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Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%). [ABSTRACT FROM AUTHOR]

Global and regional climate model simulations are frequently used for regional climate change assessments and in climate impact modeling studies. To reflect the inherent and methodological uncertainties in climate modeling, the assessment of regional climate change requires ensemble simulations from different global and regional climate model combinations. To interpret the spread of simulated results, it is useful to understand how the climate change signal is modified in the GCM-RCM modelmodelgeneral circulation model-regional climate model (GCM-RCM) chain. This kind of information can also be useful for impact modelers; for the process of experiment design and when interpreting model results. In this study, we investigate how the simulated historical and future climate of the Max-Planck-Institute earth system model (MPI-ESM) is modified by dynamic downscaling with the regional model REMO in different world regions. The historical climate simulations for 1950–2005 are driven by observed anthropogenic forcing. The climate projections are driven by projected anthropogenic forcing according to different Representative Concentration Pathways (RCPs). The global simulations are downscaled with REMO over the Coordinated Regional Climate Downscaling Experiment (CORDEX) domains Africa, Europe, South America and West Asia from 2006–2100. This unique set of simulations allows for climate type specific analysis across multiple world regions and for multi-scenarios. We used a classification of climate types by Köppen-Trewartha to define evaluation regions with certain climate conditions. A systematic comparison of near-surface temperature and precipitation simulated by the regional and the global model is done. In general, the historical time period is well represented by the GCM and the RCM. Some different biases occur in the RCM compared to the GCM as in the Amazon Basin, northern Africa and the West Asian domain. Both models project similar warming, although somewhat less so by the RCM for certain regions and climate types. A common feature in regions of tropical climate types is that REMO shows dryer climate conditions than forMax Planck Institute for Meteorology-Earth System Model (MPI-ESM) for RCP 4.5 and RCP 8.5, leading to an opposing sign in the climate change signal. With an increase in radiative forcing from RCP 2.6 to RCP 8.5 and towards the end of the 21st century, some of the detected differences between GCM and RCM are more pronounced.

The transferability of the regional climate model REMO with a standard setup over different regions of the world has been evaluated. The study is based on the idea that the modeling parameters and parameterizations in a regional climate model should be robust to adequately simulate the major climatic characteristic of different regions around the globe. If a model is not able to do that, there might be a chance of an “overtuning” to the “home-region”, which means that the model physics are tuned in a way that it might cover some more fundamental errors, e.g., in the dynamics. All simulations carried out in this study contribute to the joint effort by the international regional downscaling community called COordinated Regional climate Downscaling EXperiment (CORDEX). REMO has been integrated over six CORDEX domains forced with the so-called perfect boundary conditions obtained from the global reanalysis dataset ERA-Interim for the period 1989 to 2008. These six domains include Africa, Europe, North America, South America, West Asia and the Mediterranean region. Each of the six simulations was conducted with the identical model setup which allows investigating the transferability of a single model to regions with substantially different climate characteristics. For the consistent evaluation over the different domains, a new evaluation framework is presented by combining the Köppen-Trewartha climate classification with temperature-precipitation relationship plots and a probability density function (PDF) skill score method. The evaluation of the spatial and temporal characteristics of simulated precipitation and temperature, in comparison to observational datasets, shows that REMO is able to simulate the mean annual climatic features over all the domains quite reasonably, but still some biases remain. The regions over the Amazon and near the coast of major upwelling regions have a significant warm bias. Wet and dry biases appear over the mountainous regions and East Africa, respectively. The temperature over South America and precipitation over the tundra and highland climate of West Asia are misrepresented. The probable causes leading to these biases are discussed and ideas for improvements are suggested. The annual cycle of precipitation and temperature of major catchments in each domain are also well represented by REMO. The model has performed well in simulating the inter- and intra-seasonal characteristics of different climate types in different regions. Moreover, the model has a high ability in representing the general characteristics of different climate types as measured by the probability density function (PDF) skill score method. Although REMO seems to perform best over its home domain in Europe (domain of development and testing), the model has simulated quite well the climate characteristics of other regions with the same set of parameterization options. Therefore, these results lead us to the conclusion that REMO is well suited for long-term climate change simulations to examine projected future changes in all these regions.

This study examined shifts in climate regimes over the global land area using the Köppen–Trewartha (K–T) climate classification by analyzing observations during 1900–2010, and simulations during 1900–2100 from twenty global climate models participating in Phase 5 of the Coupled Model Inter-comparison Project (CMIP5). Under the Intergovernmental Panel on Climate Change Representative Concentration Pathways 8.5 (RCP8.5) scenario, the models projected a 3°–10°C warming in annual temperature over the global land area by the end of the twenty-first century, with strong (moderate) warming in the high (middle) latitudes of the Northern Hemisphere and weaker warming in the tropics and the Southern Hemisphere. The projected changes in precipitation vary considerably in space and present greater uncertainties among the models. Overall, the models are consistent in projecting increasing precipitation over the high-latitude of the Northern Hemisphere, and reduced precipitation in the Mediterranean, southwestern North America, northern and southern Africa and Australia. Based on the projected changes in temperature and precipitation, the K–T climate types would shift toward warmer and drier climate types from the current climate distribution. Regions of temperate, tropical and dry climate types are projected to expand, while regions of polar, sub-polar and subtropical climate types are projected to contract. The magnitudes of the projected changes are stronger in the RCP8.5 scenario than the low emission scenario RCP4.5. On average, the climate types in 31.4% and 46.3% of the global land area are projected to change by the end of the twenty-first century under RCP4.5 and RCP8.5 scenarios, respectively. Further analysis suggests that changes in precipitation played a slightly more important role in causing shifts of climate type during the twentieth century. However, the projected changes in temperature play an increasingly important role and dominate shifts in climate type when the warming becomes more pronounced in the twenty-first century. [ABSTRACT FROM AUTHOR]

Global and regional climate model simulations are frequently used for regional climate change assessments and in climate impact modeling studies. To reflect the inherent and methodological uncertainties in climate modeling, the assessment of regional climate change requires ensemble simulations from different global and regional climate model combinations. To interpret the spread of simulated results, it is useful to understand how the climate change signal is modified in the GCM-RCM modelmodelgeneral circulation model-regional climate model (GCM-RCM) chain. This kind of information can also be useful for impact modelers; for the process of experiment design and when interpreting model results. In this study, we investigate how the simulated historical and future climate of the Max-Planck-Institute earth system model (MPI-ESM) is modified by dynamic downscaling with the regional model REMO in different world regions. The historical climate simulations for 1950-2005 are driven by observed anthropogenic forcing. The climate projections are driven by projected anthropogenic forcing according to different Representative Concentration Pathways (RCPs). The global simulations are downscaled with REMO over the Coordinated Regional Climate Downscaling Experiment (CORDEX) domains Africa, Europe, South America and West Asia from 2006-2100. This unique set of simulations allows for climate type specific analysis across multiple world regions and for multi-scenarios. We used a classification of climate types by Köppen-Trewartha to define evaluation regions with certain climate conditions. A systematic comparison of near-surface temperature and precipitation simulated by the regional and the global model is done. In general, the historical time period is well represented by the GCM and the RCM. Some different biases occur in the RCM compared to the GCM as in the Amazon Basin, northern Africa and the West Asian domain. Both models project similar warming, although somewhat less so by the RCM for certain regions and climate types. A common feature in regions of tropical climate types is that REMO shows dryer climate conditions than for Max Planck Institute for Meteorology-Earth System Model (MPI-ESM) for RCP 4.5 and RCP 8.5, leading to an opposing sign in the climate change signal. With an increase in radiative forcing from RCP 2.6 to RCP 8.5 and towards the end of the 21st century, some of the detected differences between GCM and RCM are more pronounced. [ABSTRACT FROM AUTHOR]

The ecosystems in the Arctic region are known to be very sensitive to climate changes. The accelerated warming for the past several decades has profoundly influenced the lives of the native populations and ecosystems in the Arctic. Given that the Köppen-Trewartha (K-T) climate classification is based on reliable variations of land-surface types (especially vegetation), this study used the K-T scheme to evaluate climate changes and their impact on vegetation for the Arctic (north of 50°N) by analyzing observations as well as model simulations for the period 1900-2099. The models include 16 fully coupled global climate models from the Intergovernmental Panel on Climate Change Fourth Assessment. By the end of this century, the annual-mean surface temperature averaged over Arctic land regions is projected to increase by 3.1, 4.6 and 5.3°C under the Special Report on Emissions Scenario (SRES) B1, A1b, and A2 emission scenarios, respectively. Increasing temperature favors a northward expansion of temperate climate (i.e., Dc and Do in the K-T classification) and boreal oceanic climate (i.e., Eo) types into areas previously covered by boreal continental climate (i.e., Ec) and tundra; and tundra into areas occupied by permanent ice. The tundra region is projected to shrink by −1.86 × 10 km (−33.0%) in B1, −2.4 × 10 km (−42.6%) in A1b, and −2.5 × 10 km (−44.2%) in A2 scenarios by the end of this century. The Ec climate type retreats at least 5° poleward of its present location, resulting in −18.9, −30.2, and −37.1% declines in areal coverage under the B1, A1b and A2 scenarios, respectively. The temperate climate types ( Dc and Do) advance and take over the area previously covered by Ec. The area covered by Dc climate expands by 4.61 × 10 km (84.6%) in B1, 6.88 × 10 km (126.4%) in A1b, and 8.16 × 10 km (149.6%) in A2 scenarios. The projected redistributions of K-T climate types also differ regionally. In northern Europe and Alaska, the warming may cause more rapid expansion of temperate climate types. Overall, the climate types in 25, 39.1, and 45% of the entire Arctic region are projected to change by the end of this century under the B1, A1b, and A2 scenarios, respectively. Because the K-T climate classification was constructed on the basis of vegetation types, and each K-T climate type is closely associated with certain prevalent vegetation species, the projected large shift in climate types suggests extensive broad-scale redistribution of prevalent ecoregions in the Arctic. [ABSTRACT FROM AUTHOR]

The transferability of the regional climate model REMO with a standard setup over different regions of the world has been evaluated. The study is based on the idea that the modeling parameters and parameterizations in a regional climate model should be robust to adequately simulate the major climatic characteristic of different regions around the globe. If a model is not able to do that, there might be a chance of an "overtuning" to the "home-region", which means that the model physics are tuned in a way that it might cover some more fundamental errors, e.g., in the dynamics. All simulations carried out in this study contribute to the joint effort by the international regional downscaling community called COordinated Regional climate Downscaling EXperiment (CORDEX). REMO has been integrated over six CORDEX domains forced with the so-called perfect boundary conditions obtained from the global reanalysis dataset ERA-Interim for the period 1989 to 2008. These six domains include Africa, Europe, North America, South America, West Asia and the Mediterranean region. Each of the six simulations was conducted with the identical model setup which allows investigating the transferability of a single model to regions with substantially different climate characteristics. For the consistent evaluation over the different domains, a new evaluation framework is presented by combining the Köppen-Trewartha climate classification with temperature-precipitation relationship plots and a probability density function (PDF) skill score method. The evaluation of the spatial and temporal characteristics of simulated precipitation and temperature, in comparison to observational datasets, shows that REMO is able to simulate the mean annual climatic features over all the domains quite reasonably, but still some biases remain. The regions over the Amazon and near the coast of major upwelling regions have a significant warm bias. Wet and dry biases appear over the mountainous regions and East Africa, respectively. The temperature over South America and precipitation over the tundra and highland climate of West Asia are misrepresented. The probable causes leading to these biases are discussed and ideas for improvements are suggested. The annual cycle of precipitation and temperature of major catchments in each domain are also well represented by REMO. The model has performed well in simulating the inter- and intraseasonal characteristics of different climate types in different regions. Moreover, the model has a high ability in representing the general characteristics of different climate types as measured by the probability density function (PDF) skill score method. Although REMO seems to perform best over its home domain in Europe (domain of development and testing), the model has simulated quite well the climate characteristics of other regions with the same set of parameterization options. Therefore, these results lead us to the conclusion that REMO is well suited for long-term climate change simulations to examine projected future changes in all these regions. [ABSTRACT FROM AUTHOR]