the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Mar 2021
A 1-km global dataset of historical (1979–2017) and future (2020–2100) Köppen-Geiger climate classification and bioclimatic variables
Abstract. The Köppen-Geiger climate classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods. Comprehensive assessment of climate change impacts requires a more accurate depiction of fine-grained climatic conditions and continuous long-term time coverage. Here, we present a series of improved 1-km Köppen-Geiger climate classification maps for ten historical periods in 1979–2017 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The historical maps are derived from multiple downscaled observational datasets and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. In addition to climate classification maps, we calculate 12 bioclimatic variables at 1-km resolution, providing detailed descriptions of annual averages, seasonality, and stressful conditions of climates. The new maps offer higher classification accuracy and demonstrate the ability to capture recent and future projected changes in spatial distributions of climate zones. On regional and continental scales, the new maps show accurate depictions of topographic features and correspond closely with vegetation distributions. We also provide a heuristic application example to detect long-term global-scale area changes of climate zones. This high-resolution dataset of Köppen-Geiger climate classification and bioclimatic variables can be used in conjunction with species distribution models to promote biodiversity conservation and to analyze and identify recent and future interannual or interdecadal changes in climate zones on a global or regional scale. The dataset referred to as KGClim, is publicly available at http://doi.org/10.5281/zenodo.4546140 for historical climate and http://doi.org/10.5281/zenodo.4542076 for future climate.
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Preprint
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Interactive discussion
Status: closed
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RC1: 'Plagiarism', Anonymous Referee #1, 25 Mar 2021
I wonder what the editorial policy is for plagiarism, because I am concerned with the degree of copying and rewording in this paper. Below are some examples I found. I wouldn't be surprised if lots of other sentences were lifted from other papers.
Beck et al. (2018): "The Köppen-Geiger climate classification is a highly suitable means to aggregate complex climate gradients into a simple but ecologically meaningful classification scheme."
Cui et al. (2021): "The Köppen climate classification demonstrates the ability to aggregate complex and diverse climate gradients into ecologically meaningful classes and simplify spatial variability."Beck et al. (2018): "This can lead to widespread misclassifications, particularly in regions with a low station density and/or strong climatic gradients such as mountain ranges (Karger et al., 2017)."
Cui et al. (2021): "This eventually led to widespread misclassifications of Köppen climates, particularly in mountainous regions with strong climatic gradients and often low station density (Karger et al., 2017)."Beck et al. (2018): "However, caution should be exerted not to equate those changes directly with changes in actual biomes."
Cui et al. (2021): "However, we should take cautions when relating the changes to changes in actual biome distributions."Beck et al. (2018): "vegetation changes by 2100 may lag the change in climate zones."
Cui et al. (2021): "Vegetation changes may lag the climate changes when climate become less favourable."Beck et al. (2018): "Secondly, factors not accounted for in the Köppen-Geiger classification, such as higher atmospheric CO2 levels, may alter the relationship between climate classes and vegetation."
Cui et al. (2021): "Additionally, other factors not considered in the Köppen classification scheme, such as CO2 or nitrogen levels, may influence the relationship between climate and vegetation.""The highest confidence was given to the most common climate class for each grid cell." Maybe mention that this approach was adopted from Beck et al. (2018).
"The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods." The "low spatial resolution" argument is of course incorrect as the authors well know. Poor accuracy is also incorrect (the Beck et al., 2018, map has only slightly lower accuracy according to your evaluation). And what are noncomparable time periods? Noncomparable to what? Does it really matter that much whether the climatology represents 1988-2017 or 1980-2016 given the input uncertainty?
Citation: https://doi.org/10.5194/essd-2021-53-RC1 -
AC1: 'Reply on RC1', Diyang Cui, 25 Mar 2021
Comment 1: I wonder what the editorial policy is for plagiarism because I am concerned with the degree of copying and rewording in this paper. Below are some examples I found. I wouldn't be surprised if lots of other sentences were lifted from other papers.
Response: We apologize that in the manuscript we rephrased some sentences from Beck et al., 2018, and didn’t properly cite them. The part accounts for 2% similarity based on the similarity report. The editor has reviewed the manuscript and agreed that the similarity level is within the acceptable range before the preprint. To make sure that all the work presented is original work, we will make corresponding changes as suggested.
Comment 2: "The highest confidence was given to the most common climate class for each grid cell." Maybe mention that this approach was adopted from Beck et al. (2018).
Response: Thank you for pointing this out. The approach of the confidence level from Beck et al. (2018) is one of the data integration methods that we have tested, which demonstrated the highest accuracy. We will provide the citation of Beck et al. (2018) for the confidence level approach in the manuscript.
Comment 3: "The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods." The "low spatial resolution" argument is of course incorrect as the authors well know. Poor accuracy is also incorrect (the Beck et al., 2018, map has only slightly lower accuracy according to your evaluation). And what are noncomparable time periods? Noncomparable to what? Does it really matter that much whether the climatology represents 1988-2017 or 1980-2016 given the input uncertainty?
Response: Thank you for raising your concerns about the scientific importance of our work. We have summarized the currently available Köppen-Geiger climate map products and found out that most of the existing global Köppen-Geiger climate maps with long-term and continuous temporal coverage have a relatively low resolution of 0.1-0.5o ¬and low accuracy (Grieser et al. 2006; Belda et al. 2014; Kriticos et al. 2012; Rubel und Kottek 2010). Beck et al. (2018) has an unprecedented resolution of 1-km and relatively higher accuracy but is limited in use by its temporal coverage of a single historical period (1980-2016) and a single future period (2071-2000, RCP8.5). Based on this, we concluded that “The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods.” When referring to "non-comparable time periods”, we mean single and not continuous time periods, in contrast with time-series data. We apologize that the phrase is not clear and may cause confusion. We will replace it for clarity and precision.
To address the question about whether the climatology represents 1988-2017 and 1980-2016 provide significant importance considering the input uncertainty, we summarized the findings from previous literature regarding the recent changes of Köppen climates. Based on the literature review, the recent accelerated global warming since the 1980s has led to large-scale shifts in Köppen climates over approximately 5.3–5.7% (7.9–8.5 million km2) of the total land area (Rohli et al. 2015; Belda et al. 2014; Chen und Chen 2013; Chan und Wu 2015; Yoo und Rohli 2016). Statistically significant changes have been observed since 1980 for arid and tundra climates. Many of these studies applied climatology input with a one-year interval. Detection of these interannual and interdecadal changes in climate zones require continuous long-term temporal coverage. The aim of the presented Köppen-Geiger climate map series is to fill the gap. We showed an example of applying the Köppen-Geiger climate map series to estimate the long-term annual trends of area changes for each climate type in 1979-2017 in the manuscript. The results reflect a general pattern of climate shifts into warmer and drier climates. To justify the point that we make, we will revise the manuscript and put more emphasis on the application of Köppen-Geiger map series on change detection.
We agree with the reviewer and have the same concern that the input uncertainty may influence the change detection results. To address the uncertainty issue, we provided the confidence level adopted from Beck et al. 2018 and classification accuracy to quantify the uncertainty. In addition, we tested the sensitivity of methods and data input to improve the accuracy to a higher level compared with all the other map products.
References
Beck, Hylke E.; Zimmermann, Niklaus E.; McVicar, Tim R.; Vergopolan, Noemi; Berg, Alexis; Wood, Eric F. (2018): Present and future Köppen-Geiger climate classification maps at 1-km resolution. In: Scientific data 5, S. 180214. DOI: 10.1038/sdata.2018.214.
Belda, M.; Holtanová, E.; Halenka, T.; Kalvová, J. (2014): Climate classification revisited. From Köppen to Trewartha. In: Clim. Res. 59 (1), S. 1–13. DOI: 10.3354/cr01204.
Chan, Duo; Wu, Qigang (2015): Significant anthropogenic-induced changes of climate classes since 1950. In: Scientific reports 5, S. 13487. DOI: 10.1038/srep13487.
Chen, Deliang; Chen, Hans Weiteng (2013): Using the Köppen classification to quantify climate variation and change. An example for 1901-2010. In: Environmental Development 6, S. 69–79. DOI: 10.1016/j.envdev.2013.03.007.
Grieser, Jürgen; Gommes, Rene; Cofield, Stephen; Bernardi, Michele (2006): New gridded maps of Koeppen’s climate classification.
Kriticos, Darren J.; Webber, Bruce L.; Leriche, Agathe; Ota, Noboru; Macadam, Ian; Bathols, Janice; Scott, John K. (2012): CliMond. Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. In: Methods in Ecology and Evolution 3 (1), S. 53–64. DOI: 10.1111/j.2041-210X.2011.00134.x.
Rohli, Robert V.; Andrew, Joyner T.; Reynolds, Stephen J.; Shaw, Cynthia; Vázquez, Javier R. (2015): Globally Extended Kӧppen–Geiger climate classification and temporal shifts in terrestrial climatic types. In: Physical Geography 36 (2), S. 142–157. DOI: 10.1080/02723646.2015.1016382.
Rubel, Franz; Kottek, Markus (2010): Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. In: metz 19 (2), S. 135–141. DOI: 10.1127/0941-2948/2010/0430.
Yoo, Jinwoong; Rohli, Robert V. (2016): Global distribution of Köppen–Geiger climate types during the Last Glacial Maximum, Mid-Holocene, and present. In: Palaeogeography, Palaeoclimatology, Palaeoecology 446, S. 326–337. DOI: 10.1016/j.palaeo.2015.12.010.Citation: https://doi.org/10.5194/essd-2021-53-AC1
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AC1: 'Reply on RC1', Diyang Cui, 25 Mar 2021
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EC1: 'Comment on essd-2021-53', David Carlson, 26 Mar 2021
When the manuscript arrived I asked for changes and citations to reduce the too-high similarity index. As a reviewer points out, those changes have not eliminated substantial plagarism.. The authors admit that they "rephrased some sentences from Beck et al., 2018, and didn’t properly cite them".
Copeernicus nor ESSD will tolerate this type of similarity / identiy, regardless of language. Niether reviewers nor observers can have confidence in the manuscript as submitted. Please withdraw the paper. Consult the editor if you wish to re-submit in a much-revised form.
Citation: https://doi.org/10.5194/essd-2021-53-EC1
Interactive discussion
Status: closed
-
RC1: 'Plagiarism', Anonymous Referee #1, 25 Mar 2021
I wonder what the editorial policy is for plagiarism, because I am concerned with the degree of copying and rewording in this paper. Below are some examples I found. I wouldn't be surprised if lots of other sentences were lifted from other papers.
Beck et al. (2018): "The Köppen-Geiger climate classification is a highly suitable means to aggregate complex climate gradients into a simple but ecologically meaningful classification scheme."
Cui et al. (2021): "The Köppen climate classification demonstrates the ability to aggregate complex and diverse climate gradients into ecologically meaningful classes and simplify spatial variability."Beck et al. (2018): "This can lead to widespread misclassifications, particularly in regions with a low station density and/or strong climatic gradients such as mountain ranges (Karger et al., 2017)."
Cui et al. (2021): "This eventually led to widespread misclassifications of Köppen climates, particularly in mountainous regions with strong climatic gradients and often low station density (Karger et al., 2017)."Beck et al. (2018): "However, caution should be exerted not to equate those changes directly with changes in actual biomes."
Cui et al. (2021): "However, we should take cautions when relating the changes to changes in actual biome distributions."Beck et al. (2018): "vegetation changes by 2100 may lag the change in climate zones."
Cui et al. (2021): "Vegetation changes may lag the climate changes when climate become less favourable."Beck et al. (2018): "Secondly, factors not accounted for in the Köppen-Geiger classification, such as higher atmospheric CO2 levels, may alter the relationship between climate classes and vegetation."
Cui et al. (2021): "Additionally, other factors not considered in the Köppen classification scheme, such as CO2 or nitrogen levels, may influence the relationship between climate and vegetation.""The highest confidence was given to the most common climate class for each grid cell." Maybe mention that this approach was adopted from Beck et al. (2018).
"The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods." The "low spatial resolution" argument is of course incorrect as the authors well know. Poor accuracy is also incorrect (the Beck et al., 2018, map has only slightly lower accuracy according to your evaluation). And what are noncomparable time periods? Noncomparable to what? Does it really matter that much whether the climatology represents 1988-2017 or 1980-2016 given the input uncertainty?
Citation: https://doi.org/10.5194/essd-2021-53-RC1 -
AC1: 'Reply on RC1', Diyang Cui, 25 Mar 2021
Comment 1: I wonder what the editorial policy is for plagiarism because I am concerned with the degree of copying and rewording in this paper. Below are some examples I found. I wouldn't be surprised if lots of other sentences were lifted from other papers.
Response: We apologize that in the manuscript we rephrased some sentences from Beck et al., 2018, and didn’t properly cite them. The part accounts for 2% similarity based on the similarity report. The editor has reviewed the manuscript and agreed that the similarity level is within the acceptable range before the preprint. To make sure that all the work presented is original work, we will make corresponding changes as suggested.
Comment 2: "The highest confidence was given to the most common climate class for each grid cell." Maybe mention that this approach was adopted from Beck et al. (2018).
Response: Thank you for pointing this out. The approach of the confidence level from Beck et al. (2018) is one of the data integration methods that we have tested, which demonstrated the highest accuracy. We will provide the citation of Beck et al. (2018) for the confidence level approach in the manuscript.
Comment 3: "The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods." The "low spatial resolution" argument is of course incorrect as the authors well know. Poor accuracy is also incorrect (the Beck et al., 2018, map has only slightly lower accuracy according to your evaluation). And what are noncomparable time periods? Noncomparable to what? Does it really matter that much whether the climatology represents 1988-2017 or 1980-2016 given the input uncertainty?
Response: Thank you for raising your concerns about the scientific importance of our work. We have summarized the currently available Köppen-Geiger climate map products and found out that most of the existing global Köppen-Geiger climate maps with long-term and continuous temporal coverage have a relatively low resolution of 0.1-0.5o ¬and low accuracy (Grieser et al. 2006; Belda et al. 2014; Kriticos et al. 2012; Rubel und Kottek 2010). Beck et al. (2018) has an unprecedented resolution of 1-km and relatively higher accuracy but is limited in use by its temporal coverage of a single historical period (1980-2016) and a single future period (2071-2000, RCP8.5). Based on this, we concluded that “The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods.” When referring to "non-comparable time periods”, we mean single and not continuous time periods, in contrast with time-series data. We apologize that the phrase is not clear and may cause confusion. We will replace it for clarity and precision.
To address the question about whether the climatology represents 1988-2017 and 1980-2016 provide significant importance considering the input uncertainty, we summarized the findings from previous literature regarding the recent changes of Köppen climates. Based on the literature review, the recent accelerated global warming since the 1980s has led to large-scale shifts in Köppen climates over approximately 5.3–5.7% (7.9–8.5 million km2) of the total land area (Rohli et al. 2015; Belda et al. 2014; Chen und Chen 2013; Chan und Wu 2015; Yoo und Rohli 2016). Statistically significant changes have been observed since 1980 for arid and tundra climates. Many of these studies applied climatology input with a one-year interval. Detection of these interannual and interdecadal changes in climate zones require continuous long-term temporal coverage. The aim of the presented Köppen-Geiger climate map series is to fill the gap. We showed an example of applying the Köppen-Geiger climate map series to estimate the long-term annual trends of area changes for each climate type in 1979-2017 in the manuscript. The results reflect a general pattern of climate shifts into warmer and drier climates. To justify the point that we make, we will revise the manuscript and put more emphasis on the application of Köppen-Geiger map series on change detection.
We agree with the reviewer and have the same concern that the input uncertainty may influence the change detection results. To address the uncertainty issue, we provided the confidence level adopted from Beck et al. 2018 and classification accuracy to quantify the uncertainty. In addition, we tested the sensitivity of methods and data input to improve the accuracy to a higher level compared with all the other map products.
References
Beck, Hylke E.; Zimmermann, Niklaus E.; McVicar, Tim R.; Vergopolan, Noemi; Berg, Alexis; Wood, Eric F. (2018): Present and future Köppen-Geiger climate classification maps at 1-km resolution. In: Scientific data 5, S. 180214. DOI: 10.1038/sdata.2018.214.
Belda, M.; Holtanová, E.; Halenka, T.; Kalvová, J. (2014): Climate classification revisited. From Köppen to Trewartha. In: Clim. Res. 59 (1), S. 1–13. DOI: 10.3354/cr01204.
Chan, Duo; Wu, Qigang (2015): Significant anthropogenic-induced changes of climate classes since 1950. In: Scientific reports 5, S. 13487. DOI: 10.1038/srep13487.
Chen, Deliang; Chen, Hans Weiteng (2013): Using the Köppen classification to quantify climate variation and change. An example for 1901-2010. In: Environmental Development 6, S. 69–79. DOI: 10.1016/j.envdev.2013.03.007.
Grieser, Jürgen; Gommes, Rene; Cofield, Stephen; Bernardi, Michele (2006): New gridded maps of Koeppen’s climate classification.
Kriticos, Darren J.; Webber, Bruce L.; Leriche, Agathe; Ota, Noboru; Macadam, Ian; Bathols, Janice; Scott, John K. (2012): CliMond. Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. In: Methods in Ecology and Evolution 3 (1), S. 53–64. DOI: 10.1111/j.2041-210X.2011.00134.x.
Rohli, Robert V.; Andrew, Joyner T.; Reynolds, Stephen J.; Shaw, Cynthia; Vázquez, Javier R. (2015): Globally Extended Kӧppen–Geiger climate classification and temporal shifts in terrestrial climatic types. In: Physical Geography 36 (2), S. 142–157. DOI: 10.1080/02723646.2015.1016382.
Rubel, Franz; Kottek, Markus (2010): Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. In: metz 19 (2), S. 135–141. DOI: 10.1127/0941-2948/2010/0430.
Yoo, Jinwoong; Rohli, Robert V. (2016): Global distribution of Köppen–Geiger climate types during the Last Glacial Maximum, Mid-Holocene, and present. In: Palaeogeography, Palaeoclimatology, Palaeoecology 446, S. 326–337. DOI: 10.1016/j.palaeo.2015.12.010.Citation: https://doi.org/10.5194/essd-2021-53-AC1
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AC1: 'Reply on RC1', Diyang Cui, 25 Mar 2021
-
EC1: 'Comment on essd-2021-53', David Carlson, 26 Mar 2021
When the manuscript arrived I asked for changes and citations to reduce the too-high similarity index. As a reviewer points out, those changes have not eliminated substantial plagarism.. The authors admit that they "rephrased some sentences from Beck et al., 2018, and didn’t properly cite them".
Copeernicus nor ESSD will tolerate this type of similarity / identiy, regardless of language. Niether reviewers nor observers can have confidence in the manuscript as submitted. Please withdraw the paper. Consult the editor if you wish to re-submit in a much-revised form.
Citation: https://doi.org/10.5194/essd-2021-53-EC1
Data sets
KGClim future: A 1-km global dataset of future (2020-2100) Köppen-Geiger climate classification and bioclimatic variables Cui, Diyang, Liang, Shunlin, Wang, Dongdong, and Liu, Zheng http://doi.org/10.5281/zenodo.4542076
KGClim historical: A 1-km global dataset of historical (1979-2017) Köppen-Geiger climate classification and bioclimatic variables Cui, Diyang, Liang, Shunlin, Wang, Dongdong, and Liu, Zheng http://doi.org/10.5281/zenodo.4546140
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Cited
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- Southeastern Brazil inland tropicalization: Köppen system applied for detecting climate change throughout 100 years of meteorological observed data C. Alvares et al. 10.1007/s00704-022-04122-4
- Influence of phase change materials on thermal comfort, greenhouse gas emissions, and potential indoor air quality issues across different climatic regions: A critical review P. Amoatey et al. 10.1002/er.8734