Articles | Volume 13, issue 11
https://doi.org/10.5194/essd-13-5087-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-5087-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables
Diyang Cui
Department of Geographical Sciences, University of Maryland, College
Park, 20740, USA
Department of Geographical Sciences, University of Maryland, College
Park, 20740, USA
Dongdong Wang
Department of Geographical Sciences, University of Maryland, College
Park, 20740, USA
Zheng Liu
Department of Geographical Sciences, University of Maryland, College
Park, 20740, USA
Related authors
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-53, https://doi.org/10.5194/essd-2021-53, 2021
Preprint withdrawn
Short summary
Short summary
The Köppen-Geiger climate classification has been widely applied in climate change and ecology studies to characterize climatic conditions. We present a new 1-km global dataset of Köppen-Geiger climate classification and bioclimatic variables for historical and future climates. The new climate maps offer higher classification accuracy, correspond well with distributions of vegetation and topographic features, and demonstrate the ability to identify recent and future changes in climate zones.
Hui Liang, Shunlin Liang, Bo Jiang, Tao He, Feng Tian, Jianglei Xu, Wenyuan Li, Fengjiao Zhang, and Husheng Fang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-136, https://doi.org/10.5194/essd-2025-136, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper describes 1 km daily mean land surface sensible heat flux (H) and land surface – air temperature difference (Tsa) datasets on the global scale during 2000–2020. The datasets were developed using a data-driven approach and rigorously validated against in situ observations and existing H and Tsa datasets, demonstrating both high accuracy and exceptional spatial resolution.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-553, https://doi.org/10.5194/essd-2024-553, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable long-term seamless global datasets remain scarce. To fill this gap, we developed a four-decade seamless global daily 5 km SM product using multi-source datasets and deep learning techniques. This product has long-term coverage, spatial and temporal integrity, and high accuracy, making it a valuable tool for applications like SM trend analysis, drought monitoring, and assessing vegetation responses.
Bing Li, Shunlin Liang, Han Ma, Guanpeng Dong, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data, 16, 3795–3819, https://doi.org/10.5194/essd-16-3795-2024, https://doi.org/10.5194/essd-16-3795-2024, 2024
Short summary
Short summary
This study describes 1 km all-weather instantaneous and daily mean land surface temperature (LST) datasets on the global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long global-scale time series. The generated datasets were evaluated by the observations from in situ stations and other LST datasets, and the evaluation indicated that the dataset is sufficiently reliable.
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma
Earth Syst. Sci. Data, 15, 3641–3671, https://doi.org/10.5194/essd-15-3641-2023, https://doi.org/10.5194/essd-15-3641-2023, 2023
Short summary
Short summary
We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong
Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
Short summary
Short summary
Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Ruohan Li, Dongdong Wang, Weile Wang, and Ramakrishna Nemani
Earth Syst. Sci. Data, 15, 1419–1436, https://doi.org/10.5194/essd-15-1419-2023, https://doi.org/10.5194/essd-15-1419-2023, 2023
Short summary
Short summary
There has been an increasing need for high-spatiotemporal-resolution surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) data for ecological, hydrological, carbon, and solar photovoltaic research. This study produced a new 1 km hourly product of land surface DSR and PAR from the enhanced GeoNEX new-generation geostationary data. Our validation indicated that the GeoNEX DSR and PAR product has a higher accuracy than other existing products.
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, and Shuo Xu
Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
Short summary
Short summary
Satellites are now producing multiple global land surface temperature (LST) products; however, they suffer from data gaps caused by cloud cover, seriously restricting the applications, and few products provide gap-free global hourly LST. We produced global hourly, 5 km, all-sky LST data from 2011 to 2021 using geostationary and polar-orbiting satellite data. Based on the assessment, it has high accuracy and can be used to estimate evapotranspiration, drought, etc.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
Short summary
Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
Short summary
Short summary
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
Short summary
Short summary
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
Xueyuan Gao, Shunlin Liang, Dongdong Wang, Yan Li, Bin He, and Aolin Jia
Earth Syst. Dynam., 13, 219–230, https://doi.org/10.5194/esd-13-219-2022, https://doi.org/10.5194/esd-13-219-2022, 2022
Short summary
Short summary
Numerical experiments with a coupled Earth system model show that large-scale nighttime artificial lighting in tropical forests will significantly increase carbon sink, local temperature, and precipitation, and it requires less energy than direct air carbon capture for capturing 1 t of carbon, suggesting that it could be a powerful climate mitigation option. Side effects include CO2 outgassing after the termination of the nighttime lighting and impacts on local wildlife.
Xiaona Chen, Shunlin Liang, Lian He, Yaping Yang, and Cong Yin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-279, https://doi.org/10.5194/essd-2021-279, 2021
Preprint withdrawn
Short summary
Short summary
The present study developed a 39 year consistent 8-day 0.05 degree gap-free SCE dataset over the NH for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite based on the NOAA AVHRR-SR CDR and several contributory datasets. Compared with published SCE datasets, GLASS SCE has several advantages in snow cover studies, including long time series, finer spatial resolution (especially for years before 2000), and complete spatial coverage.
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, https://doi.org/10.5194/essd-13-4241-2021, 2021
Short summary
Short summary
This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-53, https://doi.org/10.5194/essd-2021-53, 2021
Preprint withdrawn
Short summary
Short summary
The Köppen-Geiger climate classification has been widely applied in climate change and ecology studies to characterize climatic conditions. We present a new 1-km global dataset of Köppen-Geiger climate classification and bioclimatic variables for historical and future climates. The new climate maps offer higher classification accuracy, correspond well with distributions of vegetation and topographic features, and demonstrate the ability to identify recent and future changes in climate zones.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
Short summary
Short summary
Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
Short summary
Short summary
Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Cited articles
Beck, C., Grieser, J., Rudolf, B., and Schneider, U.: A new monthly precipitation climatology for the global land areas for the period 1951 to 2000, Geophys. Res. Abstr., 7, 7154, available at: http://www.juergen-grieser.de/publications/publications_pdf/Beck_Grieser_Rudolf_EGU_05.pdf (last access: 3 November 2021), 2005.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A.,
and Wood, E. F.: Present and future Köppen-Geiger climate classification
maps at 1 km resolution, Scientific data, 5, 180214,
https://doi.org/10.1038/sdata.2018.214, 2018.
Belda, M., Holtanová, E., Halenka, T., and Kalvová, J.: Climate
classification revisited: From Köppen to Trewartha, Clim. Res., 59,
1–13, https://doi.org/10.3354/cr01204, 2014.
Belda, M., Holtanová, E., Kalvová, J., and Halenka, T.: Global
warming-induced changes in climate zones based on CMIP5 projections, Clim.
Res., 71, 17–31, https://doi.org/10.3354/cr01418, 2016.
Bockheim, J. G., Gennadiyev, A. N., Hammer, R. D., and Tandarich, J. P.:
Historical development of key concepts in pedology, Geoderma, 124, 23–36,
https://doi.org/10.1016/j.geoderma.2004.03.004, 2005.
Brugger, K. and Rubel, F.: Characterizing the species composition of
European Culicoides vectors by means of the Köppen-Geiger climate
classification, Parasite. Vector., 6, 333, https://doi.org/10.1186/1756-3305-6-333,
2013.
Chan, D. and Wu, Q.: Significant anthropogenic-induced changes of climate
classes since 1950, Sci. Rep., 5, 13487, https://doi.org/10.1038/srep13487,
2015.
Chen, D. and Chen, H. W.: 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, 2013.
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B., and Thomas, C. D.:
Rapid range shifts of species associated with high levels of climate
warming, Science, 333, 1024–1026,
https://doi.org/10.1126/science.1206432, 2011.
Chen, M., Xie, P., Janowiak, J. E., and Arkin, P. A.: Global land
precipitation: A 50-year monthly analysis based on gauge observations, J. Hydrometeorol., 3, 249–266, 2002.
Craven, P. and Wahba, G.: Smoothing noisy data with spline functions,
Numer. Math., 31, 377–403, https://doi.org/10.1007/BF01404567, 1978.
Cui, D., Liang, S., and Wang, D.: Observed and projected changes in global
climate zones based on Köppen climate classification, WIREs Clim. Change, 12, e701, https://doi.org/10.1002/wcc.701, 2021a.
Cui, D., Liang, S., Wang, D., and Liu, Z.: KGClim future: A 1-km global
dataset of future (2020–2100) Köppen-Geiger climate classification and
bioclimatic variables (Version V1), Zenodo [data set], https://doi.org/10.5281/zenodo.4542076, 2021b.
Cui, D., Liang, S., Wang, D., and Liu, Z.: KGClim historical: A 1-km global
dataset of historical (1979–2013) Köppen-Geiger climate classification
and bioclimatic variables (Version V1), Zenodo [data set], https://doi.org/10.5281/zenodo.5347837, 2021c.
Cui, D., Liang, S., Wang, D., and Liu, Z.: KGClim: A 1-km global dataset of historical (1979–2013) and future (2020–2100) Köppen-Geiger climate classification and bioclimatic variables, University of Maryland [data set], available at: http://glass.umd.edu/KGClim, last access: 2 November 2021d.
Dobrowski, S. Z., Abatzoglou, J., Swanson, A. K., Greenberg, J. A.,
Mynsberge, A. R., Holden, Z. A., and Schwartz, M. K.: The climate velocity
of the contiguous United States during the 20th century, Glob. Change Biol.,
19, 241–251, https://doi.org/10.1111/gcb.12026, 2013.
Fan, Y. and Dool, H. v. d.: A global monthly land surface air temperature
analysis for 1948–present, J. Geophys. Res., 113, D01103,
https://doi.org/10.1029/2007JD008470, 2008.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, 1485,
https://doi.org/10.1029/2005RG000183, 2007.
Feng, S., Ho, C.-H., Hu, Q., Oglesby, R. J., Jeong, S.-J., and Kim, B.-M.:
Evaluating observed and projected future climate changes for the Arctic
using the Köppen-Trewartha climate classification, Clim. Dyn., 38,
1359–1373, https://doi.org/10.1007/s00382-011-1020-6, 2012.
Feng, S., Hu, Q., Huang, W., Ho, C.-H., Li, R., and Tang, Z.: Projected
climate regime shift under future global warming from multi-model,
multi-scenario CMIP5 simulations, Global Planet. Change, 112, 41–52,
https://doi.org/10.1016/j.gloplacha.2013.11.002, 2014.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: New 1 km spatial resolution
climate surfaces for global land areas, Int. J. Climatol, 37, 4302–4315,
https://doi.org/10.1002/joc.5086, 2017.
Franke, R.: Smooth interpolation of scattered data by local thin plate
splines, Comput. Math. Appl., 8, 273–281,
https://doi.org/10.1016/0898-1221(82)90009-8, 1982.
Franklin, J., Davis, F. W., Ikegami, M., Syphard, A. D., Flint, L. E.,
Flint, A. L., and Hannah, L.: Modeling plant species distributions under
future climates: How fine scale do climate projections need to be?, Glob.
Change Biol., 19, 473–483, https://doi.org/10.1111/gcb.12051, 2013.
Funk, C., Verdin, A., Michaelsen, J., Peterson, P., Pedreros, D., and Husak, G.: A global satellite-assisted precipitation climatology, Earth Syst. Sci. Data, 7, 275–287, https://doi.org/10.5194/essd-7-275-2015, 2015.
Garcia, R. A., Cabeza, M., Rahbek, C., and Araújo, M. B.: Multiple
dimensions of climate change and their implications for biodiversity,
Science, 344, 1247579, https://doi.org/10.1126/science.1247579, 2014.
Geiger, R.: berarbeitete Neuausgabe von Geiger, R: Köppen-Geiger/Klima
der Erde, Wandkarte (wall map), vol. 1, p. 16, KlettPerthes, Gotha, Germany, 1961.
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for
climate models, J. Geophys. Res., 113, 1147, https://doi.org/10.1029/2007JD008972, 2008.
Grieser, J., Gommes, R., Cofield, S., and Bernardi, M.: New gridded maps of Koeppen’s climate classification, data available at: http://www.fao.org/nr/climpag/globgrids/KC_classification_en.asp (last access: 18 July 2021), 2006a.
Grieser, J., Gommes, R., Cofield, S., and Bernardi, M.: New gridded maps of Koeppen’s climate classification, methodology available at: http://www.juergen-grieser.de/downloads/Koeppen-Climatology/Koeppen_Climatology.pdf (last access: 18 July 2021), 2006b.
Hanf, F., Körper, J., Spangehl, T., and Cubasch, U.: Shifts of climate
zones in multi-model climate change experiments using the Köppen climate
classification, Meteorol. Z., 21, 111–123, https://doi.org/10.1127/0941-2948/2012/0344, 2012.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A.,
Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R.,
Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R.
G.: High-resolution global maps of 21st-century forest cover change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
Hartmann, D. L., Klein Tank, A. M. G., Rusticucci, M., Alexander, L. V.,
Brönnimann, S., Charabi, Y., Dentener, F. J., Dlugokencky, E. J.,
Easterling, D. R., Kaplan, A., Soden, B. J., Thorne, P. W., Wild, M., and Zhai, P. M.:
Observations: Atmosphere and Surface, 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, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 96 pp., 2013.
Hay, L. E., Wilby, R. L., and Leavesley, G. H.: A comparison of delta change
and downscaled GCM scenarios for three mountainous basins in the united
states, J. Am. Water Resour. As., 36, 387–397, https://doi.org/10.1111/j.1752-1688.2000.tb04276.x, 2000.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.:
Very high resolution interpolated climate surfaces for global land areas,
Int. J. Climatol, 25, 1965–1978, https://doi.org/10.1002/joc.1276, 2005.
Ho, C. K., Stephenson, D. B., Collins, M., Ferro, C. A. T., and Brown, S.
J.: Calibration strategies: A source of additional uncertainty in climate
change projections, Bull. Amer. Meteor. Soc., 93, 21–26,
https://doi.org/10.1175/2011BAMS3110.1, 2012.
Holdridge, L. R.: Determination of world plant formations from simple
climatic data, Science, 105, 367–368,
https://doi.org/10.1126/science.105.2727.367, 1947.
Jones, S. B.: Classifications of North American climates: A review, Econ.
Geogr., 8, 205–208, https://doi.org/10.2307/140250, 1932.
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H.,
Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.:
Climatologies at high resolution for the earth's land surface areas,
Scientific data, 4, 170122, https://doi.org/10.1038/sdata.2017.122, 2017.
Köppen, W. P.: Grundriss der klimakunde, Walter de Gruyter GmbH & Co KG, Berlin, Leipzig, Germany, 1931.
Köppen, W. P.: Das geographische System der Klimate: Mit 14 Textfiguren,
Gebrüder Borntraeger, Berlin, Germany, 1936.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of
the Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259–263,
https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Kriticos, D. J., Webber, B. L., Leriche, A., Ota, N., Macadam, I., Bathols,
J., and Scott, J. K.: CliMond: Global high-resolution historical and future
scenario climate surfaces for bioclimatic modelling, Methods Ecol.
Evol., 3, 53–64, https://doi.org/10.1111/j.2041-210X.2011.00134.x, 2012.
Leemans, R., Cramer, W., and van Minnen, J. G.: Prediction of global biome
distribution using bioclimatic equilibrium models, Scope-scientific
committee on problems of the environment international council of scientif
unions, 56, 413–440, 1996.
Mahlstein, I., Daniel, J. S., and Solomon, S.: Pace of shifts in climate
regions increases with global temperature, Nat. Clim. Change, 3, 739–743,
https://doi.org/10.1038/nclimate1876, 2013.
Manabe, S. and Holloway, J. L.: The seasonal variation of the hydrologic
cycle as simulated by a global model of the atmosphere, J. Geophys. Res.,
80, 1617–1649, https://doi.org/10.1029/JC080i012p01617, 1975.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
overview of the global historical climatology network-daily database,
J. Atmos. Ocean. Tech., 29, 897-910,
https://doi.org/10.1175/jtech-d-11-00103.1, 2012.
National Climatic Data Center, NESDIS, NOAA, and U.S. Department of
Commerce: Global Surface Summary of the Day – GSOD, available at:
https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00516# (last access: 18 July 2021), 2015.
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A., and
Ramirez-Villegas, J.: High-resolution and bias-corrected CMIP5 projections
for climate change impact assessments, Scientific data, 7, p. 7,
https://doi.org/10.1038/s41597-019-0343-8, 2020.
Netzel, P. and Stepinski, T.: On using a clustering approach for global
climate classification, J. Climate, 29, 3387–3401,
https://doi.org/10.1175/JCLI-D-15-0640.1, 2016.
New, M., Hulme, M., and Jones, P.: Representing twentieth-century
space–time climate variability. Part II: Development of 1901–96 monthly
grids of terrestrial surface climate, J. Climate, 13, 2217–2238,
https://doi.org/10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2,
2000.
Ordonez, A. and Williams, J. W.: Projected climate reshuffling based on
multivariate climate-availability, climate-analog, and climate-velocity
analyses: Implications for community disaggregation, Climatic Change, 119,
659–675, https://doi.org/10.1007/s10584-013-0752-1, 2013.
Peel, M. C., McMahon, T. A., Finlayson, B. L., and Watson, F. G. R.:
Identification and explanation of continental differences in the variability
of annual runoff, J. Hydrol., 250, 224–240,
https://doi.org/10.1016/S0022-1694(01)00438-3, 2001.
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007.
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L., and Levin, S. A.:
Marine taxa track local climate velocities, Science, 341,
1239–1242, https://doi.org/10.1126/science.1239352, 2013.
Poulter, B., Ciais, P., Hodson, E., Lischke, H., Maignan, F., Plummer, S., and Zimmermann, N. E.: Plant functional type mapping for earth system models, Geosci. Model Dev., 4, 993–1010, https://doi.org/10.5194/gmd-4-993-2011, 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., and Peylin, P.: Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015.
Roderfeld, H., Blyth, E., Dankers, R., Huse, G., Slagstad, D., Ellingsen,
I., Wolf, A., and Lange, M. A.: Potential impact of climate change on
ecosystems of the Barents Sea Region, Climatic Change, 87, 283–303,
https://doi.org/10.1007/s10584-007-9350-4, 2008.
Rohli, R. V., Andrew, J. T., Reynolds, S. J., Shaw, C., and Vázquez, J.
R.: Globally extended Köppen–Geiger climate classification and temporal
shifts in terrestrial climatic types, Phys. Geogr., 36, 142–157,
https://doi.org/10.1080/02723646.2015.1016382, 2015a.
Rohli, R. V., Joyner, T. A., Reynolds, S. J., and Ballinger, T. J.: Overlap
of global Köppen–Geiger climates, biomes, and soil orders, Phys.
Geogr., 36, 158–175, https://doi.org/10.1080/02723646.2015.1016384, 2015b.
Rubel, F. and Kottek, M.: Observed and projected climate shifts 1901-2100
depicted by world maps of the Köppen-Geiger climate classification,
Meteorol. Z., 19, 135–141, https://doi.org/10.1127/0941-2948/2010/0430, 2010.
Rubel, F. and Kottek, M.: Comments on: “The thermal zones of the Earth” by
Wladimir Köppen (1884), Meteorol. Z., 20, 361–365,
https://doi.org/10.1127/0941-2948/2011/0285, 2011.
Rubel, F., Brugger, K., Haslinger, K., and Auer, I.: The climate of the
European Alps: Shift of very high resolution Köppen-Geiger climate zones
1800–2100, Meteorol. Z., 26, 115–125, https://doi.org/10.1127/metz/2016/0816, 2017.
Russell, R. J.: Dry climates of the United States: I. Climatic map, 5,
University of California Press, Berkeley, California, USA, 1931.
Sanderson, M.: The Classification of Climates from Pythagoras to Koeppen,
Bull. Amer. Meteor. Soc., 80, 669–673,
https://doi.org/10.1175/1520-0477(1999)080<0669:TCOCFP>2.0.CO;2,
1999.
Schempp, W., Zeller, K., and Duchon, J. (Eds.): Splines minimizing
rotation-invariant semi-norms in Sobolev spaces: Constructive Theory of
Functions of Several Variables, Springer, Berlin, Heidelberg, Germany, 85–100, 1977.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A
review of global precipitation data sets: Data sources, estimation, and
intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574,
2018.
Tapiador, F. J., Moreno, R., and Navarro, A.: Consensus in climate
classifications for present climate and global warming scenarios,
Atmos. Res., 216, 26–36, https://doi.org/10.1016/j.atmosres.2018.09.017, 2019.
Tarkan, A. S. and Vilizzi, L.: Patterns, latitudinal clines and
countergradient variation in the growth of roach Rutilus rutilus
(Cyprinidae) in its Eurasian area of distribution, Rev. Fish. Biol. Fisher., 25, 587–602, https://doi.org/10.1007/s11160-015-9398-6, 2015.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design, Bull. Amer. Meteor. Soc., 93, 485–498,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Tererai, F. and Wood, A. R.: On the present and potential distribution of
Ageratina adenophora (Asteraceae) in South Africa, S. Afr. J.
Bot., 95, 152–158, https://doi.org/10.1016/j.sajb.2014.09.001, 2014.
Thornthwaite, C. W.: The climates of North America: According to a new
classification, Geogr. Rev., 21, 633, https://doi.org/10.2307/209372, 1931.
Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T., and Prentice, I.
C.: Climate change threats to plant diversity in Europe, P. Natl. Acad. Sci. USA, 102, 8245–8250, https://doi.org/10.1073/pnas.0409902102, 2005.
Trewartha, G. T.: An introduction to climate, McGraw-Hill Book Company, Inc.,
New York, USA, Toronto, Canada, London, UK, 1954.
Walter, S. D. and Elwood, J. M.: A test for seasonality of events with a
variable population at risk, J. Epidemiol. Commun. H.,
29, 18–21, https://doi.org/10.1136/jech.29.1.18, 1975.
Wang, M. and Overland, J. E.: Detecting Arctic climate change using
Köppen climate classification, Climatic Change, 67, 43–62,
https://doi.org/10.1007/s10584-004-4786-2, 2004.
Webber, B. L., Yates, C. J., Le Maitre, D. C., Scott, J. K., Kriticos, D.
J., Ota, N., McNeill, A., Le Roux, J. J., and Midgley, G. F.: Modelling
horses for novel climate courses: Insights from projecting potential
distributions of native and alien Australian acacias with correlative and
mechanistic models, Divers. Distrib., 17, 978–1000,
https://doi.org/10.1111/j.1472-4642.2011.00811.x, 2011.
Wilby, R. L. and Wigley, T. M. L.: Downscaling general circulation model
output: A review of methods and limitations, Prog. Phys. Geog., 21, 530–548, https://doi.org/10.1177/030913339702100403, 1997.
Willmott, C. J. and Matsuura, K.: Terrestrial air temperature and
precipitation: monthly and annual time series (1950–1999), available at:
http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html (last access: 18 July 2021), 2001.
Winsberg, E.: Values and uncertainties in the predictions of global climate
models, Kennedy Inst. Ethic. J., 22, 111–137,
https://doi.org/10.1353/ken.2012.0008, 2012.
Yoo, J. and Rohli, R. V.: Global distribution of Köppen–Geiger climate
types during the Last Glacial Maximum, Mid-Holocene, and present,
Palaeogeogr. Palaeocl., 446, 326–337,
https://doi.org/10.1016/j.palaeo.2015.12.010, 2016.
Short summary
Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Large portions of the Earth's surface are expected to experience changes in climatic conditions....
Altmetrics
Final-revised paper
Preprint