Present and future Köppen-Geiger climate classification maps at 1-km resolution Hylke E. Beck Niklaus E. Zimmermann Tim R. McVicar Noemi Vergopolan Alexis Berg Eric F. Wood

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 Present and future Köppen-Geiger climate classification maps at 1-km resolution 2018 Scientific Data 10.1038/sdata.2018.214 beck2018-koppen-geiger-climate-classification-maps beck2018-koppen-geiger-climate-classification-maps.txt, the project's staged copy 90230766e0a15457a38e97e4a98b3c3d8f11e91c cc-by environmental-science

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 102 spans located by searching the stored text for the quote's exact words, allowing any whitespace between them global present-day and projected future Köppen-Geiger climate classifications at 1-km resolution Can multi-source, topographically corrected climate ensembles produce more accurate, uncertainty-qualified present and future global climate-classification maps? new freely available 1-km present and RCP8.5 future Köppen-Geiger maps, confidence layers, classification code, and station-based validation method fine-scale spatial variation and projected change in global Köppen-Geiger climate zones computational-modelcomputational-model mechanismsMODAL ENSEMBLE VOTING assigns each grid cell its most frequent climate class and turns agreement frequency into a confidence layer mechanismsTEMPORAL HOMOGENIZATION aligns input climatologies to 1980–2016 through additive temperature offsets and multiplicative precipitation factors mechanismsANOMALY SUPERPOSITION removes climate-model mean bias by adding temperature offsets and multiplying precipitation factors into observed climatology mechanismsTOPOGRAPHIC CORRECTION AND FINER RESOLUTION improve mountain and gradient representation, raising station-based classification accuracy mechanismsMODEL SPREAD lowers future-map confidence, especially at high latitudes where projected warming varies more among models future output is conditional on the RCP8.5 scenario and the 2071–2100 period the 1-km future grid inherits change signals from climate models with typical 1–2-degree resolution sub-grid heterogeneity in warming or precipitation change can be under- or over-estimated model spatial biases can make anomalies geographically inconsistent with the observed baseline climatology future climate-zone changes should not be equated directly with biome changes vegetation change may lag climate change and elevated CO2 may alter climate–vegetation relationships most processing code other than the classification function is available only on request present-map confidence is generally lower near climate-zone borders and at high latitudes global mapsglobal maps Köppen-Geiger climate classificationKöppen-Geiger climate classification 1-km resolution1-km resolution present-daypresent-day projected future conditionsprojected future conditions ensemble of four high-resolution, topographically-corrected climatic mapsensemble of four high-resolution, topographically-corrected climatic maps ensemble of 32 climate model projectionsensemble of 32 climate model projections scenario RCP8.5scenario RCP8.5 projected climate change anomalyprojected climate change anomaly baseline high-resolution climatic mapsbaseline high-resolution climatic maps confidence levelsconfidence levels ensemble spreadensemble spread classification accuracyclassification accuracy sharp spatial or elevation gradientssharp spatial or elevation gradients species and vegetation distribution modelingspecies and vegetation distribution modeling five main classesfive main classes 30 sub-types30 sub-types threshold valuesthreshold values seasonality of monthly air temperature and precipitationseasonality of monthly air temperature and precipitation biome distributionsbiome distributions climateclimate global vegetation distributionglobal vegetation distribution climate variablesclimate variables species rangesspecies ranges habitat and topographyhabitat and topography topographic effectstopographic effects station densitystation density uncertainty estimatesuncertainty estimates 0 °C threshold0 °C threshold 3 °C threshold3 °C threshold sub-climates s (dry summer)sub-climates s (dry summer) w (dry winter)w (dry winter) WorldClim V1 and V2WorldClim V1 and V2 CHELSA V1.2CHELSA V1.2 CHPclim V1CHPclim V1 bilinear interpolationbilinear interpolation CMIP5 archiveCMIP5 archive anomaly methodanomaly method most common classmost common class confidence mapconfidence map degree of trustdegree of trust GeoTIFF formatGeoTIFF format World Geodetic Reference System 1984World Geodetic Reference System 1984 majority resamplingmajority resampling station observationsstation observations 22,078 stations22,078 stations newly derived high-resolution present-day Köppen-Geiger classificationnewly derived high-resolution present-day Köppen-Geiger classification maps of Kottek et al.maps of Kottek et al. vegetation changesvegetation changes change in climate zoneschange in climate zones changes in actual biomeschanges in actual biomes future Köppen-Geiger classificationfuture Köppen-Geiger classification mean biasesmean biases model spatial biasesmodel spatial biases model-scale mean anomaliesmodel-scale mean anomalies Köppen-Geiger climate classification → threshold valuesThe classification is based on threshold values and seasonality. ensemble of four high-resolution, topographically-corrected climatic maps → present-dayThe four-map climatic ensemble supplies the present-day classification. ensemble of 32 climate model projections → projected future conditionsThe 32-model ensemble supplies the projected future classification. ensemble spread → confidence levelsConfidence levels are calculated from ensemble spread. 1-km resolution → classification accuracyThe high 1-km resolution is identified as a primary reason for greater classification accuracy. confidence map → classification accuracyThe confidence map provides a useful indication of classification accuracy. anomaly method → mean biasesThe anomaly method removes mean biases from climate-model outputs. model spatial biases → projected climate change anomalyModel spatial biases can make projected anomalies geographically inconsistent with the observed baseline. model-scale mean anomalies → 1-km resolutionCoarse model-scale mean anomalies cannot resolve all changes represented on the 1-km output grid. majority resampling → GeoTIFF formatMajority resampling is used to upscale classification rasters distributed as GeoTIFF files. `present-day` means the harmonized 1980–2016 climatological period, not the publication year `future` means 2071–2100 under RCP8.5 and is therefore SCENARIO-CONDITIONAL rather than an unconditional forecast `confidence` is ENSEMBLE AGREEMENT—the frequency of the modal class divided by ensemble size—not a calibrated probability that a class is true `1-km future map` has a 1-km output grid but its change signal comes from climate models typically resolved at 1–2 degrees `climate-zone change` is not the same as biome change because vegetation can lag and CO2 can change the climate–vegetation relationship extends → previous global Köppen-Geiger mapsnote: The work increases resolution, adds topographic correction, confidence layers, and future classes. supports → the superior accuracy of the new present-day mapnote: Station validation reports 80.0% versus 66.1–73.4% for prior maps. supports → confidence maps as indicators of classification reliabilitynote: Correctly classified stations have materially higher mean confidence than misclassified stations. builds-on → Peel et al.'s Köppen-Geiger classification criterianote: The classification rules explicitly follow Peel et al. with stated differences. qualifies → ecological interpretation of future climate-zone mapsnote: The paper warns that climate-zone changes cannot be read directly as biome changes. climate —causes→ global vegetation distributionthe relation itself is separately evidenced · strength of the relation: 1 · note: The source states a major-driver relation without hedging; no increase/reduction sign is implied. · the relation stated: Climate is identified as the major driver of global vegetation distribution.climateglobal vegetation distribution climate variables —causes→ species rangesthe relation itself is separately evidenced · strength of the relation: 0.95 · note: The paper reports the modelling convention as primary-driver evidence; the effect concerns range pattern, not monotonic size. · the relation stated: Climate variables are considered the primary driver explaining species ranges at large spatial extents.climate variablesspecies ranges climate variables —contrasted-with→ habitat and topographythe relation itself is separately evidenced · strength of the relation: 1 · note: The explicit 'while' clause contrasts spatial roles. · the relation stated: Climate variables are primary large-scale drivers, while habitat and topography are smaller-scale modifiers.climate variableshabitat and topography 0 °C threshold —contrasted-with→ 3 °C thresholdthe relation itself is separately evidenced · strength of the relation: 1 · note: The source uses an explicit instead-of contrast. · the relation stated: The method uses a 0 °C threshold instead of a 3 °C threshold.0 °C threshold3 °C threshold sub-climates s (dry summer) —distinct-from→ w (dry winter)the relation itself is separately evidenced · strength of the relation: 1 · note: Mutual exclusion is a categorical distinction. · the relation stated: The s and w sub-climates are made mutually exclusive.sub-climates s (dry summer)w (dry winter) newly derived high-resolution present-day Köppen-Geiger classification —contrasted-with→ maps of Kottek et al.the relation itself is separately evidenced · strength of the relation: 1 · note: The validation presents a direct accuracy comparison. · the relation stated: The new classification achieved 80.0% accuracy while the earlier Kottek map achieved 66.1%.newly derived high-resolution present-day Köppen-Geiger classificationmaps of Kottek et al. vegetation changes —distinct-from→ change in climate zonesthe relation itself is separately evidenced · strength of the relation: 0.8 · note: The distinction is explicit, but the lag is modal. · the relation stated: Vegetation changes may lag changes in climate zones and should not be treated as identical.vegetation changeschange in climate zones future Köppen-Geiger classification —distinct-from→ changes in actual biomesthe relation itself is separately evidenced · strength of the relation: 1 · note: The source explicitly warns against direct equivalence. · the relation stated: Projected climate-class changes should not be equated directly with changes in actual biomes.future Köppen-Geiger classificationchanges in actual biomes

Present and future Köppen-Geiger climate classification maps at 1-km resolution

Abstract

We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980–2016) and for projected future conditions (2071–2100) under climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen .

Background & Summary

The Köppen-Geiger system classifies climate into five main classes and 30 sub-types. The classification is based on threshold values and seasonality of monthly air temperature and precipitation. Considering vegetation as “crystallized, visible climate” 1 , this classification aims to empirically map biome distributions around the world: different regions in a similar class share common vegetation characteristics. The first version of this classification was developed in the late 19th century 2 ; it is still widely used today, for many applications and studies conditioned on differences in climatic regimes, such as ecological modeling or climate change impact assessments 3–8 . This wide use reflects the fact that climate has since long been recognized as the major driver of global vegetation distribution 9–11 . In species distribution models 12 , climate variables are considered the primary driver to explain species ranges at larger spatial extents, while habitat and topography are considered to only be modifiers of plant species distributions at smaller extents 13–15 . The Köppen-Geiger climate classification is a highly suitable means to aggregate complex climate gradients into a simple but ecologically meaningful classification scheme. It is therefore often used as input when analyzing the distribution 4 , 16 , 17 or growth behavior 18 of species, or to set-up dynamic global vegetation models 19 .

Three recent versions of the world maps of the Köppen-Geiger climate classification exist 20–22 . Kottek et al. 20 produced a map (0.5° resolution) based on CRU TS 2.1 23 for temperature and VASClimO V1.1 24 for precipitation. CRU was based on approximately 7000–17,000 stations (depending on the year) and VASClimO on 9343 stations. The Peel et al. map 21 (0.1° resolution) was derived from 4844 air temperature stations and 12,396 precipitation stations. Kriticos et al. 22 produced a map (0.083° resolution) based on WorldClim V1 temperature and precipitation datasets 25 , which are based on 24,542 and 47,554 stations, respectively.

All maps have a relatively low resolution (≥0.1°) and the map of Peel et al. 21 has not been explicitly corrected for topographic effects, which influences air temperature 26 and precipitation 27 in mountainous regions. In addition, the maps of Kottek et al. 20 and Peel et al. 21 are based on a relatively small number of stations. This can lead to widespread misclassifications, particularly in regions with a low station density and/or strong climatic gradients such as mountain ranges 28 . Moreover, since these maps do not include corresponding uncertainty estimates, they may provide users a false sense of confidence.

Here, we present a new and improved Köppen-Geiger climate classification map for the present (1980–2016) with an unprecedented 0.0083° resolution (approximately 1 km at the equator), providing more accurate representation of highly heterogeneous regions ( Fig. 1a ). To maximize the accuracy and assess uncertainties in map classifications, we combine climatic air temperature and precipitation data from multiple independent sources, including WorldClim V1 and V2, CHELSA V1.2, and CHPclim V1 ( Table 1 ). These datasets have all been explicitly corrected for topographic effects and, with the exception of the CHELSA V1.2 temperature dataset, been based on a large number of stations (≥34,542 for precipitation and ≥20,268 for temperature). The use of multiple data sources allows us to provide an estimate of uncertainty in the derived classes. Further, we combine climate change projections from 32 Coupled Model Intercomparison Project phase 5 (CMIP5 29 ) models to map future (2071–2100) climate classes at the same spatial resolution ( Fig. 1b ).

Methods

Köppen-Geiger climate classification

We follow the Köppen-Geiger climate classification as described in Peel et al. 21 , which was also used by Kriticos et al. 22 ( Table 2 ). This classification is identical to that presented by Köppen in 1936 1 with three differences. First, temperate (C) and cold (D) climates are distinguished using a 0 °C threshold instead of a 3 °C threshold, following the suggestion of Russell 30 . Second, the arid (B) sub-climates W (desert) and S (steppe) were identified depending on whether 70% of precipitation occurred in summer or winter. Third, the sub-climates s (dry summer) and w (dry winter) within the C and D climates were made mutually exclusive by assigning s when more precipitation falls in winter than in summer and assigning w otherwise. Note that the tropical (A), temperate (C), cold (D), and polar (E) climates are mutually exclusive but may intersect with the arid (B) class. To account for this, climate type B was given precedence over the other classes.

Climate data

The present Köppen-Geiger classification map was derived from three climatic datasets for air temperature (WorldClim V1 and V2, and CHELSA V1.2) and four climatic datasets for precipitation (WorldClim V1 and V2, CHELSA V1.2, and CHPclim V1; Table 1 ). All datasets have a 0.0083° resolution with the exception of CHPclim V1.2, which has a 0.05° resolution. For consistency CHPclim V1.2 was downscaled to 0.0083° using bilinear interpolation.

The future Köppen-Geiger classification was produced using monthly historical and future air temperature and precipitation data from the CMIP5 archive 29 . For the future scenario, we used Representative Concentration Pathway 8.5 (RCP8.5 31 ). All climate models with data during the 1980–2016 and 2071–2100 periods were used. Data for 1980–2016 was derived by concatenating historical runs (which end in 2005) and future runs (which begin in 2006). For each model, we only considered a single initialization ensemble. In total 32 models had sufficient data and hence were used for deriving the future map ( Table 1 ).

Present-day Köppen-Geiger map

The present-day Köppen-Geiger map ( Fig. 1a ) was derived from an ensemble of high-resolution climatic datasets ( Table 1 ) using the criteria listed in Table 2 . Since the climatic datasets have inconsistent temporal coverages, we first adjusted them to reflect the period 1980–2016. To this end, we calculated, for each climatic dataset, monthly 0.5° climatologies for temperature using CRU TS V4.01 and for precipitation using GPCC FDR V7, for both the 1980–2016 period and the temporal span of the climatic dataset. Next, for each month we calculated climate change offsets (for temperature) or factors (for precipitation) between the two periods, and resampled these offsets or factors from 0.5° to 0.0083° resolution using bilinear interpolation, and adjusted the climatic maps by addition (for temperature) or multiplication (for precipitation).

For each adjusted temperature and precipitation climatic dataset combination, we derived a Köppen-Geiger map at 0.0083° resolution. From this ensemble of 4×3=12 maps we derived a final map by selecting, for each grid-cell, the most common class ( Fig. 1a ). A corresponding confidence map was derived by dividing the frequency of occurrence of the most common class by the ensemble size and converting these fractions to percentages ( Fig. 2a ). For example, if Csa is the most common class for a particular grid-cell, and it has been assigned eight times out of 12, the resulting confidence level is 100 × 8 12 = 66.6 %. This confidence level should be interpreted as the degree of trust we place in our final present-day classification. Confidence levels are generally lower in the vicinity of borders between climate zones, in particular at high latitudes where the climatic data show more uncertainty.

Future Köppen-Geiger map

The future Köppen-Geiger map ( Fig. 1b ) was derived by the so-called “anomaly method” 32 based on an ensemble of climate projections from the 32 CMIP5 models ( Table 1 ). First, observed monthly present-day reference temperature and precipitation climatologies (0.0083° resolution) were derived, by simple averaging of the ensemble of temporally-homogenized, high-resolution climatic maps. Then, for each climate model and each month, we subsequently calculated climate change offsets (for temperature) or factors (for precipitation) between 1980–2016 and 2071–2100 and resampled these offsets or factors from the native model resolution to 0.0083° using bilinear interpolation ( Fig. 3 ). Finally, future high-resolution climatic temperature and precipitation maps were derived from the present-day, observed reference climatologies by addition of the offsets (for temperature) or multiplication by the factors (for precipitation). We want to emphasize that the change factors are never excessively high (i.e., >5; Fig. 3 ), because (i) model simulations tend to overestimate the precipitation frequency 33 (resulting in the near-absence of areas with close to zero precipitation), and (ii) over the majority of arid regions the future projections tend toward drying rather than wetting 34 (resulting in factors <1).

For each climate model, we derived a future Köppen-Geiger map at 0.0083° resolution from the downscaled future temperature and precipitation data. From this ensemble of 32 maps we derived a final map by selecting, for each grid-cell, the most common class ( Fig. 1b ). A corresponding confidence map was derived by dividing the frequency of occurrence of the most common class by the ensemble size and converting these fractions to percentages ( Fig. 2b ). For example, if Cfa is the most common class for particular grid-cell, and it has been assigned 24 times out of 32, the corresponding confidence level is 100 × 24 32 = 75.0 %. This confidence level should be interpreted as the degree of trust we have in our final future classification based on the uncertainties in climate change projections. Thus, uncertainties are larger than for the present-day map. In particular, they are larger at high latitudes because of the greater model spread in projected warming in those regions.

Code availability

The new Köppen-Geiger classifications have been produced using MathWorks MATLAB version R2017a. The function used to classify the temperature and precipitation data according to the criteria listed in Table 2 (KoppenGeiger.m) is freely available via (Data Citation 1) and www.gloh2o.org/koppen . The other codes are available upon request from the first author.

Data Records

The present and future Köppen-Geiger classification maps and the corresponding confidence maps are freely available for download at (Data Citation 1) and www.gloh2o.org/koppen . The maps are stored in GeoTIFF format as unsigned 8-bit integers. We also provide a legend file (legend.txt) linking the numeric values in the maps to the Köppen-Geiger climate symbols and providing the color scheme used for displaying the maps in the current study (adapted from Peel et al. 21 ). The maps are referenced to the World Geodetic Reference System 1984 (WGS 84) ellipsoid and made available at three resolutions (0.0083°, 0.083°, and 0.5°; approximately 1 km, 10 km, and 50 km at the equator, respectively). The classifications are upscaled from 0.0083° to 0.083° and 0.5° using majority resampling and the confidence levels using bilinear averaging. Table 3 presents the file naming convention. The maps can be visualized and analyzed using most Geographic Information Systems (GIS) software (e.g., QGIS, ArcGIS, and GRASS).

Technical Validation

We validated the new high-resolution present-day Köppen-Geiger classification ( Fig. 1a ), and previous maps from Kottek et al. 20 , Peel et al. 21 , and Kriticos et al. 22 , by calculating the classification accuracy (defined as the percentage of correct classes) using station observations as reference. An initial database was compiled from the Global Historical Climatology Network-Daily (GHCN-D) database 35 and the Global Summary Of the Day (GSOD) database ( https://data.noaa.gov ). For each station, we calculated monthly mean temperature and precipitation time series (discarding months with <25 daily values), and subsequently monthly climatologies by averaging the monthly means (if ≥10 values were present). Stations with gaps in the climatologies or missing data for one of the four maps were discarded, resulting in a final dataset comprising 22,078 stations which we used to calculate the classification accuracy of each map.

The newly derived high-resolution present-day Köppen-Geiger classification ( Fig. 1a ) exhibited a classification accuracy of 80.0%, while the maps of Kottek et al. 20 , Peel et al. 21 , and Kriticos et al. 22 exhibited classification accuracies of 66.1, 70.9, and 73.4%, respectively. These results confirm that the new map is more accurate, which is primarily due to its high (1 km) resolution and use of an ensemble of topographically-corrected climatic datasets. The map of Kottek et al. 20 showed the lowest classification accuracy, due to its low (0.5°) resolution. The map of Peel et al. 21 also performed less well, due to a lack of topographic corrections and the use of a relatively small number of stations.

We also tested the usefulness of the confidence map associated with the new present-day classification ( Fig. 2a ) using station observations. We obtained a mean confidence level of 92.6% for the correctly classified stations ( n =17,667) and 77.4% for the misclassified stations ( n =4411). The mean confidence level was thus substantially lower for the misclassified stations, confirming that the confidence map provides a useful indication of the classification accuracy.

Figures 4 and 5 show historic Köppen-Geiger classification maps from all three previous studies and our present-day map for the Alps (Europe) and the central Rocky Mountains (North America), respectively, illustrating the enhanced detail in our map. The other maps sometimes fail to depict important topographic features; the map of Peel et al. 21 , for example, does not represent the Apennine mountains (Italy), due to a lack of topographic corrections ( Fig. 4e ). The new map ( Figs. 4a and 5a ) also exhibits better agreement with a Landsat-based forest cover map 36 (30-m resolution; Figs. 4c and 5c ). The spatial extent of the polar (E) climate, for example, corresponds closely with treelines in the forest cover maps. Additionally, the new present-day and future Köppen-Geiger maps ( Fig. 4a and g , respectively) agree well with equivalent high-resolution maps derived for the Alps 8 (their Figs. 1 and 2 , respectively).

Usage Notes

The future Köppen-Geiger classification ( Fig. 1b ) should be viewed as providing insights into potential spatial changes in regional climatic zones under climate change. However, caution should be exerted not to equate those changes directly with changes in actual biomes. First, vegetation changes by 2100 may lag the change in climate zones. Secondly, factors not accounted for in the Köppen-Geiger classification, such as higher atmospheric CO 2 levels, may alter the relationship between climate classes and vegetation. It is thus advised to interpret the future Köppen-Geiger classification first and foremost from a ‘climatic conditions’ perspective.

The rationale for using the anomaly method to build future maps using climate model projections, instead of directly computing present and future maps from model outputs, is that superimposing future modeled anomalies onto the observed climate removes mean biases from climate model outputs. This is a widely used method in climate change impact assessments 32 . However, an unavoidable limitation of this approach is that because of model spatial biases, modeled climate change anomalies may not be fully geographically consistent with the baseline observed climatology to which they are added 37 (e.g., if the climate of one region in a given model is spatially shifted relative to reality).

Another irredeemable limitation is that because of their coarser resolution (typically 1–2°), climate model outputs do not resolve future climate change at the same scale as our baseline climatology. Thus, in cases where there could be significant heterogeneities in precipitation change and/or warming below the model resolution (e.g., along coastlines and/or in regions with strong land-cover differences and/or elevation gradients), future changes at the 1-km scale might be under- or over-estimated, because only the model-scale mean anomalies are used to compute future changes. High-elevation mountainous regions are a prime example of this because they are expected to experience considerably more warming than adjacent valleys 38 .

Additional information

How to cite this article : Beck, H. E. et al., Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data . 5:180214 doi: 10.1038/sdata.2018.214 (2018).

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