZhang GJ, Cho HR. Parameterization of the vertical transport of momentum by cumulus clouds. Part ii: application. J Atmos Sci 1991;48(22):2448–2457. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0469(1991)048\u003C2448:POTVTO>2.0.CO;2.\nZhang GJ, Wu X, Zeng X, Mitovski T. Estimation of convective entrainment properties from a cloud-resolving model simulation during twp-ice. Clim Dyn 2016;47(7–8):2177–92.\nZhang M, Bretherton CS, Blossey PN, Austin PH, Bacmeister JT, Bony S, Brient F, Cheedela SK, Cheng A, Del Genio AD, et al. Cgils: results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models. J Adv Model Earth Syst 2013;5(4):826–42.\nZhao M, Golaz JC, Held I, Ramaswamy V, Lin SJ, Ming Y, Ginoux P, Wyman B, Donner L, Paynter D, et al. Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics. J Climate 2016;29(2):543–60.\nZhao M, Golaz JC, Held IM, Guo H, Balaji V, Benson R, Chen JH, Chen X, Donner LJ, Dunne JP, Dunne K, Durachta J, Fan SM, Freidenreich SM, Garner ST, Ginoux P, Harris LM, Horowitz LW, Krasting JP, Langenhorst AR, Liang Z, Lin P, Lin SJ, Malyshev SL, Mason E, Milly PCD, Ming Y, Naik V, Paulot F, Paynter D, Phillipps P, Radhakrishnan A, Ramaswamy V, Robinson T, Schwarzkopf D, Seman CJ, Shevliakova E, Shen Z, Shin H, Silvers LG, Wilson JR, Winton M, Wittenberg AT, Wyman B, Xiang B. The gfdl global atmosphere and land model am4.0\u002Flm4.0: 2. model description, sensitivity studies, and tuning strategies. J Adv Model Earth Syst 2018;10(3):735–69. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017MS001209.\nZuidema P, Torri G, Muller C, Chandra A. A survey of precipitation-induced atmospheric cold pools over oceans and their interactions with the larger-scale environment. Surv Geophys 2017;38(6):1283–305.",{"EN":229},"While the increase of computer power mobilizes a part of the atmospheric modeling community toward models with explicit convection or based on machine learning, we review the part of the literature dedicated to convective parameterization development for large-scale forecast and climate models. Many developments are underway to overcome endemic limitations of traditional convective parameterizations, either in unified or multiobject frameworks: scale-aware and stochastic approaches, new prognostic equations or representations of new components such as cold pools. Understanding their impact on the emergent properties of a model remains challenging, due to subsequent tuning of parameters and the limited understanding given by traditional metrics. Further effort still needs to be dedicated to the representation of the life cycle of convective systems, in particular their mesoscale organization and associated cloud cover. The development of more process-oriented metrics based on new observations is also needed to help quantify model improvement and better understand the mechanisms of climate change.",{"EN":231},"Ongoing Breakthroughs in Convective Parameterization",{"VOID":233},"10.1007\u002Fs40641-019-00127-w","2025-01-05T23:39:59.881+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40641-019-00127-w",[237,252,274],{"id":238,"sortIndex":19,"researcher":18,"roles":239,"affiliations":240,"properties":249},"8b667eb4-239b-4b19-93b2-5dc07b303856",[169],[241],{"id":18,"sortIndex":19,"affiliation":242,"properties":18},{"id":243,"createTime":244,"updateTime":244,"relativeEntities":245,"slug":18,"properties":246,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"126dd576-2f49-4e91-990a-4704c75bcbea","2024-01-10T05:42:03.024+00:00",[],{"title":247},{"VI":248},"Centre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, Toulouse, France",{"title":250},{"VI":251},"Catherine Rio",{"id":253,"sortIndex":254,"researcher":18,"roles":255,"affiliations":256,"properties":271},"d123d95a-3714-401a-819c-3040aca88ca4",1,[169],[257],{"id":258,"sortIndex":19,"affiliation":259,"properties":268},"841e9e39-40a7-4d37-879c-9140b139a8cd",{"id":260,"createTime":261,"updateTime":262,"relativeEntities":263,"slug":264,"properties":265,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"0cebf3e0-6555-45a4-8349-936360b94e97","2023-12-01T03:26:48.726+00:00","2025-01-26T03:59:42.157+00:00",[],"NASA-Goddard-Institute-for-Space-Studies-New-York-U-S-A-",{"title":266},{"VI":267},"NASA Goddard Institute for Space Studies, New York, U.S.A.",{"title":269},{"VI":270},"NASA Goddard Institute for Space Studies, New York, USA",{"title":272},{"VI":273},"Anthony D. Del Genio",{"id":275,"sortIndex":276,"researcher":18,"roles":277,"affiliations":278,"properties":287},"0afe2b3d-9374-4ee9-92ec-f6c29b9358cf",2,[169],[279],{"id":18,"sortIndex":19,"affiliation":280,"properties":18},{"id":281,"createTime":282,"updateTime":282,"relativeEntities":283,"slug":18,"properties":284,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1dd172c8-0680-4fbd-867e-70e50469a513","2024-01-10T07:41:16.041+00:00",[],{"title":285},{"VI":286},"Laboratoire de Météorologie Dynamique, IPSL, CNRS, Sorbonne Université, Paris, France",{"title":288},{"VI":289},"Frédéric Hourdin",{"url":235,"publisher":291,"properties":318},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":292,"slug":10,"properties":293,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":296,"manageAffiliations":297,"indexDatabases":298,"url":18,"thumbnailPath":18,"statistic":313,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":294,"title":295},{"VOID":13},{"EN":15},[],[],[299,306],{"id":64,"indexDatabase":300,"url":77,"indexYears":78,"academicFieldIds":305,"indexDatabaseRanking":82},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":301,"label":302,"description":303,"key":74,"publicationTags":304,"standard":18},[],{"EN":71,"VI":71},{"EN":71,"VI":73},[76],[80,81],{"id":84,"indexDatabase":307,"url":99,"indexYears":18,"academicFieldIds":312,"indexDatabaseRanking":18},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":308,"label":309,"description":310,"key":95,"publicationTags":311,"standard":18},[],{"EN":91,"VI":91},{"VI":93,"EN":94},[97,98],[101],{"impactFactor":19,"impactFactorByYear":314,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":315,"totalCitation":123,"totalCitationByYear":316,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":317,"hindexLast5Year":141,"hindex":141},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":116,"2020":60,"2021":120,"2022":121,"2024":122},{"2015":125,"2016":126,"2017":127,"2018":128,"2019":129,"2020":130,"2021":131},{"2015":134,"2016":135,"2017":136,"2018":137,"2019":138,"2020":139,"2021":140},{"volume":319,"pages":320},{"VOID":213},{"VOID":321},"95-111","2019-04-29",{"id":324,"createTime":325,"updateTime":325,"relativeEntities":326,"slug":18,"properties":327,"entityType":161,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":336,"fullTextUrl":18,"authors":337,"publicationType":182,"publisherRelationship":370,"citationCount":18,"citationInfo":18,"publishDate":403,"publishYear":404,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":218},"a2a974cd-c55d-476f-bd42-09a2d51c5618","2023-12-23T23:31:09.660+00:00",[],{"references":328,"abstract":330,"title":332,"doi":334},{"VOID":329},"Marlon JR, Bartlein PJ, Gavin DG, Long CJ, Anderson RS, Briles CE, et al. Long-term perspective on wildfires in the western USA. Proc Natl Acad Sci U S A. 2012;109:E535–43. doi:10.1073\u002Fpnas.1112839109.\nBowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM, Cochrane MA, et al. Fire in the Earth system. Science. 2009;324:481–4. doi:10.1126\u002Fscience.1163886.\nBond WJ, Woodward FI, Midgley GF. The global distribution of ecosystems in a world without fire. New Phytol. 2005;165:525–38. doi:10.1111\u002Fj.1469-8137.2004.01252.x.\nGoldammer JG. Vegetation fires and global change: challenges for concerted international action. Remagen-Oberwinter: Kessell Publishing House; 2013.\nScott AC, Glasspool IJ. The diversification of Paleozoic fire systems and fluctuations in atmospheric oxygen concentration. Proc Natl Acad Sci U S A. 2006;103:10861–5. doi:10.1073\u002Fpnas.0604090103.\nMarlon JR, Bartlein PJ, Daniau A-L, Harrison SP, Maezumi SY, Power MJ, et al. Global biomass burning: a synthesis and review of Holocene paleofire records and their controls. Quat Sci Rev. 2013;65:5–25. doi:10.1016\u002Fj.quascirev.2012.11.029. This study presents a large meta-analysis of global charcoal records and finds that climate was a dominant driver of regional to global fire activity throughout the Holocene.\nMeyn A, White PS, Buhk C, Jentsch A. Environmental drivers of large, infrequent wildfires: the emerging conceptual model. Prog Phys Geogr. 2007;31:287–312. doi:10.1177\u002F0309133307079365.\nvan der Werf GR, Randerson JT, Giglio L, Gobron N, Dolman AJ. Climate controls on the variability of fires in the tropics and subtropics. Glob Biogeochem Cycles. 2008;22, GB3028. doi:10.1029\u002F2007GB003122.\nKloster S, Mahowald NM, Randerson JT, Lawrence PJ. The impacts of climate, land use, and demography on fires during the 21st century simulated by CLM-CN. Biogeosciences. 2012;9:509–25. doi:10.5194\u002Fbg-9-509-2012.\nvan der Werf GR, Randerson JT, Giglio L, Collatz GJ, Mu M, Kasibhatla PS, et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos Chem Phys. 2010;10:11707–35. doi:10.5194\u002Facp-10-11707-2010.\nBallantyne AP, Alden CB, Miller JB, Tans PP, White JWC. Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years. Nature. 2012;488:70–2. doi:10.1038\u002Fnature11299.\nLe Quéré C, Raupach MR, Canadell JG, Marland G, Bopp L, Ciais P, et al. Trends in the sources and sinks of carbon dioxide. Nat Geosci. 2009;2:831–6. doi:10.1038\u002Fngeo689.\nWard DS, Kloster S, Mahowald NM, Rogers BM, Randerson JT, Hess PG (2012) The changing radiative forcing of fires: global model estimates for past, present and future. Atmos Chem Phys 12. doi:10.5194\u002Facp-12-10857-2012.\nFlannigan MD, Krawchuk MA, de Groot WJ, Wotton BM, Gowman LM. Implications of changing climate for global wildland fire. Int J Wildland Fire. 2009;18:483–507. doi:10.1071\u002FWF08187.\nWesterling AL, Hidalgo HG, Cayan DR, Swetnam TW. Warming and earlier spring increase western US forest wildfire activity. Science. 2006;313:940–3. doi:10.1126\u002Fscience.1128834.\nLittell JS, McKenzie D, Peterson DL, Westerling AL. Climate and wildfire area burned in Western US ecoprovinces, 1916–2003. Ecol Appl. 2009;19:1003–21. doi:10.1890\u002F07-1183.1.\nMouillot F, Field CB. Fire history and the global carbon budget: a 1° × 1° fire history reconstruction for the 20th century. Glob Chang Biol. 2005;11:398–420. doi:10.1111\u002Fj.1365-2486.2005.00920.x.\nAdams MA. Mega-fires, tipping points and ecosystem services: managing forests and woodlands in an uncertain future. For Ecol Manag. 2013;294:250–61. doi:10.1016\u002Fj.foreco.2012.11.039.\nPage SE, Siegert F, Rieley JO, Boehm H-DV, Jaya A, Limin S. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature. 2002;420:61–5. doi:10.1038\u002Fnature01131.\nvan der Werf GR, Randerson JT, Collatz GJ, Giglio L, Kasibhatla PS, Arellano AF, et al. Continental-scale partitioning of fire emissions during the 1997 to 2001 El Nino\u002FLa Nina period. Science. 2004;303:73–6. doi:10.1126\u002Fscience.1090753.\nCochrane J (2015) Indonesia’s Forest Fires Take Toll on Wildlife, Big and Small. The New York Times, 30 October 2015. http:\u002F\u002Fwww.nytimes.com\u002F2015\u002F10\u002F31\u002Fworld\u002Fasia\u002Findonesia-forest-fires-wildlife.html.\nCruz MG, Sullivan AL, Gould JS, Sims NC, Bannister AJ, Hollis JJ, et al. Anatomy of a catastrophic wildfire: the Black Saturday Kilmore East fire in Victoria, Australia. For Ecol Manag. 2012;284:269–85. doi:10.1016\u002Fj.foreco.2012.02.035.\nChubarova N, Nezval Y, Sviridenkov I, Smirnov A, Slutsker I. Smoke aerosol and its radiative effects during extreme fire event over Central Russia in summer 2010. Atmos Meas Tech. 2012;5:557–68. doi:10.5194\u002Famt-5-557-2012.\nKonovalov IB, Beekmann M, Kuznetsova IN, Yurova A, Zvyagintsev AM. Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region. Atmos Chem Phys. 2011;11:10031–56. doi:10.5194\u002Facp-11-10031-2011.\nBrando PM, Balch JK, Nepstad DC, Morton DC, Putz FE, Coe MT, et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc Natl Acad Sci U S A. 2014;111:6347–52. doi:10.1073\u002Fpnas.1305499111.\nWilliams AP, Seager R, Berkelhammer M, Macalady AK, Crimmins MA, Swetnam TW, et al. Causes and implications of extreme atmospheric moisture memand during the record-breaking 2011 wildfire season in the southwest United States. J Appl Meteorol Climatol. 2014;53:2671–84. doi:10.1175\u002FJAMC-D-14-0053.1.\nWilliams AP, Seager R, Abatzoglou JT, Cook BI, Smerdon JE, Cook ER. Contribution of anthropogenic warming to California drought during 2012–2014. Geophys Res Lett. 2015;42:6819–28. doi:10.1002\u002F2015GL064924.\nTuretsky MR, Donahue WF, Benscoter BW. Experimental drying intensifies burning and carbon losses in a northern peatland. Nat Commun. 2011;2:514. doi:10.1038\u002Fncomms1523.\nPyne SJ. Fire: Nature and Culture. London: Reaktion Books; 2012.\nPyne SJ. The fires this time, and next. Science. 2001;294:1005–6. doi:10.1126\u002Fscience.1064989.\nMoritz MA, Parisien MA, Batllori E, Krawchuk MA, Van Dorn J, Ganz DJ, et al. Climate change and disruptions to global fire activity. Ecosphere. 2012;3:1–22. doi:10.1890\u002FES11-00345.1. This study drives an empirically derived spatial model with projected climate data from 16 global climate models to project how macro-scale fire probability will compare during 2010–2039 and 2070–2099 to the observed record.\nKrawchuk MA, Moritz MA, Parisien M-A, Van Dorn J, Hayhoe K. Global pyrogeography: the current and future distribution of wildfire. PLoS ONE. 2009;4, e5102. doi:10.1371\u002Fjournal.pone.0005102.\nDavidson EA, de Araújo AC, Artaxo P, Balch JK, Brown IF, Bustamante MMC, et al. The Amazon basin in transition. Nature. 2012;481:321–8. doi:10.1038\u002Fnature10717.\nFriedlingstein P, Meinshausen M, Arora VK, Jones CD, Anav A, Liddicoat SK, et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J Clim. 2014;27:511–26. doi:10.1175\u002FJCLI-D-12-00579.1.\nGiglio L, Randerson JT, van der Werf GR, Kasibhatla PS, Collatz GJ, Morton DC, et al. Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeosciences. 2010;7:1171–86. doi:10.5194\u002Fbg-7-1171-2010.\nDaniau AL, Bartlein PJ, Harrison SP, Prentice IC, Brewer S, Friedlingstein P, et al. Predictability of biomass burning in response to climate changes. Glob Biogeochem Cycles. 2012;26, GB4007. doi:10.1029\u002F2011GB004249\u002Ffull.\nChuvieco E, Giglio L, Justice C. Global characterization of fire activity: toward defining fire regimes from Earth observation data. Glob Chang Biol. 2008;14:1488–502. doi:10.1111\u002Fj.1365-2486.2008.01585.x.\nArchibald S, Roy DP, van Wilgen BW, Scholes RJ. What limits fire? An examination of drivers of burnt area in Southern Africa. Glob Chang Biol. 2009;15:613–30. doi:10.1111\u002Fj.1365-2486.2008.01754.x.\nFischer H, Schüpbach S, Gfeller G, Bigler M, Röthlisberger R, Erhardt T, et al. Millennial changes in North American wildfire and soil activity over the last glacial cycle. Nat Geosci. 2015;8:723–7. doi:10.1038\u002Fngeo2495.\nZennaro P, Kehrwald N, McConnell JR, Schüpbach S, Maselli OJ, Marlon J, et al. Fire in ice: two millennia of boreal forest fire history from the Greenland NEEM ice core. Clim Past. 2014;10:1905–24. doi:10.5194\u002Fcp-10-1905-2014.\nZennaro P, Kehrwald N, Marlon J, Ruddiman W, Brücher T, Agostinelli C, et al. Europe on fire three thousand years ago: arson or climate? Geophys Res Lett. 2015;42:5023–33. doi:10.1002\u002F2015GL064259.\nvan der Werf GR, Peters W, van Leeuwen TT, Giglio L. What could have caused pre-industrial biomass burning emissions to exceed current rates? Clim Past. 2013;9:289–306. doi:10.5194\u002Fcp-9-289-2013.\nGirardin MP, Ali AA, Carcaillet C, Blarquez O, Hély C, Terrier A, et al. Vegetation limits the impact of a warm climate on boreal wildfires. New Phytol. 2013;199:1001–11. doi:10.1111\u002Fnph.12322.\nKelly R, Chipman ML, Higuera PE, Stefanova I, Brubaker LB, Hu FS. Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. Proc Natl Acad Sci U S A. 2013;110:13055–60. doi:10.1073\u002Fpnas.1305069110.\nBrown KJ, Giesecke T. Holocene fire disturbance in the boreal forest of central Sweden. Boreas. 2014;43:639–51. doi:10.1111\u002Fbor.12056.\nBlarquez O, Ali AA, Girardin MP, Grondin P, Fréchette B, Bergeron Y, et al. Regional paleofire regimes affected by non-uniform climate, vegetation and human drivers. Nat Sci Rep. 2015;5:13356. doi:10.1038\u002Fsrep13356.\nBarrett CM, Kelly R, Higuera PE, Hu FS. Climatic and land cover influences on the spatiotemporal dynamics of Holocene boreal fire regimes. Ecology. 2013;94:389–402. doi:10.1890\u002F12-0840.1.\nWalsh MK, Marlon JR, Goring SJ, Brown KJ, Gavin DG. A regional perspective on holocene fire–climate–human interactions in the Pacific Northwest of North America. Ann Assoc Am Geogr. 2015;105:1135–57. doi:10.1080\u002F00045608.2015.1064457.\nCalder WJ, Parker D, Stopka CJ, Jiménez-Moreno G, Shuman BN. Medieval warming initiated exceptionally large wildfire outbreaks in the Rocky Mountains. Proc Natl Acad Sci U S A. 2015;112:13261–6. doi:10.1073\u002Fpnas.1500796112.\nPower MJ, Mayle FE, Bartlein PJ, Marlon JR, Anderson RS, Behling H, et al. Climatic control of the biomass-burning decline in the Americas after AD 1500. The Holocene. 2013;23:3–13. doi:10.1177\u002F0959683612450196.\nAbrams MD, Nowacki GJ. Exploring the early Anthropocene burning hypothesis and climate-fire anomalies for the eastern US. J Sustain For. 2015;34:30–48. doi:10.1080\u002F10549811.2014.973605.\nWilliams AN, Mooney SD, Sisson SA, Marlon J. Exploring the relationship between Aboriginal population indices and fire in Australia over the last 20,000 years. Palaeogeogr Palaeoclimatol Palaeoecol. 2015;432:49–57. doi:10.1016\u002Fj.palaeo.2015.04.030.\nFeurdean A, Spessa A, Magyari EK, Willis KJ, Veres D, Hickler T. Trends in biomass burning in the Carpathian region over the last 15,000 years. Quat Sci Rev. 2012;45:111–25. doi:10.1016\u002Fj.quascirev.2012.04.001.\nKrupinski NBQ, Marlon JR, Nishri A, Street JH, Paytan A. Climatic and human controls on the late Holocene fire history of northern Israel. Quat Res. 2013;80:396–405. doi:10.1016\u002Fj.yqres.2013.06.012.\nEllis EC, Kaplan JO, Fuller DQ, Vavrus S, Goldewijk KK, Verburg PH. Used planet: a global history. Proc Natl Acad Sci U S A. 2013;110:7978–85. doi:10.1073\u002Fpnas.1217241110.\nArno SF, Sneck KM. A method for determining fire history in coniferous forests of the Mountain West. Ogden: USDA Forest Service; 1977.\nWilliams AP, Allen CD, Macalady AK, Griffin D, Woodhouse CA, Meko DM, et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat Clim Chang. 2013;3:292–7. doi:10.1038\u002Fnclimate1693.\nDugan AJ, Baker WL. Sequentially contingent fires, droughts and pluvials structured a historical dry forest landscape and suggest future contingencies. J Veg Sci. 2015;26:697–710. doi:10.1111\u002Fjvs.12266.\nHuffman DW, Zegler TJ, Fulé PZ. Fire history of a mixed conifer forest on the Mogollon Rim, northern Arizona, USA. Int J Wildland Fire. 2015;24:680–9. doi:10.1071\u002FWF14005.\nMargolis EQ, Swetnam TW. Historical fire–climate relationships of upper elevation fire regimes in the south-western United States. Int J Wildland Fire. 2013;22:588–98. doi:10.1071\u002FWF12064.\nMargolis EQ. Fire regime shift linked to increased forest density in a piñon–juniper savanna landscape. Int J Wildland Fire. 2014;23:234–45. doi:10.1071\u002FWF13053.\nO’Connor CD, Falk DA, Lynch AM, Swetnam TW. Fire severity, size, and climate associations diverge from historical precedent along an ecological gradient in the Pinaleño Mountains, Arizona, USA. For Ecol Manag. 2014;329:264–78. doi:10.1016\u002Fj.foreco.2014.06.032.\nSwetnam TW, Falk DA, Sutherland EK, Brown PM, Brown TJ (2012) Final Report: Fire and Climate in the Western US: A New Synthesis for Land Management. Fire and Climate Synthesis Project. University of Arizona, Tucson, AZ.\nSwetnam TW, Whitlock C. Ch. 3: Paleofire and Climate History: Western America and Global Perspectives. In: Goldammer JG, editor. Vegetation fires and global change: challenges for concerted international action. Germany: Kessel Publishing House; 2013. p. 21–38.\nBigio ER, Swetnam TW, Baisan CH. Local-scale and regional climate controls on historical fire regimes in the San Juan Mountains, Colorado. For Ecol Manag. 2016;360:311–22. doi:10.1016\u002Fj.foreco.2015.10.041.\nTrouet V, Taylor AH, Wahl ER, Skinner CN, Stephens SL. Fire‐climate interactions in the American West since 1400 CE. Geophys Res Lett. 2010;37, L04702. doi:10.1029\u002F2009GL041695.\nSwetnam TW, Betancourt JL. Mesoscale disturbance and ecological response to decadal climatic variability in the American Southwest. J Clim. 1998;11:3128–47. doi:10.1175\u002F1520-0442(1998)011\u003C3128:MDAERT>2.0.CO;2.\nSwetnam TW, Baisan CH. Tree-Ring Reconstructions of Fire and Climate History in the Sierra Nevada and Southwestern United States. In: Veblen TT, Baker WL, Montenegro G, Swetnam TW, editors. Fire and climatic change in temperate ecosystems of the western Americas. New York: Springer; 2003. p. 158–95.\nMundo IA, Kitzberger T, Juñent FAR, Villalba R, Barrera MD. Fire history in the Araucaria araucana forests of Argentina: human and climate influences. Int J Wildland Fire. 2013;22:194–206. doi:10.1071\u002FWF11164.\nSwetnam TW, Baisan CH (1996) Historical fire regime patterns in the southwestern United States since AD 1700. In: Allen CD (ed) Fire Effects in Southwestern Fortest : Proceedings of the 2nd La Mesa Fire Symposium, vol General Technical Report RM-GTR-286. USDA Forest Service, Rocky Mountain Research Station, pp 11–32.\nPyne SJ. Between Two Fires: A Fire History of Contemporary America. Tucson: The University of Arizona Press; 2015.\nParks SA, Miller C, Parisien M-A, Holsinger LM, Dobrowski SZ, Abatzoglou J. Wildland fire deficit and surplus in the western United States, 1984–2012. Ecosphere. 2015;6:1–13. doi:10.1890\u002FES15-00294.1.\nHarris L, Taylor AH. Topography, fuels, and fire dxclusion drive fire severity of the Rim Fire in an old Growth mixed-conifer forest, Yosemite National Park, USA. Ecosystems. 2015;18:1192–208. doi:10.1007\u002Fs10021-015-9890-9.\nHeyerdahl EK, Loehman RA, Falk DA. Mixed-severity fire in lodgepole pine dominated forests: are historical regimes sustainable on Oregon’s Pumice Plateau, USA? Can J For Res. 2014;44:593–603. doi:10.1139\u002Fcjfr-2013-0413.\nSibold JS, Veblen TT, González ME. Spatial and temporal variation in historic fire regimes in subalpine forests across the Colorado Front Range in Rocky Mountain National Park, Colorado, USA. J Biogeogr. 2006;33:631–47. doi:10.1111\u002Fj.1365-2699.2005.01404.x.\nBaker WL. Are high-severity fires burning at much higher rates recently than historically in dry-forest landscapes of the western USA? PLoS ONE. 2015;10, e0136147. doi:10.1371\u002Fjournal.pone.0136147.\nOdion DC, Hanson CT, Arsenault A, Baker WL, DellaSala DA, Hutto RL, et al. Examining historical and current mixed-severity fire regimes in ponderosa pine and mixed-conifer forests of western North America. PLoS ONE. 2014;9, e87852. doi:10.1371\u002Fjournal.pone.0087852.\nSeager R, Hooks A, Williams AP, Cook BI, Nakamura J, Henderson N. Climatology, variability and trends in United States vapor pressure deficit, an important fire-related meteorological quantity. J Appl Meteorol. 2015;54:1121–41. doi:10.1175\u002FJAMC-D-14-0321.1.\nGirardin MP, Terrier A. Mitigating risks of future wildfires by management of the forest composition: an analysis of the offsetting potential through boreal Canada. Clim Chang. 2015;130:587–601. doi:10.1007\u002Fs10584-015-1373-7.\nKharuk VI, Dvinskaya ML, Ranson KJ. Fire return intervals within the northern boundary of the larch forest in Central Siberia. Int J Wildland Fire. 2013;22:207–11. doi:10.1071\u002FWF11181.\nHéon J, Arseneault D, Parisien M-A. Resistance of the boreal forest to high burn rates. Proc Natl Acad Sci U S A. 2014;111:13888–93. doi:10.1073\u002Fpnas.1409316111.\nKoutsias N, Xanthopoulos G, Founda D, Xystrakis F, Nioti F, Pleniou M, et al. On the relationships between forest fires and weather conditions in Greece from long-term national observations (1894–2010). Int J Wildland Fire. 2013;22:493–507. doi:10.1071\u002FWF12003.\nKolden CA, Smith AMS, Abatzoglou JT. Limitations and utilisation of monitoring trends in burn severity products for assessing wildfire severity in the USA. Int J Wildland Fire. 2015;24:1023–8. doi:10.1071\u002FWF15082.\nEidenshink J, Schwind B, Brewer K, Zhu Z, Quayle B, Howard S. A project for monitoring trends in burn severity. Fire Ecol. 2007;3:3–21.\nShort KC. A spatial database of wildfires in the United States, 1992–2011. Earth Syst Sci Data. 2014;6:1–27. doi:10.5194\u002Fessd-6-1-2014.\nShort KC. Sources and implications of bias and uncertainty in a century of US wildfire activity data. Int J Wildland Fire. 2015;24:883–91. doi:10.1071\u002FWF14190.\nDennison PE, Brewer SC, Arnold JD, Moritz MA. Large wildfire trends in the western United States, 1984–2011. Geophys Res Lett. 2015;41:2928–33. doi:10.1002\u002F2014GL059576.\nWilliams AP, Seager R, Macalady AK, Berkelhammer M, Crimmins MA, Swetnam TW, et al. Correlations between components of the water balance and burned area reveal new insights for predicting fire activity in the southwest US. Int J Wildland Fire. 2015;24:14–26. doi:10.1071\u002FWF14023.\nRoy DP, Boschetti L, Justice CO, Ju J. The Collection 5 MODIS Burned Area Product–Global evaluation by comparison with the MODIS Active Fire Product. Remote Sens Environ. 2008;112:3690–707. doi:10.1016\u002Fj.rse.2008.05.013.\nHanson CT, Odion DC. Is fire severity increasing in the Sierra Nevada, California, USA? Int J Wildland Fire. 2014;23:1–8. doi:10.1071\u002FWF13016.\nMorton DC, Collatz GJ, Wang D, Randerson JT, Giglio L, Chen Y. Satellite-based assessment of climate controls on US burned area. Biogeosciences. 2013;10:247–60. doi:10.5194\u002Fbg-10-247-2013.\nWesterling A, Brown T, Schoennagel T, Swetnam T, Turner M, Veblen T. Briefing: Climate and Wildfire in Western US Forests. In: Sample VA, Bixler RP, editors. Forest Conservation and Management in the Anthropocene: Conference Proceedings, RMRS-P-71. Fort Collins, CO: USDA Forest Service Rocky Mountain Research Station; 2014. p. 81–102.\nAbatzoglou JT, Kolden CA. Relationships between climate and macroscale area burned in the western United States. Int J Wildland Fire. 2013;22:1003–20. doi:10.1071\u002FWF13019.\nSchwartz MW, Butt N, Dolanc CR, Holguin A, Moritz MA, North MP, et al. Increasing elevation of fire in the Sierra Nevada and implications for forest change. Ecosphere. 2015;6:121. doi:10.1890\u002FES15-00003.1.\nCansler CA, McKenzie D. Climate, fire size, and biophysical setting control fire severity and spatial pattern in the northern Cascade Range, USA. Ecol Appl. 2014;24:1037–56. doi:10.1890\u002F13-1077.1.\nRiley KL, Abatzoglou JT, Grenfell IC, Klene AE, Heinsch FA. The relationship of large fire occurrence with drought and fire danger indices in the western USA, 1984–2008: the role of temporal scale. Int J Wildland Fire. 2013;22:894–909. doi:10.1071\u002FWF12149.\nYoon J-H, Wang S-Y, Gilles RR, Hipps L, Kravitz B, Rasch PJ. Extreme fire season in California: a glimpse into the future? [in “Explaining Extremes of 2014 from a Climate Perspective”]. Bull Am Meteorol Soc. 2015;96:S5–9.\nBarbero R, Abatzoglou JT, Steel EA, Larkin NK. Modeling very large-fire occurrences over the continental United States from weather and climate forcing. Environ Res Lett. 2014;9:124009. doi:10.1088\u002F1748-9326\u002F9\u002F12\u002F124009.\nSedano F, Randerson JT. Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences. 2014;11:3739–55. doi:10.5194\u002Fbg-11-3739-2014.\nStavros EN, Abatzoglou J, Larkin NK, McKenzie D, Steel EA. Climate and very large wildland fires in the contiguous Western USA. Int J Wildland Fire. 2014;23:899–914. doi:10.1071\u002FWF13169.\nBarbero R, Abatzoglou JT, Kolden CA, Hegewisch KC, Larkin NK, Podschwit H. Multi-scalar influence of weather and climate on very large‐fires in the Eastern United States. Int J Climatol. 2014;35:2180–6. doi:10.1002\u002Fjoc.4090.\nJolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun. 2015;6:7537. doi:10.1038\u002Fncomms8537. This study evaluates a global reanalysis of gridded meteorological data and finds a significant increase in the global vegetation area experiencing anomalously severe fire-weather in a given year during 1979--2013..\nUrbieta IR, Zavala G, Bedia J, Gutiérrez JM, Miguel-Ayanz JS, Camia A, et al. Fire activity as a function of fire–weather seasonal severity and antecedent climate across spatial scales in southern Europe and Pacific western USA. Environ Res Lett. 2015;10:114013. doi:10.1088\u002F1748-9326\u002F10\u002F11\u002F114013.\nLasslop G, Hantson S, Kloster S. Influence of wind speed on the global variability of burned fraction: a global fire model’s perspective. Int J Wildland Fire. 2015;24:989–1000. doi:10.1071\u002FWF15052.\nDiaz HF, Swetnam TW. The wildfires of 1910: climatology of an extreme early twentieth-century event and comparison with more recent extremes. Bull Am Meteorol Soc. 2013;94:1361–70. doi:10.1175\u002FBAMS-D-12-00150.1.\nClarke H, Lucas C, Smith P. Changes in Australian fire weather between 1973 and 2010. Int J Climatol. 2013;33:931–44. doi:10.1002\u002Fjoc.3480.\nHiguera PE, Abatzoglou JT, Littell JS, Morgan P. The changing strength and nature of fire-climate relationships in the Northern Rocky Mountains, USA, 1902–2008. PLoS ONE. 2015;10, e0127563. doi:10.1371\u002Fjournal.pone.0127563. This study highlights the nonlinearity and complexities of climate-fire relationships using a century of observational data from the northern Rocky Mountains.\nPausas JG, Fernández-Muñoz S. Fire regime changes in the Western Mediterranean Basin: from fuel-limited to drought-driven fire regime. Clim Chang. 2012;110:215–26. doi:10.1007\u002Fs10584-011-0060-6.\nMoritz MA, Morais ME, Summerell LA, Carlson JM, Doyle JC. Wildfires, complexity, and highly optimized tolerance. Proc Natl Acad Sci U S A. 2005;102:17912–7. doi:10.1073\u002Fpnas.0508985102.\nParisien M-A, Moritz MA. Environmental controls on the distribution of wildfire at multiple spatial scales. Ecol Monogr. 2009;79:127–54. doi:10.1890\u002F07-1289.1.\nKrawchuk MA, Moritz MA. Burning issues: statistical analyses of global fire data to inform assessments of environmental change. Environmetrics. 2014;25:472–81. doi:10.1002\u002Fenv.2287. This paper provides an excellent review of statistical modeling of global fire activity and needed next steps for research and application.\nKrawchuk MA, Moritz MA. Constraints on global fire activity vary across a resource gradient. Ecology. 2011;92:121–32. doi:10.1890\u002F09-1843.1.\nPausas JG, Ribeiro E. The global fire–productivity relationship. Glob Ecol Biogeogr. 2013;22:728–36. doi:10.1111\u002Fgeb.12043.\nPausas JG, Bradstock RA. Fire persistence traits of plants along a productivity and disturbance gradient in mediterranean shrublands of south-east Australia. Glob Ecol Biogeogr. 2007;16:330–40. doi:10.1111\u002Fj.1466-8238.2006.00283.x.\nArchibald S, Lehmann CER, Gómez-Dans JL, Bradstock RA. Defining pyromes and global syndromes of fire regimes. Proc Natl Acad Sci U S A. 2013;110:6442–7. doi:10.1073\u002Fpnas.1211466110.\nMcWethy DB, Higuera PE, Whitlock C, Veblen TT, Bowman DMJS, Cary GJ, et al. A conceptual framework for predicting temperate ecosystem sensitivity to human impacts on fire regimes. Glob Ecol Biogeogr. 2013;22:900–12. doi:10.1111\u002Fgeb.12038.\nFaivre N, Jin Y, Goulden ML, Randerson JT. Controls on the spatial pattern of wildfire ignitions in Southern California. Int J Wildland Fire. 2014;23:799–811. doi:10.1071\u002FWF13136.\nHawbaker TJ, Radeloff VC, Stewart SI, Hammer RB, Keuler NS, Clayton MK. Human and biophysical influences on fire occurrence in the United States. Ecol Appl. 2013;23:565–82. doi:10.1890\u002F12-1816.1.\nHantson S, Pueyo S, Chuvieco E. Global fire size distribution is driven by human impact and climate. Glob Ecol Biogeogr. 2015;24:77–86. doi:10.1111\u002Fgeb.12246.\nHantson S, Lasslop G, Kloster S, Chuvieco E. Anthropogenic effects on global mean fire size. Int J Wildland Fire. 2015;24:589–96. doi:10.1071\u002FWF14208.\nKnorr W, Kaminski T, Arneth A, Weber U. Impact of human population density on fire frequency at the global scale. Biogeosciences. 2014;11:1085–102. doi:10.5194\u002Fbg-11-1085-2014.\nBistinas I, Harrison SP, Prentice IC, Pereira JM. Causal relationships versus emergent patterns in the global controls of fire frequency. Biogeosciences. 2014;11:5087–101. doi:10.5194\u002Fbg-11-5087-2014.\nFinney MA, Cohen JD, Forthofer JM, McAllister SS, Gollner MJ, Gorham DJ, et al. Role of buoyant flame dynamics in wildfire spread. Proc Natl Acad Sci U S A. 2015;112:9833–8. doi:10.1073\u002Fpnas.1504498112.\nHoffman CM, Canfield J, Linn RR, Mell W, Sieg CH, Pimont F, et al. Evaluating crown fire rate of spread predictions from physics-based models. Fire Technol. 2015;1:1–17. doi:10.1007\u002Fs10694-015-0500-3.\nHoffman CM, Linn R, Parsons R, Sieg C, Winterkamp J. Modeling spatial and temporal dynamics of wind flow and potential fire behavior following a mountain pine beetle outbreak in a lodgepole pine forest. Agric For Meteorol. 2015;204:79–93. doi:10.1016\u002Fj.agrformet.2015.01.018.\nBradstock RA. A biogeographic model of fire regimes in Australia: current and future implications. Glob Ecol Biogeogr. 2010;19:145–58. doi:10.1111\u002Fj.1466-8238.2009.00512.x.\nFlannigan M, Cantin AS, de Groot WJ, Wotton M, Newbery A, Gowman LM. Global wildland fire season severity in the 21st century. For Ecol Manag. 2013;294:54–61. doi:10.1016\u002Fj.foreco.2012.10.022.\nLiu Y, Goodrick SL, Stanturf JA. Future US wildfire potential trends projected using a dynamically downscaled climate change scenario. For Ecol Manag. 2013;294:120–35. doi:10.1016\u002Fj.foreco.2012.06.049.\nLuo L, Tang Y, Zhong S, Bian X, Heilman WE. Will future climate favor more erratic wildfires in the Western United States? J Appl Meteorol Climatol. 2013;52:2410–7. doi:10.1175\u002FJAMC-D-12-0317.1.\nStavros EN, Abatzoglou JT, McKenzie D, Larkin NK. Regional projections of the likelihood of very large wildland fires under a changing climate in the contiguous Western United States. Clim Chang. 2014;126:455–68. doi:10.1007\u002Fs10584-014-1229-6.\nYue X, Mickley LJ, Logan JA, Kaplan JO. Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century. Atmos Environ. 2013;77:767–80. doi:10.1016\u002Fj.atmosenv.2013.06.003.\nYue X, Mickley LJ, Logan JA, Hudman RC, Val Martin M, Yantosca RM. Impact of 2050 climate change on North American wildfire: consequences for ozone air quality. Atmos Chem Phys. 2015;15:10033–55. doi:10.5194\u002Facp-15-10033-2015.\nTian X, Zhao F, Shu L, Wang M. Changes in forest fire danger for south-western China in the 21st century. Int J Wildland Fire. 2014;23:185–95. doi:10.1071\u002FWF13014.\nBarbero R, Abatzoglou JT, Larkin NK, Kolden CA, Stocks B. Climate change presents increased potential for very large fires in the contiguous United States. Int J Wildland Fire. 2015;24:892–9. doi:10.1071\u002FWF15083.\nWesterling AL, Turner MG, Smithwick EAH, Romme WH, Ryan MG. Continued warming could transform Greater Yellowstone fire regimes by mid-21st century. Proc Natl Acad Sci U S A. 2011;108:13165–70. doi:10.1073\u002Fpnas.1110199108.\nBedia J, Herrera S, Gutiérrez JM, Benali A, Brands S, Mota B, et al. Global patterns in the sensitivity of burned area to fire-weather: implications for climate change. Agric For Meteorol. 2015;214:369–79. doi:10.1016\u002Fj.agrformet.2015.09.002.\nYue X, Mickley LJ, Logan JA. Projection of wildfire activity in southern California in the mid-twenty-first century. Clim Dyn. 2014;43:1973–91. doi:10.1007\u002Fs00382-013-2022-3.\nHurteau MD, Westerling AL, Wiedinmyer C, Bryant BP. Projected effects of climate and development on California wildfire emissions through 2100. Environ Sci Technol. 2014;48:2298–304. doi:10.1021\u002Fes4050133.\nWesterling AL, Bryant BP. Climate change and wildfire in California. Clim Chang. 2008;87:231–49. doi:10.1007\u002Fs10584-007-9363-z.\nBatllori E, Parisien MA, Krawchuk MA, Moritz MA. Climate change‐induced shifts in fire for Mediterranean ecosystems. Glob Ecol Biogeogr. 2013;22:1118–29. doi:10.1111\u002Fgeb.12065.\nBalshi MS, McGuire AD, Duffy P, Flannigan M, Walsh J, Melillo J. Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach. Glob Chang Biol. 2009;15:578–600. doi:10.1111\u002Fj.1365-2486.2008.01679.x.\nParisien M-A, Parks SA, Krawchuk MA, Little JM, Flannigan MD, Gowman LM, et al. An analysis of controls on fire activity in boreal Canada: comparing models built with different temporal resolutions. Ecol Appl. 2014;24:1341–56. doi:10.1890\u002F13-1477.1. This study uniquely uses both spatial and temporal variability in observations to develop a burned area model for boreal Canada.\nHu FS, Higuera PE, Duffy P, Chipman ML, Rocha AV, Young AM, et al. Arctic tundra fires: natural variability and responses to climate change. Front Ecol Environ. 2015;13:369–77. doi:10.1890\u002F150063.\nJohnstone JF, Hollingsworth TN, Chapin FS, Mack MC. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob Chang Biol. 2010;16:1281–95. doi:10.1111\u002Fj.1365-2486.2009.02051.x.\nParks SA, Holsinger LM, Miller C, Nelson CR. Wildland fire as a self-regulating mechanism: the role of previous burns and weather in limiting fire progression. Ecol Appl. 2015;25:1478–92. doi:10.1890\u002F14-1430.1.\nBalch JK, Bradley BA, D’Antonio CM, Gómez‐Dans J. Introduced annual grass increases regional fire activity across the arid western USA (1980–2009). Glob Chang Biol. 2013;19:173–83. doi:10.1111\u002Fgcb.12046.\nBoulanger Y, Gauthier S, Gray DR, Le Goff H, Lefort P, Morissette J. Fire regime zonation under current and future climate over eastern Canada. Ecol Appl. 2013;23:904–23. doi:10.1890\u002F12-0698.1.\nBoulanger Y, Gauthier S, Burton PJ. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Can J For Res. 2014;44:365–76. doi:10.1139\u002Fcjfr-2013-0372.\nBowman DMJS, Murphy BP, Williamson GJ, Cochrane MA. Pyrogeographic models, feedbacks and the future of global fire regimes. Glob Ecol Biogeogr. 2014;23:821–4. doi:10.1111\u002Fgeb.12180.\nYang J, Tian H, Tao B, Ren W, Kush J, Liu Y, et al. Spatial and temporal patterns of global burned area in response to anthropogenic and environmental factors: reconstructing global fire history for the 20th and early 21st centuries. J Geophys Res: Biogeosci. 2014;119:249–63. doi:10.1002\u002F2013JG002532.\nYang J, Tian H, Tao B, Ren W, Lu C, Pan S, et al. Century-scale patterns and trends of global pyrogenic carbon emissions and fire influences on terrestrial carbon balance. Glob Biogeochem Cycles. 2015;29:1549–66. doi:10.1002\u002F2015GB005160. This study models global fire area and emissions using a dynamic global vegetation model linked to a fire module to estimate the response of global fire activity to changes in climate, atmospheric CO 2 , and human demographics over the past 110 years.\nMarlon JR, Bartlein PJ, Carcaillet C, Gavin DG, Harrison SP, Higuera PE, et al. Climate and human influences on global biomass burning over the past two millennia. Nat Geosci. 2008;1:697–702. doi:10.1038\u002Fngeo313.\nKnorr W, Jiang L, Arneth A. Climate, CO2 and demographic impacts on global wildfire emissions. Biogeosciences. 2016;13:267–82. doi:10.5194\u002Fbg-13-267-2016. This study uses global semi-empirical fire modeling with a dynamic global vegetation model to tease apart the projected effects of 21st century changes in climate, atmospheric CO 2 , and human population\u002Fdemographics change.\nWu M, Knorr W, Thonicke K, Schurgers G, Camia A, Arneth A. Sensitivity of burned area in Europe to climate change, atmospheric CO2 levels and demography: a comparison of two fire‐vegetation models. J Geophys Res: Biogeosci. 2015;120:2256–72. doi:10.1002\u002F2015JG003036.\nKelly R, Genet H, McGuire AD, Hu FS (2015) Palaeodata-informed modelling of large carbon losses from recent burning of boreal forests. Nature Climate Change:In press. doi:10.1038\u002Fnclimate2832. This study used charcoal reconstructions of fire in Alaskan boreal forest to drive model simulations of carbon dynamics from AD 850–2006 and finds that fire was likely the dominant source of carbon-stock variability in boreal forests and that a recent increase in fire frequency since 1950 has led to large carbon losses\nde Groot WJ, Flannigan MD, Cantin AS. Climate change impacts on future boreal fire regimes. For Ecol Manag. 2013;294:35–44. doi:10.1016\u002Fj.foreco.2012.09.027.\nMurphy BP, Bowman DMJS. What controls the distribution of tropical forest and savanna? Ecol Lett. 2012;15:748–58. doi:10.1111\u002Fj.1461-0248.2012.01771.x.\nRanderson JT, Chen Y, Werf GR, Rogers BM, Morton DC. Global burned area and biomass burning emissions from small fires. J Geophys Res: Biogeosci. 2012;117, G04012. doi:10.1029\u002F2012JG002128.\nFarquhar GD. Carbon dioxide and vegetation. Science. 1997;278:1411. doi:10.1126\u002Fscience.278.5342.1411.\nFrank DC, Poulter B, Saurer M, Esper J, Huntingford C, Helle G, et al. Water-use efficiency and transpiration across European forests during the Anthropocene. Nat Clim Chang. 2015;5:579–83. doi:10.1038\u002Fnclimate2614.\nDe Kauwe MG, Medlyn BE, Zaehle S, Walker AP, Dietze MC, Hickler T, et al. Forest water use and water use efficiency at elevated CO2: a model‐data intercomparison at two contrasting temperate forest FACE sites. Glob Chang Biol. 2013;19:1759–79. doi:10.1111\u002Fgcb.12164.\nRoderick ML, Greve P, Farquhar GD. On the assessment of aridity with changes in atmospheric CO2. Water Resour Res. 2015;51:5450–63. doi:10.1002\u002F2015WR017031.\nAllen CD, Breshears DD, McDowell NG. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere. 2015;6:1–55. doi:10.1890\u002FES15-00203.1.\nZhang K, Kimball JS, Nemani RR, Running SW, Hong Y, Gourley JJ, et al. Vegetation greening and climate change promote multidecadal rises of global land evapotranspiration. Nat Scientif Rep. 2015;5:15956. doi:10.1038\u002Fsrep15956.\nDonohue RJ, Roderick ML, McVicar TR, Farquhar GD. Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys Res Lett. 2013;40:3031–5. doi:10.1002\u002Fgrl.50563.\nUkkola AM, Prentice IC, Keenan TF, van Dijk AIJM, Viney NR, Myneni RB, et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat Clim Chang. 2015. doi:10.1038\u002Fnclimate2831.\nXu C, Liu H, Williams AP, Yin Y, Wu X. Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Glob Chang Biol. 2016. doi:10.1111\u002Fgcb.13224.\nFriend AD, Lucht W, Rademacher TT, Keribin R, Betts R, Cadule P, et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc Natl Acad Sci U S A. 2014;111:3280–5. doi:10.1073\u002Fpnas.1222477110.\nAnderegg WRL, Hicke JA, Fisher RA, Allen CD, Aukema J, Bentz B, et al. Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol. 2015;208:674–83. doi:10.1111\u002Fnph.13477.\nMcDowell NG, Fischer RA, Xu C, Domec JC, Hölttä T, Mackay DS, et al. Evaluating theories of drouht-induced vegetation mortality using a multimodel-experiment framework. New Phytol. 2013;200:304–21. doi:10.1111\u002Fnph.12465.\nKeenan TF, Baker I, Barr A, Ciais P, Davis K, Dietze M, et al. Terrestrial biosphere model performance for inter‐annual variability of land‐atmosphere CO2 exchange. Glob Chang Biol. 2012;18:1971–87. doi:10.1111\u002Fj.1365-2486.2012.02678.x.\nLi F, Levis S, Ward DS. Quantifying the role of fire in the Earth system–Part 1: improved global fire modeling in the Community Earth System Model (CESM1). Biogeosciences. 2013;10:2293–314. doi:10.5194\u002Fbg-10-2293-2013.\nSchweizer VJ, O’Neill BC. Systematic construction of global socioeconomic pathways using internally consistent element combinations. Clim Chang. 2014;122:431–45. doi:10.1007\u002Fs10584-013-0908-z.\nRanders J. 2015: A Global Forecast for the Next Forty Years. White River Junction: Chelsea Green Publishing; 2012.\nvan Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, et al. The representative concentration pathways: an overview. Clim Chang. 2011;109:5–31. doi:10.1007\u002Fs10584-011-0148-z.\nKnutti R, Sedláček J. Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Chang. 2013;3:369–73. doi:10.1038\u002Fnclimate1716.\nZhang X, Liu H, Zhang M. Double ITCZ in coupled ocean–atmosphere models: from CMIP3 to CMIP5. Geophys Res Lett. 2015;42:8651–9. doi:10.1002\u002F2015GL065973.\nRomps DM, Seeley JT, Vollaro D, Molinari J. Projected increase in lightning strikes in the United States due to global warming. Science. 2014;346:851–4. doi:10.1126\u002Fscience.1259100.\nPfeiffer M, Spessa A, Kaplan JO. A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0). Geosci Model Dev. 2013;6:643–85. doi:10.5194\u002Fgmd-6-643-2013.\nMagi BI. Global lightning parameterization from CMIP5 climate model output. J Atmos Ocean Technol. 2015;32:434–52. doi:10.1175\u002FJTECH-D-13-00261.1.\nAllen DJ, Pickering KE. Evaluation of lightning flash rate parameterizations for use in a global chemical transport model. J Geophys Res: Atmos. 2002;107, ACH 15-11-21. doi:10.1029\u002F2002JD002066.\nPrice C, Rind D. A simple lightning parameterization for calculating global lightning distributions. J Geophys Res: Atmos. 1992;97:9919–33. doi:10.1029\u002F92JD00719.\nHoltslag AAM, Svensson G, Baas P, Basu S, Beare B, Beljaars ACM, et al. Stable atmospheric boundary layers and diurnal cycles: challenges for weather and climate models. Bull Am Meteorol Soc. 2013;94:1691–706. doi:10.1175\u002FBAMS-D-11-00187.1.",{"EN":331},"Fire is an integral component of the Earth system that will critically affect how terrestrial carbon budgets and living systems respond to climate change. Paleo and observational records document robust positive relationships between fire activity and aridity in many parts of the world on interannual to millennial timescales. Observed increases in fire activity and aridity in many areas over the past several decades motivate curiosity as to the degree to which anthropogenic climate change will alter global fire regimes and subsequently Earth’s terrestrial biosphere. Importantly, fire responses to warming are not ubiquitous and effects by humans, fuels, and non-temperature climate variables are also apparent in both paleo and observational datasets. The complicated and interactive relationships among these variables necessitate quantitative modeling to better understand future fire responses to global change. Macro-scale fire models exhibit a wide spectrum of complexity. Correlation-based models are inherently superior at representing the current global mean distribution of fire activity but future projections developed from these models cannot account for important processes such as CO2 fertilization and vegetation response after extreme events. Process-based models address some of these limitations by explicitly modeling vegetation dynamics, but this requires false assumptions about processes that are not yet well understood. Continued empirical evaluation of interactions between fire, vegetation, climate, and humans, and resultant improvements to both correlation- and process-based macro-fire models, are mandatory to better understand the past and future of the Earth system.",{"EN":333},"Recent Advances and Remaining Uncertainties in Resolving Past and Future Climate Effects on Global Fire Activity",{"VOID":335},"10.1007\u002Fs40641-016-0031-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40641-016-0031-0",[338,355],{"id":339,"sortIndex":19,"researcher":18,"roles":340,"affiliations":341,"properties":352},"0e88dc13-210b-4d63-8b91-360f17eacc79",[169],[342],{"id":18,"sortIndex":19,"affiliation":343,"properties":18},{"id":344,"createTime":345,"updateTime":346,"relativeEntities":347,"slug":348,"properties":349,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"868de44b-d3d9-4ec1-a841-52a73c2e329d","2023-12-06T21:17:30.884+00:00","2024-10-12T14:00:39.970+00:00",[],"Lamont-Doherty-Earth-Observatory-Columbia-University-Palisades-USA",{"title":350},{"VI":351},"Lamont Doherty Earth Observatory, Columbia University, Palisades, USA",{"title":353},{"VI":354},"A. Park Williams",{"id":356,"sortIndex":254,"researcher":18,"roles":357,"affiliations":358,"properties":367},"021b6c8d-daf8-49c1-89b3-f2ba02aac84b",[169],[359],{"id":18,"sortIndex":19,"affiliation":360,"properties":18},{"id":361,"createTime":362,"updateTime":362,"relativeEntities":363,"slug":18,"properties":364,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"6608a9b6-5556-43a4-9a0c-e0516d433087","2024-01-04T02:48:27.775+00:00",[],{"title":365},{"VI":366},"Department of Geography, University of Idaho, Moscow, USA",{"title":368},{"VI":369},"John T. Abatzoglou",{"url":336,"publisher":371,"properties":398},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":372,"slug":10,"properties":373,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":376,"manageAffiliations":377,"indexDatabases":378,"url":18,"thumbnailPath":18,"statistic":393,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":374,"title":375},{"VOID":13},{"EN":15},[],[],[379,386],{"id":64,"indexDatabase":380,"url":77,"indexYears":78,"academicFieldIds":385,"indexDatabaseRanking":82},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":381,"label":382,"description":383,"key":74,"publicationTags":384,"standard":18},[],{"EN":71,"VI":71},{"EN":71,"VI":73},[76],[80,81],{"id":84,"indexDatabase":387,"url":99,"indexYears":18,"academicFieldIds":392,"indexDatabaseRanking":18},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":388,"label":389,"description":390,"key":95,"publicationTags":391,"standard":18},[],{"EN":91,"VI":91},{"VI":93,"EN":94},[97,98],[101],{"impactFactor":19,"impactFactorByYear":394,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":395,"totalCitation":123,"totalCitationByYear":396,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":397,"hindexLast5Year":141,"hindex":141},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":116,"2020":60,"2021":120,"2022":121,"2024":122},{"2015":125,"2016":126,"2017":127,"2018":128,"2019":129,"2020":130,"2021":131},{"2015":134,"2016":135,"2017":136,"2018":137,"2019":138,"2020":139,"2021":140},{"volume":399,"pages":401},{"VOID":400},"2",{"VOID":402},"1-14","2016-02-09",2016,{"id":406,"createTime":407,"updateTime":408,"relativeEntities":409,"slug":410,"properties":411,"entityType":161,"verifyStatus":162,"verifyTime":408,"verifyNote":163,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":420,"fullTextUrl":18,"authors":421,"publicationType":182,"publisherRelationship":437,"citationCount":18,"citationInfo":18,"publishDate":469,"publishYear":470,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":218},"39f07fde-ea90-439d-849d-f3bc19af90b2","2024-01-11T03:47:33.651+00:00","2024-12-07T22:53:13.793+00:00",[],"The-Transient-Response-to-Cumulative-CO2-Emissions-a-Review",{"references":412,"abstract":414,"title":416,"doi":418},{"VOID":413},"Allen MR, Frame DJ, Huntingford C, Jones CD, Lowe JA, Meinshausen M, Meinshausen N. Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 2009;458(7242):1163–1166.\nArora VK, Boer GJ, Friedlingstein P, Eby M, Jones CD, Christian JR, Bonan G, Bopp L, Brovkin V, Cadule P, et al. Carbon-concentration and carbon-climate feedbacks in CMIP5 earth system models. J Clim 2013;26(15).\nCherubini F, Gasser T, Bright RM, Ciais P, Strømman AH. Linearity between temperature peak and bioenergy CO2 emission rates. Nat Clim Chang 2014;4(11):983–987.\nCiais P, Sabine C, Bala G, Bopp L, Brovkin V, Canadell J, Chhabra A, DeFries R, Galloway J, Heimann M, Jones C, Quéé CL, Myneni RB, Piao S, Thornton P. Carbon and other biogeochemical cycles. Working Group I Contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report Climate Change 2013: The Physical Science Basis. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, and Midgley P, editors. Cambridge University Press; 2013.\nCollins M, Knutti R, Arblaster JM, Dufresne JL, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M. Long-term climate change: Projections, commitments and irreversibility. Working Group I Contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report Climate Change 2013: The Physical Science Basis. Cambridge University Press ; 2013.\nEby M, Weaver AJ, Alexander K, Zickfeld K, Abe-Ouchi A, Cimatoribus A, Crespin E, Drijfhout S, Edwards N, Eliseev A, et al. Historical and idealized climate model experiments: an intercomparison of earth system models of intermediate complexity. Clim Past 2013;9:1111–1140. doi:10.5194\u002Fcp-9-1111-2013.\nden Elzen M, Hare W, Höhne N, Levin K, Lowe J, Riahi K, Rogelj J, Sawin E, Taylor C, van Vuuren D, et al. 2010. The emissions gap report: Are the copenhagen accord pledges sufficient to limit global warming to 2∘C or 1.5∘C? A preliminary assessment. Tech. rep., United Nations Environment Programme.\nFriedlingstein P, Cox P, Betts R, Bopp L, von Bloh W, Brovkin V, Cadule P, Doney S, Eby M, Fung I, Bala G, John J, Jones C, Joos F, Kato T, Kawamiy M, Knorr W, Lindsay K, Matthews HD, Raddatz T, Rayner P, Reick C, Roeckner E, Schnitzler KG, Schnur R, Strassmann K, Weaver AJ, Yoshikawa C, Zeng N. Climatecarbon cycle feedback analysis: Results from the C4MIP model intercomparison. J Clim 2006;19:3337–3353.\nFriedlingstein P, Andrew R, Rogelj J, Peters G, Canadell J, Knutti R, Luderer G, Raupach M, Schaeffer M, van Vuuren D, et al. Persistent growth of CO2 emissions and implications for reaching climate targets. Nat Geosci 2014;7(10): 709–715.\nFrölicher TL, Paynter DJ. Extending the relationship between global warming and cumulative carbon emissions to multi-millennial timescales. Environ Res Lett 2015;10(7):075,002.\nFrölicher TL, Sarmiento JL, Paynter DJ, Dunne JP, Krasting JP, Winton M. Dominance of the southern ocean in anthropogenic carbon and heat uptake in cmip5 models. J Clim 2014a;28(2014):862–886.\nFrölicher TL, Winton M, Sarmiento JL. Continued global warming after co2 emissions stoppage. Nat Clim Chang 2014b;4(1):40–44.\nGillett NP, Arora VK, Matthews D, Allen MR. Constraining the ratio of global warming to cumulative CO2 emissions using cmip5 simulations. J Clim 2013;26:6844–6858.\nGoodwin P, Williams RG, Ridgwell A. Sensitivity of climate to cumulative carbon emissions due to compensation of ocean heat and carbon uptake. Nat Geosci 2015;8(1):29–34.\nGregory JM, Jones CD, Cadule P, Friedlingstein P. Quantifying carbon cycle feedbacks. J Clim 2009; 22(19):5232–5250.\nHerrington T, Zickfeld K. Path independence of climate and carbon cycle response over a broad range of cumulative carbon emissions. Earth Syst Dyn 2014;5(2):409–422.\nIPCC. Summary for policymakers. Working Group I Contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report Climate Change 2013: The Physical Science Basis. In: Alexander L, Allen S, Bindoff NL, Bron FM, Church J, Cubasch U, Emori S, Forster P, Friedlingstein P, Gillett N, Gregory J, Hartmann D, Jansen E, Kirtman B, Knutti R, Kanikicharla KK, Lemke P, Marotzke J, Masson-Delmotte V, Meehl G, Mokhov I, Piao S, Plattner GK, Dahe Q, Ramaswamy V, Randall D, Rhein M, Rojas M, Sabine C, Shindell D, Stocker TF, Talley L, Vaughan D, and Xie SP, editors. Cambridge University Press; 2013.\nJeffrey A, Zwillinger D. Table of integrals, series, and products: Elsevier Science; 2000.\nKnutti R, Rogelj J. The legacy of our co2 emissions: a clash of scientific facts, politics and ethics. Clim Chang 2015:1–13.\nKrasting J, Dunne J, Shevliakova E, Stouffer R. Trajectory sensitivity of the transient climate response to cumulative carbon emissions. Geophys Res Lett 2014;41(7):2520–2527.\nMacDougall AH, Friedlingstein P. The origin and limits of the near proportionality between climate warming and cumulative CO2 emissions. J Clim 2015;28:4217–4230. doi:10.1175\u002FJCLI-D-14-00036.1.\nMacDougall AH, Eby M, Weaver AJ. If anthropogenic CO2 emissions cease, will atmospheric CO2 concentration continue to increase? J Clim 2013;26:9563–9576. doi:10.1175\u002FJCL-D-12-00751.1.\nMatthews HD, Caldeira K. Stabilizing climate requires near–zero emissions. Geophys Res Lett 2008;35: L04,705. doi:10.1029\u002F2007GL032388.\nMatthews HD, Gillett NP, Stott PA, Zickfeld K. The proportionality of global warming to cumulative carbon emissions. Nature 2009;459:829–832. doi:10.1038\u002Fnature08047.\nMatthews HD, Solomon S, Pierrehumbert R. Cumulative carbon as a policy framework for achieving climate stabilization. Philos Trans R Soc A Math Phys Eng Sci 2012;370(1974):4365–4379.\nMeinshausen M, Meinshausen N, Hare W, Raper SC, Frieler K, Knutti R, Frame DJ, Allen MR. Greenhouse-gas emission targets for limiting global warming to 2 c. Nature 2009;458(7242): 1158–1162.\nMoss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ. The next generation of scenarios for climate change research and assessment. Nature 2010;463:747–754. doi:10.1038\u002Fnature08823.\nNakicenovic N, Swart R. Special Report on Emissions Scenarios. In: Nakicenovic N and Swart R, editors. Cambridge, UK: Cambridge University Press; 2000. p. 612. ISBN 0521804 930.\nNohara D, Yoshida Y, Misumi K, Ohba M. Dependency of climate change and carbon cycle on co2 emission pathways. Environ Res Lett 2013;8(1):014,047.\nPierrehumbert R. Short-lived climate pollution. Annu Rev Earth Planet Sci 2014;42(1):341.\nRaupach M. The exponential eigenmodes of the carbon-climate system, and their implications for ratios of responses to forcings. Earth Syst Dyn 2013;4:31–49.\nRogelj J, Schaeffer M, Meinshausen M, Shindell DT, Hare W, Klimont Z, Velders GJ, Amann M, Schellnhuber HJ. Disentangling the effects of co2 and short-lived climate forcer mitigation. Proc Natl Acad Sci 2014;111(46): 16,325–16,330.\nSmith SM, Lowe JA, Bowerman NH, Gohar LK, Huntingford C, Allen MR. Equivalence of greenhouse-gas emissions for peak temperature limits. Nat Clim Chang 2012;2(7): 535–538.\nSolomon S, Plattner GK, Knutti R, Friedlingstein P. Irreversible climate change due to carbon dioxide emissions. Proc Natl Acad Sci 2009;106(6):1704–1709.\nStocker T, Qin D, Plattner GK, Alexander L, Allen S, Bindoff N, Bréon FM, Church J, Cubasch U, Emori S, Forster P, Friedlingstein P, Gillett N, Gregory J, Hartmann D, Jansen E, Kirtman B, Knutti R, Kumar KK, Lemke P, Marotzke J, Masson-Delmotte V, Meehl G, Mokhov I, Piao S, Ramaswamy V, Randall D, Rhein M, Rojas M, Sabine C, Shindell D, Talley L, Vaughan D, Xie SP. Technical summary. Working Group I Contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report Climate Change 2013: The Physical Science Basis. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, and Midgley P, editors. Cambridge University Press; 2013.\nWigley TM, Schlesinger ME. Analytical solution for the effect of increasing CO2 on global mean temperature. Nature 1985;315:649–652.\nWigley TML. Relative contributions of different trace gases to the greenhouse effect. Climate Monitor 1987;16 (1):14–28.\nZickfeld K, Eby M, Matthews HD, Weaver AJ. Setting cumulative emissions targets to reduce the risk of dangerous climate change. Proc Natl Acad Sci 2009;106(38):16,129–16,134.\nZickfeld K, Arora V, Gillett N. Is the climate response to CO2 emissions path dependent? Geophys Res Lett 2012;39(5):L05,703.\nZickfeld K, Eby M, Weaver AJ, Alexander K, Crespin E, Edwards NR, Eliseev AV, Feulner G, Fichefet T, Forest CE, et al. Long-term climate change commitment and reversibility: an EMIC intercomparison. J Clim 2013;26(16).",{"EN":415},"The transient climate response to cumulative CO2 emissions (TCRE) is a metric of climate change that directly relates the primary cause of climate change (cumulative CO2 emissions) to global mean temperature change. The metric was developed once researchers noticed that the cumulative CO2 versus temperature change curve was nearly linear for almost all Earth system model output. Here, recent literature on the origin, limits, and value of TCRE is reviewed. TCRE appears to emerge from the diminishing radiative forcing per unit mass of atmospheric CO2 being compensated by diminishing efficiency of ocean heat uptake and the modulation of airborne fraction of carbon by ocean processes. The best estimate of the value of TCRE is between 0.8 to 2.5 K EgC−1. Overall, TCRE has been shown to be a conceptually simple and robust metric of climate warming with many applications in formulating climate policy.",{"EN":417},"The Transient Response to Cumulative CO2 Emissions: a Review",{"VOID":419},"10.1007\u002Fs40641-015-0030-6","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40641-015-0030-6",[422],{"id":423,"sortIndex":19,"researcher":18,"roles":424,"affiliations":425,"properties":434},"919a7821-0d4d-4443-a631-30a9524bda1c",[169],[426],{"id":18,"sortIndex":19,"affiliation":427,"properties":18},{"id":428,"createTime":429,"updateTime":429,"relativeEntities":430,"slug":18,"properties":431,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"8e6b6296-2d8c-4d60-b443-e8e43eb6ac4f","2024-01-05T04:02:13.567+00:00",[],{"title":432},{"VI":433},"Institute for Atmospheric and Climate Science, Zurich, Switzerland",{"title":435},{"VI":436},"Andrew H. MacDougall",{"url":420,"publisher":438,"properties":465},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":439,"slug":10,"properties":440,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":443,"manageAffiliations":444,"indexDatabases":445,"url":18,"thumbnailPath":18,"statistic":460,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":441,"title":442},{"VOID":13},{"EN":15},[],[],[446,453],{"id":64,"indexDatabase":447,"url":77,"indexYears":78,"academicFieldIds":452,"indexDatabaseRanking":82},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":448,"label":449,"description":450,"key":74,"publicationTags":451,"standard":18},[],{"EN":71,"VI":71},{"EN":71,"VI":73},[76],[80,81],{"id":84,"indexDatabase":454,"url":99,"indexYears":18,"academicFieldIds":459,"indexDatabaseRanking":18},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":455,"label":456,"description":457,"key":95,"publicationTags":458,"standard":18},[],{"EN":91,"VI":91},{"VI":93,"EN":94},[97,98],[101],{"impactFactor":19,"impactFactorByYear":461,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":462,"totalCitation":123,"totalCitationByYear":463,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":464,"hindexLast5Year":141,"hindex":141},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":116,"2020":60,"2021":120,"2022":121,"2024":122},{"2015":125,"2016":126,"2017":127,"2018":128,"2019":129,"2020":130,"2021":131},{"2015":134,"2016":135,"2017":136,"2018":137,"2019":138,"2020":139,"2021":140},{"volume":466,"pages":467},{"VOID":400},{"VOID":468},"39-47","2015-11-17",2015,{"id":472,"createTime":473,"updateTime":474,"relativeEntities":475,"slug":476,"properties":477,"entityType":161,"verifyStatus":162,"verifyTime":474,"verifyNote":163,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":486,"fullTextUrl":18,"authors":487,"publicationType":182,"publisherRelationship":503,"citationCount":18,"citationInfo":18,"publishDate":536,"publishYear":537,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":218},"b2b76516-1c54-43ff-870f-43e5c4c0d8e4","2023-12-27T17:01:17.614+00:00","2024-12-11T22:42:26.335+00:00",[],"Drought-and-Fire-in-the-Western-USA-Is-Climate-Attribution-Enough-",{"references":478,"abstract":480,"title":482,"doi":484},{"VOID":479},"• Moritz MA, Batllori E, Bradstock RA, Gill AM, Handmer J, Hessburg PF, et al. Learning to coexist with wildfire. Nature. 2014;515:58–66. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature13946. This paper highlights and substantiates the idea that wildland fire cannot be managed independently of its context as a social ecological system.\nLee C, Schlemme C, Murray J, Unsworth R. The cost of climate change: ecosystem services and wildland fires. Ecol Econ. 2015;116:261–9. https:\u002F\u002Fdoi.org\u002F10.1016\u002FJ.ECOLECON.2015.04.020.\nAger AA, Barros AMG, Preisler HK, Day MA, Spies TA, Bailey JD, et al. Effects of accelerated wildfire on future fire regimes and implications for the United States federal fire policy. Ecol Soc. 2017;22:art12. https:\u002F\u002Fdoi.org\u002F10.5751\u002FES-09680-220412.\nBowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM, Cochrane MA, et al. Fire in the Earth System. Science (80- ). 2009;324:481–4. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1163886.\nFlannigan MD, Krawchuk MA, de Groot WJ, Wotton BM, Gowman LM. Implications of changing climate for global wildland fire. Int J Wildl Fire. 2009;18:483. https:\u002F\u002Fdoi.org\u002F10.1071\u002FWF08187.\n•• McKenzie D, Littell JS. Climate change and the eco-hydrology of fire: will area burned increase in a warming western USA. Ecol Appl. 2017;27:26–36. https:\u002F\u002Fdoi.org\u002F10.1002\u002Feap.1420. This paper underscores the non-stationarity in climate-fire relationships in the American West and shows that fuel- and flammability-limited systems may exhibit a wide range of behavior under climate change. It further argues that statistical climate-fire relationships are likely to be of limited use in a no-analog future.\n•• Schoennagel T, Balch JK, Brenkert-Smith H, Dennison PE, Harvey BJ, Krawchuk MA, et al. Adapt to more wildfire in western North American forests as climate changes. Proc Natl Acad Sci U S A. 2017;114:4582–90. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1617464114. This paper lays out the dimensions of problems confronted by people given current and future wildfire, and underscores the need for deliberate adaptation.\nFu Q, Feng S. Responses of terrestrial aridity to global warming. J Geophys Res Atmos. 2014;119:7863–75. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2014JD021608.\nChapin FS, Trainor SF, Huntington O, Lovecraft AL, Zavaleta E, Natcher DC, et al. Increasing wildfire in Alaska’s boreal Forest: pathways to potential solutions of a wicked problem. Bioscience. 2008;58:531–40. https:\u002F\u002Fdoi.org\u002F10.1641\u002FB580609.\nBowman DMJS, Murphy BP, Williamson GJ, Cochrane MA. Pyrogeographic models, feedbacks and the future of global fire regimes. Glob Ecol Biogeogr. 2014;23:821–4. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgeb.12180.\n• Fischer AP, Spies TA, Steelman TA, Moseley C, Johnson BR, Bailey JD, et al. Wildfire risk as a socioecological pathology. Front Ecol Environ. 2016;14:276–84. https:\u002F\u002Fdoi.org\u002F10.1002\u002Ffee.1283. This paper refines the description of fire social-ecological systems and substantiates the case that fire is a coupled human-natural system.\nO’Connor CD, Garfin GM, Falk DA, Swetnam TW. Human pyrogeography: a new synergy of fire, climate and people is reshaping ecosystems across the globe. Geogr Compass. 2011;5:329–50. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1749-8198.2011.00428.x.\n• DMJS B, Garnett ST, Barlow S, Bekessy SA, Bellairs SM, Bishop MJ, et al. Renewal ecology: conservation for the Anthropocene. Restor Ecol. 2017;25:674–80. https:\u002F\u002Fdoi.org\u002F10.1111\u002Frec.12560. This paper provides one conceptual framework for thinking about coupled human natural systems, especially ways in which fire can be embraced rather than managed by suppression.\nPohl C, Hirsch Hadorn G. Principles for designing transdisciplinary research: proposed by the Swiss Academies of Arts and Sciences. München: oekom Verlag; 2007.\nPohl C, Hirsch Hadorn G. Methodological challenges of transdisciplinary research. Nat Sci Soc. 2008;16:111–21. https:\u002F\u002Fdoi.org\u002F10.1051\u002Fnss:2008035.\nKeane RE, Ryan KC, Veblen TT, Allen CD, Logan J, Hawkes B. Cascading effects of fire exclusion in the Rocky Mountain ecosystems: a literature review. In: General Technical Report. RMRS-GTR-91. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 24 p. (Vol. 91); 2002. https:\u002F\u002Fdoi.org\u002F10.2737\u002FRMRS-GTR-91.\nMiller C. The hidden consequences of fire suppression. Park Science. 2012; 28(3). Online: http:\u002F\u002Fwww.nature.nps.gov\u002FParkScience\u002Findex.cfm?ArticleID=547&Page=1 . Retrieved from https:\u002F\u002Fwww.fs.usda.gov\u002Ftreesearch\u002Fpubs\u002F40395\n• Kitzberger T, Falk DA, Westerling AL, Swetnam TW. Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America. PLoS One. 2017;12:e0188486. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0188486. This paper describes both human and climatic contributions to fire, and describes further the hazards of projecting statistical climate-fire relationships\n•• Crausbay SD, Ramirez AR, Carter SL, Cross MS, Hall KR, Bathke DJ, et al. Defining ecological drought for the twenty-first century. Bull Am Meteorol Soc. 2017;98:2543–50. https:\u002F\u002Fdoi.org\u002F10.1175\u002FBAMS-D-16-0292.1. This paper expands the definition of drought to include ecological drought and its human dimensions.\n• Littell JS, Peterson DL, Riley KL, Liu Y, Luce CH. A review of the relationships between drought and forest fire in the United States. Glob Chang Biol. 2016;22:2353–69. This paper reviews application of physical, hydrologic, and ecohydrological drought mechanisms in fire research in the US.\nDai A. Characteristics and trends in various forms of the palmer drought severity index during 1900–2008. J Geophys Res. 2011;116:D12115. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2010JD015541.\nWesterling AL, Gershunov A, Brown TJ, Cayan DR, Dettinger MD, Westerling AL, et al. Climate and wildfire in the western United States. Bull Am Meteorol Soc. 2003;84:595–604. https:\u002F\u002Fdoi.org\u002F10.1175\u002FBAMS-84-5-595.\nLittell JS, Oneil EE, McKenzie D, Hicke JA, Lutz JA, Norheim RA, et al. Forest ecosystems, disturbance, and climatic change in Washington state, USA. Clim Chang. 2010;102:129–58. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10584-010-9858-x.\nLittell JS, Gwozdz RB (2011) Climatic water balance and regional fire years in the Pacific Northwest, USA: linking regional climate and fire at landscape scales. pp 117–139 in McKenzie, D C. Miller, and D.A. Falk, eds. The landscape ecology of fire, Dordrecht Springer Ltd.\nMcKenzie D, Littell JS. Climate change and wilderness fire regimes. Int J Wilderness. 2011;17:22–31.\nAbatzoglou JT, Kolden CA. Relationships between climate and macroscale area burned in the western United States. Int J Wildl Fire. 2013;22:1003–20. https:\u002F\u002Fdoi.org\u002F10.1071\u002FWF13019.\nSherwood S, Fu Q. A Drier future? Science. 2014;343(6172):737–9. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1247620.\nBreshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, et al. Regional vegetation die-off in response to global-change-type drought. Proc Natl Acad Sci. 2005;102:15144–8. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.0505734102.\nMo KC, Lettenmaier DP. Precipitation deficit flash droughts over the United States. J Hydrometeorol. 2016;17:1169–84. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJHM-D-15-0158.1.\nWilliams AP, Seager R, Berkelhammer M, Macalady AK, Crimmins MA, Swetnam TW, et al. Causes and implications of extreme atmospheric moisture demand during the record-breaking 2011 wildfire season in the Southwestern United States. J App Met Clim. 2014;53:2671–84. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAMC-D-14-0053.1.\nMarlier ME, Xiao M, Engel R, Livneh B, Abatzoglou JT, Lettenmaier DP. The 2015 drought in Washington state: a harbinger of things to come? Environ Res Lett. 2017;12:114008. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002Faa8fde.\n• Harpold AA, Dettinger M, Rajagopal S. Defining snow drought and why it matters. Eos. 2017;98 https:\u002F\u002Fdoi.org\u002F10.1029\u002F2017EO068775. This paper clarifies terminology used for some time and distinguishes snow drought from – and relates it to other forms of drought.\nWesterling AL, Hidalgo HG, Cayan DR, Swetnam TW. Warming and earlier spring increase western U.S. forest wildfire activity. Science. 2006;313:940–3. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1128834.\nWesterling AL. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philos Trans R Soc Lond Ser B Biol Sci. 2016;371:20150178. https:\u002F\u002Fdoi.org\u002F10.1098\u002Frstb.2015.0178.\nGergel DR, Nijssen B, Abatzoglou JT, Lettenmaier DP, Stumbaugh MR. Effects of climate change on snowpack and fire potential in the western USA. Clim Chang. 2017;141:287–99. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10584-017-1899-y.\nHolden ZA, Luce CH, Crimmins MA, Morgan P. Wildfire extent and severity correlated with annual streamflow distribution and timing in the Pacific Northwest, USA (1984-2005). Ecohydrology. 2012;5:677–84. https:\u002F\u002Fdoi.org\u002F10.1002\u002Feco.257.\nWilliams AP, Abatzoglou JT. Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity. Curr Clim Chang Rep. 2016;2:1–14. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40641-016-0031-0.\nvan der Werf GR, Randerson JT, Giglio L, Gobron N, Dolman AJ. Climate controls on the variability of fires in the tropics and subtropics. Global Biogeochem Cycles. 2008;22:n\u002Fa-n\u002Fa. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2007GB003122.\nSchroeder MJ. Critical fire weather patterns in the conterminous United States. Silver Spring, MD: Environmental Science Services Administration; 1969.\nPotter BE. Atmospheric interactions with wildland fire behaviour - I. Basic surface interactions, vertical profiles and synoptic structures. Int J Wildl Fire. 2012;21:779. https:\u002F\u002Fdoi.org\u002F10.1071\u002FWF11128.\nJohnson EA, Wowchuk DR. Wildfires in the southern Canadian Rocky Mountains and their relationship to mid-tropospheric anomalies. Can J For Res. 1993;23:1213–22. https:\u002F\u002Fdoi.org\u002F10.1139\u002Fx93-153.\nSkinner WR, Stocks BJ, Martell DL, Bonsal B, Shabbar A. The association between circulation anomalies in the mid-troposphere and area burned by wildland fire in Canada. Theor Appl Climatol. 1999;63:89–105. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs007040050095.\nGedalof Z, Peterson DL, Mantua NJ. Atmospheric, climatic, and ecological controls on extreme wildfire years in the northwestern United States. Ecol Appl. 2005;15:154–74. https:\u002F\u002Fdoi.org\u002F10.1890\u002F03-5116.\nCrimmins MA. Synoptic climatology of extreme fire-weather conditions across the southwest United States. Int J Climatol. 2006;26:1001–16. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjoc.1300.\nTrouet V, Taylor AH, Carleton AM, Skinner CN. Interannual variations in fire weather, fire extent, and synoptic-scale circulation patterns in northern California and Oregon. Theor Appl Climatol. 2009;95:349–60. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-008-0012-x.\nHostetler SW, Bartlein PJ, Holman JO. Atlas of climatic controls of wildfire in the Western United States. U.S. Geological Survey Scientific Investigations Report 5139. 2006; 67 p.\nDiaz HF, Swetnam TW. The wildfires of 1910: climatology of an extreme early twentieth-century event and comparison with more recent extremes. Bull Am Meteorol Soc. 2013;94:1361–70. https:\u002F\u002Fdoi.org\u002F10.1175\u002FBAMS-D-12-00150.1.\nRiley KL, Abatzoglou JT, Grenfell IC, Klene AE, Heinsch FA. The relationship of large fire occurrence with drought and fire danger indices in the western USA, 1984-2008: the role of temporal scale. Int J Wildl Fire. 2013;22:894–909. https:\u002F\u002Fdoi.org\u002F10.1071\u002FWF12149.\nCollins BM, Omi PN, Chapman PL. Regional relationships between climate and wildfire-burned area in the Interior West, USA. Can J For Res. 2006;36:699–709. https:\u002F\u002Fdoi.org\u002F10.1139\u002Fx05-264.\nMcKenzie D, Gedalof Z, Peterson DL, Mote P. Climatic change, wildfire, and conservation. Conserv Biol. 2004;18:890–902.\nLittell JS, Mckenzie D, Peterson DL, Westerling AL. Climate and wildfire area burned in western U.S. ecoprovinces, 1916-2003. Ecol Appl. 2009;19:1003–21. https:\u002F\u002Fdoi.org\u002F10.1890\u002F07-1183.1.\n• Keeley JE, Syphard AD. Different historical fire–climate patterns in California. Int J Wildl Fire. 2017;26:253. https:\u002F\u002Fdoi.org\u002F10.1071\u002FWF16102. This paper illustrates both the differences in climate-fire relationships across vegetation types but also the role of fire management in potentially affecting the ways climate and fire interact.\nSwetnam TW. Fire history and climate change in giant sequoia groves. Science. 1993;262:885–9.\nSwetnam TW, Betancourt JL. Fire-southern oscillation relations in the southwestern United States. Science. 1990;249:1017–20. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.249.4972.1017.\nSwetnam TW, Betancourt JL. Mesoscale disturbance and ecological response to decadal climatic variability in the American Southwest. J Clim. 1998;11:3128–47. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1998)011\u003C3128:MDAERT>2.0.CO;2.\nHeyerdahl EK, Brubaker LB, Agee JK. Annual and decadal climate forcing of historical fire regimes in the interior Pacific Northwest, USA. The Holocene. 2002;12:597–604. https:\u002F\u002Fdoi.org\u002F10.1191\u002F0959683602hl570rp.\nHessl AE, McKenzie D, Schellhaas R. Drought and pacific decadal oscillation linked to fire occurrence in the inland pacific northwest. Ecol Appl. 2004;14:425–42. https:\u002F\u002Fdoi.org\u002F10.1890\u002F03-5019.\nGavin DG, Hallett DJ, Hu FS, Lertzman KP, Prichard SJ, Brown KJ, et al. Forest fire and climate change in western North America: insights from sediment charcoal records. Front Ecol Environ. 2007;5:499–506. https:\u002F\u002Fdoi.org\u002F10.1890\u002F060161.\nHeyerdahl EK, McKenzie D, Daniels LD, Hessl AE, Littell JS, Mantua NJ. Climate drivers of regionally synchronous fires in the inland Northwest (16511900). Int J Wildl Fire. 2008;17:40–9. https:\u002F\u002Fdoi.org\u002F10.1071\u002FWF07024.\nMargolis EQ, Woodhouse CA, Swetnam TW. Drought, multi-seasonal climate, and wildfire in northern New Mexico. Clim Chang. 2017;142:433–46. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10584-017-1958-4.\nSchoennagel T, Veblen TT, Romme WH, Sibold JS, Cook ER. ENSO and PDO variability affect drought-induced fire occurrence in Rocky Mountain subalpine forests. Ecol Appl. 2000;15:2000–14.\nDettinger MD, Cayan DR, Diaz HF, Meko DM. North–south precipitation patterns in Western North America on interannual-to-decadal timescales. J Clim. 1998;11:3095–111. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1998)011\u003C3095:NSPPIW>2.0.CO;2.\nMcCabe GJ, Palecki MA, Betancourt JL. Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc Natl Acad Sci U S A. 2004;101:4136–41. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.0306738101.\nMarlon JR, Bartlein PJ, Gavin DG, Long CJ, Anderson RS, Briles CE, et al. Long-term perspective on wildfires in the western USA. Proc Natl Acad Sci U S A. 2012;109:E535–43. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1112839109.\nSimard AJ, Haines DA, Main WA. Relations between El Nino\u002FSouthern Oscillation anomalies and wildland fire activity in the United States. Agric For Meteorol. 1985;36(2):93–104. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0168-1923(85)90001-2.\nKitzberger T, Brown PM, Heyerdahl EK, Swetnam TW, Veblen TT. Contingent Pacific-Atlantic Ocean influence on multicentury wildfire synchrony over western North America. Proc Natl Acad Sci U S A. 2007;104:543–8. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.0606078104.\nGedalof Z. Climate and spatial patterns of wildfire in North America. In: McKenzie D, Miller C, Falk D, editors. The landscape ecology of fire. Ecological Studies (Analysis and Synthesis). Dordrecht: Springer; 2011.\nCrimmins MA. Interannual to decadal changes in extreme fire weather event frequencies across the southwestern United States. Int J Climatol Int J Clim. 2011;31:1573–83. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjoc.2184.\nMason SA, Hamlington PE, Hamlington BD, Matt Jolly W, Hoffman CM. Effects of climate oscillations on wildland fire potential in the continental United States. Geophys Res Lett. 2017;44:7002–10. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017GL074111.\nSchubert SD, Stewart RE, Wang H, Barlow M, Berbery EH, Cai W, et al. Global meteorological drought: a synthesis of current understanding with a focus on SST drivers of precipitation deficits. J Clim. 2016;29:3989–4019.\n• McAfee SA, McAfee SA. Consistency and the lack thereof in Pacific Decadal Oscillation impacts on North American winter climate. J Clim. 2014;27:7410–31. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJCLI-D-14-00143.1. This paper questions the stationarity of the PDO influence on North American climatology and presents evidence that its predictive capacity is limited for impacts.\nBarbero R, Abatzoglou JT, Brown TJ. Seasonal reversal of the influence of El Niño-Southern Oscillation on very large wildfire occurrence in the interior northwestern United States. Geophys Res Lett. 2015;42:3538–45. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015GL063428.\nJolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun. 2015;6:6. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fncomms8537.\n•• Abatzoglou JT, Williams AP. Impact of anthropogenic climate change on wildfire across western US forests. Proc Natl Acad Sci. 2016;113:11770–5. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1607171113. This paper is the first quantitative climate change attribution study for forest fire – it assesses the relative contribution of anthropogenic climate change and climatic variability in the recent fire history.\nHostetler SW, Bartlein PJ, Alder JR. Atmospheric and surface climate associated with 1986-2013 wildfires in North America. J Geophys Res Biogeosci. 2018;123:1588–609. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2017JG004195.\nNicolai-Shaw N, Gudmundsson L, Hirschi M, Seneviratne SI. Long-term predictability of soil moisture dynamics at the global scale: persistence versus large-scale drivers. Geophys Res Lett. 2016;43:8554–62. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016GL069847.\nSeager R, Ting M. Decadal drought variability over North America: mechanisms and predictability. Curr Clim Chang Rep. 2017;3:141–9. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40641-017-0062-1.\nOwen G, Mcleod JD, Kolden CA, Ferguson DB, Brown TJ Wildfire management and forecasting fire potential: the roles of climate information and social networks in the Southwest United States. 2012; https:\u002F\u002Fdoi.org\u002F10.1175\u002FWCAS-D-11-00038.1,.\nHiguera PE, Abatzoglou JT, Littell JS, Morgan P. The changing strength and nature of fire-climate relationships in the northern Rocky Mountains, U.S.A., 1902-2008. PLoS One. 2015:10. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0127563.\nHolling CS, Gunderson LH. Chapter 2: Resilience and adaptive cycles. In: Gunderson LH, Holling CS, editors. Panarchy : understanding transformations in human and natural systems. Washington, D.C.: Island Press; 2002.\nFath BD, Dean CA, Katzmair H. Navigating the adaptive cycle: an approach to managing the resilience of social systems. Ecol Soc. 2015;20:art24. https:\u002F\u002Fdoi.org\u002F10.5751\u002FES-07467-200224.\nLittell JS, Peterson DL, Millar CI, O’Halloran KA. U.S. National Forests adapt to climate change through science–management partnerships. Clim Chang. 2012;110:269–96. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10584-011-0066-0.\nHolling CS. Resilience and stability of ecological systems. Annu Rev Ecol Syst. 1973;4:1–23. https:\u002F\u002Fdoi.org\u002F10.1146\u002Fannurev.es.04.110173.000245.\nDoane DL, O’Laughlin J, Morgan P, Miller C. Barriers to wildland fire use: a preliminary problem analysis. Int J Wilderness. 2006;12(1):36–8.",{"EN":481},"I sought to review the contributions of recent literature and prior foundational papers to our understanding of drought and fire. In this review, I summarize recent literature on drought and fire in the western USA and discuss research directions that may increase the utility of that body of work for twenty-first century application. I then describe gaps in the synthetic knowledge of drought-driven fire in managed ecosystems and use concepts from use-inspired research to describe potentially useful extensions of current work. Fire responses to climate, and specifically various kinds of drought, are clear, but vary widely with fuel responses to surplus water and drought at different timescales. Ecological and physical factors interact with human management and ignitions to create fire regime and landscape trajectories that challenge prediction. The mechanisms by which the climate system affects regional droughts and how they translate to fire in the western USA need more attention to accelerate both forecasting and adaptation. However, projections of future fire activity under climate change will require integrated advances on both fronts to achieve decision-relevant modeling. Concepts from transdisciplinary research and coupled human-natural systems can help frame strategic work to address fire in a changing world.",{"EN":483},"Drought and Fire in the Western USA: Is Climate Attribution Enough?",{"VOID":485},"10.1007\u002Fs40641-018-0109-y","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40641-018-0109-y",[488],{"id":489,"sortIndex":19,"researcher":18,"roles":490,"affiliations":491,"properties":500},"d7c1922a-be2d-45bd-8bf2-e7dfc66fcdfa",[169],[492],{"id":18,"sortIndex":19,"affiliation":493,"properties":18},{"id":494,"createTime":495,"updateTime":495,"relativeEntities":496,"slug":18,"properties":497,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"e813160a-abfe-434e-a52a-c6b9c7dd1789","2023-12-27T17:01:17.628+00:00",[],{"title":498},{"VI":499},"US Geological Survey, Department of the Interior Alaska Climate Adaptation Science Center, Anchorage, USA",{"title":501},{"VI":502},"Jeremy S. Littell",{"url":486,"publisher":504,"properties":531},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":505,"slug":10,"properties":506,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":509,"manageAffiliations":510,"indexDatabases":511,"url":18,"thumbnailPath":18,"statistic":526,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":507,"title":508},{"VOID":13},{"EN":15},[],[],[512,519],{"id":64,"indexDatabase":513,"url":77,"indexYears":78,"academicFieldIds":518,"indexDatabaseRanking":82},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":514,"label":515,"description":516,"key":74,"publicationTags":517,"standard":18},[],{"EN":71,"VI":71},{"EN":71,"VI":73},[76],[80,81],{"id":84,"indexDatabase":520,"url":99,"indexYears":18,"academicFieldIds":525,"indexDatabaseRanking":18},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":521,"label":522,"description":523,"key":95,"publicationTags":524,"standard":18},[],{"EN":91,"VI":91},{"VI":93,"EN":94},[97,98],[101],{"impactFactor":19,"impactFactorByYear":527,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":528,"totalCitation":123,"totalCitationByYear":529,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":530,"hindexLast5Year":141,"hindex":141},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":116,"2020":60,"2021":120,"2022":121,"2024":122},{"2015":125,"2016":126,"2017":127,"2018":128,"2019":129,"2020":130,"2021":131},{"2015":134,"2016":135,"2017":136,"2018":137,"2019":138,"2020":139,"2021":140},{"volume":532,"pages":534},{"VOID":533},"4",{"VOID":535},"396-406","2018-08-09",2018,{"id":539,"createTime":540,"updateTime":541,"relativeEntities":542,"slug":543,"properties":544,"entityType":161,"verifyStatus":162,"verifyTime":541,"verifyNote":163,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":553,"fullTextUrl":18,"authors":554,"publicationType":182,"publisherRelationship":585,"citationCount":18,"citationInfo":18,"publishDate":617,"publishYear":217,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":218},"ead860ee-94cd-45b0-835b-9050202f7d67","2024-01-31T00:20:04.426+00:00","2024-12-05T22:38:12.756+00:00",[],"Aquaplanet-Simulations-of-Tropical-Cyclones",{"references":545,"abstract":547,"title":549,"doi":551},{"VOID":546},"• Emanuel K. 100 years of progress in tropical cyclone research. Meteorol Monogr 2019;59:15.1–15.68. This monograph covers a wide range of TC research from a historical perspective.\nRotunno R, Emanuel KA. An air–sea interaction theory for tropical cyclones. Part II: Evolutionary study using a nonhydrostatic axisymmetric numerical model. J Atmos Sci 1987;44:542–561.\nVecchi GA, Swanson KL, Soden BJ. Whither hurricane activity? Science 2008;322:687–689.\nEmanuel KA, Nolan DS. Tropical cyclone activity and the global climate system. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc. A, vol 10; 2004.\nTippett MK, Camargo SJ, Sobel A. A Poisson regression index for tropical cyclone genesis and the role of large-scale vorticity in genesis. J Climate 2011;24:2335–2357.\nTang B, Camargo SJ. Environmental control of tropical cyclones in CMIP5: A ventilation perspective. J Adv Model Earth Syst 2014;6:115–128.\nSugi M, Noda A, Sato N. Influence of the global warming on tropical cyclone climatology: An experiment with the JMA global model. J Meteorol Soc Japan 2002;80:249–272.\nBengtsson L, Hodges KI, Esch M, Keenlyside N, Kornblueh L, Lou JJ, Yamagata T. How may tropical cyclones change in a warmer climate? Tellus 2007;59A:539–561.\nZhao M, Held IM, Lin SJ, Vecchi GA. Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J Climate 2009;22:6653–6678.\nZhao M, et al. Robust direct effect of increasing atmospheric CO2 concentration on global tropical cyclone frequency: A multimodel inter-comparison. US CLIVAR Variations 2013;11:17–24.\nWehner M, Reed KA, Stone D, Collins WD, Bacmeister J. Resolution dependence of future tropical cyclone projections of CAM5. 1 in the US CLIVAR Hurricane Working Group idealized configurations. J Climate 2015;28:3905–3925.\n• Camargo SJ, Wing AA. Tropical cyclones in climate models. Wiley Interdiscip Rev Clim Chang 2016;7:211–237. This review article describes GCM simulations of TCs, as well as other techniques that have been used to assess changes in TC activity.\nSchenkel BA, Lin N, Chavas D, Vecchi GA, Oppenheimer M, Brammer A. Lifetime evolution of outer tropical cyclone size and structure as diagnosed from reanalysis and climate model data. J Climate 2018;31: 7985–8004.\nZhao M, Held IM, Lin SJ. Some counterintuitive dependencies of tropical cyclone frequency on parameters in a GCM. J Atmos Sci 2012;69:2272–2283.\nShaevitz DA. Coauthors: Characteristics of tropical cyclones in high-resolution models in the present climate. J Adv Model Earth Syst 2014;6:1154–1172.\nMurakami H. Coauthors: Simulation and prediction of category 4 and 5 hurricanes in the high-resolution GFDL HiFLOR coupled climate model. J Climate 2015;28:9058–9079.\nKnutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin J, Srivastava AK, Sugi M. Tropical cyclones and climate change. Nat Geosci 2010;3:157–163.\nKnutson TR, Tuleya RE. Impact of CO2-induced warming on simulated hurricane intensity and precipitation: Sensitivity to the choice of climate model and convective parameterization. J Climate 2004;17:3477–3495.\nNolan DS, Rappin ED, Emanuel KA. Tropical cyclogenesis sensitivity to environmental parameters in radiative-convective equilibrium. Quart J Roy Meteor Soc 2007;133:2085–2107.\nNolan DS, Rappin ED. Increased sensitivity of tropical cyclogenesis to wind shear in higher SST environments. Geophys Res Lett 2008;35(14):L14805.\nSchneider T, O’Gorman PA, Levine XJ. Water vapor and the dynamics of climate changes. Rev Geophys 2010;48:RG3001. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2009RG000302.\nVoigt A, Shaw TA. Circulation response to warming shaped by radiative changes of clouds and water vapour. Nat Geosci 2015;8:102–106.\nKang SM, Frierson DMW, Held IM. The tropical response to extratropical thermal forcing in an idealized GCM: the importance of radiative feedbacks and convective parameterization. J Atmos Sci 2009;66:2812–2827.\nHenry M, Merlis TM. The role of the nonlinearity of the Stefan-Boltzmann law on the structure of radiatively forced temperature change. J Climate 2019;20:335–348.\nEmanuel K. Tropical cyclones. Annu Rev Earth Planet Sci 2003;31:75–104.\nDavis CA. The formation of moist vortices and tropical cyclones in idealized simulations. J Atmos Sci 2015; 72:3499–3516.\nWing AA, Camargo SJ, Sobel A. Role of radiative–convective feedbacks in spontaneous tropical cyclogenesis in idealized numerical simulations. J Atmos Sci 2016;73:2633–2642.\nMurthy VS, Boos WR. Role of surface enthalpy fluxes in idealized simulations of tropical depression spinup. J Atmos Sci 2018;75:1811–1831.\nMontgomery MT, Smith RK. Recent developments in the fluid dynamics of tropical cyclones. Ann Rev Fluid Mech 2017;49:541–574.\nHakim GJ. The mean state of axisymmetric hurricanes in statistical equilibrium. J Atmos Sci 2011;68:1364–1376.\nChavas DR, Emanuel K. Equilibrium tropical cyclone size in an idealized state of axisymmetric radiative–convective equilibrium. J Atmos Sci 2014;71:1663–1680.\nPersing J, Montgomery MT, Smith RK, McWilliam JC. Quasi steady-state hurricanes revisited. Trop Cyclone Res Rev 2019;8:1–17.\nNeale RB, Hoskins BJ. A standard test for AGCMs including their physical parametrizations: I: The proposal. Atmos Sci Lett 2000;1:101–107.\nBallinger AP, Merlis TM, Zhao M, Held IM. The sensitivity of tropical cyclone activity to off-equatorial thermal forcing. J Atmos Sci 2015;72:2286–2302.\nGray WM. Hurricanes: Their formation, structure and likely role in the tropical circulation. Meteorology over the tropical oceans, pp. 155–218. Royal Meteorological Society. In: Shaw DB, editors; 1979.\n• Jeevanjee N, Hassanzadeh P, Hill S, Sheshadri A. 2017. A perspective on climate model hierarchies. J. Adv. Model. Earth Syst. pp. 1760–1771. This article describes novel categorizations of climate model hierarchies, which include broader range of model configurations than those discussed here.\nMaher P, Gerber EP, Medeiros B, Merlis TM, Sherwood S, Sheshadri A, Sobel A, Vallis GK, Voigt A, Zurita-Gotor P. 2019. Model hierarchies for understanding atmospheric circulation. Rev. Geophys. p. in press.\nHeld IM. The gap between simulation and understanding in climate modeling. Bull Amer Meteor Soc 2005;86: 1609–1614.\nShi X, Bretherton CS. Large-scale character of an atmosphere in rotating radiative-convective equilibrium. J Adv Earth Model Syst 2014;6:616–629.\n• Merlis TM, Zhou W, Held IM, Zhao M. Surface temperature dependence of tropical cyclone-permitting simulations in a spherical model with uniform thermal forcing. Geophys Res Lett 2016;43:2859–2865. This article compares simulations of spherical geometry with uniform thermal forcing to large-domain rotating RCE.\nViale F, Merlis TM. Variations in tropical cyclone frequency response to solar and CO2 forcing in aquaplanet simulations. J Adv Model Earth Syst 2017;9:4–18.\nMerlis TM, Zhao M, Held IM. The sensitivity of hurricane frequency to ITCZ changes and radiatively forced warming in aquaplanet simulations. Geophys Res Lett 2013;40:4109–4114.\nFrierson DMW, Held IM, Zurita-Gotor P. A gray-radiation aquaplanet moist GCM. Part I: static stability and eddy scale. J Atmos Sci 2006;63:2548–2566.\nKang SM, Held IM, Frierson DMW, Zhao M. The response of the ITCZ to extratropical thermal forcing: Idealized slab-ocean experiments with a GCM. J Climate 2008;21:3521–3532.\nTaylor KE, Stouffer RJ, Meehl GA. An overview of CMIP5 and the experiment design. Bull Amer Meteor Soc 2012;93(4):485–498.\nBlackburn M, Williamson DL, Nakajima K, Ohfuchi W, Takahashi YO, Hayashi YY, Nakamura H, Ishiwatari M, McGregor JL, Borth H, et al. The aqua-planet experiment (APE): control SST simulation. J Meteorol Soc Japan 2013;91:17–56.\nWalsh KJE, Camargo SJ, Vecchi GA, Daloz AS, Elsner J, Emanuel K, Horn M, Lim YK, Roberts M, Patricola C, et al. Hurricanes and climate: the US CLIVAR working group on hurricanes. Bull Amer Meteor Soc 2015;96:997–1017.\nLi F, Collins WD, Wehner MF, Leung LR. Hurricanes in an aquaplanet world: implications of the impacts of external forcing and model horizontal resolution. J Adv Model Earth Syst 2013;5(2):134–145.\nZhou W, Held IM, Garner ST. Tropical cyclones in rotating radiative–convective equilibrium with coupled SST. J Atmos Sci 2017;74:879–892.\nBell MM, Montgomery MT, Emanuel KA. Air–sea enthalpy and momentum exchange at major hurricane wind speeds observed during CBLAST. J Atmos Sci 2012;69:3197–3222.\nEmanuel K, DesAutels C, Holloway C, Korty R. Environmental control of tropical cyclone intensity. J Atmos Sci 2004;61:843–858.\nMei W, Xie S-P, Primeau F, McWilliams JC, Pasquero C. Northwestern pacific typhoon intensity controlled by changes in ocean temperatures. Sci Adv 2015;1:e1500014.\nHeld IM, Zhao M. Horizontally homogeneous rotating radiative-convective equilibria at GCM resolution. J Atmos Sci 2008;65:2003–2013.\nZhou W, Held IM, Garner ST. Parameter study of tropical cyclones in rotating radiative–convective equilibrium with column physics and resolution of a 25-km GCM. J Atmos Sci 2014;71:1058–1069.\nKhairoutdinov M, Emanuel K. Rotating radiative-convective equilibrium simulated by a cloud-resolving model. J Adv Model Earth Syst 2013;5:816–825.\nReed KA, Chavas DR. Uniformly rotating global radiative-convective equilibrium in the Community Atmosphere Model, version 5. J Adv Model Earth Syst 2015;7:1938–1955.\nChan JCL. The physics of tropical cyclone motion. Annu Rev Fluid Mech 2005;37:99–128.\nHeld IM, Hou AY. Nonlinear axially symmetric circulations in a nearly inviscid atmosphere. J Atmos Sci 1980;37:515–533.\nKirtman BP, Schneider EK. A spontaneously generated tropical atmospheric general circulation. J Atmos Sci 2000;57:2080–2093.\nBarsugli J, Shin SI, Sardeshmukh PD. Tropical climate regimes and global climate sensitivity in a simple setting. J Atmos Sci 2005;62:1226–1240.\nHorinouchi T. Moist Hadley circulation: Possible role of wave-convection coupling in aquaplanet experiments. J Atmos Sci 2012;69:891–907.\nPritchard MS, Yang D. Response of the superparameterized Madden–Julian oscillation to extreme climate and basic-state variation challenges a moisture mode view. J Climate 2016;29:4995–5008.\nWalsh K. Climate theory and tropical cyclone risk assessment; 2019.\n• Chavas DR, Reed KA. 2019. Dynamical aquaplanet experiments with uniform thermal forcing: implications for tropical cyclone genesis and size. J. Atmos. Sci. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJAS-D-19-0001.1. This article presents and compares theories for TC genesis and size in spherical geometry, uniform thermal forcing simulations.\n• Chavas DR, Reed KA, Knaff JA. Physical understanding of the tropical cyclone wind-pressure relationship. Nat Commun 2017;8:1360. This article presents a theory for the wind-pressure relationship and assesses it in observations, comprehensive GCM simulations, and uniform thermal forcing aquaplanet simulations.\nCorsaro CM. 2017. The β-effect in tropical cyclones: impact on intensity and sensitivity to temperature. Ph.D. thesis, Imperial College London, London UK.\nKossin JP. A global slowdown of tropical-cyclone translation speed. Nature 2018;558:104–107.\nKorty RL, Emanuel KA, Huber M, Zamora RA. Tropical cyclones downscaled from simulations with very high carbon dioxide levels. J Climate 2017;30:649–667.\nKossin JP, Emanuel KA, Vecchi GA. The poleward migration of the location of tropical cyclone maximum intensity. Nature 2014;509:349–352.\n• Fedorov AV, Muir L, Boos WR, Studholme J. 2018. Tropical cyclogenesis in warm climates simulated by a cloud-system resolving model. Clim. Dyn. pp. 1–21. This article investigates the effects of meridional temperature gradients, approaching the weak temperature gradient limit, on TC genesis in CRM simulations.\nBordoni S, Schneider T. Monsoons as eddy-mediated regime transitions of the tropical overturning circulation. Nat Geosci 2008;1:515–519.\nDonohoe A, Frierson DMW, Battisti DS. The effect of ocean mixed layer depth on climate in slab ocean aquaplanet experiments. Clim Dyn 2014;43:1041–1055.\nMerlis TM. Does humidity’s seasonal cycle affect the annual-mean tropical precipitation response to extratropical forcing. J Climate 2016;29:1451–1460.\nByrne MP, Pendergrass AG, Rapp AD, Wodzicki KR. Response of the intertropical convergence zone to climate change: location, width, and strength. Current Climate Change Reports 2018;4:355–370.\n• Seo J, Kang S, Merlis TM. A model intercomparison of the tropical precipitation response to a CO2 doubling in aquaplanet simulations. Geophys Res Lett 2017; 44: 993–1000. This article has an intercomparison of ITCZ shifts in response to increased CO 2 and shows that GCMs robustly simulate poleward shifts, with the magnitude sensitive to model parameterizations.\nBischoff T, Schneider T. Energetic constraints on the position of the intertropical convergence zone. J Climate 2014;27:4937–4951.\nEmanuel K, Sobel A. Response of tropical sea surface temperature, precipitation, and tropical cyclone-related variables to changes in global and local forcing. J Adv Model Earth Syst 2013;5:447–458.\nSobel A, Camargo SJ, Hall TM, Lee CY, Tippett MK, Wing AA. Human influence on tropical cyclone intensity. Science 2016;353:242–246.\nO’Gorman PA, Allan RP, Byrne MP, Previdi M. Energetic constraints on precipitation under climate change. Surv Geophys 2012;33:1–24.\nSobel A, Camargo SJ, Previdi M. 2019. Aerosol vs. greenhouse gas effects on tropical cyclone potential intensity and the hydrologic cycle. J Climate p. in press.\nKim D, Moon Y, Camargo SJ, Wing AA, Sobel A, Murakami H, Vecchi GA, Zhao M, Page E. Process-oriented diagnosis of tropical cyclones in high-resolution GCMs. J Climate 2018;31:1685–1702.\nCamargo SJ, Emanuel KA, Sobel A. Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J Climate 2007;20:4819–4834.\nVimont DJ, Kossin JP. The Atlantic meridional mode and hurricane activity. Geophys Res Lett 2007;34: L07709.\n• Frisius T, Abdullah SMA. Nonlocality of tropical cyclone activity in idealized climate simulations. J Adv Model Earth Syst 2017;9:3099–3115. This article is the first TC-permitting GCM study with idealized zonal asymmetries published.\nBallinger AP. 2015. Tropical cyclone activity in an aquaplanet general circulation model. Ph.D. thesis, Princeton University, Princeton NJ.\nDefforge C. 2016. Evaluating the influence of sea surface temperature on tropical cyclone genesis: observations and simulations. Master’s thesis, McGill University, Montreal, Canada.\nZarzycki CM, Levy MN, Jablonowski C, Overfelt JR, Taylor MA, Ullrich PA. Aquaplanet experiments using CAM’s variable-resolution dynamical core. J Climate 2014;27(14):5481–5503.\nHarris LM, Lin SJ, Tu C. High-resolution climate simulations using GFDL HiRAM with a stretched global grid. J Climate 2016;29:4293–4314.\nBoos WR, Fedorov A, Muir L. Convective self-aggregation and tropical cyclogenesis under the hypohydrostatic rescaling. J Atmos Sci 2016;73:525–544.\n• Wing AA, Reed KA, Satoh M, Stevens B, Bony S, Ohno T. Radiative-convective equilibrium model intercomparison project. Geosci Model Dev 2018;11:793–813. This article describes a protocol for a non-rotating RCE intercomparison, including GCMs and CRMs. Extending the protocol to rotating model configurations is a promising approach for TC research.\nBretherton CS, Blossey PN, Khairoutdinov M. An energy-balance analysis of deep convective self-aggregation above uniform SST. J Atmos Sci 2005;62:4273–4292.\nMuller CJ, Held IM. Detailed investigation of the self-aggregation of convection in cloud-resolving simulations. J Atmos Sci 2012;69:2551–2565.\nWing AA, Emanuel KA. Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations. J Adv Model Earth Syst 2014;6:59–74.\nBony S, Stevens B, Coppin D, Becker T, Reed KA, Voigt A, Medeiros B. Thermodynamic control of anvil cloud amount. Proc Nat Acad Sci 2016;113:8927–8932.\nArnold NP, Randall DA. Global-scale convective aggregation: implications for the Madden-Julian Oscillation. J Adv Model Earth Syst 2015;7:1499–1518.\nMuller CJ, Romps DM. Acceleration of tropical cyclogenesis by self-aggregation feedbacks. Proc Nat Acad Sci 2018;115:2930–2935.",{"EN":548},"Tropical cyclones (TCs) are strongly influenced by the large-scale environment of the tropics and will, therefore, be modified by climate changes. Numerical simulations designed to understand the sensitivities of TCs to environmental changes have typically followed one of two approaches: single-storm domain sizes with convection-permitting resolution and uniform thermal boundary conditions or comprehensive global high-resolution (about 50 km in the horizontal) atmospheric general circulation model (GCM) simulations. The approaches reviewed here rest between these two and are an important component of hierarchical modelling of the atmosphere: aquaplanet TC simulations. Idealized model configurations have revealed controls on equilibrium TC size in large-domain simulations of rotating radiative-convective equilibrium. Simulations that include differential rotation (spherical geometry) but retain uniform thermal forcing have revealed a new mechanism of TC propagation change via storm-scale dynamics and show a poleward shift in genesis in response to warming. Simulations with Earth-like meridional thermal forcing gradients have isolated competing influences on TC genesis via shifts in the atmospheric general circulation and the temperature dependence of TC genesis in the absence of mean circulation changes. Aquaplanet simulations of TCs with variants that include or inhibit certain processes have recently emerged as a research methodology that has advanced the understanding of the climatic controls on TC activity. Looking forward, idealized boundary condition model configurations can be used as a bridge between GCM resolution and convection-permitting resolution models and as a tool for identifying additional mechanisms through which climate changes influence TC activity.",{"EN":550},"Aquaplanet Simulations of Tropical Cyclones",{"VOID":552},"10.1007\u002Fs40641-019-00133-y","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40641-019-00133-y",[555,570],{"id":556,"sortIndex":254,"researcher":18,"roles":557,"affiliations":558,"properties":567},"43125443-cbab-481d-8a70-c72366307ea2",[169],[559],{"id":18,"sortIndex":19,"affiliation":560,"properties":18},{"id":561,"createTime":562,"updateTime":562,"relativeEntities":563,"slug":18,"properties":564,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"62975baa-4d40-4433-a9cb-5e7ee409e66f","2023-12-04T16:32:53.954+00:00",[],{"title":565},{"VI":566},"National Oceanic and Atmospheric Administration\u002FGeophysical Fluid Dynamics Laboratory, Princeton, USA",{"title":568},{"VI":569},"Isaac M. Held",{"id":571,"sortIndex":19,"researcher":18,"roles":572,"affiliations":573,"properties":582},"950e2f18-5e84-4964-8ef4-4be82b61ba5c",[169],[574],{"id":18,"sortIndex":19,"affiliation":575,"properties":18},{"id":576,"createTime":577,"updateTime":577,"relativeEntities":578,"slug":18,"properties":579,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"965dc53d-7bfd-47e8-8053-07c39effdcda","2024-01-13T09:14:32.768+00:00",[],{"title":580},{"VI":581},"Atmospheric and Oceanic Sciences, McGill University, Montreal, Canada",{"title":583},{"VI":584},"Timothy M. Merlis",{"url":553,"publisher":586,"properties":613},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":587,"slug":10,"properties":588,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":591,"manageAffiliations":592,"indexDatabases":593,"url":18,"thumbnailPath":18,"statistic":608,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":589,"title":590},{"VOID":13},{"EN":15},[],[],[594,601],{"id":64,"indexDatabase":595,"url":77,"indexYears":78,"academicFieldIds":600,"indexDatabaseRanking":82},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":596,"label":597,"description":598,"key":74,"publicationTags":599,"standard":18},[],{"EN":71,"VI":71},{"EN":71,"VI":73},[76],[80,81],{"id":84,"indexDatabase":602,"url":99,"indexYears":18,"academicFieldIds":607,"indexDatabaseRanking":18},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":603,"label":604,"description":605,"key":95,"publicationTags":606,"standard":18},[],{"EN":91,"VI":91},{"VI":93,"EN":94},[97,98],[101],{"impactFactor":19,"impactFactorByYear":609,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":610,"totalCitation":123,"totalCitationByYear":611,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":612,"hindexLast5Year":141,"hindex":141},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":116,"2020":60,"2021":120,"2022":121,"2024":122},{"2015":125,"2016":126,"2017":127,"2018":128,"2019":129,"2020":130,"2021":131},{"2015":134,"2016":135,"2017":136,"2018":137,"2019":138,"2020":139,"2021":140},{"volume":614,"pages":615},{"VOID":213},{"VOID":616},"185-195","2019-06-08",{"id":619,"createTime":620,"updateTime":621,"relativeEntities":622,"slug":623,"properties":624,"entityType":161,"verifyStatus":162,"verifyTime":621,"verifyNote":163,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":633,"fullTextUrl":18,"authors":634,"publicationType":182,"publisherRelationship":665,"citationCount":18,"citationInfo":18,"publishDate":697,"publishYear":537,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":218},"b08d6b53-841e-4de6-b073-4bd42469ad78","2024-01-29T12:56:30.264+00:00","2025-02-18T22:31:50.060+00:00",[],"Mechanisms-and-Predictability-of-Pacific-Decadal-Variability",{"references":625,"abstract":627,"title":629,"doi":631},{"VOID":626},"Abe H, Tanimoto Y, Hasegawa T, Ebuchi N. Oceanic Rossby waves over eastern tropical Pacific of both hemispheres forced by anomalous surface winds after mature phase of ENSO. J Phys Oceanogr. 2016;46(11):3397–414. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjpo-d-15-0118.1.\nAlexander MA. Midlatitude atmosphere-ocean interaction during El Niño. Part I: the North Pacific Ocean. J Clim. 1992a;5:944–58.\nAlexander MA. Extratropical air-sea interaction, SST variability and the Pacific Decadal Oscillation (PDO). In: Sun D, Bryan F, editors. Climate dynamics: why does climate vary. Washington D. C: AGU Monograph #189; 2010. p. 123–48.\nAlexander MA, Blade I, Newman M, Lanzante JR, Lau NC, Scott JD. The atmospheric bridge: the influence of ENSO teleconnections on air-sea interaction over the global oceans. J Clim. 2002;15(16):2205–31. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2002)015\u003C2205:tabtio>2.0.co;2.\nAnderson BT. Tropical Pacific sea-surface temperatures and preceding sea level pressure anomalies in the subtropical North Pacific. J Geophys Res-Atmos. 2003;108(D23):18. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2003jd003805.\nAnderson BT, Perez RC, Karspeck A. Triggering of El Nino onset through trade wind-induced charging of the equatorial Pacific. Geophys Res Lett. 2013;40(6):1212–6. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fgrl.50200.\nAshok K, Behera S, Rao S, Weng H, Yamagata T. El Nino Modoki and its possible teleconnection. J Geophys Res. 2007;112 https:\u002F\u002Fdoi.org\u002F10.1029\u002F2006JC003798.\nBarnett TP, Pierce DW, Latif M, Dommenget D, Saravanan R. Interdecadal interactions between the tropics and midlatitudes in the Pacific basin. Geophys Res Lett. 1999;26(5):615–8. https:\u002F\u002Fdoi.org\u002F10.1029\u002F1999gl900042.\nBoer G. Decadal potential predictability of twenty-first century climate. Clim Dyn. 2011;36:1119–33.\nBoer G, Kharin V, Merryfield W. Decadal predictability and forecast skill. Clim Dyn. 2013;38 https:\u002F\u002Fdoi.org\u002F10.1007\u002F\u002Fs00382-013-1705-0.\nBograd SJ, Pozo Buil M, Di Lorenzo E, Castro CG, Schroeder ID, Goericke R, et al. Changes in source waters to the Southern California Bight. Deep-Sea Research Part Ii-Topical Studies in Oceanography. 2015;112:42–52. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.dsr2.2014.04.009.\nBond NA, Cronin MF, Freeland H, Mantua N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys Res Lett. 2015;42:3414–20.\nCapotondi A, et al. Enhanced upper ocean stratification with climate change in the CMIP3 models. J Geophys Res. 2012a;117(C4):C04031.\nCapotondi A, et al. Enhanced upper ocean stratification with climate change in the CMIP3 models. J Geophys Res. 2012b;117(C4):C04031.\nCapotondi A, Wittenberg AT, Newman M, di Lorenzo E, Yu JY, Braconnot P, et al. Understanding ENSO Diversity. Bull. Amer. Meteorol. Soc. 2015;96(6):921–38. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fbams-d-13-00117.1.\nCeballos LI, Di Lorenzo E, Hoyos CD, Schneider N, Taguchi B. North Pacific gyre oscillation synchronizes climate fluctuations in the eastern and western boundary systems. J Clim. 2009;22(19):5163–74. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2009jcli2848.1.\nChen X, Tung KK. Varying planetary heat sink led to global-warming slowdown and acceleration. Science. 2014;345:897–903.\nChen XY, Wallace JM. ENSO-Like Variability: 1900-2013. J Clim. 2015;28(24):9623–41. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-15-0322.1.\nCheng J, Liu Z, Zhang S, Liu W, Dong L, Liu P, et al. Interdecadal variability of Atlantic meridional overturning circulation in global warming. PNAS. 2016;113:3175–8. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1519827113.\nChhak KC, Di Lorenzo E, Schneider N, Cummins PF. Forcing of low-frequency ocean variability in the Northeast Pacific. J Clim. 2009;22(5):1255–76. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2008jcli2639.1.\nChiang JCH, Vimont DJ. Analogous Pacific and Atlantic meridional modes of tropical atmosphere-ocean variability. J Clim. 2004;17(21):4143–58. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli4953.1.\nCloern JE, Hieb KA, Jacobson T, Sanso B, Di Lorenzo E, Stacey MT, et al. Biological communities in San Francisco Bay track large-scale climate forcing over the North Pacific. Geophys Res Lett. 2010;37 https:\u002F\u002Fdoi.org\u002F10.1029\u002F2010gl044774.\nCobb KM, Westphal N, Sayani HR, Watson JT, Di Lorenzo E, Cheng H, et al. Highly variable El Nino-southern oscillation throughout the Holocene. Science. 2013;339(6115):67–70. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1228246.\nColin de Verdière A. On mean flow instabilities within planetary geostrophic equations. J Phys Oceanogr. 1986;16:1981–4.\nColin de Verdière A, Huck T. Baroclinic instability: an oceanic wave- maker for interdecadal variability. J Phys Oceanogr. 1999;29:893–910.\nd’Orgeville MD, Peltier WR. Implications of both statistical equilibrium and global warming simulations with CCSM3. Part I: on the decadal variability in the North Pacific basin J Climate. 2009;22:5277–97.\nDai AG. The influence of the inter-decadal Pacific oscillation on US precipitation during 1923-2010. Clim Dyn. 2013;41(3–4):633–46. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-012-1446-5.\nDai A, Fyfe J, Xie S-P, Dai X. Decadal modulation of global surface temperature by internal climate variability. Nat Clim Chang. 2015;5:555–60.\nDelworth T, Zhang R, Mann M. Decadal to centennial variability of the Atlantic from observations and models. In: Ocean circulation: mechanisms and impacts, geophysical monograph series 173. Washington, DC: American Geophysical Union; 2007. p. 131–48.\nDeser C, Phillips AS, Hurrell JW. Pacific interdecadal climate variability: linkages between the tropics and the North Pacific during boreal winter since 1900. J Clim. 2004;17(16):3109–24. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2004)017\u003C3109:picvlb>2.0.co;2.\nDeser C, Alexander MA, Xie SP, Phillips AS. Sea surface temperature variability: patterns and mechanisms. Annu Rev Mar Sci. 2010;2:115–43. https:\u002F\u002Fdoi.org\u002F10.1146\u002Fannurev-marine-120408-151453.\nDi Lorenzo E, Mantua N. Multi-year persistence of the 2014\u002F15 North Pacific marine heatwave. Nat Clim Chang. 2016;6(11):1042–7. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnclimate3082.\nDi Lorenzo E, et al. North Pacific gyre oscillation links ocean climate and ecosystem change. Geophys Res Lett. 2008;35(8):L08607. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2007gl032838.\nDi Lorenzo E, et al. Nutrient and salinity decadal variations in the central and eastern North Pacific. Geophys Res Lett. 2009;36 https:\u002F\u002Fdoi.org\u002F10.1029\u002F2009gl038261.\nDi Lorenzo E, Cobb KM, Furtado JC, Schneider N, Anderson BT, Bracco A, et al. Central Pacific El Nino and decadal climate change in the North Pacific Ocean. Nat Geosci. 2010;3(11):762–5. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fngeo984.\nDi Lorenzo E, et al. Synthesis of Pacific Ocean climate and ecosystem dynamics. Oceanography. 2013;26(4):68–81.\nDi Lorenzo E, Liguori G, Schneider N, Furtado JC, Anderson BT, Alexander MA. ENSO and meridional modes: a null hypothesis for Pacific climate variability. Geophys Res Lett. 2015;42(21):9440–8. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015gl066281.\nDiffenbaugh NS, Swain DL, Touma D. Anthropogenic warming has increased drought risk in California. PNAS. 2015;112(13):3931–6. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1422385112.\nDing H, Greatbatch RJ, Latif M, Park W, Gerdes R. Hindcast of the 1976\u002F77 and 1998\u002F99 climate shifts in the Pacific. J Clim. 2013;26(19):7650–61. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-12-00626.1.\nDing RQ, Li JP, Tseng YH. The impact of South Pacific extratropical forcing on ENSO and comparisons with the North Pacific. Clim Dyn. 2015;44(7–8):2017–34. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-014-2303-5.\nDoblas-Reyes FJ, Andreu-Burillo I, Chikamoto Y, García-Serrano J, Guemas V, Kimoto M, et al. Initialized near-term regional climate change prediction. Nat Commun. 2013;4:1715.\nEmile-Geay, J. T., Cobb, K. M., Mann, M. E., Wittenberg, A. T. (2011). Estimating tropical pacific SST variability over the past millennium. Part 2: reconstructions and uncertainties. Journal of Climate.\nEngland MH, McGregor S, Spence P, Meehl GA, Timmermann A, Cai WJ, et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat Clim Chang. 2014;4(3):222–7. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnclimate2106.\nFang C, Wu L, Zhang X. The impact of global warming on the Pacific decadal oscillation and the possible mechanism. Adv Atmos Sci. 2014;31:118–30.\nFang J, Yang X-Q. Structure and dynamics of decadal anomalies in the wintertime midlatitude North Pacific ocean-atmosphere system. Clim Dyn. 2016;47:1989–2007.\nFogt RL, Bromwich DH. Decadal variability of the ENSO teleconnection to the high-latitude South Pacific governed by coupling with the southern annular mode. J Clim. 2006;19(6):979–97. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli3671.1.\nFrankignoul C, Sennechael N. Observed influence of North Pacific SST anomalies on the atmospheric circulation. J Clim. 2007;19:592–606.\nFrankignoul C, Sennechael N, Kwon YO, Alexander MA. Influence of the meridional shifts of the Kuroshio and the Oyashio extensions on the atmospheric circulation. J Clim. 2011;24(3):762–77. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2010jcli3731.1.\nFurtado JC, Di Lorenzo E, Anderson BT, Schneider N. Linkages between the North Pacific oscillation and central tropical Pacific SSTs at low frequencies. Clim Dyn. 2012;39(12):2833–46. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-011-1245-4.\nGarreaud RD, Battisti DS. Interannual (ENSO) and interdecadal (ENSO-like) variability in the southern hemisphere tropospheric circulation. J Clim. 1999;12(7):2113–23. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1999)012\u003C2113:ieaiel>2.0.co;2.\nGiannakis D, Majda AJ. Limits of predictability in the North Pacific sector of a comprehensive climate model. Geophys Res Lett. 2012;39:6. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2012gl054273.\nGriffies S, Bryan K. A predictability study of simulated North Atlantic multidecadal variability. Clim Dyn. 1997;13:459–87.\nGu DF, Philander SGH. Interdecadal climate fluctuations that depend on exchanges between the tropics and extratropics. Science. 1997;275(5301):805–7. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.275.5301.805.\nGuemas V, Doblas-Reyes FJ, Lienert F, Soufflet Y, Du H. Identifying the causes of the poor decadal climate prediction skill over the North Pacific. J Geophys Res-Atmos. 2012;117:17. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2012jd018004.\nHare SR, Mantua NJ, Francis RC. Inverse production regimes: Alaska and West Coast Pacific salmon. Fisheries. 1999;24(1):6–14. https:\u002F\u002Fdoi.org\u002F10.1577\u002F1548-8446(1999)024\u003C0006:ipr>2.0.co;2.\nHare S, Mantua N. Empirical evidence for North Pacific regime shifts in 1977 and 1989. Prog Oceanogr. 2000;47:103–45.\nHasselmann K. Stochastic climate models. Part I: theory. Tellus. 1976;28:473–85.\nHobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, Oliver ECJ, et al. A hierarchical approach to defining marine heatwaves. Prog Oceanogr. 2016;141:227–38.\nHsu HH, Chen YL. Decadal to bi-decadal rainfall variation in the western Pacific: a footprint of South Pacific decadal variability? Geophys Res Lett. 2011;38 https:\u002F\u002Fdoi.org\u002F10.1029\u002F2010gl046278.\nJin FF. A theory of interdecadal climate variability of the North Pacific ocean-atmosphere system. J Clim. 1997;10(8):1821–35. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1997)010\u003C1821:atoicv>2.0.co;2.\nJohnson H, Marshall D. A theory for the surface Atlantic response to thermohaline variability. J Phys Oceanogr. 2002;32:1121–32.\nKao HY, Yu JY. Contrasting eastern-Pacific and Central-Pacific types of ENSO. J Clim. 2009;22(3):615–32. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2008jcli2309.1.\nKaufmann R, Kaupp H, Mann M, Stock J. Reconciling anthropogenic climate change with observed temperature 1998-2008. Proc Nat Acad Sci. 2011;108:11790–3.\nKawase M. Establishment of deep ocean circulation driven by deep-water production. J Phys Oceanogr. 1987;17:2294–317.\nKilduff DP, Di Lorenzo E, Botsford LW, Teo SLH. Changing central Pacific El Ninos reduce stability of North American salmon survival rates. Proc Natl Acad Sci U S A. 2015;112(35):10962–6. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1503190112.\nKilpatrick T, Schneider N, Di Lorenzo E. Generation of low-frequency spiciness variability in the thermocline. J Phys Oceanogr. 2011;41(2):365–77. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2010jpo4443.1.\nKim H-M, Webster PJ, Curry JA. Impact of shifting patterns of Pacific Ocean warming on North Atlantic tropical cyclones. Science. 2009;325:77–80.\nKirtman, B. et al., (2013), Near-term climate change: projections and predictability, IPCC AR5, Ch. 5.\nKleeman R, McCreary JP, Klinger BA. A mechanism for generating ENSO decadal variability. Geophys Res Lett. 1999;26(12):1743–6. https:\u002F\u002Fdoi.org\u002F10.1029\u002F1999gl900352.\nKnutson TR, Manabe S. Model assessment of decadal variability and trends in the tropical Pacific Ocean. J Clim. 1998;11(9):2273–96. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1998)011\u003C2273:maodva>2.0.co;2.\nKosaka Y, Xie SP. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature. 2013;501(7467):403–7. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature12534.\nKucharski F, Ikram F, Molteni F, Farneti R, Kang IS, No HH, et al. Atlantic forcing of Pacific decadal variability. Clim Dyn. 2016;46(7–8):2337–51. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-015-2705-z.\nKumar A, Wen CH. An oceanic heat content-based definition for the Pacific decadal oscillation. Mon Weather Rev. 2016;144(10):3977–84. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fmwr-d-16-0080.1.\nKushnir Y, Robinson WA, Bladé I, Hall NMJ, Peng S, Sutton R. Atmospheric GCM response to extratropical SST anomalies: synthesis and evaluation. J Clim. 2002;15:2233–56.\nKwon Y-O, Deser C. North Pacific decadal variability in the community climate system model version 2. J Clim. 2007;20:2416–33.\nLarkin NK, Harrison DE. On the definition of El Nino and associated seasonal average US weather anomalies. Geophys Res Lett. 2005;32(13) https:\u002F\u002Fdoi.org\u002F10.1029\u002F2005gl022738.\nLatif M, Barnett TP. Causes of decadal climate variability over the North Pacific and North America. Science. 1994;266:634–7.\nLatif M, Barnett TP. Decadal climate variability over the North Pacific and North America: dynamics and predictability. J Clim. 1996;9(10):2407–23. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1996)009\u003C2407:dcvotn>2.0.co;2.\nLee S-K, et al. Pacific origin of the abrupt increases in Indian Ocean heat content during the warming hiatus. Nat Geosci. 2015;8:445–9.\nLiguori G., Di Lorenzo E. Meridional Modes and Increasing Pacific decadal variability under greenhouse forcing, Geophys. Res Lett. 2018. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017GL076548.\nLinkin ME, Nigam S. The north pacific oscillation-West Pacific teleconnection pattern: mature-phase structure and winter impacts. J Clim. 2008;21(9):1979–97. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2007jcli2048.1.\nLinsley BK, Wellington GM, Schrag DP. Decadal sea surface temperature variability in the subtropical South Pacific from 1726 to 1997 AD. Science. 2000;290(5494):1145–8. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.290.5494.1145.\nLiu Z, Philander SGH, Pacanowski R. A GCM study of tropical -subtropical upper ocean mass exchange. J Phys Oceanogr. 1994;24:2606–23.\nLiu Z, Wu W, Gallimore R, Jacob R. Search for the origins of Pacific decadal climate variability. Geophys Res Lett. 2002;29 https:\u002F\u002Fdoi.org\u002F10.1029\u002F2001GL013735.\nLiu Z, Xie SP. Equatorward propagation of coupled air-sea disturbances with application to the annual cycle of the eastern tropical Pacific. J Atmos Sci. 1994;51:3807–22.\nLiu ZY. Planetary wave modes in thermocline circulation: non-Doppler-shift mode, advective mode and green mode. Quat J Royal Meteor Soc. 1999a;125:1315–39.\nLiu ZY. Forced planetary wave response in a thermocline gyre. J Phys Oceanogr. 1999b;29:1036–55.\nLiu ZY. Dynamics of Interdecadal climate variability: a historical perspective. J Clim. 2012;25(6):1963–95. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2011jcli3980.1.\nLiu ZY, Alexander M. Atmospheric bridge, oceanic tunnel, and global climatic teleconnections. Rev Geophys. 2007;45(2):34. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2005rg000172.\nLiu QY, Wen N, Liu ZY. An observational study of the impact of the North Pacific SST on the atmosphere. Geophys Res Lett. 2006;33(18):5. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2006gl026082.\nLiu ZY, Liu Y, Wu LX, Jacob R. Seasonal and long-term atmospheric responses to reemerging North Pacific ocean variability: a combined dynamical and statistical assessment. J Clim. 2007;20(6):955–80. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli4041.1.\nLiu ZY, Fan L, Shin SI, Liu QY. Assessing atmospheric response to surface forcing in the observations. Part II: cross validation of seasonal response using GEFA and LIM. J Clim. 2012a;25(19):6817–34. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-11-00630.1.\nLiu ZY, Wen N, Fan L. Assessing atmospheric response to surface forcing in the observations. Part I: cross validation of annual response using GEFA, LIM, and FDT. J Clim. 2012b;25(19):6796–816. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-11-00545.1.\nLiu W, Xie S-P, Lu J. Tracking ocean heat uptake during the surface warming hiatus. Nature Comm. 2016;7 https:\u002F\u002Fdoi.org\u002F10.1038\u002Fncomms10926.\nMantua N, Hare SR, Zhang Y, Wallace JM, Francis RC. A Pacific interdecadal climate oscillation with impacts on salmon production. Bull Am Meteorol Soc. 1997;78:1069–79.\nMartinez E, Antoine D, D'Ortenzio F, Gentili B. Climate-driven basin-scale decadal oscillations of oceanic phytoplankton. Science. 2009;326(5957):1253–6. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1177012.\nMcGregor HV, Dima M, Fischer HW, Mulitza S. Rapid 20th-century increase in coastal upwelling off Northwest Africa. Science. 2007;315(5812):637–9. https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1134839.\nMeehl GA, Teng HY. CMIP5 multi- model hindcasts for the mid-1970s shift and early 2000s hiatus and predictions for 2016-2035. Geophys Res Lett. 2014;41(5):1711–6. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2014gl059256.\nMeehl GA, Goddard L, Murphy J, Stouffer RJ, Boer G, Danabasoglu G, et al. Decadal prediction: can it be skillful? Bull. Amer. Meteorol. Soc. 2009;90(10):1467–85. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2009bams2778.1.\nMeehl GA, Hu AX, Tebaldi C. Decadal prediction in the Pacific region. J Clim. 2010;23(11):2959–73. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2010jcli3296.1.\nMeehl GA, Hu AX, Arblaster JM, Fasullo J, Trenberth KE. Externally forced and internally generated decadal climate variability associated with the interdecadal pacific oscillation. J Clim. 2013;26(18):7298–310. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-12-00548.1.\nMeehl GA, Goddard L, Boer G, Burgman R, Branstator G, Cassou C, et al. Decadal climate prediction: an update from the trenches. Bull. Amer. Meteorol. Soc. 2014;95(2):243–67. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fbams-d-12-00241.1.\nMeehl GA, Hu AX, Teng HY. Initialized decadal prediction for transition to positive phase of the interdecadal pacific oscillation. Nat Commun. 2016;7:7. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fncomms11718.\nMo KC. Relationships between low-frequency variability in the southern hemisphere and sea surface temperature anomalies. J Clim. 2000;13(20):3599–610. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2000)013\u003C3599:rblfvi>2.0.co;2.\nMochizuki T, Ishii M, Kimoto M, Chikamoto Y, Watanabe M, Nozawa T, et al. Pacific decadal oscillation hindcasts relevant to near-term climate prediction. Proc Natl Acad Sci U S A. 2010;107(5):1833–7. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.0906531107.\nMochizuki T, Chikamoto Y, Kimoto M, Ishii M, Tatebe H, Komuro Y, et al. Decadal prediction using a recent series of MIROC global climate models. J Meteorol Soc Jpn. 2012;90A:373–83. https:\u002F\u002Fdoi.org\u002F10.2151\u002Fjmsj.2012-A22.\nNamias J, Yuan XJ, Cayan DR. Persistence of North Pacific Sea surface temperature and atmospheric flow patterns. J Clim. 1988;1(7):682–703. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1988)001\u003C0682:ponpss>2.0.co;2.\nNewman M. Interannual to decadal predictability of tropical and North Pacific sea surface temperatures. J Clim. 2007;20(11):2333–56. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli4165.1.\nNewman M. An empirical benchmark for decadal forecasts of global surface temperature anomalies. J Clim. 2013;26:5260–9.\nNewman M, Compo GP, Alexander MA. ENSO-forced variability of the Pacific decadal oscillation. J Clim. 2003;16(23):3853–7. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2003)016\u003C3853:evotpd>2.0.co;2.\nNewman M, Alexander MA, Ault TR, Cobb KM, Deser C, di Lorenzo E, et al. The Pacific decadal oscillation, revisited. J Clim. 2016;29(12):4399–427. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-15-0508.1.\nNieves V, Willis J, Patzert W. Recent biatus caused by decadal shift in indo-Pacific heating. Science. 2015;349:532–5.\nPerkins ML, Holbrook NJ. Can Pacific Ocean thermocline depth anomalies be simulated by a simple linear vorticity model? J Phys Oceanogr. 2001;31(7):1786–806. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0485(2001)031\u003C1786:cpotda>2.0.co;2.\nPierce DW, Barnett TP, Latif M. Connections between the Pacific Ocean tropics and midlatitudes on decadal timescales. J Clim. 2000;13(6):1173–94. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2000)013\u003C1173:cbtpot>2.0.co;2.\nPower S, Colman R. Multi-year predictability in a coupled general circulation model. Clim Dyn. 2006;26(2–3):247–72. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-005-0055-y.\nPower S, Casey T, Folland C, Colman A, Mehta V. Inter-decadal modulation of the impact of ENSO on Australia. Clim Dyn. 1999;15(5):319–24. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs003820050284.\nPozo Buil M, Di Lorenzo E. Decadal changes in Gulf of Alaska upwelling source waters. Geophys Res Lett. 2015;42(5):1488–95. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015gl063191.\nPozo Buil M, Di Lorenzo E. Decadal dynamics and predictability of oxygen and subsurface tracers in the California current system. Geophys Res Lett. 2017;44(9):4204–13. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2017gl072931.\nQiu B. Kuroshio extension variability and forcing of the Pacific decadal oscillations: responses and potential feedback. J Phys Oceanogr. 2003;33(12):2465–82. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2459.1.\nQiu B, Chen SM. Variability of the Kuroshio extension jet, recirculation gyre, and mesoscale eddies on decadal time scales. J Phys Oceanogr. 2005;35(11):2090–103. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjpo2807.1.\nQiu B, Schneider N, Chen SM. Coupled decadal variability in the North Pacific: an observationally constrained idealized model. J Clim. 2007;20(14):3602–20. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli4190.1.\nRevelard A, Frankignoul C, Sennechael N, Kwon YO, Qiu B. Influence of the decadal variability of the Kuroshio extension on the atmospheric circulation in the cold season. J Clim. 2016;29(6):2123–44. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-15-0511.1.\nRoemmich D, McGowan J. Climatic warming and the decline of zooplankton in the California current. Science. 1995;267:1324–6.\nRogers JC. The North Pacific oscillation. J Climatol. 1981;1:39–57. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjoc.3370010106.\nSchneider N, Cornuelle BD. The forcing of the Pacific decadal oscillation. J Clim. 2005;18(21):4355–73. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli3527.1.\nSchneider N, Miller AJ. Predicting western North Pacific Ocean climate. J Clim. 2001;14(20):3997–4002. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2001)014\u003C3997:pwnpoc>2.0.co;2.\nSchneider N, Venzke S, Miller AJ, Pierce DW, Barnett TP, Deser C, et al. Pacific thermocline bridge revisited. Geophys Res Lett. 1999b;26(9):1329–32. https:\u002F\u002Fdoi.org\u002F10.1029\u002F1999gl900222.\nSchneider N, Miller AJ, Pierce DW. Anatomy of North Pacific decadal variability. J Clim. 2002;15(6):586–605. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2002)015\u003C0586:aonpdv>2.0.co;2.\nSeager R, Harnik N, Robinson WA, Kushnir Y, Ting M, Huang HP, et al. Mechanisms of ENSO-forcing of hemispherically symmetric precipitation variability. Q J R Meteorol Soc. 2005;131(608):1501–27. https:\u002F\u002Fdoi.org\u002F10.1256\u002Fqj.04.96.\nSmirnov D, Newman M, Alexander M, Kwon Y-O, Frankignoul C. Investigating the local atmospheric response to a realistic shift in the Oyashio Sea surface temperature front. J Clim. 2015;28:1126–47.\nSolomon S, Rosenlof K, Portmann R, Daniel J, Davis S, Sanford T, et al. Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science. 2010;327:1219–23.\nSugiura N, Awaji T, Masuda S, Toyoda T, Igarashi H, Ishikawa Y, et al. Potential for decadal predictability in the North Pacific region. Geophys Res Lett. 2009;36:6. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2009gl039787.\nSun JQ, Wang HJ. Relationship between Arctic oscillation and Pacific decadal oscillation on decadal timescale. Chin Sci Bull. 2006;51(1):75–9. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11434-004-0221-3.\nSydeman, W.J., Thompson, S.A., 2010. The California current integrated ecosystem assessment (IEA), module II: trends and variability in climate-ecosystem state.\nTaguchi B, Schneider N. Origin of decadal-scale, eastward-propagating heat content anomalies in the North Pacific. J Clim. 2014;27(20):7568–86. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-13-00102.1.\nTaguchi B, Xie SP, Schneider N, Nonaka M, Sasaki H, Sasai Y. Decadal variability of the Kuroshio extension: observations and an eddy-resolving model hindcast. J Clim. 2007;20(11):2357–77. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli4142.1.\nTeng HY, Branstator G, Meehl GA. Predictability of the atlantic overturning circulation and associated surface patterns in two CCSM3 climate change ensemble experiments. J Clim. 2011;24(23):6054–76. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2011jcli4207.1.\nTrenberth K, Fasullo J, Balmaseda M. Earth’s energy imbalance. J Clim. 2014a;27:3129–44.\nTrenberth K, Fasullo J, Branstator G, Phillips A. Seasonal aspects of the recent pause in surface warming. Nat Clim Chang. 2014b;4:911–6.\nVimont DJ. The contribution of the interannual ENSO cycle to the spatial pattern of decadal ENSO-like variability. J Clim. 2005;18(12):2080–92. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli3365.1.\nVimont DJ. Transient growth of thermodynamically coupled variations in the tropics under an equatorially symmetric mean. J Clim. 2010;23(21):5771–89. https:\u002F\u002Fdoi.org\u002F10.1175\u002F2010jcli3532.1.\nVimont DJ, Battisti DS, Hirst AC. Footprinting: a seasonal connection between the tropics and mid-latitudes. Geophys Res Lett. 2001;28(20):3923–6. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2001gl013435.\nVimont D, Wallace M, Battisti D. The seasonal footprinting mechanism in the Pacific: implications for ENSO. J Clim. 2003;16:2668–75.\nWalker Sir GT, Bliss EW. World weather V. Mem R Meteorol Soc. 1932;4:53–83.\nWang SY, Hipps L, Gillies RR, Yoon JH. Probable causes of the abnormal ridge accompanying the 2013–2014 California drought: ENSO precursor and anthropogenic warming footprint. Geophys Res Lett. 2014;41:3220–6.\nWang X, Jin FF, Wang Y. A tropical ocean recharge mechanism for climate variability. Part I: equatorial heat content changes induced by the off-equatorial wind. J Clim. 2003;16:3585–98.\nWatanabe M, Shiogama H, Tatebe H, Hayashi M, Ishii M, Kimoto M. Contribution of natural decadal variability to global warming acceleration and hiatus. Nat Clim Chang. 2014;4:893–7.\nWeng HY, Behera SK, Yamagata T. Anomalous winter climate conditions in the Pacific rim during recent El NiA +\u002F− o Modoki and El NiA +\u002F− o events. Clim Dyn. 2009;32(5):663–74. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-008-0394-6.\nWu L, Liu Z, Gallimore R, Jacob R, Lee D, Zhong Y. Pacific decadal variability: the tropical Pacific mode and the North Pacific mode. J Clim. 2003;16(8):1101–20. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2003)16\u003C1101:pdvttp>2.0.co;2.\nXie SP. A dynamic ocean-atmosphere model of the tropical Atlantic decadal variability. J Clim. 1999;12(1):64–70. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442-12.1.64.\nYeh S-W, Kug J-S, Dewitte B, Kwon M-H, Kirtman B, Jin F-F. El Nino in a changing climate. Nature. 2009;461:511–4.\nZhang LP, Delworth TL. Analysis of the characteristics and mechanisms of the Pacific decadal oscillation in a suite of coupled models from the geophysical fluid dynamics laboratory. J Clim. 2015;28(19):7678–701. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-14-00647.1.\nZhang L, Delworth TL. Simulated response of the Pacific decadal oscillation to climate change. J Clim. 2016;29(16):5999–6018. https:\u002F\u002Fdoi.org\u002F10.1175\u002FJCLI-D-15-0690.1.\nZhang DX, McPhaden MJ. Decadal variability of the shallow Pacific meridional overturning circulation: relation to tropical sea surface temperatures in observations and climate change models. Ocean Model. 2006;15(3–4):250–73. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ocemod.2005.12.005.\nZhang Y, Wallace JM, Battisti DS. ENSO-like interdecadal variability: 1900-93. J Clim. 1997;10(5):1004–20. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(1997)010\u003C1004:eliv>2.0.co;2.\nZhang R, Delworth TL, Held IM. Can the Atlantic Ocean drive the observed multidecadal variability in northern hemisphere mean temperature? Geophys Res Lett. 2007;34(2):6. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2006gl028683.\nZhang H, Clement A, Di Nezio P. The South Pacific meridional mode: a mechanism for ENSO-like variability. J Clim. 2014;27(2):769–83. https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjcli-d-13-00082.1.\nZhong Y, Liu Z, Jacob R. The origin of Pacific decadal variability in the NCAR-CCSM3. J Clim. 2008;21:114–33.\nZhong YF, Liu Z. On the mechanism of Pacific multidecadal climate variability in CCSM3: the role of subpolar North Pacific Ocean. J Phys Oceanogr. 2009;39:2052–76.",{"EN":628},"This paper reviews recent progress in the understanding and prediction of pacific decadal variability (PDV). The PDV is now recognized to consist of multiple ocean-atmosphere modes and to be caused by multiple processes. At the leading order, PDV can be viewed as the reddening process of stochastic atmospheric variability on the extratropical ocean. However, PDV is also strongly tied to teleconnection dynamics interacting with the tropics, primarily the interactions between meridional modes in the extra-tropics and ENSO, and between the ENSO teleconnections and the dominant modes of atmospheric variability in the mid-latitude. Extratropical oceanic Rossby waves are found to be crucial for determining the decadal time scales of the PDV and provide potentially an important source of predictability of PDV. Preliminary experiments with GCMs and empirical linear inverse models have shown some skill for the prediction of PDV in ocean surface temperatures. While the climate predictions in the first few years depend significantly on the oceanic initial condition, predictions of near decadal time scales are contributed mostly by the global warming trend. In addition, recent studies explored the role of ocean subsurface dynamics for multi-decadal predictability in the Pacific and suggest that subsurface dynamics may lead to important sources of decadal predictability in regional upwelling systems, namely the eastern boundary and polar gyre. Overall, the predictability of PDV and the related surface and subsurface signals remain to be much studied. Recent studies also start to explore the relation between PDV and global warming. It has been suggested that PDV can slow down or accelerate the global warming trend significantly. The influence of the anthropogenic climate change on PDV, however, has remained unclear.",{"EN":630},"Mechanisms and Predictability of Pacific Decadal Variability",{"VOID":632},"10.1007\u002Fs40641-018-0090-5","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40641-018-0090-5",[635,650],{"id":636,"sortIndex":19,"researcher":18,"roles":637,"affiliations":638,"properties":647},"6f4944e7-320e-4ae0-886d-98635d46ae28",[169],[639],{"id":18,"sortIndex":19,"affiliation":640,"properties":18},{"id":641,"createTime":642,"updateTime":642,"relativeEntities":643,"slug":18,"properties":644,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"9259277d-d6f8-422e-8bbd-a4a39a1329fc","2024-02-06T07:34:13.915+00:00",[],{"title":645},{"VI":646},"Atmospheric Science Program, Department of Geography, The Ohio State University, Columbus, USA",{"title":648},{"VI":649},"Zhengyu Liu",{"id":651,"sortIndex":254,"researcher":18,"roles":652,"affiliations":653,"properties":662},"dd0d2b84-8747-4ca3-b535-61f79b9fd31d",[169],[654],{"id":18,"sortIndex":19,"affiliation":655,"properties":18},{"id":656,"createTime":657,"updateTime":657,"relativeEntities":658,"slug":18,"properties":659,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"29825ab4-769d-4d0a-8165-71446cd7f56d","2023-12-08T07:53:24.484+00:00",[],{"title":660},{"VI":661},"Program in Ocean Science and Engineering, Georgia Institute of Technology, Atlanta, USA",{"title":663},{"VI":664},"Emanuele Di Lorenzo",{"url":633,"publisher":666,"properties":693},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":667,"slug":10,"properties":668,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":671,"manageAffiliations":672,"indexDatabases":673,"url":18,"thumbnailPath":18,"statistic":688,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"eissn":669,"title":670},{"VOID":13},{"EN":15},[],[],[674,681],{"id":64,"indexDatabase":675,"url":77,"indexYears":78,"academicFieldIds":680,"indexDatabaseRanking":82},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":676,"label":677,"description":678,"key":74,"publicationTags":679,"standard":18},[],{"EN":71,"VI":71},{"EN":71,"VI":73},[76],[80,81],{"id":84,"indexDatabase":682,"url":99,"indexYears":18,"academicFieldIds":687,"indexDatabaseRanking":18},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":683,"label":684,"description":685,"key":95,"publicationTags":686,"standard":18},[],{"EN":91,"VI":91},{"VI":93,"EN":94},[97,98],[101],{"impactFactor":19,"impactFactorByYear":689,"i10Index":112,"i10IndexLast5Year":113,"totalPublication":114,"totalPublicationByYear":690,"totalCitation":123,"totalCitationByYear":691,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":692,"hindexLast5Year":141,"hindex":141},{"2016":104,"2017":105,"2018":106,"2019":107,"2020":108,"2021":109,"2022":110,"2023":111},{"2015":116,"2016":117,"2017":118,"2018":119,"2019":116,"2020":60,"2021":120,"2022":121,"2024":122},{"2015":125,"2016":126,"2017":127,"2018":128,"2019":129,"2020":130,"2021":131},{"2015":134,"2016":135,"2017":136,"2018":137,"2019":138,"2020":139,"2021":140},{"volume":694,"pages":695},{"VOID":533},{"VOID":696},"128-144","2018-04-14",{"id":699,"createTime":700,"updateTime":701,"relativeEntities":702,"slug":703,"properties":704,"entityType":161,"verifyStatus":162,"verifyTime":713,"verifyNote":163,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":714,"fullTextUrl":18,"authors":715,"publicationType":182,"publisherRelationship":745,"citationCount":18,"citationInfo":18,"publishDate":777,"publishYear":404,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":218},"82e0eeb4-99a1-462c-8ed9-5af00bf3a2e6","2023-12-03T02:42:20.763+00:00","2024-12-24T22:29:19.865+00:00",[],"The-Spectral-Signature-of-Recent-Climate-Change",{"references":705,"abstract":707,"title":709,"doi":711},{"VOID":706},"Smith W, Hickey J, Howell H, Jacobowitz H, Hilleary D, Drummond A. Nimbus-6 earth radiation budget experiment. Appl Opt. 1977;16:306–18. doi:10.1364\u002FAO.16.000306.\nJacobowitz H, Tighe R, The Nimbus-7 ERB Experiment Team. The earth radiation budget derived from the NIMBUS 7 ERB experiment. J Geophys Res. 1984;89:4997–5010. doi:10.1029\u002FJD89iD04p04997.\nBarkstrom B. The Earth Radiation Budget Experiment (ERBE). Bull Am Meteorol Soc. 1984;65:1170–85. doi:10.1175\u002F1520-0477(1984)065\u003C1170:TERBE>2.0.CO;2.\nWielicki B, Barkstrom B, Harrison E, Lee R, Smith G, Cooper J. Clouds and the earth’s radiant energy system (CERES): an earth observing experiment. Bull Am Meteorol Soc. 1996;77:853–68. doi:10.1175\u002F15200477(1996)077\u003C0853:CATERE>2.0.CO;2.\nHarries J, Hanafin J, Russell J, et al. The Geostationary Earth Radiation Budget project. Bull Amer Meteorol Soc. 2005;86:945–60. doi:10.1175\u002FBAMS-86-7-945.\nKandel R, Voillier M, Raberanto P, et al. The ScaRaB earth radiation budget dataset. Bull Am Meteorol Soc. 1998;79:765–83. doi:10.1175\u002F1520-0477(1998)079\u003C0765:TSERBD>2.0.CO;2.\nDuvel J, Viollier M, Raberanto P, et al. The ScaRaB-Resurs earth radiation budget dataset and first results. Bull Am Meteorol Soc. 2001;82:1397–408. doi:10.1175\u002F1520-0477(2001)082\u003C1397:TSRERB>2.3.CO;2.\nDesbois M, Capderou M, Eymard L, et al. Megha-Tropiques: un satellite hydrométéorologique franco-indien. La Meterologie. 2007;57:19–27.\nLoeb N, Wielicki B, Doelling D, et al. Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J Clim. 2009;22:748–66. doi:10.1175\u002F2008JCLI2637.1.\nWong T, Wielicki B, Lee R, Smith G, Bush K, Willis J. Reexamination of the observed decadal variability of the earth radiation budget using altitude-corrected ERBE\u002FERBS nonscanner WFOV data. J Clim. 2006;19:4028–40. doi:10.1175\u002FJCLI3838.1.\nHartmann D, Ramanathan V, Berroir A, Hunt G. Earth radiation budget data and climate research. Rev Geophys. 1986;24:439–68. doi:10.1029\u002FRG024i002p00439.\nRamanathan V. The role of Earth radiation budget studies in climate an general circulation research. J Geophys Res. 1987;92:4075–95. doi:10.1029\u002FJD092iD04p04075.\nForster P, Gregory J. The climate sensitivity and its components diagnosed from Earth radiation budget data. J Clim. 2006;19:39–52. doi:10.1175\u002FJCLI3611.1.\nTett S, Rowlands D, Mineter M, Cartis C. Can top-of-atmosphere radiation measurements constrain climate predictions? Part II: climate sensitivity. J Climate. 2013;26:9367–83. doi:10.1175\u002FJCLI-D-12-00596.1.\nHansen J, Nazarenko L, Ruedy R, et al. Earth’s energy imbalance: confirmation and implications. Science. 2005;308:1431–5. doi:10.1126\u002Fscience.1110252.\nHuang Y, LeroyS, Gero P, Dykema J, Anderson J. Separation of longwave climate feedbacks from spectral observations. J Geophys Res. 2010;115. doi:10.1029\u002F2009JD012766.\nFeldman D, Algieri C, Ong J, Collins W. CLARREO shortwave observing system simulation experiments of the twenty-first century. J Geophys Res. 2011;116. doi:10.1029\u002F2010JD015350.\nGoody R, Haskins R, Abdou W, Chen L. Detection of climate forcing using emission spectra. Earth Observ Remote Sens. 1996;13:713–27.\nHanel R, Conrath B, Kunde V, et al. The Nimbus 4 infrared spectroscopy experiment: 1. Calibrated thermal emission spectra. J Geophys Res. 1972;77:2629–41. doi:10.1029\u002FJC077i015p02629.\nKempe V, Oertel D, Schuster R, Becker-Ross H, Jahn H. Absolute IR-spectra from the measurement of Fourier-spectrometers aboard Meteor 25 and 28. Acta Astronautica. 1980;7:1403–16. doi:10.1016\u002F0094-5765(80)90015-6.\nThéodore B, Coppens D, Döhler W, Damiano A, Oertel D, Klaes D, Schmetz J, Spänkuch D. A Glimpse into the past: rescuing hyperspectral SI-1 data from Meteor-28 and 29, Proc. EUMETSAT Meteorological Satellite Conference, 2015; 21–25 September, Toulouse, France.\nIMG Mission Operation and Verification Committee. Interferometric monitor for greenhouse gases. IMG Project Technical Report (ed. Kobayashi, H.) (Central Research Institute of Electric Power Industry (CRIEPI), Komae Research Laboratory, Komae-shi, Tokyo, 1999).\nBovensmann H, Burrows J, Buchwitz M, et al. SCIAMACHY: mission objectives and measurement modes. J Atmos Sci. 1999;56:127–50. doi:10.1175\u002F1520-0469(1999)056\u003C0127:SMOAMM>2.0.CO;2.\nAumann H, Chahine M, Gautier C, et al. AIRS\u002FAMSU\u002FHSB on the aqua mission: design, science objectives, data products, and processing systems. IEEE Trans Geosci Remote Sens. 2003;41:253–64. doi:10.1109\u002FTGRS.2002.808356.\nSimeoni D, Astruc P, Miras D, et al. Design and development of IASI instrument. Proc SPIE. 2004;5543:208–19. doi:10.1117\u002F12.561090.\nHan Y, Revercomb H, Cromp M, Gu D, Johnson D, Mooney D, Scott D, Strow L, Bingham G, Borg L, Chen Y, DeSlover D, Esplin M, Hagan D, Jin X, Knuteson R, Motteler H, Predina J, Suwinski L, Taylor J, Tobin D, Tremblay D, Wang C, Wang L, Wang L, Zavyalov V. Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality. J Geophys Res, 2013;118, doi:10.1002\u002F2013jd020344.\nFeldman D, Collins W, Paige J. Pan-spectral observing system simulation experiments of shortwave reflectance and longwave radiance for climate model evaluation. Goesci Model Dev. 2015;8:1943–54. doi:10.5194\u002Fgmd-8-1943-2015. Gives background into how to perform a climate OSSE and shows how pan-spectral information could prove valuable in discriminating between the response of different climate models to a given forcing scenario.\nMolnar G, Susskind J. Atmospheric parameter climatologies from AIRS: monitoring short- and longer term climate variabilities and “trends”. Proc. SPIE, 2008;6966. doi:10.1117\u002F12.775446.\nPagano T, Chahine M, Olsen E. Seven years of observations of mid-tropospheric CO2 from the Atmospheric Infrared Sounder. Acta Astronautica. 2011;69:355–9. doi:10.1016\u002Fj.actaastro.2011.05.016.\nGettleman A, Collins W, Fetzer E, et al. Climatology of upper-tropospheric relative humidity from the Atmospheric Infrared Sounder and implications for climate. J Clim. 2006;19:6104–21. doi:10.1175\u002FJCLI3956.1.\nPan F, Huang X, Strow L, Gou H. Linear trends and closures of 10-yr observations of AIRS stratospheric channels. J Clim. 2015;28:8939–50. doi:10.1175\u002FJCLI-D-15-0418.1.\nMadden R, Ramanathan V. Detecting climate change due to increasing carbon dioxide. Science. 1980;209:763–8. doi:10.1126\u002Fscience.209.4458.763.\nHansen J, Johnson D, Lacis A, et al. Climate impact of increasing atmospheric carbon dioxide. Science. 1981;213:957–66. doi:10.1126\u002Fscience.213.4511.957.\nKukla G, Gavin J. Summer ice and carbon dioxide. Science. 1981;214:497–503. doi:10.1126\u002Fscience.214.4520.497.\nGornitz V, Lebedeff S, Hansen J. Global sea level trend in the past century. Science. 1982;215:1611–4. doi:10.1126\u002Fscience.215.4540.1611.\nKiehl J. Satellite detection of effects due to increased atmospheric carbon dioxide. Science. 1983;222:504–6. doi:10.1126\u002Fscience.222.4623.504.\nCharlock T. CO2 induced climatic-change and spectral variations in the outgoing terrestrial infrared radiation. Tellus-B. 1984;36:139–48.\nPrabhakara C, Dalu G, Kunde V. Search for global and seasonal-variation of methane from Nimbus-4 IRIS measurements. J Geophys Res. 1974;79:1744–9. doi:10.1029\u002FJC079i012p01744.\nPrabhakara C, Fraser R, Dalu G, Wu M, Curran R, Styles T. Thin cirrus clouds—seasonal distribution over oceans deduced from Nimbus-4 IRIS. J Appl Meteorol. 1988;27:379–99. doi:10.1175\u002F1520-0450(1988)027\u003C0379:TCCSDO>2.0.CO;2.\nNorth G, Kim K-Y, Shen S, Hardin J. Detection of forced climate signals. 1. Filter theory. J Climate. 1995;8:401–8. doi:10.1175\u002F15200442(1995)008\u003C0401:DOFCSP>2.0.CO;2.\nHaskins R, Goody R, Chen L. A statistical method for testing a general circulation model with spectrally resolved radiances. J Geophys Res. 1997;102:16563–81. doi:10.1029\u002F97JD00897.\nGoody R, Haskins R. Calibration of radiances from space. J Clim. 1998;11:754–8. doi:10.1175\u002F1520-0442(1998)011\u003C0754:CORFS>2.0.CO;2.\nHuang X, Farrara J, Leroy S, Yung Y, Goody R. Cloud variability as revealed in outgoing infrared spectra: comparing model to observations with spectral EOF analysis. Geophys Res Lett. 2002;29:1270. doi:10.1029\u002F2001GL014176.\nKeith D, Anderson J. Accurate spectrally resolved infrared radiance observation from space: implications for the detection of decade-to-century-scale climatic change. J Clim. 2001;14:979–90. doi:10.1175\u002F1520-0442(2001)014\u003C0979:ASRIRO>2.0.CO;2.\nAnderson J, Dykema J, Goody R, Hu H, Kirk-Davidoff D. Absolute, spectrally-resolved, thermal radiance: a benchmark for climate monitoring from space. J Quant Spectrosc Radiat Transf. 2004;85:367–83. doi:10.1016\u002FS0022-4073(03)00232-2.\nGoody R, Anderson J, Karl T, et al. Why monitor the climate? Bull Am Meteorol Soc. 2002;83:873–8. doi:10.1175\u002F1520-0477(2002)083\u003C0873:WWSMTC>2.3.CO;2.\nGoody R, Anderson J, North G. Testing climate models: an approach. Bull Am Meteorol Soc. 1998;79:2541–9. doi:10.1175\u002F1520-0477(1998)079\u003C2541:TCMAA>2.0.CO;2.\nSlingo A, Webb M. The spectral signature of global warming. Q J R Meteorol Soc. 1997;123:293–307. doi:10.1256\u002Fsmsqj.53802.\nHarries J, Brindley H, Geer A. Climate variability and trends from operational satellite spectral data. Geophys Res Lett. 1998;25:3975–8. doi:10.1029\u002F1998GL900056.\nBrindley H, Geer A, Harries J. Climate variability and trends in SSU radiances: a comparison of model predictions and satellite observations in the middle stratosphere. J Clim. 1999;12:3197–219. doi:10.1175\u002F1520-0442(1999)012\u003C3197:CVATIS>2.0.CO;2.\nGeer A, Harries J, Brindley H. Spatial patterns of climate variability in upper-tropospheric water vapor radiances from satellite data and climate model simulations. J Clim. 1999;12:1940–55. doi:10.1175\u002F1520-0442(1999)012\u003C1940:SPOCVI>2.0.CO;2.\nHarries J, Brindley H, Sagoo P, Bantges R. Increases in greenhouse forcing inferred from the outgoing longwave spectra of the Earth in 1970 and 1997. Nature. 2001;410:355–7. doi:10.1038\u002F35066553.\nBrindley H, Harries J. Observations of the infrared outgoing spectrum of the Earth from space: the effects of temporal and spatial sampling. J Clim. 2003;16:3820–33. doi:10.1175\u002F1520-0442(2003)016\u003C3820:OOTIOS>2.0.CO;2.\nBrindley H, Allan R. Simulations of the effects of interannual and decadal variability on the clear-sky outgoing longwave radiation spectrum. Q J R Meteorol Soc. 2003;129:2971–298. doi:10.1256\u002Fqj.02.216.\nLeroy S, Anderson J, Ohring G. Climate signal detection times and constraints on climate benchmark accuracy requirements. J Clim. 2008;21:841–6. doi:10.1175\u002F2007JCL1946.1.\nNRC. Earth science and applications from space: national imperatives for the next decade and beyond. National Academy Press, 2007, 428 pp.\nHuang X, Yung Y. Spatial and spectral variability of the outgoing thermal IR spectra from AIRS: a case study of July 2003. J Geophys Res. 2005;110. doi:10.1029\u002F2004JD005530.\nHuang Y, Ramaswamy V, Huang X, Fu Q, Bardeen C. A strict test in climate modeling with spectrally resolved radiances: GCM simulation versus AIRS observations. Geophys Res Lett, 2007;34. doi:10.1029\u002F2007GL031409.\nHuang X, Yang W, Loeb N, Ramaswamy V. Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: clear sky over the tropical oceans. J Geophys Res. 2008;113. doi:10.1029\u002F2007JD009219.\nHuang X, Loeb N, Yang W. Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: 2. Cloudy sky and band-by-band cloud radiative forcing over the tropical oceans. J Geophys Res. 2010;115. doi:10.1029\u002F2010JD013932.\nHuang Y, Ramaswamy. Observed and simulated seasonal co-variations of outgoing longwave radiation spectrum and surface temperature. Geophys Res Lett. 2008;35. doi:10.1029\u002F2008GL034859\nBony S, Lau K, Sud Y. Sea surface temperature and large-scale circulation influences on tropical greenhouse effect and cloud radiative forcing. J Clim. 1997;10:2055–77. doi:10.1175\u002F1520-0442(1997)010\u003C2055:SSTALS>2.0.CO;2.\nLeroy S, Anderson J, Dykema J, Goody R. Testing climate models using thermal infrared spectra. J Clim. 2008;21:1863–75. doi:10.1175\u002F2007JCLI2061.1.\nRoberts Y, Pilewskie P, Kindel B. Evaluating the observed variability in hyperspectral Earth-reflected solar radiance. J Geophys Res. 2011;116. doi:10.1029\u002F2011JD016448.\nJin Z, Wielicki B, Loukachine C, Charlock T, Young D, Noel S. Spectral kernel approach to study radiative response of climate variables and interannual variability of reflected solar spectrum. J Geophys Res. 2011;116. doi: 10.1029\u002F2010JD015228.\nFeldman D, Algieri C, Collins W, Roberts Y, Pilewskie P. Simulation studies for the detection of changes in broadband albedo and shortwave nadir reflectance spectra under a climate change scenario. J Geophys Res. 2011:116. doi:10.1029\u002F2011JD0160407.\nIntergovernmental Panel on Climate Change (IPCC). Special report on emissions scenarios: a special report of working group III of the Intergovernmental Panel on Climate Change. In: N. Nakicenovic et al. (eds), 2000; 599pp., Cambridge University Press, Cambridge, UK.\nFeldman D, Coleman D, Collins W. On the usage of spectral and broadband satellite instrument measurements to differentiate climate models with different cloud feedback strengths. J Clim. 2013;26:6561–74. doi:10.1175\u002FJCLI-D-12-00378.1.\nRoberts Y, Pilewskie P, Kindel B, Feldman D, Collins W. Quantitative comparison of the variability in observed and simulated shortwave reflectance. Atmos Chem Phys. 2013;13:3133–47. doi:10.5194\u002Facp-13-3133-2013.\nJin Z, Lukachin C, Roberts Y, Wielicki B, Feldman D, Collins W. Interannual variability of the Earth’s spectral solar reflectance from measurements and simulations. J Geophys Res. 2014;119:4458–70. doi:10.1002\u002F2013JD021056. Shows that the directly observed inter-annual variability across the RSW spectrum is typically very small (less than a few percent) when averaged over large spatial domains (e.g. tropics, mid-latitudes), and that this variability is well captured by radiative transfer simulations informed both by climate model output and geophysical retrievals from other satellite instruments.\nBrindley H, Bantges R, Russell J, et al. Spectral signatures of Earth’s climate variability over 5 years from IASI. J Clim. 2015;28:1649–60. doi:10.1175\u002FJCLI-D-14-00431.1. Quantifies inter-annual variability across the OLR spectrum for a range of spatial scales and shows how both the magnitude and shape of the spectral variability change as spatial scale increases.\nHuang Y, Ramaswamy V. Evolution and trend of the outgoing longwave radiation spectrum. J Clim. 2009;22:4637–51. doi:10.1175\u002F2009JCLI2874.1.\nWang L, Han Y, Jin X, Chen Y, Tremblay D. Radiometric consistency assessment of hyperspectral infrared sounders. Atmos Meas Tech. 2015;8:4831–44. doi:10.5194\u002Famt-8-4831-2015.\nIPCC. Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Doschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, 741–882, doi:10.1017\u002FCBO9781107415324.020.\nBodas-Salcedo A, Webb M, Bony S, et al. COSP satellite simulation software for model assessment. Bull Am Meteorol Soc. 2011;92:1023–43. doi:10.1175\u002F2011BAMS2856.1.\nTsushima Y, Ringer M, Webb M, Williams K. Quantitative evaluation of the seasonal variations in climate model cloud regimes. Clim Dyn. 2013;41:2679–96. doi:10.1007\u002Fs00382-012-1609-4.\nBodas-Salcedo A, Williams K, Ringer M, et al. Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models. J Clim. 2014;27:41–56. doi:10.1175\u002FJCLI-D-13-00169.1.\nCrevoisier C, Clerbaux C, Guidard V, et al. Towards IASI-New Generation (IASI-NG): impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables. Atmos Meas Tech. 2014;7:4367–85. doi:10.5194\u002Famt-7-4367-2014.\nLiu X, Smith W, Zhou D, Larar A. Principal component-based radiative transfer model for hyperspectral sensors: theoretical concept. Appl Optics. 2006;45:201–9. doi:10.1364\u002FAO.45.000201.\nChen X, Huang X, Liu X. Non-negligible effects of cloud vertical overlapping assumptions on longwave spectral fingerprinting studies. J Geophys Res. 2013;118:7309–20. doi:10.1002\u002Fjgrd.50562.\nWielicki B, Young D, Mlynczak M, et al. Achieving climate change accuracy in orbit. Bull Amer Meteorol Soc. 2013;94:1519–39. doi:10.1175\u002FBAMS-D-12-00149.1 Describes the CLARREO mission concept and proposes methods for formally determining accuracy requirements for a wide range of climate variables.\nFox N, Kaiser-Weiss A, Schmutz W, Thome K, Young D, Wielicki B, Winkler R, Woolliams E. Accurate radiometry from space: an essential tool for climate studies. Phil Trans Roy Soc. 2011;369. doi: 10.1098\u002Frsta.2011.024664.\nBantges R, Brindley H, Chen X, Huang X, Harries J, Murray J. On the detection of robust multi-decadal changes in the Earth’s outgoing longwave radiation spectrum. Accepted for publication in J. Climate. 2016. doi: 10.1175\u002FJCLI-D-15-0713.1.\nHarries J, Carli B, Rizzi R, Serio C, Mlynczak M, Palchetti L, Maestri T, Brindley H, Masiello G. The far infrared Earth. Rev Geophys. 2008;46. doi: 10.1029\u002F2007RG000233.\nFeldman D, Collins W, Pincus R, Huang X, Chen X. Far-infrared surface emissivity and climate. Proc Nat Acad Sci Amer. 2014;111:16297–302. doi:10.1073\u002Fpnas.1413640111.\nCox C, Walden V, Rowe P, Shupe M. Humidity trends imply increased sensitivity to clouds in a warming Arctic. Nature Comm. 2015;6. doi:10.1038\u002Fncomms10117.",{"EN":708},"Spectrally resolved measurements of the Earth’s reflected shortwave (RSW) and outgoing longwave radiation (OLR) at the top of the atmosphere intrinsically contain the imprints of a multitude of climate relevant parameters. Here, we review the progress made in directly using such observations to diagnose and attribute change within the Earth system over the past four decades. We show how changes associated with perturbations such as increasing greenhouse gases are expected to be manifested across the spectrum and illustrate the enhanced discriminatory power that spectral resolution provides over broadband radiation measurements. Advances in formal detection and attribution techniques and in the design of climate model evaluation exercises employing spectrally resolved data are highlighted. We illustrate how spectral observations have been used to provide insight into key climate feedback processes and quantify multi-year variability but also indicate potential barriers to further progress. Suggestions for future research priorities in this area are provided.",{"EN":710},"The Spectral Signature of Recent Climate Change",{"VOID":712},"10.1007\u002Fs40641-016-0039-5","2024-12-24T22:29:19.864+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs40641-016-0039-5",[716,733],{"id":717,"sortIndex":19,"researcher":18,"roles":718,"affiliations":719,"properties":730},"3b68d450-bf6f-40a4-8f4b-1007caa50c09",[169],[720],{"id":18,"sortIndex":19,"affiliation":721,"properties":18},{"id":722,"createTime":723,"updateTime":724,"relativeEntities":725,"slug":726,"properties":727,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"b937e01b-a2c4-4d59-8000-00a12038673c","2023-12-03T02:42:20.776+00:00","2024-04-05T22:59:50.792+00:00",[],"Space-and-Atmospheric-Physics-Group-and-NERC-National-Centre-for-Earth-Observation-Imperial-College-London-London-UK",{"title":728},{"VI":729},"Space and Atmospheric Physics Group, and NERC National Centre for Earth Observation, Imperial College London, London, UK",{"title":731},{"VI":732},"H. 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Links between annual, Milankovitch and continuum temperature variability. Nature. 2006;441(7091):329–32. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature04745\n                    \n                  .",{"doi":979},"10.1038\u002Fnature04745",{"id":18,"text":981,"url":18,"identifiers":982},"Ghil M, Yiou P, Hallegatte S, Malamud BD, Naveau P, Soloviev A, et al. Extreme events: dynamics, statistics and prediction. Nonlinear Proc Geoph. 2011;18(3):295–350. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fnpg-18-295-2011\n                    \n                  .",{"doi":983},"10.5194\u002Fnpg-18-295-2011",{"id":18,"text":985,"url":18,"identifiers":986},"Smith MD. An ecological perspective on extreme climatic events: a synthetic definition and framework to guide future research. J Ecol. 2011;99(3):656–63. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-2745.2011.01798.x\n                    \n                  .",{"doi":987},"10.1111\u002Fj.1365-2745.2011.01798.x",{"id":18,"text":989,"url":18,"identifiers":990},"Reichstein M, Bahn M, Ciais P, Frank D, Mahecha MD, Seneviratne SI, et al. Climate extremes and the carbon cycle. Nature. 2013;500(7462):287–95. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature12350\n                    \n                  .",{"doi":991},"10.1038\u002Fnature12350",{"id":18,"text":993,"url":18,"identifiers":994},"Frank DA, Reichstein M, Bahn M, Thonicke K, Frank D, Mahecha MD, et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob Chang Biol. 2015;21(8):2861–80. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.12916\n                    \n                  .",{"doi":995},"10.1111\u002Fgcb.12916",{"id":18,"text":997,"url":18,"identifiers":998},"Saatchi S, Asefi-Najafabady S, Malhi Y, Aragao LEOC, Anderson LO, Myneni RB, et al. Persistent effects of a severe drought on Amazonian forest canopy. Proc Natl Acad Sci U S A. 2013;110(2):565–70. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1204651110\n                    \n                  .",{"doi":999},"10.1073\u002Fpnas.1204651110",{"id":18,"text":1001,"url":18,"identifiers":1002},"Anderegg WRL, Schwalm C, Biondi F, Camarero JJ, Koch G, Litvak M, et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science. 2015;349(6247):528–32. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.aab1833\n                    \n                  .",{"doi":1003},"10.1126\u002Fscience.aab1833",{"id":18,"text":1005,"url":18,"identifiers":1006},"Ciais P, Reichstein M, Viovy N, Granier A, Ogee J, Allard V, et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature. 2005;437(7058):529–33. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature03972\n                    \n                  .",{"doi":1007},"10.1038\u002Fnature03972",{"id":18,"text":1009,"url":18,"identifiers":1010},"Vetter M, Churkina G, Jung M, Reichstein M, Zaehle S, Bondeau A, et al. Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models. Biogeosciences. 2008;5(2):561–83. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-5-561-2008\n                    \n                  .",{"doi":1011},"10.5194\u002Fbg-5-561-2008",{"id":18,"text":1013,"url":18,"identifiers":1014},"Bastos A, Gouveia CM, Trigo RM, Running SW. Analysing the spatio-temporal impacts of the 2003 and 2010 extreme heatwaves on plant productivity in Europe. Biogeosciences. 2014;11(13):3421–35. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-11-3421-2014\n                    \n                  .",{"doi":1015},"10.5194\u002Fbg-11-3421-2014",{"id":18,"text":1017,"url":18,"identifiers":1018},"Barriopedro D, Fischer EM, Luterbacher J, Trigo R, Garcia-Herrera R. The hot summer of 2010: redrawing the temperature record map of Europe. Science. 2011;332(6026):220–4. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1201224\n                    \n                  .",{"doi":1019},"10.1126\u002Fscience.1201224",{"id":18,"text":1021,"url":18,"identifiers":1022},"Reichstein M, Ciais P, Papale D, Valentini R, Running S, Viovy N, et al. Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: a joint flux tower, remote sensing and modelling analysis. Glob Chang Biol. 2007;13(3):634–51. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-2486.2006.01224.x\n                    \n                  .",{"doi":1023},"10.1111\u002Fj.1365-2486.2006.01224.x",{"id":18,"text":1025,"url":18,"identifiers":1026},"Chapin FS, Woodwell GM, Randerson JT, Rastetter EB, Lovett GM, Baldocchi DD, et al. Reconciling carbon-cycle concepts, terminology, and methods. Ecosystems. 2006;9(7):1041–50. \n                    https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10021-005-0105-7\n                    \n                  .",{"doi":1027},"10.1007\u002Fs10021-005-0105-7",{"id":18,"text":1029,"url":18,"identifiers":1030},"Guo M, Li J, Xu JW, Wang XF, He HS, Wu L. CO2 emissions from the 2010 Russian wildfires using GOSAT data. Environ Pollut. 2017;226:60–8. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.envpol.2017.04.014\n                    \n                  .",{"doi":1031},"10.1016\u002Fj.envpol.2017.04.014",{"id":18,"text":1033,"url":18,"identifiers":1034},"Le Quere C, Andrew RM, Canadell JG, Sitch S, Korsbakken JI, Peters GP, et al. Global carbon budget 2016. Earth Syst Sci Data. 2016;8(2):605–49. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-8-605-2016\n                    \n                  .",{"doi":1035},"10.5194\u002Fessd-8-605-2016",{"id":18,"text":1037,"url":18,"identifiers":1038},"Ahlstrom A, Raupach MR, Schurgers G, Smith B, Arneth A, Jung M, et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science. 2015;348(6237):895–9. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.aaa1668\n                    \n                  .",{"doi":1039},"10.1126\u002Fscience.aaa1668",{"id":18,"text":1041,"url":18,"identifiers":1042},"Fang YY, Michalak AM, Schwalm CR, Huntzinger DN, Berry JA, Ciais P, et al. Global land carbon sink response to temperature and precipitation varies with ENSO phase. Environ Res Lett. 2017;12(6):064007. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002Faa6e8e\n                    \n                  .",{"doi":1043},"10.1088\u002F1748-9326\u002Faa6e8e",{"id":18,"text":1045,"url":18,"identifiers":1046},"Zscheischler J, Mahecha MD, von Buttlar J, Harmeling S, Jung M, Rammig A, et al. A few extreme events dominate global interannual variability in gross primary production. Environ Res Lett. 2014;9(3):035001. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002F9\u002F3\u002F035001\n                    \n                  .",{"doi":1047},"10.1088\u002F1748-9326\u002F9\u002F3\u002F035001",{"id":18,"text":1049,"url":18,"identifiers":1050},"Seneviratne SI, Nicholls N, Easterling D, Goodess CM, Kanae S, Kossin J, et al. Changes in climate extremes and their impacts on the natural physical environmenty. In: Field CB, Barros V, Stocker TF, et al., editors. Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2012. p. 109–230.",{},{"id":18,"text":1052,"url":18,"identifiers":1053},"Fischer EM, Knutti R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change. 2015;5(6):560. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnclimate2617\n                    \n                  .",{"doi":1054},"10.1038\u002Fnclimate2617",{"id":18,"text":1056,"url":18,"identifiers":1057},"Otto FEL, Massey N, van Oldenborgh GJ, Jones RG, Allen MR. Reconciling two approaches to attribution of the 2010 Russian heat wave. Geophys Res Lett. 2012;39:L04702. \n                    https:\u002F\u002Fdoi.org\u002F10.1029\u002F2011gl050422\n                    \n                  .",{"doi":1058},"10.1029\u002F2011gl050422",{"id":18,"text":1060,"url":18,"identifiers":1061},"Bahn M, Reichstein M, Dukes JS, Smith MD, McDowell NG. Climate-biosphere interactions in a more extreme world. New Phytol. 2014;202(2):356–9. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fnph.12662\n                    \n                  .",{"doi":1062},"10.1111\u002Fnph.12662",{"id":18,"text":1064,"url":18,"identifiers":1065},"Sheffield J, Wood EF, Roderick ML. Little change in global drought over the past 60 years. Nature. 2012;491(7424):435. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature11575\n                    \n                  .",{"doi":1066},"10.1038\u002Fnature11575",{"id":18,"text":1068,"url":18,"identifiers":1069},"Orlowsky B, Seneviratne SI. Elusive drought: uncertainty in observed trends and short- and long-term CMIP5 projections. Hydrol Earth Syst Sc. 2013;17(5):1765–81. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fhess-17-1765-2013\n                    \n                  .",{"doi":1070},"10.5194\u002Fhess-17-1765-2013",{"id":18,"text":1072,"url":18,"identifiers":1073},"Trenberth KE, Dai AG, van der Schrier G, Jones PD, Barichivich J, Briffa KR, et al. Global warming and changes in drought. Nat Clim Chang. 2014;4(1):17–22. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNclimate2067\n                    \n                  .",{"doi":1074},"10.1038\u002FNclimate2067",{"id":18,"text":1076,"url":18,"identifiers":1077},"Pendergrass AG, Knutti R, Lehner F, Deser C, Sanderson BM. Precipitation variability increases in a warmer climate. 2017;7:17966. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-017-17966-y\n                    \n                  .",{"doi":1078},"10.1038\u002Fs41598-017-17966-y",{"id":18,"text":1080,"url":18,"identifiers":1081},"Swann ALS, Hoffman FM, Koven CD, Randerson JT. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc Natl Acad Sci U S A. 2016;113(36):10019–24. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1604581113\n                    \n                  .",{"doi":1082},"10.1073\u002Fpnas.1604581113",{"id":18,"text":1084,"url":18,"identifiers":1085},"Stahl K, Hisdal H, Hannaford J, Tallaksen LM, van Lanen HAJ, Sauquet E, et al. Streamflow trends in Europe: evidence from a dataset of near-natural catchments. Hydrol Earth Syst Sc. 2010;14(12):2367–82. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fhess-14-2367-2010\n                    \n                  .",{"doi":1086},"10.5194\u002Fhess-14-2367-2010",{"id":18,"text":1088,"url":18,"identifiers":1089},"Friedlingstein P, Meinshausen M, Arora VK, Jones CD, Anav A, Liddicoat SK, et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J Clim. 2014;27(2):511–26. \n                    https:\u002F\u002Fdoi.org\u002F10.1175\u002FJcli-D-12-00579.1\n                    \n                  .",{"doi":1090},"10.1175\u002FJcli-D-12-00579.1",{"id":18,"text":1092,"url":18,"identifiers":1093},"Zaehle S, Dalmonech D. Carbon-nitrogen interactions on land at global scales: current understanding in modelling climate biosphere feedbacks. Curr Opin Env Sust. 2011;3(5):311–20. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cosust.2011.08.008\n                    \n                  .",{"doi":1094},"10.1016\u002Fj.cosust.2011.08.008",{"id":18,"text":1096,"url":18,"identifiers":1097},"Körner C. Biosphere responses to CO2 enrichment. Ecol Appl. 2000;10(6):1590–619. \n                    https:\u002F\u002Fdoi.org\u002F10.2307\u002F2641226\n                    \n                  .",{"doi":1098},"10.2307\u002F2641226",{"id":18,"text":1100,"url":18,"identifiers":1101},"Friend AD, Lucht W, Rademacher TT, Keribin R, Betts R, Cadule P, et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc Natl Acad Sci U S A. 2014;111(9):3280–5. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1222477110\n                    \n                  .",{"doi":1102},"10.1073\u002Fpnas.1222477110",{"id":18,"text":1104,"url":18,"identifiers":1105},"Körner C. A matter of tree longevity. Science. 2017;355(6321):130–1. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.aaal2449\n                    \n                  .",{"doi":1106},"10.1126\u002Fscience.aaal2449",{"id":18,"text":1108,"url":18,"identifiers":1109},"Leonard M, Westra S, Phatak A, Lambert M, van den Hurk B, McInnes K, et al. A compound event framework for understanding extreme impacts. Wires Clim Change. 2014;5(1):113–28. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002Fwcc.252\n                    \n                  .",{"doi":1110},"10.1002\u002Fwcc.252",{"id":18,"text":1112,"url":18,"identifiers":1113},"Mahony CR, Cannon AJ, Wang TL, Aitken SN. A closer look at novel climates: new methods and insights at continental to landscape scales. Glob Chang Biol. 2017;23(9):3934–55. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13645\n                    \n                  .",{"doi":1114},"10.1111\u002Fgcb.13645",{"id":18,"text":1116,"url":18,"identifiers":1117},"Bevacqua E, Maraun D, Haff IH, Widmann M, Vrac M. Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy). Hydrol Earth Syst Sc. 2017;21(6):2701–23. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fhess-21-2701-2017\n                    \n                  .",{"doi":1118},"10.5194\u002Fhess-21-2701-2017",{"id":18,"text":1120,"url":18,"identifiers":1121},"Flach M, Gans F, Brenning A, Denzler J, Reichstein M, Rodner E, et al. Multivariate anomaly detection for earth observations: a comparison of algorithms and feature extraction techniques. Earth Syst Dynam. 2017;8(3):677–96. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fesd-8-677-2017\n                    \n                  .",{"doi":1122},"10.5194\u002Fesd-8-677-2017",{"id":18,"text":1124,"url":18,"identifiers":1125},"Williams AP, Abatzoglou JT. Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity. Curr Climate Change Rep. 2016;2(1):1–14. \n                    https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40641-016-0031-0",{"doi":335},{"id":18,"text":1127,"url":18,"identifiers":1128},"Jactel H, Petit J, Desprez-Loustau ML, Delzon S, Piou D, Battisti A, et al. Drought effects on damage by forest insects and pathogens: a meta-analysis. Glob Chang Biol. 2012;18(1):267–76. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-2486.2011.02512.x\n                    \n                  .",{"doi":1129},"10.1111\u002Fj.1365-2486.2011.02512.x",{"id":18,"text":1131,"url":18,"identifiers":1132},"Schlesinger WH, Dietze MC, Jackson RB, Phillips RP, Rhoades CC, Rustad LE, et al. Forest biogeochemistry in response to drought. Glob Chang Biol. 2016;22(7):2318–28. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13105\n                    \n                  .",{"doi":1133},"10.1111\u002Fgcb.13105",{"id":18,"text":1135,"url":18,"identifiers":1136},"Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G, et al. Forest disturbances under climate change. Nat Clim Chang. 2017;7(6):395–402. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNclimate3303\n                    \n                  .",{"doi":1137},"10.1038\u002FNclimate3303",{"id":18,"text":1139,"url":18,"identifiers":1140},"Larcher W. Physiological plant ecology : ecophysiology and stress physiology of functional groups. 4th edn. Berlin; New York: Springer; 2003.",{"doi":1141},"10.1007\u002F978-3-662-05214-3",{"id":18,"text":1143,"url":18,"identifiers":1144},"Jentsch A, Kreyling J, Elmer M, Gellesch E, Glaser B, Grant K, et al. Climate extremes initiate ecosystem-regulating functions while maintaining productivity. J Ecol. 2011;99(3):689–702. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-2745.2011.01817.x\n                    \n                  .",{"doi":1145},"10.1111\u002Fj.1365-2745.2011.01817.x",{"id":18,"text":1147,"url":18,"identifiers":1148},"Schwalm CR, Williams CA, Schaefer K, Arneth A, Bonal D, Buchmann N, et al. Assimilation exceeds respiration sensitivity to drought: a FLUXNET synthesis. Glob Chang Biol. 2010;16(2):657–70. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-2486.2009.01991.x\n                    \n                  .",{"doi":1149},"10.1111\u002Fj.1365-2486.2009.01991.x",{"id":18,"text":1151,"url":18,"identifiers":1152},"von Buttlar J, Zscheischler J, Rammig A, Sippel S, Reichstein M, Knohl A, et al. Impacts of droughts and extreme temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones. Biogeosci Discuss. 2017;2017:1–39. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-2017-393\n                    \n                  .",{"doi":1153},"10.5194\u002Fbg-2017-393",{"id":18,"text":1155,"url":18,"identifiers":1156},"Zscheischler J, Michalak AM, Schwalm C, Mahecha MD, Huntzinger DN, Reichstein M, et al. Impact of large-scale climate extremes on biospheric carbon fluxes: an intercomparison based on MsTMIP data. Global Biogeochem Cy. 2014;28(6):585–600. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2014gb004826\n                    \n                  .",{"doi":1157},"10.1002\u002F2014gb004826",{"id":18,"text":1159,"url":18,"identifiers":1160},"Xia JY, Niu SL, Ciais P, Janssens IA, Chen JQ, Ammann C, et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc Natl Acad Sci U S A. 2015;112(9):2788–93. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1413090112\n                    \n                  .",{"doi":1161},"10.1073\u002Fpnas.1413090112",{"id":18,"text":1163,"url":18,"identifiers":1164},"Choat B, Jansen S, Brodribb TJ, Cochard H, Delzon S, Bhaskar R, et al. Global convergence in the vulnerability of forests to drought. Nature. 2012;491(7426):752. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature11688\n                    \n                  .",{"doi":1165},"10.1038\u002Fnature11688",{"id":18,"text":1167,"url":18,"identifiers":1168},"O'Sullivan OS, Heskel MA, Reich PB, Tjoelker MG, Weerasinghe LK, Penillard A, et al. Thermal limits of leaf metabolism across biomes. Glob Chang Biol. 2017;23(1):209–23. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13477\n                    \n                  .",{"doi":1169},"10.1111\u002Fgcb.13477",{"id":18,"text":1171,"url":18,"identifiers":1172},"Teskey R, Wertin T, Bauweraerts I, Ameye M, McGuire MA, Steppe K. Responses of tree species to heat waves and extreme heat events. Plant Cell Environ. 2015;38(9):1699–712. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fpce.12417\n                    \n                  .",{"doi":1173},"10.1111\u002Fpce.12417",{"id":18,"text":1175,"url":18,"identifiers":1176},"Drake JE, Tjoelker MG, Vårhammar A, Medlyn Belinda E, Reich PB, Leigh A, et al. Trees tolerate an extreme heatwave via sustained transpirational cooling and increased leaf thermal tolerance. Global Change Biol. 2018;24:2390–402. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.14037\n                    \n                  .",{"doi":1177},"10.1111\u002Fgcb.14037",{"id":18,"text":1179,"url":18,"identifiers":1180},"Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, et al. Investigating soil moisture-climate interactions in a changing climate: a review. Earth-Sci Rev. 2010;99(3–4):125–61. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.earscirev.2010.02.004\n                    \n                  .",{"doi":1181},"10.1016\u002Fj.earscirev.2010.02.004",{"id":18,"text":1183,"url":18,"identifiers":1184},"Hoover DL, Knapp AK, Smith MD. The immediate and prolonged effects of climate extremes on soil respiration in a mesic grassland. J Geophys Res-Biogeo. 2016;121(4):1034–44. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015jg003256\n                    \n                  .",{"doi":1185},"10.1002\u002F2015jg003256",{"id":18,"text":1187,"url":18,"identifiers":1188},"Balogh J, Papp M, Pinter K, Foti S, Posta K, Eugster W, et al. Autotrophic component of soil respiration is repressed by drought more than the heterotrophic one in dry grasslands. Biogeosciences. 2016;13(18):5171–82. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-13-5171-2016\n                    \n                  .",{"doi":1189},"10.5194\u002Fbg-13-5171-2016",{"id":18,"text":1191,"url":18,"identifiers":1192},"Wang X, Liu LL, Piao SL, Janssens IA, Tang JW, Liu WX, et al. Soil respiration under climate warming: differential response of heterotrophic and autotrophic respiration. Glob Chang Biol. 2014;20(10):3229–37. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.12620\n                    \n                  .",{"doi":1193},"10.1111\u002Fgcb.12620",{"id":18,"text":1195,"url":18,"identifiers":1196},"Baldocchi D. Measuring and modelling carbon dioxide and water vapour exchange over a temperate broad-leaved forest during the 1995 summer drought. Plant Cell Environ. 1997;20(9):1108–22. \n                    https:\u002F\u002Fdoi.org\u002F10.1046\u002Fj.1365-3040.1997.d01-147.x\n                    \n                  .",{"doi":1197},"10.1046\u002Fj.1365-3040.1997.d01-147.x",{"id":18,"text":1199,"url":18,"identifiers":1200},"Jarvis P, Rey A, Petsikos C, Wingate L, Rayment M, Pereira J, et al. Drying and wetting of Mediterranean soils stimulates decomposition and carbon dioxide emission: the “Birch effect”. Tree Physiol. 2007;27(7):929–40. \n                    https:\u002F\u002Fdoi.org\u002F10.1093\u002Ftreephys\u002F27.7.929\n                    \n                  .",{"doi":1201},"10.1093\u002Ftreephys\u002F27.7.929",{"id":18,"text":1203,"url":18,"identifiers":1204},"Ma XL, Huete A, Moran S, Ponce-Campos G, Eamus D. Abrupt shifts in phenology and vegetation productivity under climate extremes. J Geophys Res-Biogeo. 2015;120(10):2036–52. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015jg003144\n                    \n                  .",{"doi":1205},"10.1002\u002F2015jg003144",{"id":18,"text":1207,"url":18,"identifiers":1208},"Wu J, Albert LP, Lopes AP, Restrepo-Coupe N, Hayek M, Wiedemann KT, et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science. 2016;351(6276):972–6. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.aad5068\n                    \n                  .",{"doi":1209},"10.1126\u002Fscience.aad5068",{"id":18,"text":1211,"url":18,"identifiers":1212},"Ma SX, Pitman AJ, Lorenz R, Kala J, Srbinovsky J. Earlier green-up and spring warming amplification over Europe. Geophys Res Lett. 2016;43(5):2011–8. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016gl068062\n                    \n                  .",{"doi":1213},"10.1002\u002F2016gl068062",{"id":18,"text":1215,"url":18,"identifiers":1216},"Zhang Y, Xiao XM, Zhou S, Ciais P, McCarthy H, Luo YQ. Canopy and physiological controls of GPP during drought and heat wave. Geophys Res Lett. 2016;43(7):3325–33. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016gl068501\n                    \n                  .",{"doi":1217},"10.1002\u002F2016gl068501",{"id":18,"text":1219,"url":18,"identifiers":1220},"Teuling AJ, Seneviratne SI, Stockli R, Reichstein M, Moors E, Ciais P, et al. Contrasting response of European forest and grassland energy exchange to heatwaves. Nat Geosci. 2010;3(10):722–7. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNGEO950\n                    \n                  .",{"doi":1221},"10.1038\u002FNGEO950",{"id":18,"text":1223,"url":18,"identifiers":1224},"Breda N, Huc R, Granier A, Dreyer E. Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann Forest Sci. 2006;63(6):625–44. \n                    https:\u002F\u002Fdoi.org\u002F10.1051\u002Fforest:2006042\n                    \n                  .",{"doi":1225},"10.1051\u002Fforest:2006042",{"id":18,"text":1227,"url":18,"identifiers":1228},"Wolf S, Keenan TF, Fisher JB, Baldocchi DD, Desai AR, Richardson AD, et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc Natl Acad Sci. 2016;113(21):5880–85. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1519620113\n                    \n                  .",{"doi":1229},"10.1073\u002Fpnas.1519620113",{"id":18,"text":1231,"url":18,"identifiers":1232},"Jung M, Reichstein M, Schwalm CR, Huntingford C, Sitch S, Ahlstrom A, et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature. 2017;541(7638) \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature20780\n                    \n                  .",{"doi":1233},"10.1038\u002Fnature20780",{"id":18,"text":1235,"url":18,"identifiers":1236},"Knapp AK, Carroll CJW, Denton EM, La Pierre KJ, Collins SL, Smith MD. Differential sensitivity to regional-scale drought in six central US grasslands. Oecologia. 2015;177(4):949–57. \n                    https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00442-015-3233-6\n                    \n                  .",{"doi":1237},"10.1007\u002Fs00442-015-3233-6",{"id":18,"text":1239,"url":18,"identifiers":1240},"Taylor SH, Ripley BS, Martin T, De-Wet LA, Woodward FI, Osborne CP. Physiological advantages of C-4 grasses in the field: a comparative experiment demonstrating the importance of drought. Glob Chang Biol. 2014;20(6):1992–2003. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.12498\n                    \n                  .",{"doi":1241},"10.1111\u002Fgcb.12498",{"id":18,"text":1243,"url":18,"identifiers":1244},"Roman DT, Novick KA, Brzostek ER, Dragoni D, Rahman F, Phillips RP. The role of isohydric and anisohydric species in determining ecosystem-scale response to severe drought. Oecologia. 2015;179(3):641–54. \n                    https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00442-015-3380-9\n                    \n                  .",{"doi":1245},"10.1007\u002Fs00442-015-3380-9",{"id":18,"text":1247,"url":18,"identifiers":1248},"Yi K, Dragoni D, Phillips RP, Roman DT, Novick KA. Dynamics of stem water uptake among isohydric and anisohydric species experiencing a severe drought. Tree Physiol. 2017;37(10):1379–92. \n                    https:\u002F\u002Fdoi.org\u002F10.1093\u002Ftreephys\u002Ftpw126\n                    \n                  .",{"doi":1249},"10.1093\u002Ftreephys\u002Ftpw126",{"id":18,"text":1251,"url":18,"identifiers":1252},"Bloom AJ, Chapin FS, Mooney HA. Resource limitation in plants—an economic analogy. Annu Rev Ecol Syst. 1985;16:363–92. \n                    https:\u002F\u002Fdoi.org\u002F10.1146\u002Fannurev.es.16.110185.002051\n                    \n                  .",{"doi":1253},"10.1146\u002Fannurev.es.16.110185.002051",{"id":18,"text":1255,"url":18,"identifiers":1256},"Denton EM, Dietrich JD, Smith MD, Knapp AK. Drought timing differentially affects above- and belowground productivity in a mesic grassland. Plant Ecol. 2017;218(3):317–28. \n                    https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11258-016-0690-x\n                    \n                  .",{"doi":1257},"10.1007\u002Fs11258-016-0690-x",{"id":18,"text":1259,"url":18,"identifiers":1260},"Sevanto S, Dickman LT. Where does the carbon go?-Plant carbon allocation under climate change. Tree Physiol. 2015;35(6):581–4. \n                    https:\u002F\u002Fdoi.org\u002F10.1093\u002Ftreephys\u002Ftpv059\n                    \n                  .",{"doi":1261},"10.1093\u002Ftreephys\u002Ftpv059",{"id":18,"text":1263,"url":18,"identifiers":1264},"Blessing CH, Werner RA, Siegwolf R, Buchmann N. Allocation dynamics of recently fixed carbon in beech saplings in response to increased temperatures and drought. Tree Physiol. 2015;35(6):585–98. \n                    https:\u002F\u002Fdoi.org\u002F10.1093\u002Ftreephys\u002Ftpv024\n                    \n                  .",{"doi":1265},"10.1093\u002Ftreephys\u002Ftpv024",{"id":18,"text":1267,"url":18,"identifiers":1268},"Doughty CE, Metcalfe DB, Girardin CAJ, Amezquita FF, Cabrera DG, Huasco WH, et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature. 2015;519(7541):78–U140. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature14213\n                    \n                  .",{"doi":1269},"10.1038\u002Fnature14213",{"id":18,"text":1271,"url":18,"identifiers":1272},"Børja I, Godbold DL, Světlík J, Nagy NE, Gebauer R, Urban J, et al. Norway spruce fine roots and fungal hyphae grow deeper in forest soils after extended drought. In: Soil Biological Communities and Ecosystem Resilience. Berlin: Springer; 2017. p. 123–42.",{"doi":1273},"10.1007\u002F978-3-319-63336-7_8",{"id":18,"text":1275,"url":18,"identifiers":1276},"Gherardi LA, Sala OE. Enhanced precipitation variability decreases grass- and increases shrub-productivity. Proc Natl Acad Sci U S A. 2015;112(41):12735–40. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1506433112\n                    \n                  .",{"doi":1277},"10.1073\u002Fpnas.1506433112",{"id":18,"text":1279,"url":18,"identifiers":1280},"Musavi T, Migliavacca M, Reichstein M, Kattge J, Wirth C, Black TA, et al. Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity. Nat Ecol Evol. 2017;1:0048. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41559-016-0048\n                    \n                  .",{"doi":1281},"10.1038\u002Fs41559-016-0048",{"id":18,"text":1283,"url":18,"identifiers":1284},"Roy J, Picon-Cochard C, Augusti A, Benot ML, Thiery L, Darsonville O, et al. Elevated CO2 maintains grassland net carbon uptake under a future heat and drought extreme. Proc Natl Acad Sci U S A. 2016;113(22):6224–9. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1524527113\n                    \n                  .",{"doi":1285},"10.1073\u002Fpnas.1524527113",{"id":18,"text":1287,"url":18,"identifiers":1288},"Zhu ZC, Piao SL, Myneni RB, Huang MT, Zeng ZZ, Canadell JG, et al. Greening of the earth and its drivers. Nature Climate Change. 2016;6(8):791. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNclimate3004\n                    \n                  .",{"doi":1289},"10.1038\u002FNclimate3004",{"id":18,"text":1291,"url":18,"identifiers":1292},"Obermeier WA, Lehnert LW, Kammann CI, Muller C, Grunhage L, Luterbacher J, et al. Reduced CO2 fertilization effect in temperate C3 grasslands under more extreme weather conditions. Nature Climate Change. 2017;7(2):137. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNclimate3191\n                    \n                  .",{"doi":1293},"10.1038\u002FNclimate3191",{"id":18,"text":1295,"url":18,"identifiers":1296},"Leuzinger S, Zotz G, Asshoff R, Körner C. Responses of deciduous forest trees to severe drought in Central Europe. Tree Physiol. 2005;25(6):641–50. \n                    https:\u002F\u002Fdoi.org\u002F10.1093\u002Ftreephys\u002F25.6.641\n                    \n                  .",{"doi":1297},"10.1093\u002Ftreephys\u002F25.6.641",{"id":18,"text":1299,"url":18,"identifiers":1300},"Lemordant L, Gentine P, Stefanon M, Drobinski P, Fatichi S. Modification of land-atmosphere interactions by CO2 effects: implications for summer dryness and heat wave amplitude. Geophys Res Lett. 2016;43(19):10240–8. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016gl069896\n                    \n                  .",{"doi":1301},"10.1002\u002F2016gl069896",{"id":18,"text":1303,"url":18,"identifiers":1304},"Zscheischler J, Reichstein M, von Buttlar J, Mu MQ, Randerson JT, Mahecha MD. Carbon cycle extremes during the 21st century in CMIP 5 models: future evolution and attribution to climatic drivers. Geophys Res Lett. 2014;41(24):8853–61. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2014gl062409\n                    \n                  .",{"doi":1305},"10.1002\u002F2014gl062409",{"id":18,"text":1307,"url":18,"identifiers":1308},"Broughton KJ, Smith RA, Duursma RA, Tan DKY, Payton P, Bange MP, et al. Warming alters the positive impact of elevated CO2 concentration on cotton growth and physiology during soil water deficit. Funct Plant Biol. 2017;44(2):267–78. \n                    https:\u002F\u002Fdoi.org\u002F10.1071\u002FFp16189\n                    \n                  .",{"doi":1309},"10.1071\u002FFp16189",{"id":18,"text":1311,"url":18,"identifiers":1312},"Dieleman WIJ, Vicca S, Dijkstra FA, Hagedorn F, Hovenden MJ, Larsen KS, et al. Simple additive effects are rare: a quantitative review of plant biomass and soil process responses to combined manipulations of CO2 and temperature. Glob Chang Biol. 2012;18(9):2681–93. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-2486.2012.02745.x\n                    \n                  .",{"doi":1313},"10.1111\u002Fj.1365-2486.2012.02745.x",{"id":18,"text":1315,"url":18,"identifiers":1316},"Fatichi S, Leuzinger S, Paschalis A, Langley JA, Barraclough AD, Hovenden MJ. Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2. Proc Natl Acad Sci U S A. 2016;113(45):12757–62. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1605036113\n                    \n                  .",{"doi":1317},"10.1073\u002Fpnas.1605036113",{"id":18,"text":1319,"url":18,"identifiers":1320},"De Boeck HJ, Dreesen FE, Janssens IA, Nijs I. Whole-system responses of experimental plant communities to climate extremes imposed in different seasons. New Phytol. 2011;189(3):806–17. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1469-8137.2010.03515.x\n                    \n                  .",{"doi":1321},"10.1111\u002Fj.1469-8137.2010.03515.x",{"id":18,"text":1323,"url":18,"identifiers":1324},"Beierkuhnlein C, Thiel D, Jentsch A, Willner E, Kreyling J. Ecotypes of European grass species respond differently to warming and extreme drought. J Ecol. 2011;99(3):703–13. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-2745.2011.01809.x\n                    \n                  .",{"doi":1325},"10.1111\u002Fj.1365-2745.2011.01809.x",{"id":18,"text":1327,"url":18,"identifiers":1328},"Darenova E, Holub P, Krupkova L, Pavelka M. Effect of repeated spring drought and summer heavy rain on managed grassland biomass production and CO2 efflux. J Plant Ecol. 2017;10(3):476–85. \n                    https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjpe\u002Frtw058\n                    \n                  .",{"doi":1329},"10.1093\u002Fjpe\u002Frtw058",{"id":18,"text":1331,"url":18,"identifiers":1332},"Sippel S, Zscheischler J, Reichstein M. Ecosystem impacts of climate extremes crucially depend on the timing. Proc Natl Acad Sci U S A. 2016;113(21):5768–70. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1605667113\n                    \n                  .",{"doi":1333},"10.1073\u002Fpnas.1605667113",{"id":18,"text":1335,"url":18,"identifiers":1336},"Desai AR. Influence and predictive capacity of climate anomalies on daily to decadal extremes in canopy photosynthesis. Photosynth Res. 2014;119(1–2):31–47. \n                    https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11120-013-9925-z\n                    \n                  .",{"doi":1337},"10.1007\u002Fs11120-013-9925-z",{"id":18,"text":1339,"url":18,"identifiers":1340},"Buermann W, Bikash PR, Jung M, Burn DH, Reichstein M. Earlier springs decrease peak summer productivity in North American boreal forests. Environ Res Lett. 2013;8(2):024027. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002F8\u002F2\u002F024027\n                    \n                  .",{"doi":1341},"10.1088\u002F1748-9326\u002F8\u002F2\u002F024027",{"id":18,"text":1343,"url":18,"identifiers":1344},"Papagiannopoulou C, Miralles DG, Dorigo WA, Verhoest NEC, Depoorter M, Waegeman W. Vegetation anomalies caused by antecedent precipitation in most of the world. Environ Res Lett. 2017;12(7):074016. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002Faa7145\n                    \n                  .",{"doi":1345},"10.1088\u002F1748-9326\u002Faa7145",{"id":18,"text":1347,"url":18,"identifiers":1348},"Seddon AWR, Macias-Fauria M, Long PR, Benz D, Willis KJ. Sensitivity of global terrestrial ecosystems to climate variability. Nature. 2016;531(7593):229. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature16986\n                    \n                  .",{"doi":1349},"10.1038\u002Fnature16986",{"id":18,"text":1351,"url":18,"identifiers":1352},"Kwon H, Pendall E, Ewers BE, Cleary M, Naithani K. Spring drought regulates summer net ecosystem CO2 exchange in a sagebrush-steppe ecosystem. Agric For Meteorol. 2008;148(3):381–91. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agrformet.2007.09.010\n                    \n                  .",{"doi":1353},"10.1016\u002Fj.agrformet.2007.09.010",{"id":18,"text":1355,"url":18,"identifiers":1356},"Sippel S, Forkel M, Rammig A, Thonicke K, Flach M, Heimann M, et al. Contrasting and interacting changes in simulated spring and summer carbon cycle extremes in European ecosystems. Environ Res Lett. 2017;12(7):075006. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002Faa7398\n                    \n                  .",{"doi":1357},"10.1088\u002F1748-9326\u002Faa7398",{"id":18,"text":1359,"url":18,"identifiers":1360},"Körner C, Basler D. Phenology under global warming. Science. 2010;327(5972):1461–2. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1186473\n                    \n                  .",{"doi":1361},"10.1126\u002Fscience.1186473",{"id":18,"text":1363,"url":18,"identifiers":1364},"Hufkens K, Friedl MA, Keenan TF, Sonnentag O, Bailey A, O'Keefe J, et al. Ecological impacts of a widespread frost event following early spring leaf-out. Glob Chang Biol. 2012;18(7):2365–77. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1365-2486.2012.02712.x\n                    \n                  .",{"doi":1365},"10.1111\u002Fj.1365-2486.2012.02712.x",{"id":18,"text":1367,"url":18,"identifiers":1368},"Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecol Manag. 2010;259(4):660–84. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.foreco.2009.09.001\n                    \n                  .",{"doi":1369},"10.1016\u002Fj.foreco.2009.09.001",{"id":18,"text":1371,"url":18,"identifiers":1372},"Hagedorn F, Joseph J, Peter M, Luster J, Pritsch K, Geppert U, et al. Recovery of trees from drought depends on belowground sink control. Nat Plants. 2016;2(8):16111. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNplants.2016.111\n                    \n                  .",{"doi":1373},"10.1038\u002FNplants.2016.111",{"id":18,"text":1375,"url":18,"identifiers":1376},"Wu X, Liu H, Li X, Ciais P, Babst F, Guo W, et al. Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Global Change Biol:n\u002Fa-n\u002Fa. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13920\n                    \n                  .",{"doi":1377},"10.1111\u002Fgcb.13920",{"id":18,"text":1379,"url":18,"identifiers":1380},"Anderegg WRL, Plavcova L, Anderegg LDL, Hacke UG, Berry JA, Field CB. Drought's legacy: multiyear hydraulic deterioration underlies widespread aspen forest die-off and portends increased future risk. Glob Chang Biol. 2013;19(4):1188–96. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.12100\n                    \n                  .",{"doi":1381},"10.1111\u002Fgcb.12100",{"id":18,"text":1383,"url":18,"identifiers":1384},"Schwalm CR, Anderegg WRL, Michalak AM, Fisher JB, Biondi F, Koch G, et al. Global patterns of drought recovery. Nature. 2017;548(7666):202. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature23021\n                    \n                  .",{"doi":1385},"10.1038\u002Fnature23021",{"id":18,"text":1387,"url":18,"identifiers":1388},"Peltier DMP, Fell M, Ogle K. Legacy effects of drought in the southwestern United States: a multi-species synthesis. Ecol Monogr. 2016;86(3):312–26. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002Fecm.1219\n                    \n                  .",{"doi":1389},"10.1002\u002Fecm.1219",{"id":18,"text":1391,"url":18,"identifiers":1392},"Wagg C, O'Brien MJ, Vogel A, Scherer-Lorenzen M, Eisenhauer N, Schmid B, et al. Plant diversity maintains long-term ecosystem productivity under frequent drought by increasing short-term variation. Ecology. 2017;98(11):2952–61.",{"doi":1393},"10.1002\u002Fecy.2003",{"id":18,"text":1395,"url":18,"identifiers":1396},"Rouault G, Candau JN, Lieutier F, Nageleisen LM, Martin JC, Warzee N. Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Ann Forest Sci. 2006;63(6):613–24. \n                    https:\u002F\u002Fdoi.org\u002F10.1051\u002Fforest:2006044\n                    \n                  .",{"doi":1397},"10.1051\u002Fforest:2006044",{"id":18,"text":1399,"url":18,"identifiers":1400},"Rousk J, Smith AR, Jones DL. Investigating the long-term legacy of drought and warming on the soil microbial community across five European shrubland ecosystems. Glob Chang Biol. 2013;19(12):3872–84. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.12338\n                    \n                  .",{"doi":1401},"10.1111\u002Fgcb.12338",{"id":18,"text":1403,"url":18,"identifiers":1404},"McDowell NG, Beerling DJ, Breshears DD, Fisher RA, Raffa KF, Stitt M. The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol Evol. 2011;26(10):523–32. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tree.2011.06.003\n                    \n                  .",{"doi":1405},"10.1016\u002Fj.tree.2011.06.003",{"id":18,"text":1407,"url":18,"identifiers":1408},"Hartmann H, Ziegler W, Kolle O, Trumbore S. Thirst beats hunger - declining hydration during drought prevents carbon starvation in Norway spruce saplings. New Phytol. 2013;200(2):340–9. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fnph.12331\n                    \n                  .",{"doi":1409},"10.1111\u002Fnph.12331",{"id":18,"text":1411,"url":18,"identifiers":1412},"Rowland L, da Costa ACL, Galbraith DR, Oliveira RS, Binks OJ, Oliveira AAR, et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature. 2015;528(7580):119. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature15539\n                    \n                  .",{"doi":1413},"10.1038\u002Fnature15539",{"id":18,"text":1415,"url":18,"identifiers":1416},"Zeng N, Mariotti A, Wetzel P. Terrestrial mechanisms of interannual CO(2) variability. Global Biogeochem Cy. 2005;19(1):Gb1016. \n                    https:\u002F\u002Fdoi.org\u002F10.1029\u002F2004gb0022763\n                    \n                  .",{"doi":1417},"10.1029\u002F2004gb0022763",{"id":18,"text":1419,"url":18,"identifiers":1420},"Zscheischler J, Reichstein M, Harmeling S, Rammig A, Tomelleri E, Mahecha MD. Extreme events in gross primary production: a characterization across continents. Biogeosciences. 2014;11(11):2909–24. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-11-2909-2014\n                    \n                  .",{"doi":1421},"10.5194\u002Fbg-11-2909-2014",{"id":18,"text":1423,"url":18,"identifiers":1424},"Budyko M. Climate and life. New York: Academic Press; 1974.",{},{"id":18,"text":1426,"url":18,"identifiers":1427},"Jung M, Reichstein M, Margolis HA, Cescatti A, Richardson AD, Arain MA, et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J Geophys Res-Biogeo. 2011;116:G00j07. \n                    https:\u002F\u002Fdoi.org\u002F10.1029\u002F2010jg001566\n                    \n                  .",{"doi":1428},"10.1029\u002F2010jg001566",{"id":18,"text":1430,"url":18,"identifiers":1431},"Zscheischler J, Mahecha MD, Harmeling S, Reichstein M. Detection and attribution of large spatiotemporal extreme events in Earth observation data. Ecol Inform. 2013;15:66–73. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecoinf.2013.03.004\n                    \n                  .",{"doi":1432},"10.1016\u002Fj.ecoinf.2013.03.004",{"id":18,"text":1434,"url":18,"identifiers":1435},"Dai A, Wigley TML. Global patterns of ENSO-induced precipitation. Geophys Res Lett. 2000;27(9):1283–6. \n                    https:\u002F\u002Fdoi.org\u002F10.1029\u002F1999gl011140\n                    \n                  .",{"doi":1436},"10.1029\u002F1999gl011140",{"id":18,"text":1438,"url":18,"identifiers":1439},"Lyon B, Barnston AG. ENSO and the spatial extent of interannual precipitation extremes in tropical land areas. J Clim. 2005;18(23):5095–109. \n                    https:\u002F\u002Fdoi.org\u002F10.1175\u002FJcli3598.1\n                    \n                  .",{"doi":1440},"10.1175\u002FJcli3598.1",{"id":18,"text":1442,"url":18,"identifiers":1443},"Marshall J, Kushner Y, Battisti D, Chang P, Czaja A, Dickson R, et al. North Atlantic climate variability: phenomena, impacts and mechanisms. Int J Climatol. 2001;21(15):1863–98. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjoc.693\n                    \n                  .",{"doi":1444},"10.1002\u002Fjoc.693",{"id":18,"text":1446,"url":18,"identifiers":1447},"Trenberth KE. The definition of El Nino. Bull Am Meteorological Soc. 1997;78(12):2771–7. \n                    https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0477(1997)078\u003C2771:TDOENO>2.0.CO;2\n                    \n                  .",{"doi":1448},"10.1175\u002F1520-0477(1997)078\u003C2771:TDOENO>2.0.CO;2",{"id":18,"text":1450,"url":18,"identifiers":1451},"Collins M, An SI, Cai WJ, Ganachaud A, Guilyardi E, Jin FF, et al. The impact of global warming on the tropical Pacific ocean and El Nino. Nat Geosci. 2010;3(6):391–7. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNgeo868\n                    \n                  .",{"doi":1452},"10.1038\u002FNgeo868",{"id":18,"text":1454,"url":18,"identifiers":1455},"Gibson PB, Pitman AJ, Lorenz R, Perkins-Kirkpatrick SE. The role of circulation and land surface conditions in current and future Australian heat waves. J Clim. 2017;30:9933-47. \n                    https:\u002F\u002Fdoi.org\u002F10.1175\u002FJCLI-D-17-0265.1\n                    \n                  .",{"doi":1456},"10.1175\u002FJCLI-D-17-0265.1",{"id":18,"text":1458,"url":18,"identifiers":1459},"Ummenhofer CC, Sen Gupta A, Briggs PR, England MH, McIntosh PC, Meyers GA, et al. Indian and Pacific Ocean influences on southeast Australian drought and soil moisture (vol 24, pg 1313, 2011). J Clim. 2011;24(14):3796–6. \n                    https:\u002F\u002Fdoi.org\u002F10.1175\u002FJcli-D-11-00229.1\n                    \n                  .",{"doi":1460},"10.1175\u002FJcli-D-11-00229.1",{"id":18,"text":1462,"url":18,"identifiers":1463},"Richey JE, Nobre C, Deser C. Amazon River discharge and climate variability—1903 to 1985. Science. 1989;246(4926):101–3. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.246.4926.101\n                    \n                  .",{"doi":1464},"10.1126\u002Fscience.246.4926.101",{"id":18,"text":1466,"url":18,"identifiers":1467},"Holmgren M, Scheffer M, Ezcurra E, Gutierrez JR, Mohren GMJ. El Nino effects on the dynamics of terrestrial ecosystems. Trends Ecol Evol. 2001;16(2):89–94. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0169-5347(00)02052-8\n                    \n                  .",{"doi":1468},"10.1016\u002FS0169-5347(00)02052-8",{"id":18,"text":1470,"url":18,"identifiers":1471},"Hallett TB, Coulson T, Pilkington JG, Clutton-Brock TH, Pemberton JM, Grenfell BT. Why large-scale climate indices seem to predict ecological processes better than local weather. Nature. 2004;430(6995):71–5. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature02708\n                    \n                  .",{"doi":1472},"10.1038\u002Fnature02708",{"id":18,"text":1474,"url":18,"identifiers":1475},"Bastos A, Running SW, Gouveia C, Trigo RM. The global NPP dependence on ENSO: La Nina and the extraordinary year of 2011. J Geophys Res-Biogeo. 2013;118(3):1247–55. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002Fjgrg.20100\n                    \n                  .",{"doi":1476},"10.1002\u002Fjgrg.20100",{"id":18,"text":1478,"url":18,"identifiers":1479},"Kim JS, Kug JS, Yoon JH, Jeong SJ. Increased atmospheric CO2 growth rate during El Nino driven by reduced terrestrial productivity in the CMIP5 ESMs. J Clim. 2016;29(24):8783–805. \n                    https:\u002F\u002Fdoi.org\u002F10.1175\u002FJcli-D-14-00672.1\n                    \n                  .",{"doi":1480},"10.1175\u002FJcli-D-14-00672.1",{"id":18,"text":1482,"url":18,"identifiers":1483},"Cleverly J, Eamus D, Luo QY, Coupe NR, Kljun N, Ma XL, et al. The importance of interacting climate modes on Australia’s contribution to global carbon cycle extremes. Sci Rep-Uk. 2016;6:23113. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fsrep23113\n                    \n                  .",{"doi":1484},"10.1038\u002Fsrep23113",{"id":18,"text":1486,"url":18,"identifiers":1487},"Cai W, Cowan T, Raupach M. Positive Indian Ocean Dipole events precondition southeast Australia bushfires. Geophys Res Lett. 2009;36:L19710. \n                    https:\u002F\u002Fdoi.org\u002F10.1029\u002F2009gl039902\n                    \n                  .",{"doi":1488},"10.1029\u002F2009gl039902",{"id":18,"text":1490,"url":18,"identifiers":1491},"Chen Y, Morton DC, Andela N, Giglio L, Randerson JT. How much global burned area can be forecast on seasonal time scales using sea surface temperatures? Environ Res Lett. 2016;11(4):045001. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002F11\u002F4\u002F045001\n                    \n                  .",{"doi":1492},"10.1088\u002F1748-9326\u002F11\u002F4\u002F045001",{"id":18,"text":1494,"url":18,"identifiers":1495},"Liu JJ, Bowman KW, Schimel DS, Parazoo NC, Jiang Z, Lee M, et al. Contrasting carbon cycle responses of the tropical continents to the 2015-2016 El Nino. Science. 2017;358(6360):191. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.aam5690\n                    \n                  .",{"doi":1496},"10.1126\u002Fscience.aam5690",{"id":18,"text":1498,"url":18,"identifiers":1499},"Belmecheri S, Babst F, Hudson AR, Betancourt J, Trouet V. Northern Hemisphere jet stream position indices as diagnostic tools for climate and ecosystem dynamics. Earth Interact. 2017;21:1–23. \n                    https:\u002F\u002Fdoi.org\u002F10.1175\u002FEi-D-16-0023.1\n                    \n                  .",{"doi":1500},"10.1175\u002FEi-D-16-0023.1",{"id":18,"text":1502,"url":18,"identifiers":1503},"Bastos A, Janssens IA, Gouveia CM, Trigo RM, Ciais P, Chevallier F, et al. European land CO2 sink influenced by NAO and East-Atlantic Pattern coupling. Nat Commun. 2016;7:10315. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fncomms10315\n                    \n                  .",{"doi":1504},"10.1038\u002Fncomms10315",{"id":18,"text":1506,"url":18,"identifiers":1507},"Parazoo NC, Barnes E, Worden J, Harper AB, Bowman KB, Frankenberg C, et al. Influence of ENSO and the NAO on terrestrial carbon uptake in the Texas-northern Mexico region. Global Biogeochem Cy. 2015;29(8):1247–65. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015gb005125\n                    \n                  .",{"doi":1508},"10.1002\u002F2015gb005125",{"id":18,"text":1510,"url":18,"identifiers":1511},"Desai AR, Wohlfahrt G, Zeeman MJ, Katata G, Eugster W, Montagnani L, et al. Montane ecosystem productivity responds more to global circulation patterns than climatic trends. Environ Res Lett. 2016;11(2):024013. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002F11\u002F2\u002F024013\n                    \n                  .",{"doi":1512},"10.1088\u002F1748-9326\u002F11\u002F2\u002F024013",{"id":18,"text":1514,"url":18,"identifiers":1515},"Bastos A, Ciais P, Park T, Zscheischler J, Yue C, Barichivich J, et al. Was the extreme Northern Hemisphere greening in 2015 predictable? Environ Res Lett. 2017;12(4):044016. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002Faa67b5\n                    \n                  .",{"doi":1516},"10.1088\u002F1748-9326\u002Faa67b5",{"id":18,"text":1518,"url":18,"identifiers":1519},"Shepherd TG. Atmospheric circulation as a source of uncertainty in climate change projections. Nat Geosci. 2014;7(10):703–8. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNgeo2253\n                    \n                  .",{"doi":1520},"10.1038\u002FNgeo2253",{"id":18,"text":1522,"url":18,"identifiers":1523},"Cai WJ, Borlace S, Lengaigne M, van Rensch P, Collins M, Vecchi G, et al. Increasing frequency of extreme El Nino events due to greenhouse warming. Nat Clim Chang. 2014;4(2):111–6. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNclimate2100\n                    \n                  .",{"doi":1524},"10.1038\u002FNclimate2100",{"id":18,"text":1526,"url":18,"identifiers":1527},"Coumou D, Lehmann J, Beckmann J. The weakening summer circulation in the Northern Hemisphere mid-latitudes. Science. 2015;348(6232):324–7. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1261768\n                    \n                  .",{"doi":1528},"10.1126\u002Fscience.1261768",{"id":18,"text":1530,"url":18,"identifiers":1531},"Trouet V, Babst F, Meko M. Recent enhanced high-summer North Atlantic Jet variability emerges from three-century context. Nat Commun. 2018;9:180. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-017-02699-3\n                    \n                  .",{"doi":1532},"10.1038\u002Fs41467-017-02699-3",{"id":18,"text":1534,"url":18,"identifiers":1535},"Zemp DC, Schleussner CF, Barbosa HMJ, Hirota M, Montade V, Sampaio G, et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat Commun. 2017;8:14681. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fncomms14681\n                    \n                  .",{"doi":1536},"10.1038\u002Fncomms14681",{"id":18,"text":1538,"url":18,"identifiers":1539},"Keys PW, van der Ent RJ, Gordon LJ, Hoff H, Nikoli R, Savenije HHG. Analyzing precipitationsheds to understand the vulnerability of rainfall dependent regions. Biogeosciences. 2012;9(2):733–46. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-9-733-2012\n                    \n                  .",{"doi":1540},"10.5194\u002Fbg-9-733-2012",{"id":18,"text":1542,"url":18,"identifiers":1543},"Garcia ES, Swann ALS, Villegas JC, Breshears DD, Law DJ, Saleska SR, et al. Synergistic ecoclimate teleconnections from forest loss in different regions structure global ecological responses. Plos One. 2016;11(11):e0165042. \n                    https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0165042\n                    \n                  .",{"doi":1544},"10.1371\u002Fjournal.pone.0165042",{"id":18,"text":1546,"url":18,"identifiers":1547},"Shepherd TG. A common framework for approaches to extreme event attribution. Curr Clim Change Rep. 2016;2(1):28–38. \n                    https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40641-016-0033-y\n                    \n                  .",{"doi":1548},"10.1007\u002Fs40641-016-0033-y",{"id":18,"text":1550,"url":18,"identifiers":1551},"Vautard R, Yiou P, Otto F, Stott P, Christidis N, Van Oldenborgh GJ, et al. Attribution of human-induced dynamical and thermodynamical contributions in extreme weather events. Environ Res Lett. 2016;11(11):114009. \n                    https:\u002F\u002Fdoi.org\u002F10.1088\u002F1748-9326\u002F11\u002F11\u002F114009\n                    \n                  .",{"doi":1552},"10.1088\u002F1748-9326\u002F11\u002F11\u002F114009",{"id":18,"text":1554,"url":18,"identifiers":1555},"Hoover DL, Rogers BM. Not all droughts are created equal: the impacts of interannual drought pattern and magnitude on grassland carbon cycling. Glob Chang Biol. 2016;22(5):1809–20. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13161\n                    \n                  .",{"doi":1556},"10.1111\u002Fgcb.13161",{"id":18,"text":1558,"url":18,"identifiers":1559},"Miralles DG, Teuling AJ, van Heerwaarden CC, de Arellano JVG. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat Geosci. 2014;7(5):345–9. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNGEO2141\n                    \n                  .",{"doi":1560},"10.1038\u002FNGEO2141",{"id":18,"text":1562,"url":18,"identifiers":1563},"Knapp AK, Avolio ML, Beier C, Carroll CJW, Collins SL, Dukes JS, et al. Pushing precipitation to the extremes in distributed experiments: recommendations for simulating wet and dry years. Glob Chang Biol. 2017;23(5):1774–82. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13504\n                    \n                  .",{"doi":1564},"10.1111\u002Fgcb.13504",{"id":18,"text":1566,"url":18,"identifiers":1567},"Jentsch A, Kreyling J, Beierkuhnlein C. A new generation of climate-change experiments: events, not trends. Front Ecol Environ. 2007;5(7):365–74. \n                    https:\u002F\u002Fdoi.org\u002F10.1890\u002F1540-9295(2007)5%5B365:Angoce%5D2.0.Co;2\n                    \n                  .",{"doi":1568},"10.1890\u002F1540-9295(2007)5%5B365:Angoce%5D2.0.Co;2",{"id":18,"text":1570,"url":18,"identifiers":1571},"Wilcox KR, Shi Z, Gherardi LA, Lemoine NP, Koerner SE, Hoover DL, et al. Asymmetric responses of primary productivity to precipitation extremes: a synthesis of grassland precipitation manipulation experiments. Glob Chang Biol. 2017;23(10):4376–85. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13706\n                    \n                  .",{"doi":1572},"10.1111\u002Fgcb.13706",{"id":18,"text":1574,"url":18,"identifiers":1575},"Shi Z, Thomey ML, Mowll W, Litvak M, Brunsell NA, Collins SL, et al. Differential effects of extreme drought on production and respiration: synthesis and modeling analysis. Biogeosciences. 2014;11(3):621–33. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-11-621-2014\n                    \n                  .",{"doi":1576},"10.5194\u002Fbg-11-621-2014",{"id":18,"text":1578,"url":18,"identifiers":1579},"Ruehr NK, Gast A, Weber C, Daub B, Arneth A. Water availability as dominant control of heat stress responses in two contrasting tree species. Tree Physiol. 2016;36(2):164–78. \n                    https:\u002F\u002Fdoi.org\u002F10.1093\u002Ftreephys\u002Ftpv102\n                    \n                  .",{"doi":1580},"10.1093\u002Ftreephys\u002Ftpv102",{"id":18,"text":1582,"url":18,"identifiers":1583},"Fraser LH, Henry HAL, Carlyle CN, White SR, Beierkuhnlein C, Cahill JF, et al. Coordinated distributed experiments: an emerging tool for testing global hypotheses in ecology and environmental science. Front Ecol Environ. 2013;11(3):147–55. \n                    https:\u002F\u002Fdoi.org\u002F10.1890\u002F110279\n                    \n                  .",{"doi":1584},"10.1890\u002F110279",{"id":18,"text":1586,"url":18,"identifiers":1587},"Novick KA, Ficklin DL, Stoy PC, Williams CA, Bohrer G, Oishi AC, et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat Clim Chang. 2016;6(11):1023–7. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNclimate3114\n                    \n                  .",{"doi":1588},"10.1038\u002FNclimate3114",{"id":18,"text":1590,"url":18,"identifiers":1591},"Beier C, Beierkuhnlein C, Wohlgemuth T, Penuelas J, Emmett B, Korner C, et al. Precipitation manipulation experiments - challenges and recommendations for the future. Ecol Lett. 2012;15(8):899–911. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1461-0248.2012.01793.x\n                    \n                  .",{"doi":1592},"10.1111\u002Fj.1461-0248.2012.01793.x",{"id":18,"text":1594,"url":18,"identifiers":1595},"Wu D, Ciais P, Viovy N, Knapp AK, Wilcox K, Bahn M, et al. Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three longterm grassland sites. Biogeosci Discuss. 2018;2018:1–27. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-2018-53\n                    \n                  .",{"doi":1596},"10.5194\u002Fbg-2018-53",{"id":18,"text":1598,"url":18,"identifiers":1599},"Mahecha MD, Gans F, Sippel S, Donges JF, Kaminski T, Metzger S, et al. Detecting impacts of extreme events with ecological in situ monitoring networks. Biogeosciences. 2017;14(18):4255–77. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-14-4255-2017\n                    \n                  .",{"doi":1600},"10.5194\u002Fbg-14-4255-2017",{"id":18,"text":1602,"url":18,"identifiers":1603},"Schwalm CR, Williams CA, Schaefer K, Baldocchi D, Black TA, Goldstein AH, et al. Reduction in carbon uptake during turn of the century drought in western North America. Nat Geosci. 2012;5(8):551–6. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNgeo1529\n                    \n                  .",{"doi":1604},"10.1038\u002FNgeo1529",{"id":18,"text":1606,"url":18,"identifiers":1607},"Biederman JA, Scott RL, Goulden ML, Vargas R, Litvak ME, Kolb TE, et al. Terrestrial carbon balance in a drier world: the effects of water availability in southwestern North America. Glob Chang Biol. 2016;22(5):1867–79. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13222\n                    \n                  .",{"doi":1608},"10.1111\u002Fgcb.13222",{"id":18,"text":1610,"url":18,"identifiers":1611},"Yu GR, Chen Z, Piao SL, Peng CH, Ciais P, Wang QF, et al. High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region. Proc Natl Acad Sci U S A. 2014;111(13):4910–5. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1317065111\n                    \n                  .",{"doi":1612},"10.1073\u002Fpnas.1317065111",{"id":18,"text":1614,"url":18,"identifiers":1615},"Foken T. The energy balance closure problem: an overview. Ecol Appl. 2008;18(6):1351–67. \n                    https:\u002F\u002Fdoi.org\u002F10.1890\u002F06-0922.1\n                    \n                  .",{"doi":1616},"10.1890\u002F06-0922.1",{"id":18,"text":1618,"url":18,"identifiers":1619},"Korner C. Slow in, rapid out—carbon flux studies and Kyoto targets. Science. 2003;300(5623):1242–3. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.1084460\n                    \n                  .",{"doi":1620},"10.1126\u002Fscience.1084460",{"id":18,"text":1622,"url":18,"identifiers":1623},"Asner GP, Alencar A. Drought impacts on the Amazon forest: the remote sensing perspective. New Phytol. 2010;187(3):569–78. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1469-8137.2010.03310.x\n                    \n                  .",{"doi":1624},"10.1111\u002Fj.1469-8137.2010.03310.x",{"id":18,"text":1626,"url":18,"identifiers":1627},"Sims DA, Brzostek ER, Rahman AF, Dragoni D, Phillips RP. An improved approach for remotely sensing water stress impacts on forest C uptake. Glob Chang Biol. 2014;20(9):2856–66. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.12537\n                    \n                  .",{"doi":1628},"10.1111\u002Fgcb.12537",{"id":18,"text":1630,"url":18,"identifiers":1631},"Vicca S, Balzarolo M, Filella I, Granier A, Herbst M, Knohl A, et al. Remotely-sensed detection of effects of extreme droughts on gross primary production. Sci Rep-Uk. 2016;6:28269. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fsrep28269\n                    \n                  .",{"doi":1632},"10.1038\u002Fsrep28269",{"id":18,"text":1634,"url":18,"identifiers":1635},"Leuning R, Cleugh HA, Zegelin SJ, Hughes D. Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet\u002Fdry savanna in Australia: measurements and comparison with MODIS remote sensing estimates. Agric For Meteorol. 2005;129(3–4):151–73. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agrformet.2004.12.004\n                    \n                  .",{"doi":1636},"10.1016\u002Fj.agrformet.2004.12.004",{"id":18,"text":1638,"url":18,"identifiers":1639},"Hwang T, Gholizadeh H, Sims DA, Novick KA, Brzostek ER, Phillips RP, et al. Capturing species-level drought responses in a temperate deciduous forest using ratios of photochemical reflectance indices between sunlit and shaded canopies. Remote Sens Environ. 2017;199:350–9. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2017.07.033\n                    \n                  .",{"doi":1640},"10.1016\u002Fj.rse.2017.07.033",{"id":18,"text":1642,"url":18,"identifiers":1643},"Rascher U, Pieruschka R. Spatio-temporal variations of photosynthesis: the potential of optical remote sensing to better understand and scale light use efficiency and stresses of plant ecosystems. Precis Agric. 2008;9(6):355–66. \n                    https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-008-9074-0\n                    \n                  .",{"doi":1644},"10.1007\u002Fs11119-008-9074-0",{"id":18,"text":1646,"url":18,"identifiers":1647},"Guanter L, Zhang YG, Jung M, Joiner J, Voigt M, Berry JA, et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc Natl Acad Sci U S A. 2014;111(14):E1327–33. \n                    https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1320008111\n                    \n                  .",{"doi":1648},"10.1073\u002Fpnas.1320008111",{"id":18,"text":1650,"url":18,"identifiers":1651},"Sun Y, Frankenberg C, Wood JD, Schimel DS, Jung M, Guanter L, et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science. 2017;358(6360):189. \n                    https:\u002F\u002Fdoi.org\u002F10.1126\u002Fscience.aam5747\n                    \n                  .",{"doi":1652},"10.1126\u002Fscience.aam5747",{"id":18,"text":1654,"url":18,"identifiers":1655},"Lee JE, Frankenberg C, van der Tol C, Berry JA, Guanter L, Boyce CK, et al. Forest productivity and water stress in Amazonia: observations from GOSAT chlorophyll fluorescence. P Roy Soc B-Biol Sci. 2013;280(1761):20130171. \n                    https:\u002F\u002Fdoi.org\u002F10.1098\u002Frspb.2013.0171\n                    \n                  .",{"doi":1656},"10.1098\u002Frspb.2013.0171",{"id":18,"text":1658,"url":18,"identifiers":1659},"Ma XL, Huete A, Cleverly J, Eamus D, Chevallier F, Joiner J, et al. Drought rapidly diminishes the large net CO2 uptake in 2011 over semi-arid Australia. Sci Rep-Uk. 2016;6:37747. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fsrep37747\n                    \n                  .",{"doi":1660},"10.1038\u002Fsrep37747",{"id":18,"text":1662,"url":18,"identifiers":1663},"Yoshida Y, Joiner J, Tucker C, Berry J, Lee JE, Walker G, et al. The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence: insights from modeling and comparisons with parameters derived from satellite reflectances. Remote Sens Environ. 2015;166:163–77. \n                    https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2015.06.008\n                    \n                  .",{"doi":1664},"10.1016\u002Fj.rse.2015.06.008",{"id":18,"text":1666,"url":18,"identifiers":1667},"Chevallier F, Maksyutov S, Bousquet P, Breon FM, Saito R, Yoshida Y, et al. On the accuracy of the CO2 surface fluxes to be estimated from the GOSAT observations. Geophys Res Lett. 2009;36:L19807. \n                    https:\u002F\u002Fdoi.org\u002F10.1029\u002F2009gl040108\n                    \n                  .",{"doi":1668},"10.1029\u002F2009gl040108",{"id":18,"text":1670,"url":18,"identifiers":1671},"Detmers RG, Hasekamp O, Aben I, Houweling S, van Leeuwen TT, Butz A, et al. Anomalous carbon uptake in Australia as seen by GOSAT. Geophys Res Lett. 2015;42(19):8177–84. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2015gl065161\n                    \n                  .",{"doi":1672},"10.1002\u002F2015gl065161",{"id":18,"text":1674,"url":18,"identifiers":1675},"Sulman BN, Roman DT, Yi K, Wang LX, Phillips RP, Novick KA. High atmospheric demand for water can limit forest carbon uptake and transpiration as severely as dry soil. Geophys Res Lett. 2016;43(18):9686–95. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016gl069416\n                    \n                  .",{"doi":1676},"10.1002\u002F2016gl069416",{"id":18,"text":1678,"url":18,"identifiers":1679},"Stoy PC, Richardson AD, Baldocchi DD, Katul GG, Stanovick J, Mahecha MD, et al. Biosphere-atmosphere exchange of CO2 in relation to climate: a cross-biome analysis across multiple time scales. Biogeosciences. 2009;6(10):2297–312. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-6-2297-2009\n                    \n                  .",{"doi":1680},"10.5194\u002Fbg-6-2297-2009",{"id":18,"text":1682,"url":18,"identifiers":1683},"De Kauwe MG, Medlyn BE, Walker AP, Zaehle S, Asao S, Guenet B, et al. Challenging terrestrial biosphere models with data from the long-term multifactor prairie heating and CO2 enrichment experiment. Glob Chang Biol. 2017;23(9):3623–45. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13643\n                    \n                  .",{"doi":1684},"10.1111\u002Fgcb.13643",{"id":18,"text":1686,"url":18,"identifiers":1687},"Rogers A, Medlyn BE, Dukes JS, Bonan G, von Caemmerer S, Dietze MC, et al. A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol. 2017;213(1):22–42. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fnph.14283\n                    \n                  .",{"doi":1688},"10.1111\u002Fnph.14283",{"id":18,"text":1690,"url":18,"identifiers":1691},"Xu XT, Medvigy D, Powers JS, Becknell JM, Guan KY. Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests. New Phytol. 2016;212(1):80–95. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fnph.14009\n                    \n                  .",{"doi":1692},"10.1111\u002Fnph.14009",{"id":18,"text":1694,"url":18,"identifiers":1695},"Sakschewski B, von Bloh W, Boit A, Poorter L, Pena-Claros M, Heinke J, et al. Resilience of Amazon forests emerges from plant trait diversity. Nat Clim Change. 2016;6(11):1032. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002FNclimate3109\n                    \n                  .",{"doi":1696},"10.1038\u002FNclimate3109",{"id":18,"text":1698,"url":18,"identifiers":1699},"Reichstein M, Mahecha MD, Ciais P, Seneviratne SI, Blyth EM, Carvalhais N, et al. Elk–testing climate–carbon cycle models: a case for pattern–oriented system analysis. iLEAPS Newsletter. 2011;11:14–21.",{},{"id":18,"text":1701,"url":18,"identifiers":1702},"Rammig A, Wiedermann M, Donges JF, Babst F, von Bloh W, Frank D, et al. Coincidences of climate extremes and anomalous vegetation responses: comparing tree ring patterns to simulated productivity. Biogeosciences. 2015;12(2):373–85. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-12-373-2015\n                    \n                  .",{"doi":1703},"10.5194\u002Fbg-12-373-2015",{"id":18,"text":1705,"url":18,"identifiers":1706},"Luo YQ, Randerson JT, Abramowitz G, Bacour C, Blyth E, Carvalhais N, et al. A framework for benchmarking land models. Biogeosciences. 2012;9(10):3857–74. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-9-3857-2012\n                    \n                  .",{"doi":1707},"10.5194\u002Fbg-9-3857-2012",{"id":18,"text":1709,"url":18,"identifiers":1710},"Sippel S, Otto FEL, Forkel M, Allen MR, Guillod BP, Heimann M, et al. A novel bias correction methodology for climate impact simulations. Earth Syst Dynam. 2016;7(1):71–88. \n                    https:\u002F\u002Fdoi.org\u002F10.5194\u002Fesd-7-71-2016\n                    \n                  .",{"doi":1711},"10.5194\u002Fesd-7-71-2016",{"id":18,"text":1713,"url":18,"identifiers":1714},"Ahlstrom A, Canadell JG, Schurgers G, Wu MC, Berry JA, Guan KY, et al. Hydrologic resilience and Amazon productivity. Nat Commun. 2017;8:387. \n                    https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-017-00306-z\n                    \n                  .",{"doi":1715},"10.1038\u002Fs41467-017-00306-z",{"id":18,"text":1717,"url":18,"identifiers":1718},"Powell TL, Galbraith DR, Christoffersen BO, Harper A, Imbuzeiro HMA, Rowland L, et al. Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought. New Phytol. 2013;200(2):350–64. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fnph.12390\n                    \n                  .",{"doi":1719},"10.1111\u002Fnph.12390",{"id":18,"text":1721,"url":18,"identifiers":1722},"Huang YY, Gerber S, Huang TY, Lichstein JW. Evaluating the drought response of CMIP5 models using global gross primary productivity, leaf area, precipitation, and soil moisture data. Global Biogeochem Cy. 2016;30(12):1827–46. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002F2016gb005480\n                    \n                  .",{"doi":1723},"10.1002\u002F2016gb005480",{"id":18,"text":1725,"url":18,"identifiers":1726},"Restrepo-Coupe N, Levine NM, Christoffersen BO, Albert LP, Wu J, Costa MH, et al. Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. Glob Chang Biol. 2017;23(1):191–208. \n                    https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcb.13442\n                    \n                  .",{"doi":1727},"10.1111\u002Fgcb.13442",{"id":18,"text":1729,"url":18,"identifiers":1730},"Hegerl GC, Hoegh-Guldberg O, Casassa G, Hoerling MP, Kovats RS, Parmesan C, et al. Good practice guidance paper on detection and attribution related to anthropogenic climate change. In Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change. IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland; 2010.",{},{"id":18,"text":1732,"url":18,"identifiers":1733},"Stott PA, Christidis N, Otto FEL, Sun Y, Vanderlinden JP, van Oldenborgh GJ, et al. Attribution of extreme weather and climate-related events. Wires Clim Change. 2016;7(1):23–41. \n                    https:\u002F\u002Fdoi.org\u002F10.1002\u002Fwcc.380\n                    \n                  .",{"doi":1734},"10.1002\u002Fwcc.380",{"id":1736,"createTime":1737,"updateTime":1738,"relativeEntities":1739,"slug":1740,"properties":1741,"entityType":161,"verifyStatus":162,"verifyTime":1738,"verifyNote":163,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1750,"fullTextUrl":18,"authors":1751,"publicationType":182,"publisherRelationship":1826,"citationCount":18,"citationInfo":18,"publishDate":1859,"publishYear":1860,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":218},"ec565d57-3096-4125-97ed-cace480ce392","2023-12-04T16:48:30.464+00:00","2025-01-08T22:00:57.884+00:00",[],"Finding-the-Fingerprint-of-Anthropogenic-Climate-Change-in-Marine-Phytoplankton-Abundance",{"references":1742,"abstract":1744,"title":1746,"doi":1748},{"VOID":1743},"Friedlingstein P, Jones MW, O’Sullivan M, et al. Global carbon budget 2019. Earth Syst Sci Data. 2019;11:1783–838.\nFalkowski P. The power of plankton. Nature. 2012;483:S17–20.\nMcKinley GA, Fay AR, Lovenduski NS, Pilcher DJ. Natural variability and anthropogenic trends in the ocean carbon sink. Annu Rev Mar Sci. 2017;9:125–50.\nBindoff NL, Cheung WWL, Kairo JG, et al. Changing ocean, marine ecosystems, and dependent communities. In: Intergovernmental Panel on Climate Change 2019: Summary for Policymakers. 2019.\nLevitus S, Antonov JI, Boyer TP, et al. Global ocean heat content 1955-2008 in light of recently revealed instrumentation problems. Geophys Res Lett. 2009;36:1–5.\nBopp L, Monfray P, Aumont O, et al. Potential impact of climate change on marine export production. Glob Biogeochem Cycles. 2001;15:81–99.\nLozier MS, Dave AC, Palter B, Gerber LM, Barber RT. On the relationship between stratification and primary productivity in the North Atlantic. Geophys Res Lett. 2011;38:L18609.\nBopp L, Resplandy L, Orr JC, et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences. 2013;10:6225–45.\nKrumhardt KM, Lovenduski NS, Long MC, Lindsay K. Avoidable impacts of ocean warming on marine primary production: Insights from the CESM ensembles. Glob Biogeochem Cycles. 2017;31:114–33 This paper uses two ensembles of an Earth system model under different forcing scenarios to identify anthropogenic impacts on marine net primary production.\nHenson SA, Sarmiento JL, Dunne JP, et al. Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity. Biogeosciences. 2010;7:621–40.\nDoney SC, Lima I, Moore JK, et al. Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data. J Mar Syst. 2009;76:95–112.\nDeser C, Philips A, Bourdette V, Teng H. Uncertainty in climate change projections: the role of internal variability. Clim Dyn. 2012;38:527–46.\nMcKinnon KA, Poppick A, Dunn-Sigouin E, Deser C. An “observational large ensemble” to compare observed and modeled temperature trend uncertainty due to internal variability. J Clim. 2017;30:7585–98.\nMcKinnon KA, Deser C. Internal variability and regional climate trends in an Observational Large Ensemble. J Clim. 2018;31:6783–802 This paper generates a synthetic ensemble of temperature, precipitation, and global sea level pressure.\nSigman DM, Hain MP. The biological productivity of the ocean. Nat Educ. 2012;3:1–16.\nBehrenfeld M, O’Malley RT, Boss ES, et al. Revaluating ocean warming impacts on global phytoplankton. Nat Clim Chang. 2016;6:323–30 This paper examines the influence of photoacclimation on phytoplankton intracellular chlorophyll concentrations.\nLalli CM, Parsons TR. Phytoplankton and primary production. In: Biological oceanography: An introduction. Amsterdam: Elsevier Butterworth-Heinemann; 2006.\nGiovannoni SJ, Vergin KL. Seasonality in ocean microbial communities. Science. 2012;335:671–6.\nSaba VS, Friedrichs MAM, Antoine D, et al. An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe. Biogeosciences. 2011;8:489–503.\nSarmiento JL, Gruber N. Organic matter export and remineralization. In: Ocean Biogeochemical Dynamics. Princeton, Woodstock: Princeton University Press; 2006.\nEmerson S, Hedges J. Life processes in the ocean. In: Chemical Oceanography and the Marine Carbon Cycle. Cambridge University Press; 2008.\nSunda WG. Trace metal interactions with marine phytoplankton. Biol Oceanogr. 2013;6:411–42.\nMoore JK, Doney SC, Glover DM, Fung IY. Iron cycling and nutrient-limitation patterns in surface waters of the World Ocean. Deep-Sea Res II Top Stud Oceanogr. 2002;49:463–507.\nCheung WWL, Lam VWY, Sarmiento JL, et al. Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Glob Chang Biol. 2010;16:24–35.\nPörtner HO, Karl DM, Boyd PW, et al. Ocean systems. In: Climate Change 2014: Impacts, adaptation, and vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.\nRhein M, Rintoul SR, Aoki S, et al. Observations: Ocean. In: Climate Change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.\nPolovina JJ, Howell EA, Abecassis M. Ocean’s least productive waters are expanding. Geophys Res Lett. 2008;35:2–6.\nIrwin AJ, Oliver MJ. Are ocean deserts getting larger? Geophys Res Lett. 2009;36:1–5.\nSchmittner A, Oschlies A, Matthews HD, Galbraith ED. Future changes in climate, ocean circulation, ecosystems, and biogeochemical cycling simulated for a business-as-usual CO2 emission scenario until year 4000 AD. Glob Biogeochem Cycles. 2008;22:1–21.\nSteinacher M, Joos F, Frölicher TL, et al. Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences. 2010;7:979–1005.\nMarinov I, Doney SC, Lima ID, Lindsay K, Moore JK, Mahowald N. North-south asymmetry in the modeled phytoplankton community response to climate change over the 21st century. Glob Biogeochem Cycles. 2013;27:1274–90.\n• Laufkötter C, Vogt M, Gruber N, et al. Drivers and uncertainties in future global marine primary production in marine ecosystem models. Biogeosciences. 2015;12:6955–6984.4 This paper compares projected changes in marine net primary productivity in multiple Earth system under high-emission scenario RCP 8.5.\nKwiatkowski L, Bopp L, Aumont O. Emergent constraints on projections of declining primary production in the tropical oceans. Nat Clim Chang. 2017;7:355–8 This article integrates Earth system model projections and remotely sensed observations to constrain long-term trends in marine primary production.\nNeville RA, Gower JFR. Passive remote sensing of phytoplankton via chlorophyll a fluorescence. J Geophys Res. 1977;82:3487–93.\nMeister G, Franz BA, Kwiatkowska EJ, McClain CR. Corrections to the calibration of MODIS Aqua ocean color bands derived from SeaWiFS data. IEEE Trans. 2012;60:310–9.\nSiegel DA, Behrenfeld MJ, Maritorena S, et al. Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission. Remote Sens Environ. 2013;135:77–91.\nFeng L, Hu C. Cloud adjacency effects on top-of-atmosphere radiance and ocean color data products: a statistical assessment. Remote Sens Environ. 2016;174:301–13.\nGordon HR, Morel AY. Water algorithms. In: Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. New York: Springer-Verlag; 1983.\nMaritorena S, Siegel DA, Peterson A. Optimization of a semi-analytical ocean color model for global scale applications. Appl Opt. 2002;41:2705–14.\nHu C, Lee Z, Franz B. Chlorophyll a algorithms for oligotrophic oceans: a novel approach based on three-band reflectance difference. J Geophys Res Ocean. 2012;117:1–25.\nBeaulieu C, Henson SA, Sarmiento JL, et al. Factors challenging our ability to detect long-term trends in ocean chlorophyll. Biogeosciences. 2013;10:2711–24.\nHenson SA. Slow science: the value of long ocean biogeochemistry records. Philos Trans R Soc. 2014;372:1-22.\nHenson SA, Beaulieu C, Lampitt R. Observing climate change trends in ocean biogeochemistry: when and where. Glob Chang Biol. 2016;22:1561–71 This article uses an ensemble of Earth system models to quantify timescales required to detect long-term trends in several biogeochemical variables.\nHammond ML, Beaulieu C, Sahu SK, Henson SA. Assessing trends and uncertainties in satellite-era ocean chlorophyll using space-time modeling. Glob Biogeochem Cycles. 2017;31:1103–17.\nSanter BD, Mears C, Doutriaux C, et al. Separating signal and noise in atmospheric temperature changes: the importance of timescales. J Geophys Res Atmos. 2011;116:1–19.\nDeser C, Knutti R, Solomon S. Communication on the role of natural variability in future North American climate. Nat Clim Chang. 2012b;2:775–9.\nMeehl GA, Hu A. Externally forced and internally generated decadal climate variability associated with the Interdecadal Pacific Oscillation. J Clim. 2013;26:7298–7310.\nSchneider DP, Deser C. Tropically driven and externally forced patterns of Antarctic Sea ice change: reconciling observed and modeled trends. Clim Dyn. 2018;50:4099–618.\nBehrenfeld MJ, O’Malley RT, Siegel DA, et al. Climate-driven trends in contemporary ocean productivity. Nature. 2006;444:752–5.\nDel Castillo CE, Signorini SR, Karaköylü EM, Rivero-Calle S. Is the Southern Ocean getting greener? Geophys Res Lett. 2019;46:6034–40. This paper highlights regional increases in chlorophyll concentration in the Southern Ocean.\nGregg WW, Rousseaux CS. Global ocean primary production trends in the modern ocean color satellite record (1998–2015). Environ Res Lett. 2019;14:1–9. This paper assimilates ocean color data into an Earth system model to estimate changes in marine primary production.\nBoyce DG, Lewis MR, Worm B. Global phytoplankton decline over the past century. Nature. 2010;466:591–6.\nOsman MB, Das SB, Trusel LD, et al. Industrial-era decline in subarctic Atlantic productivity. Nature. 2019;569:551–5. This paper integrates observational datasets in the Arctic to identify regional declines in productivity.\nSaba VS, Friedrichs MAM, Carr ME, et al. Challenges of modeling depth-integrated marine primary productivity over multiple decades: a case study at BATS and HOT. Glob Biogeochem Cycles. 2010;24:1–21.\nGregg WW, Rousseaux CS. Decadal trends in global pelagic chlorophyll: a new assessment integrating multiple satellites, in situ data, and models. J Geophys Res Oceans. 2014;119:5921–33.\nTiao GC, Reinsel GC, Xu D, et al. Effects of autocorrelation and temporal sampling schemes on estimates of trend and spatial correlation. J Geophys Res. 1990;95:507–20.\nWeatherhead EC, Reinsel GC, Tiao GC, et al. Factors affecting the detection of trends: statistical considerations and applications to environmental data. J Geophys Res Atmos. 1998;103:17149–61.\nHenson S, Cole H, Beaulieu C, Yool A. The impact of global warming on seasonality of ocean primary production. Biogeosciences. 2013;10:4357–69.\nCabré A, Marinov I, Leung S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 Earth system models. Clim Dyn. 2015;45:1253–80.\nLeung S, Cabré A, Marinov I. A latitudinally banded phytoplankton response to 21st century climate change in the Southern Ocean across the CMIP5 model suite. Biogeosciences. 2015;12:5715–34.\nMarinov I, Doney SC, Lima ID. Response of ocean phytoplankton community structure to climate change over the 21st century: partitioning the effects of nutrients, temperature and light. Biogeosciences. 2010;7:3941–59.\nMoore JK, Lindsay SC, Doney MC, Long MC, Misumi K. Marine ecosystem dynamics and biogeochemical cycling in the Community Earth System Model [CESM1(BGC)]: comparison of the 1990s with the 2090s under the RCP4.5 and RCP8.5 scenarios. J Clim. 2013;26:9291–312.\nRodgers KB, Lin J, Frölicher TL. Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an earth system model. Biogeosciences. 2015;12:3301–20.\nLong MC, Deutsch C, Ito T. Finding forced trends in oceanic oxygen. Glob Biogeochem Cycles. 2016;30:381–97.\nMcKinley GA, Pilcher DJ, Fay AR, Lindsay K, Long MC, Lovenduski NS. Timescales for detection of trends in the ocean carbon sink. Nature. 2016;530:469–72.\nLovenduski NS, McKinley GA, Fay AR, Lindsay K, Long MC. Partitioning uncertainty in ocean carbon uptake projections: internal variability, emission scenario, and model structure. Glob Biogeochem Cycles. 2016;30:1276–87.\nFrölicher TL, Rodgers KB, Stock CA, Cheung WWL. Sources and uncertainties in 21st century projections of potential ocean ecosystem stressors. Glob Biogeochem Cycles. 2016;30:1224–43.\nBrady RX, Lovenduski NS, Alexander MA, Jacox M, Gruber N. On the role of climate modes in modulating air-sea CO2 fluxes in eastern boundary upwelling systems. Biogeosciences. 2019;16:329–46.\nSchlunegger S, Rodgers KB, Sarmiento JL, et al. Emergence of anthropogenic signals in the ocean carbon cycle. Nat Clim Chang. 2019;9:719–25. This paper identifies the emergence of marine net primary production the latest of several ocean biosphere stressors.\nKay JE, Deser C, Phillips A, et al. The community earth system model (CESM) large ensemble project. Bull Am Meteorol Soc. 2015;96:1333–49.\nGregg WW, Conkright ME. Decadal changes in global ocean chlorophyll. Geophys Res Lett. 2002;29:1-4.\nYoder JA, Kennelly MA. Seasonal and ENSO variability in global ocean phytoplankton chlorophyll derived from 4 years of SeaWiFS measurements. Glob Biogeochem Cycles. 2003;17:1112.\nRadenac M, Léger F, Singh A, Delcroix T. Sea surface chlorophyll signature in the tropical Pacific during eastern and central Pacific ENSO events. J Geophys Res. 2012;117:C04007.\nWilks DS. Resampling hypothesis tests for autocorrelated fields. J Clim. 1997;10:65–82.\nTheiler J, Eubank S, Longin A, Galdrikian B, Farmer JD. Testing for nonlinearity in time series: the method of surrogate data. Phys D. 1992;58:77–94.\nSchreibner T, Schmitz A. Surrogate time series. Phys D. 2000;142:346–82.",{"EN":1745},"We review how phytoplankton abundance may be responding to the increase in stratification associated with anthropogenic climate change, providing context on the utility of remote sensing datasets and Earth system model output to understand these perturbations. Assessing disruption in the ocean biosphere using remote sensing datasets is challenged by the relatively short length of the observational record, restricting our ability to disentangle fluctuations due to internal climate variability from those imposed by externally forced anthropogenic trends. Ensembles of Earth system models can be used to quantify past and future drivers, but may not skillfully predict observed spatial patterns and temporal dynamics in marine phytoplankton. To better understand the role of internal climate variability in the observational record, we construct a synthetic ensemble of global chlorophyll concentration over the MODIS satellite mission using statistical emulation techniques. We emphasize the use of a synthetic ensemble to illuminate the role of internal climate variability in the evolution of the ocean biosphere over time.",{"EN":1747},"Finding the Fingerprint of Anthropogenic Climate Change in Marine Phytoplankton Abundance",{"VOID":1749},"10.1007\u002Fs40641-020-00156-w","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs40641-020-00156-w",[1752,1767,1782,1794,1811],{"id":1753,"sortIndex":19,"researcher":18,"roles":1754,"affiliations":1755,"properties":1764},"50d477ca-0da1-4f85-a98a-467fe93b13ec",[169],[1756],{"id":18,"sortIndex":19,"affiliation":1757,"properties":18},{"id":1758,"createTime":1759,"updateTime":1759,"relativeEntities":1760,"slug":18,"properties":1761,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"871ce77c-4af5-40a7-a12c-b415c16b17f2","2023-12-04T16:48:30.539+00:00",[],{"title":1762},{"VI":1763},"Department of Geological Sciences and Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, USA",{"title":1765},{"VI":1766},"Geneviève W. Elsworth",{"id":1768,"sortIndex":254,"researcher":18,"roles":1769,"affiliations":1770,"properties":1779},"0e795bcb-1ae3-4420-9ba3-d73733c6c020",[169],[1771],{"id":18,"sortIndex":19,"affiliation":1772,"properties":18},{"id":1773,"createTime":1774,"updateTime":1774,"relativeEntities":1775,"slug":18,"properties":1776,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"67fb0ede-d080-4649-abf1-9c3e6a3150b7","2023-12-04T16:48:30.641+00:00",[],{"title":1777},{"VI":1778},"Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, USA",{"title":1780},{"VI":1781},"Nicole S. Lovenduski",{"id":1783,"sortIndex":823,"researcher":18,"roles":1784,"affiliations":1785,"properties":1791},"fa2fb38f-7252-4874-b4c6-6177b498007b",[169],[1786],{"id":18,"sortIndex":19,"affiliation":1787,"properties":18},{"id":1773,"createTime":1774,"updateTime":1774,"relativeEntities":1788,"slug":18,"properties":1789,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1790},{"VI":1778},{"title":1792},{"VI":1793},"Riley X. Brady",{"id":1795,"sortIndex":122,"researcher":18,"roles":1796,"affiliations":1797,"properties":1808},"e674d14b-de23-490f-9b03-8a48fd09ad75",[169],[1798],{"id":18,"sortIndex":19,"affiliation":1799,"properties":18},{"id":1800,"createTime":1801,"updateTime":1802,"relativeEntities":1803,"slug":1804,"properties":1805,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"3620575b-6bed-4add-a430-86f0a58f0f1a","2023-12-28T14:46:00.689+00:00","2025-01-26T03:59:42.206+00:00",[],"Climate-and-Global-Dynamics-Laboratory-National-Center-for-Atmospheric-Research-Boulder-USA",{"title":1806},{"VI":1807},"Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, USA",{"title":1809},{"VI":1810},"Kristen M. Krumhardt",{"id":1812,"sortIndex":276,"researcher":18,"roles":1813,"affiliations":1814,"properties":1823},"7345d8e2-2d8a-4f15-9f60-09ae9d72a086",[169],[1815],{"id":18,"sortIndex":19,"affiliation":1816,"properties":18},{"id":1817,"createTime":1818,"updateTime":1818,"relativeEntities":1819,"slug":18,"properties":1820,"entityType":47,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"ded3fec0-015d-4926-b742-abd8ed8ec9c8","2024-01-30T03:06:43.126+00:00",[],{"title":1821},{"VI":1822},"Department of Statistics and Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, USA",{"title":1824},{"VI":1825},"Karen A. 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