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Observed and projected changes in global climate zones based on Köppen climate classification

Advanced Review

Diyang Cui, Shunlin Liang, Dongdong Wang

Published Online: Jan 22 2021

DOI: 10.1002/wcc.701

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Abstract In recent years, there has been a growing body of literature applying the Köppen classification scheme to investigate the changes in the distribution of bioclimatic conditions. Area changes and latitude and elevation shifts of Köppen climate zones have been examined based on the observed and projected datasets. This review article provides a comprehensive insight into the changes in global Köppen climate zones. First, we summarize the advancements and limitations of different climate zone definitions and assess the available climate classification map products. We then review recent detection and assessment studies on observed and projected climate zone changes. Finally, we summarize the findings of the previous studies. It has been proven that changes in climate zones under global warming can have far‐reaching impacts on ecological systems. Since the 1980s, anthropogenic accelerated global warming has already led to shifts in climatic conditions over a large land area. Hot tropics and arid climates are projected to expand into large areas of middle and high latitudes, an expansion that is potentially linked to the intensification of the global hydrologic cycle. Driven by increased warming in the Arctic, high‐latitude climates will shift poleward and upward, leading to a significant area shrinkage of the polar climate zones. However, due to the large model uncertainties, the detectability of significant climate zone changes through observations and projections, the rate and time of the changes, and their causes remain unclear. In this paper, we identify the research gaps and propose directions for future research. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change

Global distribution of (a) major climate zones with data from present day (1980–2016) and (b) future (2071‐2100) Köppen‐Geiger climate classification map (Beck et al., 2018), (c) biomes with data from Terrestrial Ecoregions of the World (TEOW) (Olson et al., 2001), (d) World Reference Base (WRB) soil groups based on data from SoilGrids250m (Hengl et al., 2017), (e, f) present‐day (1980‐2016) and future (2071‐2100) 30 climate subtypes based on data from (Beck et al., 2018)

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Shifts between climate zones in the 21st century under different scenarios from previous literature (Beck et al., 2018; Rubel & Kottek, 2010). The detailed summary of the studies is presented in Table 5

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Regions that are projected to undergo changes by the end of the 21st century. (a,b) The major climates of these regions for the present‐day (1980–2016) and projected conditions (2071–2100), respectively. (c,d) The confidence levels (%) associated with the classification accuracy for the present‐day (1980–2016) and projected conditions (2071–2100), respectively. Data from present and future Köppen–Geiger climate classification maps at 1‐km resolution (Beck et al., 2018)

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Decadal trends in total area, average elevation, and average absolute latitude for climate zones, estimated from observational datasets, UD, GISS, and CRU, as well as HIST‐ALL, HIST‐GHG, and HIST‐NAT CMIP5 runs. Only significant model‐simulated trends are shown. HIST‐ALL runs are driven by forcing reconstructed from observational data, such as greenhouse gas concentrations and volcanic eruptions. HIST‐GHG is forced by greenhouse gas concentrations only, and HIST‐NAT is forced by natural factors only. Reprinted with the permission of Chan and Wu (2015)

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Observed and projected changes in (a) area, (b) mean latitude, and (c) mean elevation of the five major climate zones for the historical period (1950–2003) and projected future period (2003–2093). The inset figure in each panel shows the changes in the decadal trends with grading shades representing the long‐term trends forced by only natural climate fluctuations at the 95% confidence level. Reprinted with the permission of Chan and Wu (2015)

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Regions observed to undergo climate zone changes since the 1980s. (a) The historical major climates of these regions in 1961–1990 from historical data of Köppen–Geiger climatic zones (Kriticos et al., 2012). (b) The present major climates of these regions in 1980–2016 from the present‐day Köppen‐Geiger climate classification map (Beck et al., 2018)

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References

Araújo,, M. B., Alagador,, D., Cabeza,, M., Nogués‐Bravo,, D., & Thuiller,, W. (2011). Climate change threatens European conservation areas. Ecology letters, 14(5), 484–492. https://doi.org/10.1111/j.1461-0248.2011.01610.x
Arnell,, N. W., Livermore,, M. J. L., Kovats,, S., Levy,, P. E., Nicholls,, R., Parry,, M. L., & Gaffin,, S. R. (2004). Climate and socio‐economic scenarios for global‐scale climate change impacts assessments. Characterising the SRES storylines. Global Environmental Change, 14(1), 3–20. https://doi.org/10.1016/j.gloenvcha.2003.10.004
Beaumont,, L. J., Pitman,, A., Perkins,, S., Zimmermann,, N. E., Yoccoz,, N. G., & Thuiller,, W. (2011). Impacts of climate change on the world`s most exceptional ecoregions. Proceedings of the National Academy of Sciences of the United States of America, 108(6), 2306–2311. https://doi.org/10.1073/pnas.1007217108
Beck,, C., Grieser,, J., Kottek,, M., Rubel,, F., & Rudolf,, B. (2005). Characterizing global climate change by means of Kӧppen climate classification. Retrieved from https://www.dwd.de/DE/leistungen/klimastatusbericht/publikationen/ksb2005_pdf/10_2005.pdf?__blob=publicationFile%26v=1
Beck,, C., Grieser,, J., & Rudolf,, B. (2005). A New Monthly Precipitation Climatology for the Global Land Areas for the Period 1951 to 2000. Geophysical Research Abstracts.7, Abstract. 07154
Beck,, H. E., Zimmermann,, N. E., McVicar,, T. R., Vergopolan,, N., Berg,, A., & Wood,, E. F. (2018). Present and future Köppen–Geiger climate classification maps at 1‐km resolution. Scientific Data, 5, 180214. https://doi.org/10.1038/sdata.2018.214
Belda,, M., Holtanová,, E., Halenka,, T., & Kalvová,, J. (2014). Climate classification revisited: From Köppen to Trewartha. Climate Research, 59(1), 1–13. https://doi.org/10.3354/cr01204
Belda,, M., Holtanová,, E., Halenka,, T., Kalvová,, J., & Hlávka,, Z. (2015). Evaluation of CMIP5 present climate simulations using the Köppen–Trewartha climate classification. Climate Research, 64(3), 201–212. https://doi.org/10.3354/cr01316
Belda,, M., Holtanová,, E., Kalvová,, J., & Halenka,, T. (2016). Global warming‐induced changes in climate zones based on CMIP5 projections. Climate Research, 71(1), 17–31. https://doi.org/10.3354/cr01418
Bockheim,, J. G., Gennadiyev,, A. N., Hammer,, R. D., & Tandarich,, J. P. (2005). Historical development of key concepts in pedology. Geoderma, 124(1–2), 23–36. https://doi.org/10.1016/j.geoderma.2004.03.004
Brugger,, K., & Rubel,, F. (2013). Characterizing the species composition of European Culicoides vectors by means of the Köppen–Geiger climate classification. Parasites %26 Vectors, 6(1), 333. https://doi.org/10.1186/1756-3305-6-333
Bunkers,, M. J., Miller,, J. R., & Degaetano,, A. T. (1996). Definition of climate regions in the northern plains using an objective cluster modification technique. Journal of Climate, 9(1), 130–146. https://doi.org/10.1175/1520-0442(1996)009%3C0130:DOCRIT%3E2.0.CO;2
Burrows,, M. T., Schoeman,, D. S., Buckley,, L. B., Moore,, P., Poloczanska,, E. S., Brander,, K. M., … Richardson,, A. J. (2011). The pace of shifting climate in marine and terrestrial ecosystems. Science (New York, N.Y.), 334(6056), 652–655. https://doi.org/10.1126/science.1210288
Brito‐Morales,, I., García Molinos,, J., Schoeman,, D. S., Burrows,, M. T., Poloczanska,, E. S., Brown,, C. J., … Richardson,, A. J. (2018). Climate Velocity Can Inform Conservation in a Warming World. Trends in ecology %26 evolution, 33(6), 441–457. https://doi.org/10.1016/j.tree.2018.03.009
Chan,, D., & Wu,, Q. (2015). Significant anthropogenic‐induced changes of climate classes since 1950. Scientific Reports, 5, 13487. https://doi.org/10.1038/srep13487
Chan,, D., Wu,, Q., Jiang,, G., & Dai,, X. (2016). Projected shifts in Köppen climate zones over China and their temporal evolution in CMIP5 multi‐model simulations. Advances in Atmospheric Sciences, 33(3), 283–293. https://doi.org/10.1007/s00376-015-5077-8
Chen,, D., & Chen,, H. W. (2013). Using the Köppen classification to quantify climate variation and change: An example for 1901–2010. Environmental Development, 6, 69–79. https://doi.org/10.1016/j.envdev.2013.03.007
Chen,, I.‐C., Hill,, J. K., Ohlemüller,, R., Roy,, D. B., & Thomas,, C. D. (2011). Rapid range shifts of species associated with high levels of climate warming. Science (New York, N.Y.), 333(6045), 1024–1026. https://doi.org/10.1126/science.1206432
de Castro,, M., Gallardo,, C., Jylha,, K., & Tuomenvirta,, H. (2007). The use of a climate‐type classification for assessing climate change effects in Europe from an ensemble of nine regional climate models. Climatic Change, 81(Suppl. 1), 329–341. https://doi.org/10.1007/s10584-006-9224-1
Defrance,, D., Catry,, T., Rajaud,, A., Dessay,, N., & Sultan,, B. (2020). Impacts of Greenland and Antarctic Ice Sheet melt on future Köppen climate zone changes simulated by an atmospheric and oceanic general circulation model. Applied Geography, 119, 102216. https://doi.org/10.1016/j.apgeog.2020.102216
Degaetano,, A. T. (1996). Delineation of mesoscale climate zones in the North‐eastern United States using a novel approach to cluster analysis. Journal of Climate, 9(8), 1765–1782. https://doi.org/10.1175/1520-0442(1996)009%3C1765:DOMCZI%3E2.0.CO;2
Díaz,, S., Fargione,, J., Chapin,, F. S., & Tilman,, D. (2006). Biodiversity loss threatens human well‐being. PLoS Biology, 4(8), e277. https://doi.org/10.1371/journal.pbio.0040277
Essenwanger,, O. M., & Landsberg,, H. E. (2001). General climatology. 1C, classification of climate. World survey of climatology: 1C. Amsterdam: Elsevier.
FAO. (2001). FRA 2000: Global ecological zoning for the global forest resources assessment 2000.
Feng,, S., Hu,, Q., Huang,, W., Ho,, C.‐H., Li,, R., & Tang,, Z. (2014). Projected climate regime shift under future global warming from multi‐model, multi‐scenario CMIP5 simulations. Global and Planetary Change, 112, 41–52. https://doi.org/10.1016/j.gloplacha.2013.11.002
Fick,, S. E., & Hijmans,, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
Fovell,, R. G., & Fovell,, M.‐Y. C. (1993). Climate zones of the conterminous United States defined using cluster analysis. Journal of Climate, 6(11), 2103–2135. https://doi.org/10.1175/1520-0442(1993)006%3C2103:CZOTCU%3E2.0.CO;2
Fraedrich,, K., Gerstengarbe,, F. W., & Wnerner,, P. C. (2001). Climate shifts during the last century. Climatic Change, 50, 405–417.
Funk,, C., Verdin,, A., Michaelsen,, J., Peterson,, P., Pedreros,, D., & Husak,, G. (2015). A global satellite‐assisted precipitation climatology. Earth System Science Data, 7(2), 275–287. https://doi.org/10.5194/essd-7-275-2015
Garcia,, R. A., Cabeza,, M., Rahbek,, C., & Araújo,, M. B. (2014). Multiple dimensions of climate change and their implications for biodiversity. Science (New York, N.Y.), 344(6183), 1247579. https://doi.org/10.1126/science.1247579
Giorgi,, F. (2006). Climate change hot‐spots. Geophysical Research Letters, 33(8), 89. https://doi.org/10.1029/2006GL025734
Gnanadesikan,, A., & Stouffer,, R. J. (2006). Diagnosing atmosphere‐ocean general circulation model errors relevant to the terrestrial biosphere using the Köppen climate classification. Geophysical Research Letters, 33(22), 643. https://doi.org/10.1029/2006GL028098
Grieser,, J., Gommes,, R., Cofield,, S. & Bernardi,, M. (2006). New gridded maps of Koeppen`s climate classification. Internet publication in FAO administrative Approval. Data, methodology and gridded data available at: http://www.fao.org/nr/climpag/globgrids/KC_classification_en.asp. Methodology also downloadable from http://www.juergen-grieser.de/downloads/Koeppen-Climatology/Koeppen_Climatology.pdf
Hanf,, F., Körper,, J., Spangehl,, T., & Cubasch,, U. (2012). Shifts of climate zones in multi‐model climate change experiments using the Köppen climate classification. Meteorologische Zeitschrift, 21(2), 111–123. https://doi.org/10.1127/0941-2948/2012/0344
Harris,, I., Jones,, P. D., Osborn,, T. J., & Lister,, D. H. (2014). Updated high‐resolution grids of monthly climatic observations – The CRU TS3.10 dataset. International Journal of Climatology, 34(3), 623–642. https://doi.org/10.1002/joc.3711
Hartmann,, D. L., Klein Tank,, A. M. G., Rusticucci,, M., Alexander,, L. V., Brönnimann,, S., Charabi,, Y., … Zhai,, P. M. (2013). Observations: Atmosphere and surface. In T. F. Stocker,, D. Qin,, G.‐K. Plattner,, M. Tignor,, S. K. Allen,, J. Boschung,, A. Nauels,, Y. Xia,, V. Bex,, & P. M. Midgley, (Eds.), Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, England/New York, NY: Cambridge University Press.
Heikkinen,, R. K., Luoto,, M., Araújo,, M. B., Virkkala,, R., Thuiller,, W., & Sykes,, M. T. (2016). Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography: Earth and Environment, 30(6), 751–777. https://doi.org/10.1177/0309133306071957
Hengl,, T., Mendes de Jesus,, J., Heuvelink,, G. B. M., Ruiperez Gonzalez,, M., Kilibarda,, M., Blagotić,, A., Shangguan,, W., Wright,, M. N., Geng,, X., Bauer‐Marschallinger,, B., Guevara,, M. A., Vargas,, R., MacMillan,, R. A., Batjes,, N. H., Leenaars,, J. G. B., Ribeiro,, E., Wheeler,, I., Mantel,, S., & Kempen,, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748
Hijmans,, R. J., Cameron,, S. E., Parra,, J. L., Jones,, P. G., & Jarvis,, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965–1978. https://doi.org/10.1002/joc.1276
Hoffman,, F. M., Hargrove,, W. W., Erickson,, D. J., & Oglesby,, R. J. (2005). Using clustered climate regimes to analyze and compare predictions from fully coupled general circulation models. Earth Interactions, 9(10), 1–27. https://doi.org/10.1175/EI110.1
Hof,, C., Levinsky,, I., Araújo,, M. B., & Rahbek,, C. (2011). Rethinking species` ability to cope with rapid climate change. Global Change Biology, 17(9), 2987–2990. https://doi.org/10.1111/j.1365-2486.2011.02418.x
IPCC (2018). Summary for policymakers. In V. Masson‐Delmotte,, P. Zhai,, H. O. Pörtner,, D. Roberts,, J. Skea,, P. R. Shukla,, A. Pirani,, W. Moufouma‐Okia,, C. Péan,, R. Pidcock,, S. Connors,, J. B. R. Matthews,, Y. Chen,, X. Zhou,, M. I. Gomis,, E. Lonnoy,, T. Maycock,, M. Tignor,, & T. Waterfield, (Eds.), Global warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre‐industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (pp. 45–64). Geneva, Switzerland: World Meteorological Organization. https://doi.org/10.1553/aar14s45
Jones,, S. B. (1932). Classifications of North American Climates. A Review. Economic Geography, 8(2), 205–208. https://doi.org/10.2307/140250
Jiménez,, M. A., Jaksic,, F. M., Armesto,, J. J., Gaxiola,, A., Meserve,, P. L., Kelt,, D. A., & Gutiérrez,, J. R. (2011). Extreme climatic events change the dynamics and invasibility of semi‐arid annual plant communities. Ecology Letters, 14(12), 1227–1235. https://doi.org/10.1111/j.1461-0248.2011.01693.x
Kalvová,, J., Halenka,, T., Bezpalcová,, K., & Nemešová,, I. (2003). Köppen climate types in observed and simulated climates. Studia Geophysica et Geodaetica, 47(1), 185–202. https://doi.org/10.1023/A:1022263908716
Karger,, D. N., Conrad,, O., Böhner,, J., Kawohl,, T., Kreft,, H., Soria‐Auza,, R. W., Zimmermann,, N. E., Linder,, H. P., & Kessler,, M. (2017). Climatologies at high resolution for the earth`s land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122
Köppen,, W. (1884). Die Wärmezonen der Erde, nach der Dauer der heissen, gemässigten und kalten Zeit und nach der Wirkung der Wärme auf die organische Welt betrachtet. Meteorologische Zeitschrift, 1(21), 5–226.
Köppen,, W. P. (1923). Die klimate der erde: Grundriss der klimakunde. Berlin: Walter de Gruyter.
Köppen,, W. P. (1931). Grundriss der klimakunde. Berlin: W. de Gruyter.
Köppen,, W. P. (1936). Das geographische System der Klimate: Mit 14 Textfiguren. Berlin: Borntraeger.
Köppen,, W. (1900). Versuch einer Klassifikation der Klimate, vorzugsweise nach ihren Beziehungen zur Pflanzenwelt. Geographische Zeitschrift, 6(11), 593–611.
Kottek,, M., Grieser,, J., Beck,, C., Rudolf,, B., & Rubel,, F. (2006). World map of the Köppen–Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130
Kriticos,, D. J., Webber,, B. L., Leriche,, A., Ota,, N., Macadam,, I., Bathols,, J., & Scott,, J. K. (2012). CliMond: Global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution, 3(1), 53–64. https://doi.org/10.1111/j.2041-210X.2011.00134.x
Lane,, J. E., Kruuk,, L. E. B., Charmantier,, A., Murie,, J. O., & Dobson,, F. S. (2012). Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature, 489(7417), 554–557. https://doi.org/10.1038/nature11335
Larson,, P. R., & Lohrengel,, C. F. (2011). A new tool for climatic analysis using the Köppen climate classification. Journal of Geography, 110(3), 120–130. https://doi.org/10.1080/00221341.2011.537672
Legates,, D. R., & Willmott,, C. J. (1990). Mean seasonal and spatial variability in global surface air temperature. Theoretical and Applied Climatology, 41(1–2), 11–21. https://doi.org/10.1007/BF00866198
Loarie,, S. R., Duffy,, P. B., Hamilton,, H., Asner,, G. P., Field,, C. B., & Ackerly,, D. D. (2009). The velocity of climate change. Nature, 462(7276), 1052–1055. https://doi.org/10.1038/nature08649
Lobell,, D. B., & Gourdji,, S. M. (2012). The influence of climate change on global crop productivity. Plant Physiology, 160(4), 1686–1697. https://doi.org/10.1104/pp.112.208298
Luoto,, M., Virkkala,, R., & Heikkinen,, R. K. (2007). The role of land cover in bioclimatic models depends on spatial resolution. Global Ecology and Biogeography, 16(1), 34–42. https://doi.org/10.1111/j.1466-8238.2006.00262.x
Mahlstein,, I., Daniel,, J. S., & Solomon,, S. (2013). Pace of shifts in climate regions increases with global temperature. Nature Climate Change, 3(8), 739–743. https://doi.org/10.1038/nclimate1876
Mahlstein,, I., & Knutti,, R. (2010). Regional climate change patterns identified by cluster analysis. Climate Dynamics, 35(4), 587–600. https://doi.org/10.1007/s00382-009-0654-0
Menne,, M. J., Durre,, I., Vose,, R. S., Gleason,, B. E., & Houston,, T. G. (2012). An overview of the global historical climatology network‐daily database. Journal of Atmospheric and Oceanic Technology, 29, 897–910. https://doi.org/10.1175/jtech-d-11-00103.1
Metzger,, M. J., Bunce,, R. G. H., Jongman,, R. H. G., Sayre,, R., Trabucco,, A., Zomer,, R., & Sykes,, M. (2013). A high‐resolution bioclimate map of the world: A unifying framework for global biodiversity research and monitoring. Global Ecology and Biogeography, 22(5), 630–638. https://doi.org/10.1111/geb.12022
Mitchell,, T. D., Carter,, T. R., Jones,, P., & Hulme,, M. (2004). A comprehensive set of high‐resolution grids of monthly climate for Europe and the globe: The observed record (1901–2000) and 16 scenarios (2001–2100) (Tyndall Centre Working Paper No. 55).
Mitchell,, T. D., & Jones,, P. D. (2005). An improved method of constructing a database of monthly climate observations and associated high‐resolution grids. International Journal of Climatology, 25(6), 693–712. https://doi.org/10.1002/joc.1181
Netzel,, P., & Stepinski,, T. (2016). On using a clustering approach for global climate classification. Journal of Climate, 29(9), 3387–3401. https://doi.org/10.1175/JCLI-D-15-0640.1
Olson,, D. M., Dinerstein,, E., Wikramanayake,, E. D., Burgess,, N. D., Powell,, G. V. N., Underwood,, E. C., D`amico,, J. A., Itoua,, I., Strand,, H. E., Morrison,, J. C., Loucks,, C. J., Allnutt,, T. F., Ricketts,, T. H., Kura,, Y., Lamoreux,, J. F., Wettengel,, W. W., Hedao,, P., & Kassem,, K. R. (2001). Terrestrial ecoregions of the world: A new map of life on Earth. Bioscience, 51(11), 933. https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2
Parmesan,, C. (2006). Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution, and Systematics, 37(1), 637–669. https://doi.org/10.1146/annurev.ecolsys.37.091305.110100
Parmesan,, C., & Yohe,, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421(6918), 37–42. https://doi.org/10.1038/nature01286
Pearson,, R. G., & Dawson,, T. P. (2003). Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful? Global Ecology and Biogeography, 12(5), 361–371. https://doi.org/10.1046/j.1466-822X.2003.00042.x
Peel,, M. C., Finlayson,, B. L., & McMahon,, T. A. (2007). Updated world map of the Köppen–Geiger climate classification. Hydrology and Earth System Sciences Discussions, 4(2), 439–473. https://doi.org/10.5194/hessd-4-439-2007
Peterson,, T. C., & Vose,, R. S. (1997). An overview of the global historical climatology network temperature database. Bulletin of the American Meteorological Society, 78(12), 2837–2849. https://doi.org/10.1175/1520-0477(1997)078%3C2837:AOOTGH%3E2.0.CO;2
Phillips,, T. J., & Bonfils,, C. J. W. (2015). Köppen bioclimatic evaluation of CMIP historical climate simulations. Environmental Research Letters, 10(6), 64005. https://doi.org/10.1088/1748-9326/10/6/064005
Poulter,, B., Ciais,, P., Hodson,, E., Lischke,, H., Maignan,, F., Plummer,, S., & Zimmermann,, N. E. (2011). Plant functional type mapping for earth system models. Geoscientific Model Development, 4(4), 993–1010. https://doi.org/10.5194/gmd-4-993-2011
Riahi,, K., Rao,, S., Krey,, V., Cho,, C., Chirkov,, V., Fischer,, G., Kindermann,, G., Nakicenovic,, N., & Rafaj,, P. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y
Rohli,, R. V., Joyner,, T. A., Reynolds,, S. J., & Ballinger,, T. J. (2015). Overlap of global Köppen–Geiger climates, biomes, and soil orders. Physical Geography, 36(2), 158–175. https://doi.org/10.1080/02723646.2015.1016384
Rohli,, R. V., Joyner,, T. A., Reynolds,, S. J., Shaw,, C., & Vázquez,, J. R. (2015). Globally extended Kӧppen–Geiger climate classification and temporal shifts in terrestrial climatic types. Physical Geography, 36(2), 142–157. https://doi.org/10.1080/02723646.2015.1016382
Rubel,, F., Brugger,, K., Haslinger,, K., & Auer,, I. (2017). The climate of the European Alps: Shift of very high resolution Köppen‐Geiger climate zones 1800–2100. Meteorologische Zeitschrift, 26(2), 115–125. https://doi.org/10.1127/metz/2016/0816
Rubel,, F., & Kottek,, M. (2010). Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen–Geiger climate classification. Meteorologische Zeitschrift, 19(2), 135–141. https://doi.org/10.1127/0941-2948/2010/0430
Rubel,, F., & Kottek,, M. (2011). Comments on: “The thermal zones of the Earth” by Wladimir Köppen (1884). Meteorologische Zeitschrift, 20(3), 361–365. https://doi.org/10.1127/0941-2948/2011/0285
Russell,, R. J. (1931). Dry climates of the United States: I. Climatic map (Vol. 5). University of California Press.
Sanderson,, M. (1999). The classification of climates from Pythagoras to Koeppen. Bulletin of the American Meteorological Society, 80(4), 669–673. https://doi.org/10.1175/1520-0477(1999)080%3C0669:TCOCFP%3E2.0.CO;2
Scott,, R. C. (1996). Introduction to physical geography. St. Paul/Minneapolis: West Publishing Co.
Stooksbury,, D. E., & Michaels,, P. J. (1991). Cluster analysis of south‐eastern U.S. climate stations. Theoretical and Applied Climatology, 44(3–4), 143–150. https://doi.org/10.1007/BF00868169
Sunday,, J. M.. Bates,, A. E., & Dulvy,, N. K. (2012). Thermal tolerance and the global redistribution of animals. Nature Clim Change, 2(9), 686–690. https://doi.org/10.1038/nclimate1539
Tapiador,, F. J., Moreno,, R., & Navarro,, A. (2019). Consensus in climate classifications for present climate and global warming scenarios. Atmospheric Research, 216, 26–36. https://doi.org/10.1016/j.atmosres.2018.09.017
Tarkan,, A. S., & Vilizzi,, L. (2015). Patterns, latitudinal clines and counter gradient variation in the growth of roach Rutilus rutilus (Cyprinidae) in its Eurasian area of distribution. Reviews in Fish Biology and Fisheries, 25(4), 587–602. https://doi.org/10.1007/s11160-015-9398-6
Taylor,, K. E., Stouffer,, R. J., & Meehl,, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
Taylor,, R. G., Scanlon,, B., Döll,, P., Rodell,, M., van Beek,, R., Wada,, Y., Longuevergne,, L., Leblanc,, M., Famiglietti,, J. S., Edmunds,, M., Konikow,, L., Green,, T. R., Chen,, J., Taniguchi,, M., Bierkens,, M. F. P., MacDonald,, A., Fan,, Y., Maxwell,, R. M., Yechieli,, Y., … Treidel,, H. (2013). Ground water and climate change. Nature Climate Change, 3(4), 322–329. https://doi.org/10.1038/nclimate1744
Tererai,, F., & Wood,, A. R. (2014). On the present and potential distribution of Ageratina adenophora (Asteraceae) in South Africa. South African Journal of Botany, 95, 152–158. https://doi.org/10.1016/j.sajb.2014.09.001
Thuiller,, W., Lavorel,, S., Araújo,, M. B., Sykes,, M. T., & Prentice,, I. C. (2005). Climate change threats to plant diversity in Europe. Proceedings of the National Academy of Sciences of the United States of America, 102(23), 8245–8250. https://doi.org/10.1073/pnas.0409902102
Trewartha,, G. T. (1954). An introduction to climate. New York/Toronto/London: McGraw‐Hill Book Company.
Triantafyllou,, G. N., & Tsonis,, A. A. (1994). Assessing the ability of the Köppen system to delineate the general world pattern of climates. Geophysical Research Letters, 21(25), 2809–2812. https://doi.org/10.1029/94GL01992
Thomas,, C. D., Cameron,, A., Green,, R. E., Bakkenes,, M., Beaumont,, L. J., Collingham,, Y. C., … Williams,, S. E. (2004). Extinction risk from climate change. Nature, 427(6970), 145–148. https://doi.org/10.1038/nature02121
Unal,, Y., Kindap,, T., & Karaca,, M. (2003). Redefining the climate zones of Turkey using cluster analysis. International Journal of Climatology, 23(9), 1045–1055. https://doi.org/10.1002/joc.910
Walther,, G.‐R., Post,, E., Convey,, P., Menzel,, A., Parmesan,, C., Beebee,, T. J. C., … Bairlein,, F. (2002). Ecological responses to recent climate change. Nature, 416, 389. https://doi.org/10.1038/416389a
Webber,, B. L., Yates,, C. J., Le Maitre,, D. C., Scott,, J. K., Kriticos,, D. J., Ota,, N., … Midgley,, G. F. (2011). Modelling horses for novel climate courses: Insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models. Diversity and Distributions, 17(5), 978–1000. https://doi.org/10.1111/j.1472-4642.2011.00811.x
Weigel,, A. P., Liniger,, M. A., & Appenzeller,, C. (2008). Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts? Quarterly Journal of the Royal Meteorological Society, 134(630), 241–260. https://doi.org/10.1002/qj.210
Williams,, J. W., Jackson,, S. T., & Kutzbach,, J. E. (2007). Projected distributions of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences of the United States of America, 104(14), 5738–5742. https://doi.org/10.1073/pnas.0606292104
Yoo,, J., & Rohli,, R. V. (2016). Global distribution of Köppen–Geiger climate types during the last glacial maximum, mid‐Holocene, and present. Palaeogeography, Palaeoclimatology, Palaeoecology, 446, 326–337. https://doi.org/10.1016/j.palaeo.2015.12.010
Zscheischler,, J., Mahecha,, M. D., & Harmeling,, S. (2012). Climate classifications: The value of unsupervised clustering. Procedia Computer Science, 9, 897–906. https://doi.org/10.1016/j.procs.2012.04.096
Zhou,, G., & Wang,, Y. (2000). Global change and climate‐vegetation classification. Chinese Science Bulletin, 45(7), 577–585. https://doi.org/10.1007/BF02886031
Zhang,, X., & Cai,, X. (2013). Climate change impacts on global agricultural water deficit. Geophysical Research Letters, 40(6), 1111–1117. https://doi.org/10.1002/grl.50279
Zhang,, X., Xiong,, Z., Zhang,, X., Shi,, Y., Liu,, J., Shao,, Q., & Yan,, X. (2016). Using multi‐model ensembles to improve the simulated effects of land use/cover change on temperature: A case study over northeast China. Climate Dynamics, 46(3–4), 765–778. https://doi.org/10.1007/s00382-015-2611-4
Zhang,, X., & Yan,, X. (2014). Spatiotemporal change in geographical distribution of global climate types in the context of climate warming. Climate Dynamics, 43(3–4), 595–605. https://doi.org/10.1007/s00382-013-2019-y
Zhang,, X., & Yan,, X. (2016). Deficiencies in the simulation of the geographic distribution of climate types by global climate models. Climate Dynamics, 46(9–10), 2749–2757. https://doi.org/10.1007/s00382-015-2727-6
Zhang,, X., Yan,, X., & Chen,, Z. (2017). Geographic distribution of global climate zones under future scenarios. International Journal of Climatology, 37(12), 4327–4334. https://doi.org/10.1002/joc.5089
Watson,, J. E. M., Iwamura,, T., & Butt,, N. (2013). Mapping vulnerability and conservation adaptation strategies under climate change. Nature Clim Change, 3(11), 989–994. https://doi.org/10.1038/nclimate2007

Further Reading

National Academies of Sciences, Engineering, and Medicine. (2018). Thriving on our changing planet: A decadal strategy for earth observation from space climate classification. Washington, DC: The National Academies Press.

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