Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces

Climate Risk Management - Tập 38 - Trang 100466 - 2022
Stefan Fronzek1, Yasushi Honda2,3, Akihiko Ito3, João Pedro Nunes4,5, Nina Pirttioja1, Jouni Räisänen6, Kiyoshi Takahashi3, Emma Terämä1, Minoru Yoshikawa7, Timothy R. Carter1
1Finnish Environment Institute SYKE, Finland
2The University of Tsukuba, Japan
3National Institute for Environmental Studies, Japan
4CE3C: Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Portugal
5Soil Physics and Land Management Group, Wageningen University and Research, Wageningen, The Netherlands
6Institute for Atmospheric and Earth System Research, University of Helsinki, Finland
7Mizuho Information and Research Institute, Japan

Tài liệu tham khảo

Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: guidelines for computing crop water requirements. Irrigation and drainage Paper No. 56. FAO, Rome D05109. Arnell, 2021, Changing climate risk in the UK: A multi-sectoral analysis using policy-relevant indicators, Clim. Risk Manag., 31 Azose, 2016, Probabilistic population projections with migration uncertainty Baccini, M., Kosatsky, T., Analitis, A., Anderson, H.R., D’Ovidio, M., Menne, B., Michelozzi, P., Biggeri, A., the PHEWE Collaborative Group, 2011. Impact of heat on mortality in 15 European cities: attributable deaths under different weather scenarios. Journal of Epidemiology & Community Health 65:64–70. https://doi.org/10.1136/jech.2008.085639. Børgesen, 2011, A probabilistic assessment of climate change impacts on yield and nitrogen leaching from winter wheat in Denmark, Nat. Hazards Earth Syst. Sci., 11, 2541, 10.5194/nhess-11-2541-2011 Borgomeo, 2014, Risk-based water resources planning: Incorporating probabilistic nonstationary climate uncertainties, Water Resour. Res., 50, 6850, 10.1002/2014WR015558 Brown, C., Ghile, Y., Laverty, M., Li, K., 2012. Decision scaling: Linking bottom‐up vulnerability analysis with climate projections in the water sector. Water Resour Res 48:2011WR011212. https://doi.org/10.1029/2011WR011212. Carter, 2022, A Model-Based Response Surface Approach for Evaluating Climate Change Risks and Adaptation Urgency, 67 Challinor, 2014, A meta-analysis of crop yield under climate change and adaptation, Nat. Clim. Chang., 4, 287, 10.1038/nclimate2153 Ciscar, 2018 Collins, 2013, Long-term climate change: projections, commitments and irreversibility, 1029 Culley, 2019, Generating realistic perturbed hydrometeorological time series to inform scenario-neutral climate impact assessments, J. Hydrol., 576, 111, 10.1016/j.jhydrol.2019.06.005 Dessai, 2004, Does climate adaptation policy need probabilities?, Clim. Pol., 4, 107, 10.1080/14693062.2004.9685515 Dias, 2020, Integrating a hydrological model into regional water policies: Co-creation of climate change dynamic adaptive policy pathways for water resources in southern Portugal, Environ Sci Policy, 114, 519, 10.1016/j.envsci.2020.09.020 Easterling, 1992, Simulations of crop response to climate change: effects with present technology and no adjustments (the ‘dumb farmer’ scenario), Agric. For. Meteorol., 59, 53, 10.1016/0168-1923(92)90086-J EEA, 2019. The European environment — state and outlook 2020. Knowledge for transition to a sustainable Europe. European Environment Agency (EEA), Luxembourg. Engström, 2016, Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework, Earth Syst. Dyn., 7, 893, 10.5194/esd-7-893-2016 Fronzek, 2019, Determining sectoral and regional sensitivity to climate and socio-economic change in Europe using impact response surfaces, Reg. Environ. Chang., 19, 679, 10.1007/s10113-018-1421-8 Fronzek, 2007, Assessing uncertainties in climate change impacts on resource potential for Europe based on projections from RCMs and GCMs, Clim. Change, 81, 357, 10.1007/s10584-006-9214-3 Fronzek, 2011, Evaluating sources of uncertainty in modelling the impact of probabilistic climate change on sub-arctic palsa mires, Natural Hazards and Earth System Science, 11, 2981, 10.5194/nhess-11-2981-2011 Fronzek, 2010, Applying probabilistic projections of climate change with impact models: a case study for sub-arctic palsa mires in Fennoscandia, Clim. Change, 99, 515, 10.1007/s10584-009-9679-y Fronzek, 2018, Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change, Agr. Syst., 159, 209, 10.1016/j.agsy.2017.08.004 Gasparrini, 2015, Temporal Variation in Heat-Mortality Associations: A Multicountry Study, Environ. Health Perspect., 123, 1200, 10.1289/ehp.1409070 Gerland, 2014, World population stabilization unlikely this century, Science, 346, 234, 10.1126/science.1257469 Gosling, 2017, Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality, Environ. Health Perspect., 125, 10.1289/EHP634 Gutiérrez, J.M., Jones, R.G., Narisma, G.T., Alves, L.M., Amjad, M., Gorodetskaya, I.V., Grose, M., Klutse, N.A.B., Krakovska, S., Li, J., Martínez-Castro, D., Mearns, L., Mernild, S.H., Ngo-Duc, T., van den Hurk, B., Yoon, J.-.H, 2021. Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Available from http://interactive-atlas.ipcc.ch. Haasnoot, 2013, Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world, Glob. Environ. Chang., 23, 485, 10.1016/j.gloenvcha.2012.12.006 Harris, 2013, Probabilistic projections of transient climate change, Clim. Dyn., 40, 2937, 10.1007/s00382-012-1647-y Harrison, 2019, Understanding high-end climate change: from impacts to co-creating integrated and transformative solutions, Reg. Environ. Chang., 19, 621, 10.1007/s10113-019-01477-9 Hasegawa, 2015, Consequence of Climate Mitigation on the Risk of Hunger, Environ. Sci. Tech., 49, 7245, 10.1021/es5051748 Hewitson, B., Janetos, A.C., Carter, T.R., Giorgi, F., Jones, R.G., Kwon, W.-T., Mearns, L.O., Schipper, E.L.F., van Aalst, M., 2014. Regional context. In: Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 1133–1197. Hoegh-Guldberg, 2018, Intergovernmental Panel on Climate Change Holman, 2019, Improving the representation of adaptation in climate change impact models, Reg. Environ. Chang., 19, 711, 10.1007/s10113-018-1328-4 Holmberg, 2014, Effects of changing climate on the hydrology of a boreal catchment and lake DOC–Probabilistic assessment of a dynamic model chain, Boreal Environ. Res., 19, 66 Honda, 2014, Heat-related mortality risk model for climate change impact projection, Environ. Health Prev. Med., 19, 56, 10.1007/s12199-013-0354-6 International Hydropower Association (2019) Hydropower Sector Climate Resilience Guide. Ito, 2012, Water-Use Efficiency of the Terrestrial Biosphere: A Model Analysis Focusing on Interactions between the Global Carbon and Water Cycles, J. Hydrometeorol., 13, 681, 10.1175/JHM-D-10-05034.1 Jacob, 2020, Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg Environ Change, 20, 51, 10.1007/s10113-020-01606-9 Jones, 2000, Analysing the risk of climate change using an irrigation demand model, Climate Research, 14, 89, 10.3354/cr014089 Jones, 2001, An Environmental Risk Assessment/Management Framework for Climate Change Impact Assessments, Nat. Hazards, 23, 197, 10.1023/A:1011148019213 Kaspar-Ott, 2019, Weights for general circulation models from CMIP3/CMIP5 in a statistical downscaling framework and the impact on future Mediterranean precipitation, Int J Climatol joc.6045. Kay, 2014, Probabilistic impacts of climate change on flood frequency using response surfaces I: England and Wales, Reg. Environ. Chang., 14, 1215, 10.1007/s10113-013-0563-y Kc, 2017, The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100, Glob. Environ. Chang., 42, 181, 10.1016/j.gloenvcha.2014.06.004 Knutti, 2017, A climate model projection weighting scheme accounting for performance and interdependence: Model Projection Weighting Scheme, Geophys Res Lett, 10.1002/2016GL072012 Lowe, 2018 Luomaranta, 2014, Multimodel estimates of the changes in the Baltic Sea ice cover during the present century, Tellus A: Dynamic Meteorology and Oceanography, 66, 22617, 10.3402/tellusa.v66.22617 Lutz, W., 2017. How population growth relates to climate change. Proceedings of the National Academy of Sciences 114:12103–12105. https://doi.org/10.1073/pnas.1717178114. Lutz, 2007, Probabilistic Population Projections for India with Explicit Consideration of the Education-Fertility Link, Int. Stat. Rev., 72, 81, 10.1111/j.1751-5823.2004.tb00225.x Mäkelä, 2014, Probabilistic projections of climatological forest fire danger in Finland, Clim Res, 60, 73, 10.3354/cr01223 Martinez, 2016, Projected heat-related mortality under climate change in the metropolitan area of Skopje, BMC Public Health, 16, 407, 10.1186/s12889-016-3077-y Masutomi, 2009, Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models, Agr.Ecosyst. Environ., 131, 281, 10.1016/j.agee.2009.02.004 Mendoza, 2018 Murphy, 2004, Quantification of modelling uncertainties in a large ensemble of climate change simulations, Nature, 430, 768, 10.1038/nature02771 New, 2007, Challenges in using probabilistic climate change information for impact assessments: an example from the water sector, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 365, 2117, 10.1098/rsta.2007.2080 Nissan, 2019, On the use and misuse of climate change projections in international development, WIREs Clim Change, 10, e579, 10.1002/wcc.579 Nunes, 2017, Combined impacts of climate and socio-economic scenarios on irrigation water availability for a dry Mediterranean reservoir, Sci. Total Environ., 584–585, 219, 10.1016/j.scitotenv.2017.01.131 Nunes, 2011, Modelling the impacts of climate change on water balance and agricultural and forestry productivity in Southern Portugal using SWAT, 366 Ostro, 2012, The impact of future summer temperature on public health in Barcelona and Catalonia, Spain, Int. J. Biometeorol., 56, 1135, 10.1007/s00484-012-0529-7 Parry, 1998 Piniewski, 2013, Effect of modelling scale on the assessment of climate change impact on river runoff, Hydrol. Sci. J., 58, 737, 10.1080/02626667.2013.778411 Pirttioja, 2015, Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces, Clim. Res., 65, 87, 10.3354/cr01322 Pirttioja, 2019, Using impact response surfaces to analyse the likelihood of impacts on crop yield under probabilistic climate change, Agric. For. Meteorol., 264, 213, 10.1016/j.agrformet.2018.10.006 Portmann, 2010, MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochem. Cycles, 24, 10.1029/2008GB003435 Prudhomme, 2015, Low flow response rurfaces for drought decision support: a case study from the UK, J. Extreme Events, 02, 1550005, 10.1142/S2345737615500050 Prudhomme, 2010, Scenario-neutral approach to climate change impact studies: Application to flood risk, J. Hydrol., 390, 198, 10.1016/j.jhydrol.2010.06.043 Raftery AE, Li N, Sevcikova H, Gerland P, Heilig GK (2012) Bayesian probabilistic population projections for all countries. Proceedings of the National Academy of Sciences 109:13915–13921. https://doi.org/10.1073/pnas.1211452109. Räisänen, 2001, A probability and decision-model analysis of a multimodel ensemble of climate change simulations, J. Clim., 14, 3212, 10.1175/1520-0442(2001)014<3212:APADMA>2.0.CO;2 Räisänen, 2006, Probabilistic forecasts of near-term climate change based on a resampling ensemble technique, Tellus A: Dynamic Meteorology and Oceanography, 58, 461, 10.1111/j.1600-0870.2006.00189.x Riahi, 2017, The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Glob. Environ. Chang., 42, 153, 10.1016/j.gloenvcha.2016.05.009 Rötter, 2011, What would happen to barley production in Finland if global warming exceeded 4°C? A model-based assessment, Eur. J. Agron., 35, 205, 10.1016/j.eja.2011.06.003 Rozell, 2017, Using population projections in climate change analysis, Clim. Change, 142, 521, 10.1007/s10584-017-1968-2 Ruiz-Ramos, 2018, Adaptation response surfaces for managing wheat under perturbed climate and CO 2 in a Mediterranean environment, Agr. Syst., 159, 260, 10.1016/j.agsy.2017.01.009 Sanderson, 2017, The use of climate information to estimate future mortality from high ambient temperature: A systematic literature review, PLoS One, 12, e0180369, 10.1371/journal.pone.0180369 Sillmann, 2013, Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections, J. Geophys. Res. Atmos., 118, 2473, 10.1002/jgrd.50188 Stainforth, 2007, Confidence, uncertainty and decision-support relevance in climate predictions, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 365, 2145, 10.1098/rsta.2007.2074 Stigter, 2014, Comparative assessment of climate change and its impacts on three coastal aquifers in the Mediterranean, Reg. Environ. Chang., 14, 41, 10.1007/s10113-012-0377-3 Supit, 2012, Assessing climate change effects on European crop yields using the Crop Growth Monitoring System and a weather generator, Agric. For. Meteorol., 164, 96, 10.1016/j.agrformet.2012.05.005 Takakura, 2019, Dependence of economic impacts of climate change on anthropogenically directed pathways, Nat Clim Chang, 9, 737, 10.1038/s41558-019-0578-6 Taylor, 2012, An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93, 485, 10.1175/BAMS-D-11-00094.1 Tebaldi, 2014, Pattern scaling: Its strengths and limitations, and an update on the latest model simulations, Clim. Change, 122, 459, 10.1007/s10584-013-1032-9 Tebaldi, 2021, Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6, Earth Syst. Dynam., 12, 253, 10.5194/esd-12-253-2021 Tian, 2011, Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study, Autom. Constr., 20, 1096, 10.1016/j.autcon.2011.04.011 United Nations, 2017, World Population Prospects: The 2017 Revision, Methodology of the United Nations Population Estimates and Projections, United Nations, Department of Economic and Social Affairs, Population Division, Working Paper, No. ESA/P/WP.250 Van Minnen, 2000, Deriving and Applying Response Surface Diagrams for Evaluating Climate Change Impacts on Crop Production, Clim. Change, 46, 317, 10.1023/A:1005651327499 van Vuuren, 2011, The representative concentration pathways: an overview, Clim. Change, 109, 5, 10.1007/s10584-011-0148-z van Vuuren, 2014, A new scenario framework for Climate Change Research: scenario matrix architecture, Clim. Change, 122, 373, 10.1007/s10584-013-0906-1 Watkiss, 2021, Method Webber, 2018, Diverging importance of drought stress for maize and winter wheat in Europe, Nat. Commun., 9, 4249, 10.1038/s41467-018-06525-2 Weiß, 2011, Future water availability in selected European catchments: a probabilistic assessment of seasonal flows under the IPCC A1B emission scenario using response surfaces, Natural Hazards and Earth System Science, 11, 2163, 10.5194/nhess-11-2163-2011 Weiß, 2011, A systematic approach to assessing the sensitivity and vulnerability of water availability to climate change in Europe, Water Resour. Res., 47, W02549, 10.1029/2009WR008516 Wetterhall, 2011, Using ensemble climate projections to assess probabilistic hydrological change in the Nordic region, Nat. Hazards Earth System Sci., 11, 2295, 10.5194/nhess-11-2295-2011 Wilby, 2010, Robust adaptation to climate change, Weather, 65, 180, 10.1002/wea.543