Climate change is predicted to lead to major changes in terrestrial ecosystems (Portner et al., 2022). However, significant differences in climate model projections for given scenarios of greenhouse gas emissions (Masson-Delmotte et al., 2021), continue to hinder detailed assessment. Here we show, using a traditional Koppen-Geiger bioclimate classification system (Koppen, 1884), that the latest CMIP6 Earth System Models actually agree very well on the fraction of the global land-surface that will undergo a significant change per degree of global warming. Data from 'historical' and 'ssp585' model runs are used to create bioclimate maps at various degrees of global warming, and to investigate the performance of the ensemble mean when classifying climate data into discrete categories. Using a streamlined Koppen-Geiger scheme with 13 classifications, global bioclimate classification maps at 2K and 4K of global warming above a 1901 -1931 reference period are presented. These projections show large shifts in bioclimate distribution, with an almost exclusive change from colder, wetter bioclimates to hotter, dryer ones. Historical model run performance is assessed and examined by comparison with the bioclimatic classifications derived from the observed climate over the same time period. The fraction (f ) of the land experiencing a change in its bioclimatic class as a function of global warming (T ) is estimated by combining the results from the individual models. Despite the discrete nature of the bioclimatic classification scheme, we find only a weaklysaturating dependence of this fraction on global warming f = 1  e 0.17T , which implies about 12% of land experiencing a significant change in climate, per 1K increase in global mean temperature between the global warming levels of 1 and 3K. Therefore, we estimate that stabilising the climate at 1.5K rather than 2K of global warming, would save over 7 million square kilometres of land from a major bioclimatic change. Understanding the impacts that climate change will have at a regional level yields vital information for adaptation to climate change. Furthermore, quantifying the performance of climate models is important for the continued improvement of climate models, and for understanding the regional areas where particular models under perform. Here we use the Koppen-Geiger (KG) bioclimate classification to examine and quantify changes in biome under various levels of projected future global warming within the phase 6 (CMIP6) climate models. We examine the differences between climate projections in terms of traditional KG classifications which summarise the aspects of regional climates which are most relevant to biomes, and therefore to the impacts of climate change on the natural environment. To remove the zeroth order uncertainty that arises from different climate model sensitivities to radiative forcing (Sherwood et al., 2020;Nijsse et al., 2020), and to make our results relevant to the Paris climate targets, we look specifically at changes in KG classification at different levels of global warming (1.5K, 2K, and 4K). A streamlined KG scheme is also implemented to visually demonstrate the impacts of warming on global biome distribution. Climate classification empirically separates regions of the globe based on climate data. The KG bioclimate classification scheme is one of the most established, first developed by Wladimir Koppen (Koppen, 1884) and then enhanced by Rudolf Geiger. The original KG classification scheme consists of thirty separate bioclimates. These classifications are based on monthly average temperature and precipitation at each location. The seasonality of these variables, combined with threshold values, determines the bioclimate classification of the region (Peel et al., 2007). CMIP6 is an international collaboration to run a standardised set of potential future scenarios with a range of climate models developed at various institutions. The results make a compelling case for the need to further prioritise climate change mitigation policies. However, this may not be immediately clear to public or policy makers. Improved understanding of the consequences of climate change is needed, and climate classification schemes can help in that respect. Bioclimate classification systems, such as the KG and Holdridge schemes (Lugo et al., 1999), have been used to map regions or even the entire globe. These maps have been created using observational (Kottek et al., 2006) as well as climate model data, the latter including CMIP5 (Rahimi et al., 2020) and CMIP6 climate models (Kim and Bae, 2021). Despite the changes and updates suggested by various authors, the classification scheme as originally developed by Koppen, and updated by Geiger is still the most popular climate classification system. The KG system has been applied to a broad spectrum of scientific interests, including to locally adjust an irradiation model (Every et al., 2020), in hydrological studies (Peel et al., 2001), and in modelling the distribution of Lyme disease (Cox et al., 2021). Although bioclimate maps for specific years (such as 2100) have previously been created (Beck et al., 2018), an area that is less explored are global KG climate maps at specific levels of global warming. Here we present KG classification maps at 1.5K, 2K, and 4K of global warming above reference period levels (taken as the 1901 -1931 global mean temperature). Due to the 30 different classifications in the traditional KG scheme, it can be difficult to identify the changes in bioclimate classification, we present a novel "streamlined" classification system that allows for easy identification of bioclimate change, with a naming scheme that is more intuitive. To enhance this, classification change matrices have been produced to quantify the changes displayed in the maps. Furthermore, we utilise the KG system as an exploratory technique for understanding CMIP6 model output, the KG classification scheme has been previously applied to CMIP5 data to to evaluate simulations (Phillips and Bonfils, 2015). By comparing the classifications given by models under the historical experimental run to the known historical observational values, and by assessing model deviation from their initial classifications we gain insight into the performance and behaviours of individual models as well as their ensemble mean. The Koppen-Geiger (KG) classification scheme has been described extensively in other publications (Peel et al., 2007;Beck et al., 2018). The scheme has also undergone many alterations.
Here we follow (Peel et al., 2007), whose criteria for each classification are given in These classifications have three differences to those described by (Koppen, 1936). First, C and D climates follow a 0 C threshold instead of 3 C. Secondly, BW and BS are distinguished using a 70% threshold for precipitation seasonality. Finally, climates C and D subclassifications s and w are made mutually exclusive. In this analysis, each month is set to have the same length of time -one twelfth of a year. Historical observations of monthly mean temperature and precipitation are from the CRU TS v. 4.05 dataset (Harris et al., 2020). Analogous climate model data comes from the 'historical' CMIP6 experiments (Eyring et al., 2016). Six models within the CMIP6 multi-model ensemble were chosen for reasons of data management, these being CanESM5, CanESM5-CanOE, CESM2, CESM2-WACCM, IPSL-CM6A-LR and UKESM1-0-LL. CMIP6 model data is regridded to 0.5 by 0.5 , the same spatial resolution as CRU TS observations. Antarctica is excluded as observations are limited in this region, and no significant changes in bioclimatic classification are expected in this region. Comparison of KG observed classifications with the CMIP6 models simulated classifications is made for the years 1901-2014. To reduce the effect of short-term variability model and observational data is smoothed with a 30 year centred rolling mean. The ability of individual models in the CMIP6 ensemble to simulate KG classifications correctly during the historical period is assessed in two ways: (i) Percentage land area that a model has correctly classified for each year relative to observations. (ii) Percentage change in land area classification at each year compared to the initial mean 1901-1931 classifications. Future KG classification maps under 1.5, 2 and 4K of annual mean global warming above pre-industrial levels were created from the CMIP6 model 'ssp585' 2015-2100 future scenario. We used ssp585 because all models pass 4K under ssp585, which enables us to define changes in bioclimatic 1. Classification criteria for the Koppen-Geiger classification scheme. Tmin = Average temperature of month with lowest average temperature. Tmax = Average temperature of month with highest average temperature. Pmin = Average precipitation of driest month. Pmax = Average precipitation of wettest month. Tavg = Mean annual temperature. Pyear = Mean annual precipitation. Pthresh varies according to the following rules (if 70% of Pyear occurs in winter then Pthresh = 2 x Tavg, if 70% of Pyear occurs in summer then Pthresh = 2 x Tavg + 28, otherwise Pthresh = 2 x Tavg + 14). , Psdry = precipitation of the driest month in summer, Pwdry = precipitation of the driest month in winter, Pswet = precipitation of the wettest month in summer, Pwwet = precipitation of the wettest month in winter. In the northern Hemisphere Summer is defined as AMJJAS and Winter as ONDJFM, the opposite is true for the Southern hemisphere. Due to overlapping criteria, dry (B) climates are prioritised above all others. Temperature is in C and precipitation is cm/month and cm/year. zones consistently for these different levels of global warming. The timing of each warming level is found from the centred 30 year annual mean global surface air temperature above the model's reference temperature, here defined as 1901 -1931. Monthly mean anomalies of precipitation and surface air temperature are calculated relative to this same reference period. Where anomaly corrected fields of precipitation and temperature are used, these are calculated as the sum of the mean observations and an individual model's anomalies at the specified warming level. Ensemble mean KG classification maps are calculated using the ensemble mean of the anomalous temperature and precipitation fields at each warming level. A key goal of bioclimatic classifications is to illustrate climate change in a way that is more intuitive for many people. To this end we designed a simplified Koppen-Geiger scheme which combines classifications to make changes clearer in both scale and the nature of projected transitions. Additionally, the new scheme implements a more traditional naming system. A breakdown of this streamlined system, and the constituent traditional classifications involved in each of the thirteen streamlined classifications is given in Difference maps are also plotted to demonstrate the geographical locations of major transitions between bioclimatic classifications. These difference maps plot the ten most significant transitions globally (by total land area). Classification change matrices are used to quantify bioclimate transitions in terms of global land area, at key levels of global warming. The columns represent the initial classification coverage, and the rows indicate the altered classification distribution. Shading highlights the significance of changes, in terms of the projected change as a fraction of the initial area of a given bioclimatic class. To gain insight into the behaviour of individual models, we create KG maps of individual models and compare them with maps derived from the observed climate. As expected, there is variation in the classification distribution of models and the observational data. For example, desertification in the Amazon is apparent in CanESM5 and CanESM5-CanOE models (Appendix A). This may show that these models have a tendency towards reduced precipitation in the tropics when compared to other models. Another area of disagreement between the models is the change of biome classification in northern Eurasia and America at various levels of global warming. The ensemble mean model state however reduces the effect of individual model discrepancies and compares favourably with observations. In To assess the performance of individual models and their ensemble mean in correctly classifying the bioclimate distribution for a particular year, the percentage land area correctly classified by each model every year is shown in At 4K these areas of classification change represent over 15% of land area. The change in % of total land-area in Figure 5a gives some alarming perspectives, for example, at +4K Arctic Tundra is indicated to cover over 40% less land-area than in in the reference period. At 2K the models already project significant changes to the global distribution of bioclimates; at 4K these changes become even more pronounced.
Figure 6 displays a weakly-saturating increase with global warming, the fraction of land area that experiences a change in classification follows Eq. ( 1): where f is the fraction of land area that experiences a change in bioclimatic classification, T is the global warming relative to the reference period climate, and k is a fitting parameter. The mean response across the models suggests a value of k  0.17 K 1 . For the range of global warming of particular interest to the Paris climate agreement (1 to 3 degrees of warming) the land area experiencing a change in bioclimatic classification is approximately 12% of the global land per Kelvin of global warming. The total land area (neglecting Antarctica) is approximately 146 million square kilometres, so this implies a bioclimatic change for over 17.5 million square kilometres of land per degree of warming between 1K and 3K. This highlights the benefits of keeping global warming to 1.5K as opposed to 2K of warming, as the 0.5K difference represents an additional bioclimatic change for over 7 million square kilometres of land. A quantitative distribution of climate classification changes between global warming levels of 1.5K and 2K can be seen in Appendix b (note that this breakdown uses the streamlined KG system and subsequently will not represent all changes included in Fig. 6). Despite the difference in climate projections for given greenhouse gas emissions, we present strong evidence that climate models agree well on the extent of bioclimatic change the global land-surface will undergo per degree of global warming. The Koppen-Geiger scheme has been used to present the impact of global warming at 1.5K, 2K, and 4K of warming above reference period levels in the form of climate mapsshowing the global distribution of bioclimates, and as graphs and classification change matrices -quantifying the degree of significant climate change for all classifications at various levels of warming. Despite the fact that these bioclimate classifications are fundamentally climate classifiers, they are designed to represent and correlate with biome distribution. In this way the warming maps and classification changes represent tangible shifts in the global distribution of ecosystems, giving insight into the nature of Earth at various levels of warming. This paper also uses the Koppen-Geiger scheme as a method for climate model verification which is relevant to the impacts of climate change on ecosystems. The Koppen-Geiger maps at levels of global warming demonstrate the impact that climate change will have. The transition matrices present an easily interpretable method for understanding and quantifying the scale of all classification changes. The results presented by the maps and matrices predict large changes in global bioclimate distribution, with hotter, drier bioclimates expanding and colder, wetter bioclimates shrinking and moving further towards the poles. The combination of the techniques presented in this paper indicate that the impact of global warming on KG bioclimates is roughly linear for levels of warming between 1 and 3K. We find that 12% of land will experience a significant change in bioclimate per o C of global warming.