standOff. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote shown on the same row, so the borrowing can be checked against the thing borrowed from without leaving this page. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| contribution | contribution | what the paper claims to add |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| object of study | object_of_study | what the paper is about — the thing it investigates |
| phenomenon | phenomenon | the effect or regularity under study |
| research question | research_question | the question the paper sets itself |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| unrecordable relation | unhoused_relations | a relation the paper asserts that this vocabulary cannot express, with the reason |
The reference list is not part of the text we hold for this paper. Our copy was extracted from the publisher's page and the bibliography did not survive that extraction, so the article below ends before its references. Nothing has been removed by us.
Present and future Köppen-Geiger climate classification maps at 1-km resolution
Abstract
object_of_studycontributionphenomenonconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationsunhoused_relationsWe 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
concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsconcept_edgesconcept_edgesconcept_edgesThe 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.
concepts↗concepts↗concepts↗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.
research_questionclaim_typeclaim_relationsHere, 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
concepts↗concepts↗concepts↗concepts↗claim_relationsconcept_edgesconcept_edgesWe 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
concepts↗concepts↗concepts↗concepts↗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.
limitationsconcepts↗sensesThe 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 . mechanisms↗sensesSince 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).
mechanisms↗limitationsconcepts↗concepts↗concepts↗sensesFor 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
mechanisms↗concepts↗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).
mechanisms↗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
limitationsThe 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
concepts↗concepts↗concepts↗unhoused_relationsThe 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
method↗concepts↗concepts↗concepts↗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.
mechanisms↗concepts↗concepts↗unhoused_relationsclaim_relationsconcept_edgesThe 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.
concepts↗unhoused_relationsclaim_relationsWe 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
limitationslimitationsconcepts↗concepts↗concepts↗concepts↗sensesclaim_relationsconcept_edgesconcept_edgesThe 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.
limitationsconcepts↗concepts↗unhoused_relationsunhoused_relationsThe 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).
limitationslimitationsconcepts↗unhoused_relationssensesAnother 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).
Publisher’s note : Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Material
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 8 of 8 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
This record holds no controls. A layer is empty when the tagger recorded nothing in it — which is not the same as the paper having nothing to record.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | global present-day and projected future Köppen-Geiger climate classifications at 1-km resolution | object_of_study | the paper's words | |
| research_question | Can multi-source, topographically corrected climate ensembles produce more accurate, uncertainty-qualified present and future global climate-classification maps? | research_question | the paper's words | |
| contribution | new freely available 1-km present and RCP8.5 future Köppen-Geiger maps, confidence layers, classification code, and station-based validation | contribution | the paper's words | |
| claim_type | method | claim_type | the paper's words | |
| phenomenon | fine-scale spatial variation and projected change in global Köppen-Geiger climate zones | phenomenon | the paper's words | |
| method | computational-model also in: computational-model | method | the paper's words | |
| mechanisms | mechanisms | mechanisms[0] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[1] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[2] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[3] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[4] | 0.99 | the tagger's words |
| limitations | future output is conditional on the RCP8.5 scenario and the 2071–2100 period | limitations[0] | 0.99 | the tagger's words |
| limitations | the 1-km future grid inherits change signals from climate models with typical 1–2-degree resolution | limitations[1] | 0.99 | the tagger's words |
| limitations | sub-grid heterogeneity in warming or precipitation change can be under- or over-estimated | limitations[2] | 0.99 | the tagger's words |
| limitations | model spatial biases can make anomalies geographically inconsistent with the observed baseline climatology | limitations[3] | 0.99 | the tagger's words |
| limitations | future climate-zone changes should not be equated directly with biome changes | limitations[4] | 0.99 | the tagger's words |
| limitations | vegetation change may lag climate change and elevated CO2 may alter climate–vegetation relationships | limitations[5] | 0.99 | the tagger's words |
| limitations | most processing code other than the classification function is available only on request | limitations[6] | 0.99 | the tagger's words |
| limitations | present-map confidence is generally lower near climate-zone borders and at high latitudes | limitations[7] | 0.99 | the tagger's words |
| concepts | global maps also in: global maps | concepts[0] | 0.99 | the paper's words |
| concepts | Köppen-Geiger climate classification also in: Köppen-Geiger climate classification | concepts[1] | 0.99 | the paper's words |
| concepts | 1-km resolution also in: 1-km resolution | concepts[2] | 0.99 | the paper's words |
| concepts | present-day also in: present-day | concepts[3] | 0.99 | the paper's words |
| concepts | projected future conditions also in: projected future conditions | concepts[4] | 0.99 | the paper's words |
| concepts | ensemble of four high-resolution, topographically-corrected climatic maps | concepts[5] | 0.99 | the paper's words |
| concepts | ensemble of 32 climate model projections | concepts[6] | 0.99 | the paper's words |
| concepts | scenario RCP8.5 also in: scenario RCP8.5 | concepts[7] | 0.99 | the paper's words |
| concepts | projected climate change anomaly also in: projected climate change anomaly | concepts[8] | 0.99 | the paper's words |
| concepts | baseline high-resolution climatic maps | concepts[9] | 0.99 | the paper's words |
| concepts | confidence levels also in: confidence levels | concepts[10] | 0.99 | the paper's words |
| concepts | ensemble spread also in: ensemble spread | concepts[11] | 0.99 | the paper's words |
| concepts | classification accuracy also in: classification accuracy | concepts[12] | 0.99 | the paper's words |
| concepts | sharp spatial or elevation gradients also in: sharp spatial or elevation gradients | concepts[13] | 0.99 | the paper's words |
| concepts | species and vegetation distribution modeling | concepts[14] | 0.99 | the paper's words |
| concepts | five main classes also in: five main classes | concepts[15] | 0.99 | the paper's words |
| concepts | 30 sub-types also in: 30 sub-types | concepts[16] | 0.99 | the paper's words |
| concepts | threshold values also in: threshold values | concepts[17] | 0.99 | the paper's words |
| concepts | seasonality of monthly air temperature and precipitation | concepts[18] | 0.99 | the paper's words |
| concepts | biome distributions also in: biome distributions | concepts[19] | 0.99 | the paper's words |
| concepts | climate also in: climate | concepts[20] | 0.99 | the paper's words |
| concepts | global vegetation distribution also in: global vegetation distribution | concepts[21] | 0.99 | the paper's words |
| concepts | climate variables also in: climate variables | concepts[22] | 0.99 | the paper's words |
| concepts | species ranges also in: species ranges | concepts[23] | 0.99 | the paper's words |
| concepts | habitat and topography also in: habitat and topography | concepts[24] | 0.99 | the paper's words |
| concepts | topographic effects also in: topographic effects | concepts[25] | 0.99 | the paper's words |
| concepts | station density also in: station density | concepts[26] | 0.99 | the paper's words |
| concepts | uncertainty estimates also in: uncertainty estimates | concepts[27] | 0.99 | the paper's words |
| concepts | 0 °C threshold also in: 0 °C threshold | concepts[28] | 0.99 | the paper's words |
| concepts | 3 °C threshold also in: 3 °C threshold | concepts[29] | 0.99 | the paper's words |
| concepts | sub-climates s (dry summer) also in: sub-climates s (dry summer) | concepts[30] | 0.99 | the paper's words |
| concepts | w (dry winter) also in: w (dry winter) | concepts[31] | 0.99 | the paper's words |
| concepts | WorldClim V1 and V2 also in: WorldClim V1 and V2 | concepts[32] | 0.99 | the paper's words |
| concepts | CHELSA V1.2 also in: CHELSA V1.2 | concepts[33] | 0.99 | the paper's words |
| concepts | CHPclim V1 also in: CHPclim V1 | concepts[34] | 0.99 | the paper's words |
| concepts | bilinear interpolation also in: bilinear interpolation | concepts[35] | 0.99 | the paper's words |
| concepts | CMIP5 archive also in: CMIP5 archive | concepts[36] | 0.99 | the paper's words |
| concepts | anomaly method also in: anomaly method | concepts[37] | 0.99 | the paper's words |
| concepts | most common class also in: most common class | concepts[38] | 0.99 | the paper's words |
| concepts | confidence map also in: confidence map | concepts[39] | 0.99 | the paper's words |
| concepts | degree of trust also in: degree of trust | concepts[40] | 0.99 | the paper's words |
| concepts | GeoTIFF format also in: GeoTIFF format | concepts[41] | 0.99 | the paper's words |
| concepts | World Geodetic Reference System 1984 also in: World Geodetic Reference System 1984 | concepts[42] | 0.99 | the paper's words |
| concepts | majority resampling also in: majority resampling | concepts[43] | 0.99 | the paper's words |
| concepts | station observations also in: station observations | concepts[44] | 0.99 | the paper's words |
| concepts | 22,078 stations also in: 22,078 stations | concepts[45] | 0.99 | the paper's words |
| concepts | newly derived high-resolution present-day Köppen-Geiger classification | concepts[46] | 0.99 | the paper's words |
| concepts | maps of Kottek et al. also in: maps of Kottek et al. | concepts[47] | 0.99 | the paper's words |
| concepts | vegetation changes also in: vegetation changes | concepts[48] | 0.99 | the paper's words |
| concepts | change in climate zones also in: change in climate zones | concepts[49] | 0.99 | the paper's words |
| concepts | changes in actual biomes also in: changes in actual biomes | concepts[50] | 0.99 | the paper's words |
| concepts | future Köppen-Geiger classification also in: future Köppen-Geiger classification | concepts[51] | 0.99 | the paper's words |
| concepts | mean biases also in: mean biases | concepts[52] | 0.99 | the paper's words |
| concepts | model spatial biases also in: model spatial biases | concepts[53] | 0.99 | the paper's words |
| concepts | model-scale mean anomalies also in: model-scale mean anomalies | concepts[54] | 0.99 | the paper's words |
| unhoused_relations | Köppen-Geiger climate classification → threshold values The classification is based on threshold values and seasonality. | unhoused_relations[0] | 0.99 | the tagger's words |
| unhoused_relations | ensemble of four high-resolution, topographically-corrected climatic maps → present-day The four-map climatic ensemble supplies the present-day classification. | unhoused_relations[1] | 0.99 | the tagger's words |
| unhoused_relations | ensemble of 32 climate model projections → projected future conditions The 32-model ensemble supplies the projected future classification. | unhoused_relations[2] | 0.99 | the tagger's words |
| unhoused_relations | ensemble spread → confidence levels Confidence levels are calculated from ensemble spread. | unhoused_relations[3] | 0.99 | the tagger's words |
| unhoused_relations | 1-km resolution → classification accuracy The high 1-km resolution is identified as a primary reason for greater classification accuracy. | unhoused_relations[4] | 0.99 | the tagger's words |
| unhoused_relations | confidence map → classification accuracy The confidence map provides a useful indication of classification accuracy. | unhoused_relations[5] | 0.99 | the tagger's words |
| unhoused_relations | anomaly method → mean biases The anomaly method removes mean biases from climate-model outputs. | unhoused_relations[6] | 0.99 | the tagger's words |
| unhoused_relations | model spatial biases → projected climate change anomaly Model spatial biases can make projected anomalies geographically inconsistent with the observed baseline. | unhoused_relations[7] | 0.99 | the tagger's words |
| unhoused_relations | model-scale mean anomalies → 1-km resolution Coarse model-scale mean anomalies cannot resolve all changes represented on the 1-km output grid. | unhoused_relations[8] | 0.99 | the tagger's words |
| unhoused_relations | majority resampling → GeoTIFF format Majority resampling is used to upscale classification rasters distributed as GeoTIFF files. | unhoused_relations[9] | 0.99 | the tagger's words |
| senses | `present-day` means the harmonized 1980–2016 climatological period, not the publication year | senses[0] | 0.99 | the tagger's words |
| senses | `future` means 2071–2100 under RCP8.5 and is therefore SCENARIO-CONDITIONAL rather than an unconditional forecast | senses[1] | 0.99 | the tagger's words |
| senses | `confidence` is ENSEMBLE AGREEMENT—the frequency of the modal class divided by ensemble size—not a calibrated probability that a class is true | senses[2] | 0.99 | the tagger's words |
| senses | `1-km future map` has a 1-km output grid but its change signal comes from climate models typically resolved at 1–2 degrees | senses[3] | 0.99 | the tagger's words |
| senses | `climate-zone change` is not the same as biome change because vegetation can lag and CO2 can change the climate–vegetation relationship | senses[4] | 0.99 | the tagger's words |
| claim_relations | extends → previous global Köppen-Geiger maps note: The work increases resolution, adds topographic correction, confidence layers, and future classes. | claim_relations[0] | 0.99 | the tagger's words |
| claim_relations | supports → the superior accuracy of the new present-day map note: Station validation reports 80.0% versus 66.1–73.4% for prior maps. | claim_relations[1] | 0.99 | the tagger's words |
| claim_relations | supports → confidence maps as indicators of classification reliability note: Correctly classified stations have materially higher mean confidence than misclassified stations. | claim_relations[2] | 0.99 | the tagger's words |
| claim_relations | builds-on → Peel et al.'s Köppen-Geiger classification criteria note: The classification rules explicitly follow Peel et al. with stated differences. | claim_relations[3] | 0.99 | the tagger's words |
| claim_relations | qualifies → ecological interpretation of future climate-zone maps note: The paper warns that climate-zone changes cannot be read directly as biome changes. | claim_relations[4] | 0.99 | the tagger's words |
| concept_edges | climate —causes→ global vegetation distribution the 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. also in: climate · global vegetation distribution | concept_edges[0] | 0.99 | the tagger's words |
| concept_edges | climate variables —causes→ species ranges the 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. also in: climate variables · species ranges | concept_edges[1] | 0.99 | the tagger's words |
| concept_edges | climate variables —contrasted-with→ habitat and topography the 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. also in: climate variables · habitat and topography | concept_edges[2] | 0.99 | the tagger's words |
| concept_edges | 0 °C threshold —contrasted-with→ 3 °C threshold the 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. also in: 0 °C threshold · 3 °C threshold | concept_edges[3] | 0.99 | the tagger's words |
| concept_edges | 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. also in: sub-climates s (dry summer) · w (dry winter) | concept_edges[4] | 0.99 | the tagger's words |
| concept_edges | 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%. | concept_edges[5] | 0.99 | the tagger's words |
| concept_edges | vegetation changes —distinct-from→ change in climate zones the 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. also in: vegetation changes · change in climate zones | concept_edges[6] | 0.99 | the tagger's words |
| concept_edges | future Köppen-Geiger classification —distinct-from→ changes in actual biomes the 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. | concept_edges[7] | 0.99 | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.