Characterizing uncertainties in the ESA-CCI land cover map of the epoch 2010 and their impacts on MPI-ESM climate simulations
Tóm tắt
Limitations of mapping land surface properties and their conversion into climate model boundary conditions are major sources of uncertainty in climate simulations. In this paper, the range of the largest possible uncertainty in satellite-derived land cover (LC) map is estimated and its impact on climate simulations is quantified with the Earth System Model of the Max-Planck Institute for Meteorology utilizing prescribed sea surface temperature and sea ice. Two types of uncertainty in the LC map are addressed: (i) uncertainty due to classification algorithm of spectral reflectance into LC classes, and (ii) uncertainty due to conversion of LC classes into the climate model vegetation distribution. For forest cover, each of them is about the same order of magnitude as the uncertainty range in recent observations (∼± 700 Mha). Superposing two sources of uncertainty results in LC maps that feature the range of vegetation deviation that is about the same order of magnitude as the recent (since year 1700) forest loss due to agriculture (forest cover uncertainty range ∼± 1700 Mha). These uncertainties in vegetation distribution lead to noticeable variations in near-surface climate variables, local, regional, and global climate forcing. Temperature does not show significant uncertainty in global mean, but rather exhibits regional deviations with an opposite response to LC uncertainty that compensate each other in the global mean (e.g., albedo feedback controls temperature in boreal North America resulting in cooling (warming) with decrease (increase) of vegetation while evaporative cooling controls temperature in South America and sub-Saharan Africa resulting in cooling (warming) with increase (decrease) of vegetation). Large-scale circulation is also affected by the LC uncertainty, and consequently precipitation pattern as well. It is demonstrated that precipitation uncertainty in the monsoonal regions are about the same order of magnitude as in previous studies with idealized perturbations of vegetation. These findings indicate that the range of uncertainty in satellite-derived vegetation maps for climate models is about the same order of magnitude as the uncertainty in recent observations of forest cover or as the forest lost due to agriculture. Consequently, climate simulations have a similar range of uncertainty in variables representing near-surface climate as the observed climate change due to land use. Hence, more accurate methods are needed for mapping and converting LC properties into model vegetation in order to increase reliability of climate model simulations.
Tài liệu tham khảo
Achard F, Beuchle R, Mayaux P, Stibig H-J, Bodart C, Brink A, Carboni S, Desclée B, Donnay F, Eva HD, Lupi A, Rasi R, Seliger R, Simonetti D (2014) Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob Chang Biol 20(8):2540–2554. https://doi.org/10.1111/gcb.12605. https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.12605
Alkama R, Cescatti A (2016) Biophysical climate impacts of recent changes in global forest cover. Science 351(6273):600–604. ISSN 0036-8075. https://doi.org/10.1126/science.aac8083. http://science.sciencemag.org/content/351/6273/600
Anav A, Friedlingstein P, Beer C, Ciais P, Harper A, Jones C, Murray-Tortarolo G, Papale D, Parazoo NC, Peylin P, Piao S, Sitch S, Viovy N, Wiltshire A, Zhao M (2015) Spatiotemporal patterns of terrestrial gross primary production: a review. Rev Geophys 53(31):785–818. ISSN 1944-9208. https://doi.org/10.1002/2015RG000483
Arino O, Ramos Perez JJ, Kalogirou V, Bontemps S, Defourny P, Van Bogaert E (2012) Global land cover map for 2009 (GlobCover 2009). https://doi.org/10.1594/PANGAEA.787668
Bastin J-F, Berrahmouni N, Grainger A, Maniatis D, Mollicone D, Moore R, Patriarca C, Picard N, Sparrow B, Abraham EM, Aloui K, Atesoglu A, Attore F, Bassüllü Ç, Bey A, Garzuglia M, García-Montero LG, Groot N, Guerin G, Laestadius L, Lowe AJ, Mamane B, Marchi G, Patterson P, Rezende M, Ricci S, Salcedo I, Diaz AS-P, Stolle F, Surappaeva V, Castro R (2017) The extent of forest in dryland biomes. Science 356 (6338):635–638. ISSN 0036-8075. https://doi.org/10.1126/science.aam6527. http://science.sciencemag.org/content/356/6338/635
Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320 (5882):1444–1449. ISSN 0036-8075. https://doi.org/10.1126/science.1155121. http://science.sciencemag.org/content/320/5882/1444
Brovkin V, Raddatz T, Reick CH, Claussen M, Gayler V (2009) Global biogeophysical interactions between forest and climate, vol 36
Congalton RG, Gu J, Yadav K, Thenkabail P, Ozdogan M (2014) Global land cover mapping: a review and uncertainty analysis. Remote Sens 6(12):12070–12093. ISSN 2072-4292. https://doi.org/10.3390/rs61212070. http://www.mdpi.com/2072-4292/6/12/12070
Curry J (2011) Reasoning about climate uncertainty. Climatic Change 108(4):723 . ISSN 1573-1480. https://doi.org/10.1007/s10584-011-0180-z
de Vrese P, Hagemann S (2016) Explicit representation of spatial sub-grid scale heterogeneity in an esm, vol 17
Defourny P, Boettcher M, Bontemps S, Kirches G, Krueger O, Lamarche C, Lembrée C, Radoux J, Verheggen A (2014) Algorithm theoretical basis document for land cover climate change initiative. Technical report, European Space Agency
Defourny P, Boettcher M, Bontemps S, Kirches G, Lamarche C, Peters M, Santoro M, Schlerf M (2016) Land cover cci Product user guide version 2. Technical report, European Space Agency
Devaraju N, Bala G, Modak A (2015) Effects of large-scale deforestation on precipitation in the monsoon regions: remote versus local effects. Proc Natl Acad Sci 112(11):3257–3262. https://doi.org/10.1073/pnas.1423439112
Di Gregorio A, Jansen L (2005) Land cover classification system (lccs) version 2: classification concepts and user manual. Technical report, Environmental and Natural Resources Series, FAO, Rome
Duveiller G, Hooker J, Cescatti A (2018) The mark of vegetation change on earth’s surface energy balance. Nat Commun 9. https://doi.org/10.1038/s41467-017-02810-8
Ellison D, Futter MN, Bishop K (2012) On the forest cover–water yield debate: from demand- to supply-side thinking. Glob Chang Biol 18(3):806–820. https://doi.org/10.1111/j.1365-2486.2011.02589.x. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3597246/
Endo H, Kitoh A, Mizuta R, Ishii M (2017) Future changes in precipitation extremes in east asia and their uncertainty based on large ensemble simulations with a high-resolution agcm. SOLA 13:7–12. https://doi.org/10.2151/sola.2017-002
Feddema JJ, Oleson KW, Bonan GB, Mearns LO, Buja LE, Meehl GA, Washington WM (2005) The importance of land-cover change in simulating future climates. Science 310:1674–1678. https://doi.org/10.1126/science.1118160
Flato GM (2011) Earth system models: an overview. Wiley Interdiscip Rev Clim Chang 2(6):783–800. https://doi.org/10.1002/wcc.148
Goll DS, Brovkin V, Liski J, Raddatz T, Thum T, Todd-Brown KEO (2015) Strong dependence of CO2emissions from anthropogenic land cover change on initial land cover and soil carbon parametrization. Glob Biogeochem Cycles 29(9):1511–1523. https://doi.org/10.1002/2014gb004988
Gross D, Achard F, Dubois G, Brink A, Prins HHT (2017) Uncertainties in tree cover maps of sub-saharan africa and their implications for measuring progress towards cbd aichi targets. Remote Sensing in Ecology and Conservation 4(2):94–112. https://doi.org/10.1002/rse2.52. https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.52
Hagemann S, Stacke T (2014) Impact of the soil hydrology scheme on simulated soil moisture memory. Clim Dyn 44(7-8):1731–1750. https://doi.org/10.1007/s00382-014-2221-6
Hansen MC, Stehman SV, Potapov PV (2010) Quantification of global gross forest cover loss. Proc Natl Acad Sci 107(19):8650–8655. https://doi.org/10.1073/pnas.0912668107
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853. https://doi.org/10.1126/science.1244693
Hartley A, MacBean N, Georgievski G, Bontemps S (2017) Uncertainty in plant functional type distributions and its impact on land surface models. Remote Sensing of Environment 203:(71–89). ISSN 0034-4257. https://doi.org/10.1016/j.rse.2017.07.037
He T, Liang S, Song D-X (2014) Analysis of global land surface albedo climatology and spatial-temporal variation during 1981–2010 from multiple satellite products. J Geophys Res-Atmos 119(17):10,281–10,298. ISSN 2169-8996. https://doi.org/10.1002/2014JD021667
Hellesen T, Matikainen L (2013) An object-based approach for mapping shrub and tree cover on grassland habitats by use of lidar and cir orthoimages. Remote Sens 5(2):558–583. ISSN 2072-4292. https://doi.org/10.3390/rs5020558
Herold N, Alexander LV, Donat MG, Contractor S, Becker A (2016) How much does it rain over land?. Geophys Res Lett 43(1):341–348. ISSN 1944-8007. https://doi.org/10.1002/2015GL066615
Herold N, Behrangi A, Alexander LV (2017) Large uncertainties in observed daily precipitation extremes over land. J Geophys Res-Atmos 122(2):668–681. ISSN 2169-8996. https://doi.org/10.1002/2016JD025842
IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland. https://www.ipcc.ch/report/ar5/syr/
Keenan RJ, Reams GA, Achard F, de Freitas JV, Grainger A, Lindquist E (2015) Dynamics of global forest area results from the FAO global forest resources assessment 2015. For Ecol Manag 352:9–20. https://doi.org/10.1016/j.foreco.2015.06.014
Kumar S, Dirmeyer PA, Merwade V, DelSole T, Adams JM, Niyogi D (2013) Land use/cover change impacts in CMIP5 climate simulations: a new methodology and 21st century challenges. J Geophys Res-Atmos 118 (12):6337–6353. https://doi.org/10.1002/jgrd.50463
Le Quéré C, Andrew RM, Canadell JG, Sitch S, Korsbakken JI, Peters GP, Manning AC, Boden TA, Tans PP, Houghton RA, Keeling RF, Alin S, Andrews OD, Anthoni P, Barbero L, Bopp L, Chevallier F, Chini LP, Ciais P, Currie K, Delire C, Doney SC, Friedlingstein P, Gkritzalis T, Harris I, Hauck J, Haverd V, Hoppema M, Klein Goldewijk K, Jain AK, Kato E, Körtzinger A, Landschützer P, Lefèvre N, Lenton A, Lienert S, Lombardozzi D, Melton JR, Metzl N, Millero F, Monteiro PMS, Munro DR, Nabel JEMS, Nakaoka S-I, O’Brien K, Olsen A, Omar AM, Ono T, Pierrot D, Poulter B, Rödenbeck C, Salisbury J, Schuster U, Schwinger J, Séférian R, Skjelvan I, Stocker BD, Sutton AJ, Takahashi T, Tian H, Tilbrook B, van der Laan-Luijkx IT, van der Werf GR, Viovy N, Walker AP, Wiltshire AJ, Zaehle S (2016) Global carbon budget 2016. Earth Syst Sci Data 8(2):605–649. https://doi.org/10.5194/essd-8-605-2016. https://www.earth-syst-sci-data.net/8/605/2016/
Li W, MacBean N, Ciais P, Defourny P, Lamarche C, Bontemps S, Houghton RA, Peng S (2018) Gross and net land cover changes in the main plant functional types derived from the annual esa cci land cover maps (1992–2015). Earth Syst Sci Data 10(1):219–234. https://doi.org/10.5194/essd-10-219-2018. https://www.earth-syst-sci-data.net/10/219/2018/
Ma J, Yan X, Dong W, Chou J (2015) Gross primary production of global forest ecosystems has been overestimated. Scientific Reports, 5(10820). https://doi.org/10.1038/srep10820
Mahmood R, Pielke RA, Hubbard KG, Niyogi D, Dirmeyer PA, McAlpine C, Carleton AM, Hale R, Gameda S, Beltrán-Przekurat A, Baker B, McNider R, Legates DR, Shepherd M, Du J, Blanken PD, Frauenfeld OW, Nair U, Fall S (2014) Land cover changes and their biogeophysical effects on climate. Int J Climatol 34(4):929–953. https://doi.org/10.1002/joc.3736. ISSN 1097-0088
Maslin M (2013) Cascading uncertainty in climate change models and its implications for policy. Geogr J 179 (3):264–271. ISSN 1475-4959. https://doi.org/10.1111/j.1475-4959.2012.00494.x
Muller J-P (2013) Globalbedo final validation report. Technical report, University College London. http://www.globalbedo.org/docs/GlobAlbedo_FVR_V1_2_web.pdf
Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11(5):1633–1644. https://doi.org/10.5194/hess-11-1633-2007
Poulter B, Ciais P, Hodson E, Lischke H, Maignan F, Plummer S, Zimmermann NE (2011) Plant functional type mapping for earth system models. Geosci Model Dev 4(4):993–1010. https://doi.org/10.5194/gmd-4-993-2011
Poulter B, MacBean N, Hartley A, Khlystova I, Arino O, Betts R, Bontemps S, Boettcher M, Brockmann C, Defourny P, Hagemann S, Herold M, Kirches G, Lamarche C, Lederer D, Ottlé C, Peters M, Peylin P (2015) Plant functional type classification for earth system models: results from the european space agency’s land cover climate change initiative. Geosci Model Dev 8(7):2315–2328. https://doi.org/10.5194/gmd-8-2315-2015
Quaife T, Quegan S, Disney M, Lewis P, Lomas M, Woodward FI (2008) Impact of land cover uncertainties on estimates of biospheric carbon fluxes. Glob Biogeochem Cycles 22(4): n/a–n/a. ISSN 1944–9224. https://doi.org/10.1029/2007GB003097
Raddatz TJ, Reick CH, Knorr W, Kattge J, Roeckner E, Schnur R, Schnitzler K-G, Wetzel P, Jungclaus J (2007) Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century? . Clim Dyn 29(6):565–574. https://doi.org/10.1007/s00382-007-0247-8
Ramankutty N, Foley JA (1999) Estimating historical changes in global land cover: croplands from 1700 to 1992. Glob Biogeochem Cycles 13(4):997–1027. https://doi.org/10.1029/1999gb900046
Sellers PJ, Dickinson RE, Randall DA, Betts AK, Hall FG, Berry JA, Collatz GJ, Denning AS, Mooney HA, Nobre CA, Sato N, Field CB, Henderson-Sellers A (1997) Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275:502–509. https://doi.org/10.1126/science.275.5299.502
Sheil D, Murdiyarso D (2009) How forests attract rain an examination of a new hypothesis. Bioscience 59 (4):341–347. https://doi.org/10.1525/bio.2009.59.4.12
Stevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S, Salzmann M, Schmidt H, Bader J, Block K, Brokopf R, Fast I, Kinne S, Kornblueh L, Lohmann U, Pincus R, Reichler T, Roeckner E (2013) Atmospheric component of the MPI-m earth system model: ECHAM6. J Adv Model Earth Syst 5(2):146–172. https://doi.org/10.1002/jame.20015
Still C, Berry J, Collatz G, Defries R (2009) Islscp ii c4 vegetation percentage. https://doi.org/10.3334/ornldaac/932
Swann ALS, Fung IY, Chiang JCH (2011) Mid-latitude afforestation shifts general circulation and tropical precipitation. Proc Natl Acad Sci 109 (3):712–716. https://doi.org/10.1073/pnas.1116706108
Taylor K, Williamson D, Zwiers F (2000) The sea surface temperature and sea ice concentration boundary conditions for amip ii simulations. Technical report, PCMDI Report 60, Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory. http://www-pcmdi.llnl.gov/publications/pdf/60.pdf
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. https://doi.org/10.1175/bams-d-11-00094.1
Trenberth KE, Smith L, Qian T, Dai A, Fasullo J (2007) Estimates of the global water budget and its annual cycle using observational and model data. J Hydrometeorol 8(4):758–769. https://doi.org/10.1175/jhm600.1
Wang B, Ding Q (2006) Changes in global monsoon precipitation over the past 56 years. Geophysical Research Letters, 33(6). https://doi.org/10.1029/2005gl025347
Yang Y, Xiao P, Feng X, Li H (2017) Accuracy assessment of seven global land cover datasets over china, vol 125. ISSN 0924-2716. https://doi.org/10.1016/j.isprsjprs.2017.01.016. http://www.sciencedirect.com/science/article/pii/S0924271616301332
Zhang Y, Pena-Arancibia JL, McVicar TR, Chiew FHS, Vaze J, Liu C, Lu X, Zheng H, Wang Y, Liu YY, Miralles DG, Pan M (2016) Multi-decadal trends in global terrestrial evapotranspiration and its components. Scientific Reports, 6(1). https://doi.org/10.1038/srep19124