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Warming climate and precipitation changes induce notable shifts in climate zones. In this study, the latest generation of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the previous generation CMIP5 under high‐emission scenarios are used together with observations and applied to the Köppen‐Geiger climate classification. The aim is to shed light on how projected warming and changes in precipitation will influence future climate zones and their associated ecosystems while revealing differences between the two model generations. Compared to CMIP5 models, CMIP6 models exhibit slightly improved performance in replicating the observed Köppen‐Geiger map for the historical (1976–2005) period and similar inter‐model agreement for the future. The models show major changes in climate zones with a range of projections depending on which ensemble subset is used: 37.9%–48.1% of the global land area is projected to change climate zone by the end of the century, with the most pronounced changes expected over Europe (71.4%–88.6%) and North America (51.2%–65.8%). CMIP6 models project a higher rate of areal climate zone change (km2/year) throughout the 21st century, which is mainly driven by their greater global land warming rates. Using a likely equilibrium climate sensitivity subset of CMIP6 models that is consistent with the latest evidence constrains the climate zone shifts, and their projections better match the results of CMIP5 simulations. Although the high warming rates of some CMIP6 models are less credible, the risks associated with them are greater, and they heighten the need for urgent action to preserve terrestrial ecosystems. Plain Language Summary: Köppen‐Geiger climate classification is a tool to map regional climate zones based on a region's temperature and precipitation. In recent decades, increasing temperatures and changing precipitation trends have started to affect the distribution of those climate zones, and more extensive changes are expected throughout the 21st century. In this paper, we assess the observed and expected changes in the global distribution of Köppen‐Geiger climate zones using observations along with the latest and the previous generation of global climate model projections. We find that shifts in climate zones are more pronounced in the latest generation of models due to their questionable greater global warming rate projections. Depending on which subset of model projections is used, up to half of the Earth's land area faces the risk of shifting to a different climate zone by the end of the century, with the greatest changes expected in Europe and North America. The rate of change (affected area per year) is also projected to accelerate through the 21st century, suggesting that vulnerable species and agricultural practices might have less time to adapt to changes in climate zones than previously projected. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibits slightly improved performance in simulating the globally observed historical Köppen‐Geiger climate zones compared to CMIP5By the end of the century, 38%–48% of the global land area is projected to be in a different climate zone than todayThe rate of climate zone change is projected to accelerate, with more pronounced rates in CMIP6 due to considerably higher warming rates [ABSTRACT FROM AUTHOR]

This paper analyzes the ensemble of regional climate model (RCM) projections for Europe completed within the EURO‐CORDEX project. Projections are available for the two greenhouse gas concentration scenarios RCP2.6 (22 members) and RCP8.5 (55 members) at 0.11° resolution from 11 RCMs driven by eight global climate models (GCMs). The RCM ensemble results are compared with the driving CMIP5 global models but also with a subset of available last generation CMIP6 projections. Maximum warming is projected by all ensembles in Northern Europe in winter, along with a maximum precipitation increase there; in summer, maximum warming occurs in the Mediterranean and Southern European regions associated with a maximum precipitation decrease. The CMIP6 ensemble shows the largest signals, both for temperature and precipitation, along with the largest inter‐model spread. There is a high model consensus across the ensembles on an increase of extreme precipitation and drought frequency in the Mediterranean region. Extreme temperature indices show an increase of heat extremes and a decrease of cold extremes, with CMIP6 showing the highest values and EURO‐CORDEX the finest spatial details. This data set of unprecedented size and quality will provide the basis for impact assessment and climate service activities for the European region. Key Points: This paper presents the first of this size regional climate model ensemble to investigate and understand the climate change response over the whole of EuropeThe paper confirms previous findings for mean and extreme climate change but is able to show the added value information of the high‐resolution regional ensembleThe paper assesses the regional and global model consensus in the projection and presents also the uncertainty of the signal [ABSTRACT FROM AUTHOR]

For years, carbon cycle scientists have sought to understand how a warming climate will impact the decay of soil carbon. However, there have been relatively few investigations of how soils will warm in comparison to the atmosphere. Three recent papers in American Geophysical Union journals have looked at expected soil warming by synthesizing predictions in the Climate Model Intercomparison Project 5 (CMIP5). Collectively, these papers show that soil warming will keep pace with air warming except where snow and ice occur, and that the magnitude of soil warming in northern latitudes is uncertain due to model variability in snow and ice extent. They also show that a considerable portion of anthropogenic warming is being stored in deep soils, and that in comparison to observations, soil heat storage is underrepresented by land models. These studies highlight how important continued investigations of soil climatology are to understanding carbon cycling and Earth energy balance. Key Points: Three recent papers examined soil warming predictions over the 21st centuryThey show air warming is a good proxy for soil warming, except at high latitudesMore efforts to measure and model soil temperatures will broadly benefit the biogeosciences and climate change science [ABSTRACT FROM AUTHOR]

Antarctic sea ice trends have to date been linked to surface winds, through sea ice motion and atmospheric thermal advection. This paper analyzes sea ice volume in 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) model configurations under pre‐industrial and historical climate forcings, to compare the relative importance of ice motion and thermodynamic processes. We find that the models' responses to historical forcings is dependent on their sea ice motion formulation; models with low‐magnitude sea ice motion tend to have historical trends that are dominated by thermodynamic processes, while sea ice models with higher‐magnitude motion have more spatially variable relative contributions from dynamic and thermodynamic processes. Trends at the sea ice edge during the season of sea ice advance are generally dominated by dynamic processes, whereas during retreat thermodynamic trends dominate. The models show more disagreement in the sea ice interior. This analysis highlights the different estimates and patterns of sea ice volume among global climate models and offers insight into the drivers of sea ice volume change as well as the subsequent implications for simulated atmosphere‐sea ice‐ocean interactions. Plain Language Summary: Increasing observed Antarctic sea ice is thought to be largely driven by surface winds, through driving sea ice motion and advecting warm air southward to melt ice in some regions. However, global climate models do not reproduce observed sea ice trends. This paper analyzes the sea ice volume budget in 10 climate models to see how much simulated sea ice change is driven by dynamic processes such as wind‐driven sea ice motion, and how much is due to thermodynamic freeze and melt processes. We find that the models generally agree when it comes to mean sea ice volume change, with dynamic processes dominating the sea ice edge and thermodynamic processes dominating in the interior of the sea ice pack. However, the models disagree about trends of sea ice volume. In the sea ice interior, each process dominates approximately half the models in all sea ice sectors, and thermodynamic processes tend to dominate in models with low‐magnitude average sea ice motion. These results provide insight into the estimates and patterns of Antarctic sea ice in global climate models, helping to understand the disparity between simulated and observed sea ice trends. Key Points: Thermodynamic drivers tend to dominate trends of Antarctic sea ice volume in models with low‐magnitude mean sea ice mass transportDrivers of sea ice volume trends in models with higher‐magnitude mean sea ice mass transport have large spatial and seasonal variabilityBiases in mean Antarctic sea ice concentration are unrelated to the dominant processes driving sea ice volume trends [ABSTRACT FROM AUTHOR]

Based on the Coupled Model Intercomparison Project Phase 5 multimodel simulations, this paper investigates the future changes in extreme wet‐bulb temperatures (TWs) and extreme dry‐bulb temperatures (Ts) globally under both low‐emission and high‐emission scenarios (Representative Concentration Pathways of 4.5 [RCP4.5] and 8.5 [RCP8.5]). Under a warming climate, T and TW exhibit a higher absolute increase in their mean values over the mid–high latitudes while they show higher relative increases in their mean values and variability over tropics. Humid and dry heat events which consist of consecutive extreme TWs and Ts show higher occurrences over tropics, governed by the joint increases in mean values and variability of TW and T therein. Humid heat events show intensifications to dry heat events with higher frequency, duration, and intensity, driven by the lower variability of TW than T. In the mid–high latitudes, heat events will start earlier and last longer in warm season under RCP8.5 than RCP4.5, motivating the need for global warming mitigation. Plain Language Summary: The adverse impacts of high temperatures and high humidity on health and economics are worse than extreme temperatures alone. Wet‐bulb temperature (TW) combining temperature (T) and humidity is a useful index for humid heat. We investigate future changes in extreme TWs and Ts based on a set of simulations under low‐emission and high‐emission scenarios. In a warmer climate, both extreme TWs and Ts show apparent absolute increases over the mid–high latitudes but higher increases in mean values and variability in relative terms over tropics. Governed by the joint impacts of mean values and variability of TWs and Ts, humid and dry heat events featuring consecutive days of extreme TWs and Ts show higher occurrences in the tropics. Future humid heat events are more frequent, intense, and longer lasting than dry heat events as TW is more sensitive to global warming than T. Both humid and dry heat events start earlier and last longer under the high‐emission than low‐emission scenario. Key Points: Extreme high wet‐bulb temperature is more sensitive to the changing climate than extreme high dry‐bulb temperatureFuture heat events have relatively higher occurrences in the tropics, and humid heat events are substantially intensified than dry heatThe intensification of humid heat events is associated with the projected increases in specific humidity in the warmer climate [ABSTRACT FROM AUTHOR]

This paper has examined the relative significance of uncertainty in future climate projections from a subset of the coupled model intercomparison project phase 5 (CMIP5) global climate models for the Prairie Provinces of western Canada. This was undertaken by determining: (a) the contribution of model and scenario uncertainty and natural variability to the total variance of these future projections, and (b) the timing of climate signal emergence from the background noise of natural climate variability. We examined future projections of mean temperature, precipitation and summer climate moisture index (CMI). In this region, natural climate variability plays an important role in future uncertainty until the end of this century, particularly for precipitation and to a lesser extent, summer CMI. Model uncertainty also contributes to total uncertainty for these variables throughout this century, while scenario uncertainty becomes more important towards the end of the century. For the region as a whole, significant climate change (i.e., signal/noise >2) occurs earliest for summer mean temperature, with median time of emergence around 2035 for the RCP8.5 radiative forcing scenario. Although the median precipitation signal emerges from the noise (i.e., signal/noise >1) around the 2070s in winter and the 2080s in spring, significant values do not occur in any season for this variable before 2100. For summer CMI, the median time of emergence for significant change is around 2085. At the grid scale, signal‐to‐noise ratios are significant for all seasons for mean surface air temperature, with earliest times of emergence occurring in summer. In contrast, the summer precipitation signal is not significant this century; for summer CMI, significant values are obtained in the eastern half of the region, occurring from about 2065 onwards. Median times of emergence are towards the end of the century for summer CMI in western Saskatchewan and in Alberta, although some areas of Alberta do not exhibit significant signals this century. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the variability and its temporal‐scale components of daily air temperature and precipitation simulated by two regional climate models (RCMs) and two driving global climate models (GCMs) from the EURO‐CORDEX and CMIP5 projects. The analysis was performed for eight geographical areas of central Europe for the period 1971–2000. After a brief evaluation of total variance, we focus on the ability of the simulations to represent short‐term, seasonal and long‐term variability components. This analysis was carried out for original time series as well as for series with the annual cycle removed. A fast Fourier transformation and a Kolmogorov–Zurbenko filter were used to separate the three temporal components. The two methods of time series decomposition generally agree, which confirms the robustness of the results. Although the observed total variability of daily air temperature and precipitation is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. Regarding the air temperature, the results for the time series with the annual cycle removed are generally somewhat worse than for original series. The simulations tend to underestimate the short‐term component of temperature variability, while the results for the other two components are inconclusive. Moreover, it was found that temperature variability caused by the annual cycle is overestimated by the simulation of HadGEM2 GCM and CCLM4‐8‐17 RCM driven by HadGEM2, which explains the relatively poor results of these simulations regarding air temperature total variance and short‐term as well as seasonal components in the original temperature series. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. Although the observed total variability of daily air temperature and precipitation over selected European areas is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. In the figure, location of the eight studied areas situated in the vicinity of central Europe and the orography of the E‐OBS data set. [ABSTRACT FROM AUTHOR]

Over the last decades, general circulation model (GCM) simulations have been regularly evaluated in terms of their ability to reproduce the historical frequency of significantly recurrent weather patterns (WP) observed at the regional scale. Thus, a good simulation of the frequency of these particular WP by the GCM is generally conditioning the good representation of the regional statistics of surface variables such as temperature and precipitation. In this paper, the seasonal frequency of eight particular WP have been calculated using the daily geopotential height fields simulated by an ensemble of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM. These WP are known as significantly influencing the French regional hydro‐climatology in terms of both frequency of low‐flows and high‐precipitation events. Four different bias correction methods have been applied on the simulated geopotential height fields before the calculation of the seasonal WP frequencies. The GCM ensemble showed overall good performances in terms of the simulation of WP seasonal frequencies. The application of a spatially and temporally non‐homogenous correction of simulated geopotential height fields improved significantly the simulation of WP frequencies for the four seasons. Finally, the evolution of the WP frequencies over the next century has been quantified. Three WP (WP2, WP4 and WP8) have pronounced seasonal changes, with WP2 and WP4 being less frequent in summer and autumn seasons, respectively, while WP8 being more frequent over spring, summer and autumn seasons. The strong simulated frequency evolution of WP2 and WP8 is an interesting result, which predicts the climate to be drier with time for France. Thus, WP2 (western oceanic circulation), grouping rainy days over the northern France region, is simulated as less frequent in future summers, while WP8 (anticyclonic situations), which groups non‐rainy days over France, is simulated as more frequent in future summers. [ABSTRACT FROM AUTHOR]

Watershed-scale hydrologic simulation models generally require climate data inputs including precipitation and temperature. These climate inputs can be derived from downscaled global climate simulations which have the potential to drive runoff forecasts at the scale of local watersheds. While a simulation designed to drive a local watershed model would ideally be constructed at an appropriate scale, global climate simulations are, by definition, arbitrarily determined large rectangular spatial grids. This paper addresses the technical challenge of making climate simulation model results readily available in the form of downscaled datasets that can be used for watershed scale models. Specifically, we present the development and deployment of a new Coupled Model Intercomparison Project phase 5 ( CMIP5) based database which has been prepared through a scaling and weighted averaging process for use at the level of U.S. Geological Survey (USGS) Hydrologic Unit Code (HUC)-8 watersheds. The resulting dataset includes 2,106 virtual observation sites (watershed centroids) each with 698 associated time series datasets representing average monthly temperature and precipitation between 1950 and 2099 based on 234 unique climate model simulations. The new dataset is deployed on a HydroServer and distributed using WaterOneFlow web services in the WaterML format. These methods can be adapted for downscaled General Circulation Model (GCM) results for specific drainage areas smaller than HUC-8. Two example use cases for the dataset also are presented. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper studies how the anthropogenic-induced global warming affects the East Asian winter monsoon ( EAWM) by using 26 Coupled Model Intercomparison Project phase 5 ( CMIP5) models. The simulated present-day EAWM is evaluated and future projections under RCP4.5 and RCP8.5 scenarios are presented in terms of the climatology and interannual variability. All 26 models can well reproduce the spatial pattern of EAWM climatology and 16 out of the 26 models can reasonably capture major features of interannual variability. The projection made by 26-model ensemble mean indicate that winter surface air temperature averaged over 20°-60°N, 100°-140°E will increase by 3 °C in RCP4.5 and 5.5 °C in RCP8.5 towards the end of the 21st century. The corresponding regional mean precipitation will increase by 12.3% in RCP4.5 and 21.8% in RCP8.5. The strong warming over high-latitude North Pacific due to melting sea ice in the Bering Sea and Okhotsk Sea leads to significant intensification and a northward shift of the Aleutian Low, resulting in prominent increase in the low-level northerly along the coastal regions of northeastern Asia. At 500 hPa the characteristic East Asian Trough is projected to weaken slightly and tilt more eastward with latitude. The selected 16-model ensemble mean projects future enhanced interannual variability of surface air temperature and sea-level pressure over mid-latitude North Pacific and high-latitude East Asia ( EA) and reduced variability over eastern China, suggesting that the EAWM will be more variable in the high-latitude EA and mid-latitude North Pacific but less variable in East China. Accordingly, the year-to-year precipitation variability will be significantly enhanced over high-latitude EA. Majority of the 26 models project that the leading mode of EAWM interannual variation (the 'northern mode') will become more dominant in the warmer climate. [ABSTRACT FROM AUTHOR]

One of the major globally relevant systematic biases in previous generations of climate models has been an equatorward bias in the latitude of the Southern Hemisphere (SH) mid‐latitude tropospheric eddy driven westerly jet. The far‐reaching implications of this for Southern Ocean heat and carbon uptake and Antarctic land and sea ice are key reasons why addressing this bias is a high priority. It is therefore of primary importance to evaluate the representation of the SH westerly jet in the latest generation of global climate and earth system models that comprise the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this paper we assess the representation of major indices of SH extratropical atmospheric circulation in CMIP6 by comparison against both observations and the previous generation of CMIP5 models. Indices assessed are the latitude and speed of the westerly jet, variability of the Southern Annular Mode (SAM), and representation of the Amundsen Sea Low (ASL). These are calculated from the historical forcing simulations of both CMIP5 and CMIP6 for time periods matching available observational and reanalysis data sets. From the 39 CMIP6 models available at the time of writing there is an overall reduction in the equatorward bias of the annual mean westerly jet from 1.9° in CMIP5 to 0.4° in CMIP6 and from a seasonal perspective the reduction is clearest in austral spring and summer. This is accompanied by a halving of the bias of SAM decorrelation timescales compared to CMIP5. However, no such overall improvements are evident for the ASL. Plain Language Summary: Computer models that simulate the position, strength, and spatio‐temporal behavior of winds in the Southern Hemisphere around the continent of Antarctica often show typical errors when compared to reality. This can impact answers to very relevant questions, such as how much heat and carbon are taken up by the ocean or how the sea ice cover will evolve in the future. Here we document how the newly available next generation of global climate models that form the basis for the next Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) performs with respect to observed Southern Hemisphere winds. We also analyze potential improvements compared to the previous generation of computer models. Overall, some important differences to observations (biases) are much smaller than in the previous models (by up to 50%). Other diagnostics are, however, virtually unchanged, which indicates that the improvements are rather limited between model generations. However, our study could help to identify possible reasons for the remaining biases and to further reduce errors in upcoming models. Key Points: Representation of Southern Hemisphere mid‐latitude tropospheric westerlies is improved in the Coupled Model Intercomparison Project Phase 6Key improvements are a reduced equatorward bias in jet latitude and more realistic timescales of variabilityThere is no clear improvement in the representation of the Amundsen Sea Low [ABSTRACT FROM AUTHOR]

The Mediterranean basin and Northern Africa are projected to be among the most vulnerable areas to climate change. This research documents, analyzes, and synthesizes the projected changes in precipitation P, evapotranspiration E, net water supply from the atmosphere to the surface P–E, and surface soil moisture over these regions as simulated by 17 global climate models from the sixth exercise of the Coupled Model Intercomparison Project (CMIP6) under two Shared Socioeconomic Pathways, SSP2‐4.5, and SSP5‐8.5. It also explores the sensitivity of the results to the chosen climate scenario and model resolution and assesses how the projections have evolved from the fifth exercise (CMIP5). Models project a statistically robust drying over the entire Mediterranean and coastal North Africa. Over the Northern Mediterranean sector, a significant precipitation decrease reaching −0.4 ∓ 0.1 mm day−1 is projected during the 21st century under the SSP5‐8.5 scenario. Conversely, a significant increase in precipitation of +0.05 to 0.3 ∓ 0.1 mm day−1 is projected over South‐Eastern Sahara under the same scenario. Evapotranspiration and soil moisture exhibit decreasing trends over the Mediterranean basin and an increase over the Sahara for both SSPs, with a notable acceleration from the 2020s. As a result, P‐E is projected to decrease at a rate of about −0.3 mm day−1 under the high‐end scenario SSP5‐8.5 over the Mediterranean whilst no significant changes are expected over the Sahara due to evapotranspiration compensation effects. CMIP6 and CMIP5 models project qualitatively similar patterns of changes but CMIP6 models exhibit more intense changes over the Mediterranean basin and South‐Eastern Sahara, especially during winter. [ABSTRACT FROM AUTHOR]

The new Max-Planck-Institute Earth System Model (MPI-ESM) is used in the Coupled Model Intercomparison Project phase 5 (CMIP5) in a series of climate change experiments for either idealized CO2-only forcing or forcings based on observations and the Representative Concentration Pathway (RCP) scenarios. The paper gives an overview of the model configurations, experiments related forcings, and initialization procedures and presents results for the simulated changes in climate and carbon cycle. It is found that the climate feedback depends on the global warming and possibly the forcing history. The global warming from climatological 1850 conditions to 2080-2100 ranges from 1.5°C under the RCP2.6 scenario to 4.4°C under the RCP8.5 scenario. Over this range, the patterns of temperature and precipitation change are nearly independent of the global warming. The model shows a tendency to reduce the ocean heat uptake efficiency toward a warmer climate, and hence acceleration in warming in the later years. The precipitation sensitivity can be as high as 2.5% K−1 if the CO2 concentration is constant, or as small as 1.6% K−1, if the CO2 concentration is increasing. The oceanic uptake of anthropogenic carbon increases over time in all scenarios, being smallest in the experiment forced by RCP2.6 and largest in that for RCP8.5. The land also serves as a net carbon sink in all scenarios, predominantly in boreal regions. The strong tropical carbon sources found in the RCP2.6 and RCP8.5 experiments are almost absent in the RCP4.5 experiment, which can be explained by reforestation in the RCP4.5 scenario. [ABSTRACT FROM AUTHOR]

Ocean biogeochemistry is a novel standard component of fifth phase of the Coupled Model Intercomparison Project (CMIP5) experiments which project future climate change caused by anthropogenic emissions of greenhouse gases. Of particular interest here is the evolution of the oceanic sink of carbon and the oceanic contribution to the climate-carbon cycle feedback loop. The Hamburg ocean carbon cycle model (HAMOCC), a component of the Max Planck Institute for Meteorology Earth system model (MPI-ESM), is employed to address these challenges. In this paper we describe the version of HAMOCC used in the CMIP5 experiments (HAMOCC 5.2) and its implementation in the MPI-ESM to provide a documentation and basis for future CMIP5-related studies. Modeled present day distributions of biogeochemical variables calculated in two different horizontal resolutions compare fairly well with observations. Statistical metrics indicate that the model performs better at the ocean surface and worse in the ocean interior. There is a tendency for improvements in the higher resolution model configuration in representing deeper ocean variables; however, there is little to no improvement at the ocean surface. An experiment with interactive carbon cycle driven by emissions of CO2 produces a 25% higher variability in the oceanic carbon uptake over the historical period than the same model forced by prescribed atmospheric CO2 concentrations. Furthermore, a climate warming of 3.5 K projected at atmospheric CO2 concentration of four times the preindustrial value, reduced the atmosphere-ocean CO2 flux by 1 GtC yr−1. Overall, the model shows consistent results in different configurations, being suitable for the type of simulations required within the CMIP5 experimental design. [ABSTRACT FROM AUTHOR]

The present paper addresses the origin of natural variability arising internally from the climate system of the global carbon cycle at centennial time scales. The investigation is based on the Max Planck Institute for Meteorology, Coupled Model Intercomparison Project Phase 5 (MPI-MCMIP5) preindustrial control simulations with the MPI Earth System Model in low resolution (MPI-ESM-LR) supplemented by additional simulations conducted for further analysis. The simulations show a distinct low-frequency component in the global terrestrial carbon content that induces atmospheric CO2 variations on centennial time scales of up to 3 ppm. The main drivers for these variations are low-frequency fluctuations in net primary production (NPP) of the land biosphere. The signal arises from small regions scattered across the whole globe with a pronounced source in North America. The main reason for the global NPP fluctuations is found in climatic changes leading to long-term variations in leaf area index, which largely determines the strength of photosynthetic carbon assimilation. The underlying climatic changes encompass several spatial diverse climatic alterations. For the particular case of North America, the carbon storage changes are (besides NPP) also dependent on soil respiration. This second mechanism is strongly connected to low-frequency variations in incoming shortwave radiation at the surface. [ABSTRACT FROM AUTHOR]

The ECHAM6 atmospheric general circulation model is the atmosphere component of the Max Planck Institute Earth System Model (MPI-ESM) that is used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. As ECHAM6 has its uppermost layer centered at 0.01 hPa in the upper mesosphere, these simulations offer the opportunity to study the middle atmosphere climate change and its relation to the troposphere on the basis of a very comprehensive set of state-of-the-art model simulations. The goals of this paper are (a) to introduce those new features of ECHAM6 particularly relevant for the middle atmosphere, including external forcing data, and (b) to evaluate the simulated middle atmosphere and describe the simulated response to natural and anthropogenic forcings. New features in ECHAM6 with respect to ECHAM5 include a new short-wave radiation scheme, the option to vary spectral irradiance independent of total solar irradiance, and a latitude-dependent gravity-wave source strength. The description of external forcing data focuses on solar irradiance and ozone. Stratospheric temperature trends simulated with the MPI-ESM for the last decades of the 20th century agree well with observations. The future projections depend strongly on the scenario. Under the high emission scenario RCP8.5, simulated temperatures are locally lower by more than 20 K than preindustrial values. Many of the simulated patterns of the responses to natural forcings as provided by solar variability, volcanic aerosols, and El Niño-Southern Oscillation, largely agree with the observations. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper analyses the influence of different types of El Niño-Southern Oscillation ( ENSO) events on South American precipitation under a climate change scenario. We use two sub-selections of Coupled Model Intercomparison Project Phase 5 (CMIP5) models that have a good representation of teleconnections to the region, albeit with reduced magnitude anomalies. Comparing historical simulations and the RCP 8.5 scenario experiments, after removing the trend in the mean due climate change, the sub-ensemble means show similar global sea surface temperature anomalies and South American precipitation anomaly patterns in the future. No robust changes in the pattern or magnitude of precipitation anomalies are found, despite significant ENSO-related shifts in the Pacific basin, as highlighted in other studies. However, if the models are analysed individually, some show relatively strong changes in ENSO-related precipitation anomalies for both canonical and Modoki events. We show that, in individual models, tropical teleconnection changes associated with ENSO are primarily driven by changes in the magnitude of ENSO-related circulation but with no agreement on the sign. Increases in background moisture, which have been hypothesized to drive an intensification of ENSO-related precipitation anomalies, are of secondary importance. [ABSTRACT FROM AUTHOR]

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models. Plain Language Summary: Given the serious hazards of extreme temperature events, understanding changes in extreme temperatures and effectively predicting their future changes are critical to climate mitigation and adaptation. According to station observations and model data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6), we find besides the varying temperature mean, the varying temperature variance also has an important impact on changes in extreme temperatures, and CMIP6 models have overall better performance than CMIP5 models in reproducing changes in extreme temperatures in China. Our results also emphasize that good performances in historical simulation cannot guarantee better projections in the future and projections of extreme temperatures from the multimodel ensemble sacrifice the spatial heterogeneity relative to individual reference models. Key Points: The impact of the varying natural variability on changes in temperature extremes depends on the variability indexCMIP6 models perform better than CMIP5 in simulating temperature extremes changes in ChinaMultimodel ensemble projections of extreme temperatures sacrifice spatial heterogeneity [ABSTRACT FROM AUTHOR]

The Regional Atmospheric Modelling System (RAMS) driven by data from the CMIP5 Earth System Model HadGEM2‐ES was used to simulate daily precipitation over the tropical Americas for both current and future climate, including distinct scenarios (representative concentration pathways 4.5 and 8.5) and different time horizons (short‐term, mid‐term and long‐term changes). The major objective was to evaluate possible future changes in extreme events, with emphasis on the intensity of precipitation events and the duration of wet and dry spells. According to RAMS, longer dry spells are expected over most regions of the tropical Americas in the future, with indications for Northeast Brazil, Caribbean, Northern Amazon, and shorter wet spells over Central America and Amazon. With the exception of the Caribbean, there is a general tendency towards the increased frequency of intense precipitation in the tropical Americas. [ABSTRACT FROM AUTHOR]

Extreme temperatures were considered natural hazards because they could increase morbidity and mortality. Understanding the extreme temperature changes at different warming levels is crucial to climate change mitigation and adoption for human health. This study projected climate change effects on the intensity, occurrence, and duration of extreme temperatures in Taiwan with 2 and 4°C global warming scenarios using the Weather Research and Forecasting model. The future climate simulations were conducted with the pseudo‐global warming approach, and the future climate changes were obtained from the ensemble mean of simulations in the Coupled Model Intercomparison Project 5. The simulated daily mean temperature increased by 1.40 and 3.09°C under 2 and 4°C global warmings. In a warming world, the daily maximum temperature was projected to increase by 1.35–3.00°C, whereas the daily minimum temperature was even higher, leading to weaker diurnal temperature variation in most regions. The simulation results show that intensified heatwaves with frequent and prolonged durations become par for the course, whereas extremely cold days disappear gradually. The occurrence of heatwaves in the future is projected to be five times that in the current climate. Comparing the global warming impacts over different land‐use types, the heatwave occurrence over urban areas rose more quickly than over other land‐use types; forests are less vulnerable to global warming. On the contrary, the changes in extremely cold days over urban areas were weaker than over other land‐use types. Overall, the effects of global warming on temperature revealed that extreme events were more severe with increased temperature than with the mean state of air temperature. Nonlinear behaviours indicated that global warming should be limited to 2°C, and the additional 2°C warming (from 2 to 4°C) should be addressed carefully. [ABSTRACT FROM AUTHOR]

The overall performance of the simulated seasonal precipitation response to local terrestrial forcings, namely vegetation abundance and soil moisture, in the Sahel among the Coupled Model Intercomparison Project Phase Five (CMIP5) Earth System Models (ESMs) is systematically investigated and compared with its observational counterpart using a multivariate statistical method. The observed seasonal precipitation response is evaluated against a large ensemble of observational, reanalysis, and satellite data sets to provide quantification of uncertainties. The behaviour of models with and without a Dynamic Global Vegetation Model (DGVM) component is also explored, along with the mechanisms responsible for terrestrial feedback on rainfall. In general, the CMIP5 models can reasonably capture the seasonal evolution of Sahel precipitation and soil moisture, albeit with wet biases during the pre‐monsoon period and dry biases during the peak monsoon period. The non‐DGVM ESMs simulate comparable leaf area indices (LAIs) with observations, while DGVM‐enabled ESMs simulate too much year‐round LAI. The variance of precipitation that is attributed to oceanic forcings in CMIP5 is comparable with observations; however, the variance of precipitation that is attributed to terrestrial forcings is smaller in CMIP5 models than observed, especially for non‐DGVM ESMs. CMIP5 models, especially those without DGVMs, undervalue precipitation's observed response strength to soil moisture anomalies. In both observations and CMIP5 models, none of the atmospheric variables show significant responses to direct vegetation forcing, except for the response in transpiration. Although vegetation has minimal direct effect on the atmospheric state, it can affect the atmosphere by modifying soil moisture and transpiration rate indirectly, which helps explain the more realistic simulation of rainfall in DGVM‐enabled ESMs than non‐DGVM ESMs. Coupling of an ESM to a DGVM is critical in generating reasonable land–atmosphere feedback and examining future ecological and climatic changes over the Sahel. [ABSTRACT FROM AUTHOR]

We evaluate the performance of climate models from the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6) in simulating the weather regimes over East Asia during the period from 1961 to 2005. The circulation patterns, frequencies and transition probabilities of the weather regimes are obtained by categorizing the daily 500‐hPa geopotential height (Z500) anomalies using the k‐means clustering algorithm. We identify four winter (WWR1‐4) and three summer weather regimes (SWR1‐3) in both reanalysis datasets and CMIP5/6 models. Significant increasing (declining) frequency trends in WWR3 (WWR4) and SWR2 (SWR1) are found in the ERA5 and NCEP/NCAR reanalysis datasets. On the whole, both generations of models demonstrate reasonable abilities to reproduce the circulation patterns of weather regimes based on the Z500 anomaly fields but overall underestimate the intensities of weather regime patterns, underestimate the occurrence frequencies of WWR3, SWR2, and SWR3, and overestimate the frequencies of WWR1, WWR2, WWR4, and SWR1. In terms of both the ensemble‐mean and pairwise comparisons, the CMIP6 models generally perform better than their CMIP5 counterparts in reproducing the circulation pattern intensities, occurrence frequencies during the whole period and the linear frequency trends and transition probabilities of the weather regimes both in winter and summer. Further analysis shows a highly positive correlation between the total frequency and transition performances but a lower correlation between the biases in the total frequency and intensity of weather regime patterns. The improved skill of CMIP6 over the CMIP5 models in reproducing the weather regimes over East Asia may be partly related to the increased vertical atmospheric resolution in the model. [ABSTRACT FROM AUTHOR]

Emergent constraints on carbon cycle feedbacks in response to warming and increasing atmospheric CO2 concentration have previously been identified in Earth system models participating in the Coupled Model Intercomparison Project (CMIP) Phase 5. Here, we examine whether two of these emergent constraints also hold for CMIP6. The spread of the sensitivity of tropical land carbon uptake to tropical warming in an idealized simulation with a 1% per year increase of atmospheric CO2 shows only a slight decrease in CMIP6 (−52 ± 35 GtC/K) compared to CMIP5 (−49 ± 40 GtC/K). For both model generations, the observed interannual variability in the growth rate of atmospheric CO2 yields a consistent emergent constraint on the sensitivity of tropical land carbon uptake with a constrained range of −37 ± 14 GtC/K for the combined ensemble (i.e., a reduction of ∼30% in the best estimate and 60% in the uncertainty range relative to the multimodel mean of the combined ensemble). A further emergent constraint is based on a relationship between CO2 fertilization and the historical increase in the CO2 seasonal cycle amplitude in high latitudes. However, this emergent constraint is not evident in CMIP6. This is in part because the historical increase in the amplitude of the CO2 seasonal cycle is more accurately simulated in CMIP6, such that the models are all now close to the observational constraint. Plain Language Summary: The statistical model of so‐called emergent constraints help to better understand the sensitivity of Earth system processes in a changing climate. Here, we analyze the robustness of two previously found emergent constraints on carbon cycle feedbacks, using models from the Coupled Model Intercomparison Project (CMIP) of Phases 5 and 6. First the decrease of carbon storage in the tropics due to increasing near‐surface air temperatures, which is found to be robust on the choise of model ensemble. Giving a constraint estimate of −52 ± 35 GtC/K for CMIP6 models, being within the range of uncertainty for the previously estimated result for CMIP5. Second, the increase of carbon storage in high latitudes due to CO2 fertilization effect, which is found to be not evident among CMIP6 models. This is in part because the historical increase in the amplitude of the CO2 seasonal cycle is more accurately simulated in CMIP6, such that the models are all now close to the observational constraint. Key Points: An emergent constraint on the sensitivity of tropical land carbon to global warming, originally derived from Coupled Model Intercomparison Project Phase 5 (CMIP5), also holds for CMIP6The combined CMIP5 + CMIP6 ensemble gives an emergent constraint on the sensitivity of tropical land carbon to global warming of −37 ± 14 GtC/KAn emergent constraint on the fertilization feedback due to rising CO2 levels, previously derived, is not evident in CMIP6 [ABSTRACT FROM AUTHOR]

Historical simulations and projections of climate extremes indices of precipitation and temperature were analysed over South America until the end of the 21st century through 31 general circulation models (GCMs) under four Representative Concentration Pathways. Simulations were compared with reanalysis data, and a Bayesian inference method was used to assess the uncertainties involved in the multi‐model climate projections. Regarding the precipitation extremes indices, the GCMs' simulations reasonably approached the reanalysis data, but with heterogeneous biases, both in sign and in the location of the highest values. The temperature extremes indices presented the smallest biases when compared to precipitation. Projections show a gradual growth of precipitation extremes events as the analysed radiative forcing scenario increases, both in magnitude and extent, over a large part of South America. Projections also indicate a decrease in cold days and nights and an increase in warm days and nights, more pronounced in the equatorial region. Bayesian inference method smoothed changes in precipitation extremes events, both in magnitude and extent, compared to the simple GCMs' ensemble mean. There was no considerable variation in the temperature indices when applying the Bayesian inference. Finally, the probability density functions resulted in a predominance of multimodal and wide curves for the precipitation indices, showing great uncertainties in the GCMs' results, differently from those for the temperature indices, where the GCMs presented good agreement represented through unimodal and narrow curves. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on the hydrology and soil thermal regime of 10 sub‐arctic watersheds (northern Manitoba, Canada) using the Variable Infiltration Capacity (VIC) model. We utilize statistically downscaled and bias‐corrected forcing datasets based on 17 general circulation model (GCM) ‐ representative concentration pathways (RCPs) scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to run the VIC model for three 30‐year periods: a historical baseline (1981–2010: 1990s), and future projections (2021–2050: 2030s and 2041–2070: 2050s), under RCPs 4.5 and 8.5. Future warming increases the average soil column temperature by ~2.2°C in the 2050s and further analyses of soil temperature trends at three different depths show the most pronounced warming in the top soil layer (1.6°C 30‐year−1 in the 2050s). Trend estimates of mean annual frozen soil moisture fraction in the soil column show considerable changes from 0.02 30‐year−1 (1990s) to −0.11 30‐year−1 (2050s) across the study area. Soil column water residence time decreases significantly (by 5 years) during the 2050s when compared with the 1990s as soil thawing intensifies the infiltration process thereby contributing to faster conversion to baseflow. Future warming results in 40%–50% more baseflow by the 2050s, where it increases substantially by 19.7% and 46.3% during the 2030s and 2050s, respectively. These results provide crucial information on the potential future impacts of warming soil temperatures on the hydrology of sub‐arctic watersheds in north‐central Canada and similar hydro‐climatic regimes. [ABSTRACT FROM AUTHOR]

It is now well established that global to local climate is impacted by volcanic forcing associated with major eruptions. Much attention has been given to the way in which climate is affected by such eruptions, particularly for the Northern Hemisphere, and to a considerably lesser extent for the Southern Hemisphere. Research knowledge gaps remain on such forcing impacts at finer sub‐regional spatial scales. To this end, we explore temperature responses across eight sub‐regions of Australia following three major volcanic eruptions (i.e., Krakatau, 1883; Santa Maria, 1902; Pinatubo, 1991) as simulated by CMIP5 models. We then compare such responses with station and reanalysis datasets. Model outputs indicate strongest temperature responses over more northerly regions than southerly regions of Australia, with weakest responses over Tasmania. Eastern regions of Australia seem to have strongest seasonal cooling during austral autumn, while that for northwestern coastal regions is during austral winter. In contrast, central regions of Australia cool most substantially during austral summer and/or winter, depending on the eruption. Despite such variability, initial temperature responses occur during the warmer austral months (September to February). Uncertainties exist in the reliability of CMIP5 data. For instance, temperature responses from the later Pinatubo eruption seem in stronger agreement with reanalysis data than earlier eruptions (Krakatau and Santa Maria), and while most seasonal temperature responses are stronger than those provided through reanalysis data, they are often weaker in austral spring. [ABSTRACT FROM AUTHOR]

A warmer climate impacts streamflows and these changes need to be quantified to assess future risk, vulnerability, and to implement efficient adaptation measures. The climate simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which have been the basis of most such assessments over the past decade, are being gradually superseded by the more recent Coupled Model Intercomparison Project Phase 6 (CMIP6). Our study portrays the added value of the CMIP6 ensemble over CMIP5 in a first North America wide comparison using 3,107 catchments. Results show a reduced spread of the CMIP6 ensemble compared to the CMIP5 ensemble for temperature and precipitation projections. In terms of flow indicators, the CMIP6 driven hydrological projections result in a smaller spread of future mean and high flow values, except for mountainous areas. Overall, we assess that the CMIP6 ensemble provides a narrower band of uncertainty of future climate projections, bringing more confidence for hydrological impact studies. Plain Language Summary: Greenhouse gas emissions are causing the climate to warm significantly, which in turn impacts flows in rivers worldwide. To adapt to these changes, it is essential to quantify them and assess future risk and vulnerability. Climate models are the primary tools used to achieve this. The main data set that provides scientists with state‐of‐the‐art climate model simulations is known as the Coupled Model Intercomparison Project (CMIP). The fifth phase of that project (CMIP5) has been used over the past decade in multiple hydrological studies to assess the impacts of climate change on streamflow. The more recent sixth phase (CMIP6) has started to generate projections, which brings the following question: is it necessary to update the hydrological impact studies performed using CMIP5 with the new CMIP6 models? To answer this question, a comparison between CMIP5 and CMIP6 using 3,107 catchments over North America was conducted. Results show that there is less spread in temperature and precipitation projections for CMIP6. This translates into a smaller spread of future mean, high and low flow values, except for mountainous areas. Overall, we assess that using the CMIP6 data set would provide a more concerted range of future climate projections, leading to more confident hydrological impact studies. Key Points: A comparison of hydrological impacts using Coupled Model Intercomparison Project version 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles is performed over 3,107 catchments in North AmericaThe CMIP6 ensembles provide a narrower band of uncertainty for hydrological indicators in the futureIt is recommended to update hydrological impact studies performed using CMIP5 with the CMIP6 ensemble [ABSTRACT FROM AUTHOR]

Water resources systems in arid regions are sensitive to climate change, which critically impacts the water cycle. In this study, we applied historical hydrometeorological data, CMIP5 data, and a large‐scale hydrological model (Community Land Model–Distributed Time‐Variant Gain Model [CLM–DTVGM]) to study the impact of climate change on the hydrological cycle (i.e., runoff, actual evapotranspiration (ETa), and terrestrial water storage [TWS]) under different scenarios at the Aksu River basin (ARB) located in the arid region of northwest China. The primary findings of this study: (a) As the determination coefficient (R2) and Nash–Sutcliffe efficiency coefficient (NSE) reached desirable levels (R2 ≥ 0.583, NSE ≥ 0.371, and root‐mean‐square error ≤ 155.727), the CLM–DTVGM achieved a better simulation of runoff in the ARB; under climate change, the runoff depth in the irrigation area became shallower and followed a decreasing trend, with a minimum depth of 0.5 mm and a significant decreasing trend of −8 mm·a−1. (b) Along with changes in the precipitation and temperature of the baseline period (i.e., 1980–2010), runoff, ETa, and TWS were predicted to change significantly in future scenarios. (c) Climate change significantly impacted the historical runoff from the Shaliguilanke and Xiehela hydrological stations, with correlation coefficients larger than 0.9; however, the runoff from the Alaer hydrological station was not affected. However, runoff, ETa, and TWS in the ARB were very closely correlated with climate change in the future scenarios, with correlation coefficients exceeding 0.9. Related research in the future would be important for guiding the sustainable development of the ARB. [ABSTRACT FROM AUTHOR]

The global climate models (GCMs) performances of the recently released Coupled Model Intercomparison Project phase 6 (CMIP6) compared to its predecessor, CMIP5, are evaluated to anticipate the expected changes in climate over Egypt. Thirteen GCMs and their multi‐model ensemble (MME) of both CMIPs were used for this purpose. The future projections were compared for two radiative concentration pathways (RCP 4.5 and 8.5) and two shared socio‐economic pathways (SSP 2–4.5 and 5–8.5). The results revealed improvement in most CMIP6 models in replicating historical rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) climatology over Egypt. The MME of the CMIPs revealed that both could reproduce Egypt's spatial distribution and seasonal climate variability. However, the bias in CMIP5 was higher than that for CMIP6. The uncertainties in simulating seasonal variability of rainfall and temperatures were lower for CMIP6 compared to CMIP5. The future projection of rainfall using CMIP6 MME revealed a higher reduction of precipitation (10–26 mm) in the economically crucial northern region than that estimated using CMIP5 (0–17 mm), compared to 133.5 mm rainfall in the base period. CMIP6 also projected a 0.74–1.63°C more rise in Tmax and Tmin compared to CMIP5 by the end of the century. The study indicates more aggravated scenarios of climate changes in Egypt than anticipated earlier, using the CMIP5 models. Therefore, Egypt needs to streamline the existing adaptation and mitigation measures to account for climate projections. [ABSTRACT FROM AUTHOR]

The strong multidecadal variability in North Atlantic (NA) winter atmospheric circulation is poorly understood and appears too weak in climate models. Recent research has shown peak atmospheric multidecadal variability over the NA in late winter, particularly March, linked to Atlantic multidecadal variability (AMV) of the ocean. Here a range of NA atmospheric circulation indices are assessed to provide a comprehensive picture of early‐to‐late winter low‐frequency variability and its representation in the latest generation of climate models (Coupled Model Intercomparison Project Phase 6 [CMIP6]). As found for CMIP5, CMIP6 models exhibit too‐weak multidecadal NA atmospheric variability compared to reanalysis data over the period 1862–2005. Consistent with previous research, the eastern part of the NA westerly jet (U700NA) exhibits peak low‐frequency variability in March. However, for NA‐wide jet speed and the NAO, low‐frequency variability and model‐reanalysis discrepancies are strongest in January and February, associated with too‐weak NA ocean‐atmosphere linkages. Plain Language Summary: The strong low‐frequency multidecadal variability of large‐scale atmospheric circulation in the North Atlantic (NA) drives variability in climate conditions over western Europe. Reliably reproducing this variability in climate models is of high importance for estimating future climatic conditions. However, a current issue in climate modeling is that models generally exhibit weak winter multidecadal variability compared to estimates based on observationally constrained reconstructions (reanalyses). In particular recent research has shown that strong low‐frequency atmospheric variability and associated discrepancies between models and reanalyses are most pronounced in late winter, particularly March. The first step in this study is to establish that the current generation of climate models still exhibit the long‐standing issue that simulated multidecadal variability of NA atmospheric circulation is weaker than in reanalyses. A subseasonal analysis shows that, consistent with previous research, the eastern part of the NA westerly jet exhibits peak low‐frequency variability in March. However, for NA‐wide indices, tropospheric westerly jet speed and the NAO, low‐frequency variability is strongest in January and February. Evidence is shown suggesting that these inter‐index differences in subseasonal variability are associated with differences in monthly correlations with NA subpolar gyre SSTs. Key Points: Climate models exhibit systematically weak winter multidecadal North Atlantic (NA) atmospheric variability relative to reanalysis outputThe subseasonal timing of 20th century winter variability is sensitive to the choice of atmospheric circulation indexOnset of strong variability occurs earliest for NA jet speed (December), followed by NAO (January) and eastern‐NA jet speed (February) [ABSTRACT FROM AUTHOR]

There is low confidence in projections of Antarctic sea ice area (SIA), due to deficiencies in climate model sea ice processes. Ensemble regression techniques can help to reduce this uncertainty. We investigate relationships between SIA climatology and 21st century change in the Coupled Model Intercomparison Project, phase 6 (CMIP6) multi‐model ensemble. In summer, under a strong forcing scenario, each model loses the majority of its sea ice. Therefore, models with greater historical SIA exhibit greater reductions, so the observed climatology of SIA strongly constrains projections. Ensemble spread in historical summer SIA is smaller than in CMIP phase 5 (CMIP5), and CMIP6 gives a more robust constraint on future SIA. In winter, by 2100 under a strong forcing scenario, 40% of SIA disappears on average, and ensemble spread in historical mean SIA explains approximately half the spread in projected change. A greater winter ice loss in CMIP6 than CMIP5 is explained by the higher climate sensitivities of some CMIP6 models. Plain Language Summary: Antarctic sea ice area is important for ecosystems, human activity, and for the dynamics of the atmosphere and ocean. However, major climate change assessments such as the Intergovernmental Panel on Climate Change Sixth Assessment Report have placed little confidence in projections of Antarctic sea ice area. This is because climate models struggle to simulate many aspects of sea ice area as observed by satellites. However, not all models should be treated equally. Models with relatively more sea ice area in the recent past project greater losses in the future, especially in summer. This suggests that models with historical sea ice area closest to what we have observed may be more reliable in their future simulations. We use this information in a simple statistical model to show that, firstly, the newest climate models largely show near‐total sea ice loss in summer by the end of the 21st century, which was not universally true for older climate models. Secondly, newer climate models lose more winter sea ice than their predecessors in the same time period, which is related to the known greater global warming response to given greenhouse gas concentrations in the newer models. Key Points: We compare Coupled Model Intercomparison Project (CMIP), phase 5 and the CMIP, phase 6 (CMIP6) projections of Antarctic sea ice loss, assessing drivers of uncertainty and the robustness of projectionsUnder strong forcing, CMIP6 models lose most Antarctic summer sea ice by 2100, so projections are constrained by historical climatologyIncreased winter sea ice loss in CMIP6 is related to increased equilibrium climate sensitivity [ABSTRACT FROM AUTHOR]

Convective aggregation refers to the clustering of convective events and occurs on a wide range of spatial scales. It has been suggested that the behavior of convective aggregation may change under global warming, with potential implications for future changes in precipitation extremes. Here, convective regions of the tropics are defined from a percentile threshold on gridded daily precipitation data and used to quantify large‐scale convective aggregation in an ensemble of global climate models. Applying three separate indices for aggregation, it is found that large‐scale convective aggregation increases in 17 of the 19 analyzed models under future warming. However, aggregation is found not to be correlated with tropical‐mean precipitation extremes, either climatologically or with respect to the sensitivity to warming. The large model spread in aggregation indices across the ensemble suggests the possible utility of large‐scale convective aggregation as a target for model evaluation. Plain Language Summary: Rainfall in the tropics is not evenly distributed, rather it occurs in clusters of clouds of various sizes and shapes, from a line of thunderstorms to "superclusters" spanning thousands of km. The size and spatial distribution of these clusters are hypothesized to influence the frequency and intensity of heavy rainfall in the tropics. In this study, we develop a method for quantifying the amount of clustering in the tropics, and we investigate whether it is projected to change in the future in a suite of state‐of‐the‐art climate models. Almost all the models project increases in clustering in the future, but, surprisingly, a model's projection of clustering does not seem to affect its projection of changes to heavy rainfall. The results suggest that effects other than clustering play a dominant role in determining the range of heavy rainfall outcomes projected by climate models. Key Points: According to three separate metrics, large‐scale convective aggregation increases with warming in 17 of 19 global climate models (GCMs) examinedThere is substantial spread in large‐scale convective aggregation behavior among the GCMs consideredNo statistically significant inter‐model relationship between degree of aggregation and tropical‐mean precipitation extremes was found [ABSTRACT FROM AUTHOR]

During the period 2018–2020, Europe experienced a series of hot and dry weather conditions with significant socioeconomic and environmental consequences. Yet, the extremity of these multi‐year dry conditions is not recognized. Here, we provide a comprehensive spatio‐temporal assessment of the drought hazard over Europe by benchmarking past exceptional events during the period from 1766 to 2020. We identified the 2018–2020 drought event as a new benchmark having an unprecedented intensity that persisted for more than 2 years, exhibiting a mean areal coverage of 35.6% and an average duration of 12.2 months. What makes this event truly exceptional compared with past events is its near‐surface air temperature anomaly reaching +2.8 K, which constitutes a further evidence that the ongoing global warming is exacerbating present drought events. Furthermore, future events based on climate model simulations Coupled Model Intercomparison Project v5 suggest that Europe should be prepared for events of comparable intensity as the 2018–2020 event but with durations longer than any of those experienced in the last 250 years. Our study thus emphasizes the urgent need for adaption and mitigation strategies to cope with such multi‐year drought events across Europe. Plain Language Summary: This manuscript demonstrates that the 2018–2020 multi‐year drought event constitutes a new benchmark in Europe, with an unprecedented level of intensity over the past 250 years. What makes this event truly exceptional compared with past events is its temperature anomaly reaching +2.8 K. This finding provides new evidence that the ongoing global warming exacerbates current drought events. The key message of this study is that the projected future events across the European continent will have a comparable intensity as the 2018–2020 drought but exhibit considerably longer durations than any of those observed during the last 250 years. Our analysis also shows that these exceptional temperature‐enhanced droughts significantly negatively impact commodity crops across Europe. Key Points: The 2018–2020 multi‐year drought shows unprecedented level of intensity during the past 250 yearsThe 2018–2020 event reached record‐breaking +2.8 K temperature anomaly and negatively impacted major cropsFuture drought events reach comparable intensity of 2018–2020 but with considerably longer durations [ABSTRACT FROM AUTHOR]

Even if the thresholds set out in the Paris Agreement are reached, several impacts on the climate system are still inevitable, generating new vulnerabilities and amplifying those that already exist. In this sense, the objective of this work was to analyse the impact of a global warming of 1.5 and 2°C in the regional climatological patterns of the near‐surface air temperature and precipitation over South America, locating the most affected regions and briefly discussing the possible impacts to be faced on biodiversity and agriculture. The simulations and projections of 26 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 were used, forced in four different Representative Concentration Pathways (RCPs) scenarios. Projections for 1.5 or 2°C global warming indicate a local increase even higher, of at least +0.5°C, in almost the entire South American continent. Regarding precipitation, a similar pattern was also found between the two thresholds of global warming. GCMs project an increase of about 100 mm year−1 in the southern region of Brazil and in the northern portion of the Brazilian Northeast, in northern Argentina, Uruguay, and parts of Peru, Ecuador, Colombia and Venezuela; while the areas between the south of Chile and Argentina and the extreme north of South America show reductions of up to 150 mm year−1. In addition, both the temperature and precipitation patterns were practically similar between the four analysed RCPs scenarios. The results presented in this study indicate that even if the Paris Agreement is very successful, South America will still suffer several impacts and will need to take effective adaptation measures in the short term. This may have pervasive implications for the biodiversity and genetic resource base of the subcontinent, as well as may impair agricultural productivity or incur into considerable adaptation costs for the sector. [ABSTRACT FROM AUTHOR]

Performance of six models in the Coupled Model Intercomparison Project phase 5 (CMIP5) and their new versions in CMIP phase 6 (CMIP6) in representing the climatological (1976–2005) precipitation extremes over China were evaluated based on five precipitation indices. Improvements are found in CMIP6 models in simulating the climatology of all five indices, in which GFDL‐CM4 and GFDL‐ESM4 show significant improvement. Dry biases over South China (SC) are reduced in five CMIP6 models (BCC‐CSM, CanESM, GFDL‐CM, GFDL‐ESM, and IPSL‐CM), with the largest decreased root‐mean‐square error (RMSE) of 59.2% in GFDL‐CM. The reduced dry biases in CMIP6 can be attributed to more moisture transported into SC from the southern boundary and dynamic processes of the atmosphere except in BCC‐CSM, where the increased evaporation dominates. Additionally, increased heavy precipitation events (>20 mm·day−1) are produced over SC in CMIP6 models. Wet biases over West China (WC) are also reduced. with the largest reduced RMSE of 46.8% in GFDL‐CM, which are related to the reduced precipitation frequency (more than 40%) and weakened precipitation intensity. In addition, the CMIP6 models show a higher skill in simulating the frequency distribution of daily precipitation intensity. More heavy precipitation over SC and Northeast China, and fewer weak precipitation (<20 mm·day−1) over WC can be reasonably reproduced. Although the CMIP6 models have obviously improved in simulating total precipitation on wet days, wet days (WD), simple daily intensity index (SDII), and extreme precipitation amount (R95T), the bias still exists in simulating consecutive dry days (CDD). [ABSTRACT FROM AUTHOR]

Decadal experiments' output of the coupled model inter‐comparison project phase‐5 (CMIP5) contains significant model drift. For practical use of CMIP5 decadal climate variables, it is necessary to correct this model drift. In previous studies, drift correction of CMIP5 decadal data of temperature and temperature‐based climate indices have been investigated, but there is no study that investigated the drift correction of decadal precipitation at a catchment scale. This study investigates the practical use of CMIP5 decadal precipitation data on seasonal scale using different drift correction alternatives for Brisbane catchment in Australia. For this, decadal monthly precipitation data from eight GCMs (EC‐EARTH, MRI‐CGCM3, MPI‐ESM‐LR, MPI‐ESM‐MR, MIROC4h, MIROC5 and CanCM4) were cut for the Australian region. By using the bi‐linear interpolation, the GCM data were re‐gridded to 0.05° × 0.05° spatial resolution matching with the observed gridded precipitation data collected from the Australian Bureau of Meteorology. Both model and observed data were subset for the Brisbane catchment and aggregated into seasonal means for Australian seasons. Four drift correction alternatives including one new modified method were employed for the selected GCM models and the models were categorized based on their performances using different skill scores. The results revealed that the performance of drift correction alternatives vary among different models and initialization years. Although some drift correction methods showed better performances than others for a given model but it is still difficult to suggest the most suitable method for seasonal precipitation. Drift correction approaches for other time scales such as monthly precipitation, and their application for individual ensemble members may be investigated further to better understand the implications of alternative drift corrections for decadal forecasting. [ABSTRACT FROM AUTHOR]

Surface albedo plays a key role in the energy and water cycles, and reasonable parameterizations of surface albedo will be greatly helpful to improve the simulation of radiation partition in climate models. In‐situ measurements of albedo from five sites over the Tibetan Plateau (TP) and MODIS albedo product are used to evaluate monthly, annual, and seasonal variations of the surface albedo simulated by 24 Global Climate Models (GCMs) archived by the Coupled Model Intercomparison Project Phase 5 (CMIP5). Potential factors contributing to the bias of simulated albedo were investigated. The results show that the monthly albedo of 24 GCMs varied across models, and the difference among the models was smaller in the June–July–August period than the December–January–February period. The ensemble mean albedo of the 24 GCMs was more consistent with the in‐situ measurements than the individual model. The albedo calculated from the BNU‐ESM, GFDL‐CM3, INM‐CM4, MIROC4h, MIROC‐ESM, and MIROC‐ESM‐CHEM models coincided with the observations from June to September, increasing rapidly to above 0.4 from November. However, the annual cycle of surface albedo was insignificant when simulated by some models, such as CanESM2, CSIRO‐Mk3.6.0, CMCC‐CMS, IPSL‐CM5A‐MR, and MPI‐ESM‐MR. Additionally, the surface albedo was overestimated by CMIP5 multi‐model ensemble mean—with a smaller amplitude of daily albedo—compared with the values estimated from in‐situ observations and MCD43A3. In all 24 models, snow albedo parameterization schemes were found to variably fit the attenuation of snow albedo over time. The cold temperature, relatively more precipitation, fresh snow density, and albedo in these models led to large biases in snow depth and ablation. The surface albedo tends to increase without the influence of blowing snow and withered grass on the model's albedo. [ABSTRACT FROM AUTHOR]

Aridity Index (AI) indicates the balance between water supply and water demand on the atmosphere–land interface. Despite continuous improvements, coupled climate models still have significant systematic errors in simulating AI in terms of temporal and spatial variabilities. One of the approaches to bias‐correct simulations is extracting the linear relationship between historical observations and model outputs by utilizing the empirical orthogonal function (EOF). In this study, the methodology of ensemble EOF‐based bias‐correction by observational constraints is developed based on previous bias‐correction approach, with the improvement on seeking the optimal combinations of the leading modes with sensitivity test and replacing the certain correction with the ensemble means of optimal members. In verification, the ensemble mean of Coupled Model Intercomparison Project phase 5 (CMIP5‐EM) is bias‐corrected towards the CPC/GLDAS observations, and the extracted leading modes present high correlations with internal climate variability. By cross‐validation and posteriori independent validation of hindcasts over the historical period (1948–2005), the ensemble EOF‐based bias‐correction could better present spatial patterns compared to the CMIP5‐EM after systematic bias‐correction, as indicated by the anomaly correlation and the root mean square error. The verifications also indicate that the temporal variability in aridity over different dryland regions is much closer to that in the observations and that the dryland subtype changes are improved significantly by bias‐corrections. Besides, another observational dataset of UDel/CRU is applied to assess the uncertainty on different datasets and the improvement on skill scores is robust. The above results verify that the ensemble EOF‐based bias‐corrections provide better reference for assessing and projecting global aridity changes by climate models. [ABSTRACT FROM AUTHOR]

The assessment of climate change impacts in regions with complex orography and land‐sea interfaces poses a challenge related to shortcomings of global climate models. Furthermore, climate indices based on absolute thresholds are especially sensitive to systematic model biases. Here we assess the effect of bias adjustment (BA) on the projected changes in temperature extremes focusing on the number of annual days with maximum temperature above 35°C. To this aim, we use three BA methods of increasing complexity (from simple scaling to empirical quantile mapping) and present a global analysis of raw and BA CMIP5 projections under different global warming levels. The main conclusions are (1) BA amplifies the magnitude of the climate change signal (in some regions by a factor 2 or more) achieving a more plausible representation of future heat threshold‐based indices; (2) simple BA methods provide similar results to more complex ones, thus supporting the use of simple and parsimonious BA methods in these studies. [ABSTRACT FROM AUTHOR]

This study analyzes local‐scale temperature and precipitation projections in the Shire River Basin (SRB) in Malawi using 10 global circulation models (GCMs) available in the coupled model intercomparison project phase 5 (CMIP5), under two representative concentration pathways (RCP 4.5 and RCP 8.5). For nine stations in the study area, large‐scale maximum temperature (Tmax), minimum temperature (Tmin) and precipitation data from the selected GCMs were downscaled by the sixth version of the Long Ashton Research Station Weather Generator (LARS‐WG6). The mean seasonal and annual change projections for Tmax, Tmin and precipitation during two future periods, that is, the middle future (2041–2070) and late future (2071–2100) periods were analyzed. Modelling results demonstrated that the LARS‐WG model is capable of simulating temperature more accurately than precipitation in the SRB. All 10 GCMs revealed that continually rising temperatures are anticipated in the study area; however, the projected magnitude of change varied across GCMs and between RCPs. Generally, the increase in average Tmax and Tmin was observed to be higher under RCP 8.5 compared with RCP 4.5 due to unmitigated greenhouse gas emissions (GHGs). Future precipitation change results showed more complexity and uncertainty than for temperature; not all GCMs agree on whether there will be positive or negative changes in precipitation and no systematic variations under RCP4.5 and RCP8.5 were observed during the two future time period, illustrating that both GCMs and RCPs are important sources of the relatively large uncertainties in future precipitation projections in the SRB. Thus, this study indicated that uncertainties constrained by both GCMs and RCPs are crucial and need to always be considered when executing climate impact studies and adaptation, particularly at river basin level. [ABSTRACT FROM AUTHOR]

In this study the simulated extreme and moderate El Niño events (e.g., frequency of occurrence and spatial variability) are characterized and the historical teleconnections on precipitation and temperature climate extremes in South America are analysed. Future projected changes in these teleconnections under a global warming context are also addressed. For this purpose a set of 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) simulations derived from three experiments (historical, RCP4.5 and RCP8.5) are considered. The study period is 1916–2100. As El Niño is seasonally phase‐locked, peaking in late austral spring and summer, the present analysis is carried out for these two seasons, as well as annually. Results indicate that extreme El Niño events are projected to double their occurrence considering RCP4.5 and RCP8.5, while a less pronounced increase is projected for moderate El Niño events. Most of these events show a similar pattern of sea surface temperature (SST) anomalies: a positive centre in the central equatorial Pacific that extends eastward to the coast of America. In a global warming context, the resemblance between these events may decrease. Overall, the spatial structure of the impacts of El Niño are expected to be similar between moderate and extreme events, although in some cases (regions and seasons), a higher intensity is observed considering the events characterized as extreme El Niños rather than moderate ones. As extreme El Niño events are expected to be more frequent in the future, the impacts on climate extremes as well as the associated socioeconomic impacts would also take place much more frequently too. [ABSTRACT FROM AUTHOR]

A new algorithm is proposed for estimating time‐evolving global forcing in climate models. The method is a further development of the work of Forster et al. (2013), https://doi.org/10.1002/jgrd.50174, taking into account the non‐constancy of the global feedbacks. We assume that the non‐constancy of this global feedback can be explained as a time‐scale dependence, associated with linear temperature responses to the forcing on different time scales. With this method we obtain stronger forcing estimates than previously assumed for the representative concentration pathway experiments in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The reason for the higher future forcing is that the global feedback parameter is more negative at shorter time scales than at longer time scales, consistent with the equilibrium climate sensitivity increasing with equilibration time. Our definition of forcing provides a clean separation of forcing and response, and we find that linear temperature response functions estimated from experiments with abrupt quadrupling of CO2 ${\mathrm{C}\mathrm{O}}_{2}$ can be used to predict responses also for future scenarios. In particular, we demonstrate that for most models, the response to our new forcing estimate applied on the 21st century scenarios provides a global surface temperature up to year 2100 consistent with the output of coupled model versions of the respective model. Key Points: We present a new method for estimating radiative forcing and apply it to abrupt4xCO2, 1%CO2, historical, and future scenario experimentsIncluding a time‐scale dependent feedback parameter results in stronger forcing estimates for the 21st centuryThe temperature responses to the new forcing are well described by a linear response [ABSTRACT FROM AUTHOR]

In tropical regions, particularly in Central and South America (CSA), the projections of climate seasonality under climate change are still uncertain. This is especially true for ecologically‐relevant variables such as precipitation and temperature. However, assessments of model‐based projections of seasonal climate for this region are scarce. We analyzed the simulation of seasonal precipitation and air surface temperature in CSA and six sub‐regions within from 49 models included in the Coupled Intercomparison Project Phase 5 (CMIP5) and 33 models from CMIP6. In general, continental patterns and seasonality of both variables are moderately well resembled, while most models show systematic biases over the oceans, producing unrealistic spatial patterns. To quantify how well CMIP5/CMIP6 models simulate these variables, we used Taylor diagrams with respect to TRMM for precipitation and ERA5 for temperature. Precipitation shows the largest spread among models. Conversely, temperature shows a better simulation. CMIP5/CMIP6 models exhibit a better performance simulating both variables during December–January–February and March–April–May than during the other seasons. This is partly due to the reduced model biases in representing the Intertropical Convergence Zone during these two seasons. In general, biases are reduced in the CMIP6 models with respect to CMIP5. Regarding regional evaluations, precipitation patterns for Mesoamerica, Cerrado and Chaco regions are better reproduced compared to TRMM, while the annual cycles for the Andes hotspot, Central Chile and Guianas are not well simulated, mainly during their wet seasons. However, these biases are reduced in CMIP6 models. In regard to precipitation projections, models only agree over most of the regions with decreasing precipitation. Conversely, temperature exhibits a general consensus on persistent warming even during the historical period, with an average increase of 6°C by the end of the century, according to the CMIP6 models. [ABSTRACT FROM AUTHOR]

This study analyses the long‐term (1950–2100) observed and projected changes in springtime (March–May) heat waves (HWs) in West Africa under climate change. To that end, 28 climate models participating to the fifth Coupled Model Intercomparison Project (CMIP5) are considered, after a statistical post‐correction of their biases. A multi‐scale approach is proposed, covering the Sahel, Senegal, and three thermally‐coherent zones within Senegal. HWs are defined as a sequence of at least three consecutive days above a moving 95th percentile of current temperature distributions. Climate change over Senegal translates into a general shift of the whole statistical distribution towards higher temperature values, with a general stability in the shape of the distribution. Ongoing mean warming could reach +5°C in 2100 under RCP8.5 scenario, implying that coastal Senegal could experience then a mean climate comparable to the hinterland parts today. HWs have increased in intensity, frequency and duration across Sahel and Senegal over the past years, such intensification being higher on recent decades. Future HWs over all regions present intrinsic properties that radically differ from those observed so far. The severity and length of HWs displayed stationary conditions until the late 1990s, but started increasing since then. Projected changes show marked and rapid increase in these variables, the amplitude of which is primarily RCP‐dependent, and secondarily region‐dependent. For both metrics, the largest changes occur over hinterland Senegal and Sahel. There, under RCP8.5 and after the 2070s, the whole spring season could be considered as a permanent HW lasting 3 months. Along the coast, by contrast, average temperatures are both weaker and more variable, causing more frequent threshold crossings and limiting the duration of HWs. The multi‐scale approach used here highlights contrast within Senegal, which constitutes important information for public policy decision‐makers and its inhabitants in terms of adaptation to climate change. [ABSTRACT FROM AUTHOR]

Heatwave frequency, duration and intensity has increased since the 1950s, a trend likely to continue with anthropogenic climate change. Although climate model projections are an important source of information on how heatwaves and other climate extremes may evolve with climate change it is necessary to understand whether climate models are able to adequately reproduce historical statistics of these events. We provide the first global evaluation of CMIP6 models to characterize heatwave characteristics between 1950 and 2014. Further, we also include a global heatwave evaluation of the CMIP5 models, as no study currently exists. Our results demonstrate that both ensembles underestimate mean heatwave frequency with region‐dependent biases for mean heatwave duration, intensity, and cumulative heat. Comparisons between CMIP5 and CMIP6 show that improvements in skill for the heatwave metrics evaluated here are marginal, suggesting that future improvements in simulating heatwaves may only be possible with significant advances in climate modeling capabilities. Plain Language Summary: Heatwaves are increasing in response to climate change associated with human activity, a trend that is likely to continue. In order to build confidence in projected changes in heatwaves it is necessary to first understand whether the models producing these projections can represent historical heatwave characteristics. We present the first global evaluation of the current and previous suite of climate models that inform the Intergovernmental Panel on Climate Change Assessment Reports. Our analysis considers four different heatwave characteristics that measure the frequency, duration, average intensity, and cumulative intensity over the period 1950–2014. Our results show that most climate models underestimate historical heatwave frequency with regional under‐ and overestimation of heatwave duration, average intensity and cumulative intensity. Our analyses are useful for decision makers and other users of climate model projections as they show which regions and heatwave characteristics have higher skill over the present‐day period. Key Points: First global evaluation of CMIP6 and CMIP5 models for historical heatwavesHeatwave frequency is systematically underestimated by both CMIP cyclesCumulative heat is systematically overestimated by both CMIP cycles [ABSTRACT FROM AUTHOR]

For the 1960–2000 period, the latest generation of climate models (Coupled Model Intercomparison Project Phase 6 [CMIP6]) shows less global mean surface temperature change relative to pre‐industrial than that seen in observations. In contrast, the previous generation of models (CMIP5) performed well over this period. It has been hypothesized that this suppressed late‐20th Century warming seen in CMIP6 is caused by a stronger aerosol forcing. However, we find this to be only part of the story. Not only is the aerosol forcing marginally more negative in CMIP6 compared to CMIP5, the greenhouse gas forcing in CMIP6 is also weaker than in CMIP5. These forcing differences are amplified by differences in climate sensitivity between the CMIP5 and CMIP6 ensemble, which leads to both a stronger aerosol cooling over 1960–1990 and a stronger greenhouse gas induced warming from 1990, returning the warming post‐2000 toward the observed level. Plain Language Summary: Climate models are our best tools for predicting how the climate will change in the future. Confidence in future projections relies on the ability to accurately simulate the past. Many of the latest climate models show less warming than observations around the 1960–2000 period, so understanding why is key to making more confident projections. Models respond to human and natural forcings such as greenhouse gases, air pollutants (aerosols), volcanic eruptions, and solar activity. We show that the latest models simulate a lower forcing from greenhouse gases and aerosols than older models, which is part of the reason why the climate does not warm in line with observations in the late 20th Century. The other part of the reason for the lower warming is that the newer models are more sensitive: they project a greater temperature change for a given amount of forcing. The higher sensitivity is found to result in more cooling from aerosols in the new models, but it opposes the lower forcing from greenhouse gases. The aerosol effects win out, causing suppressed warming in the late 20th Century. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) models warm less in the multimodel mean than CMIP5 models in the 1960–2000 periodImplied forcing from greenhouse gases and aerosols is lower (more negative) in CMIP6 than in CMIP5Stronger climate feedbacks in CMIP6 amplifies the cooling from aerosols [ABSTRACT FROM AUTHOR]

East Africa is vulnerable to hydroclimatic variability and change, and therefore reliable projections of future rainfall are important for climate change adaptation planning. However, the region's climate is affected by complex multi‐scalar processes and poorly represented in climate models, leading to uncertainty surrounding rainfall change. The importance of circulation features in controlling long‐term rainfall variability provides an opportunity to constrain projections. We use a process‐based climate model evaluation methodology to demonstrate links between Coupled Model Intercomparison Project Phase 5 (CMIP5) rainfall biases over Kenya and circulation biases in the Indian Ocean Walker Circulation (IOWC). During both the long and short rains, models with wet biases in historical runs continue to be wet in future. Wet future projections are associated with enhanced easterly winds over the equatorial Indian Ocean, as well as decreasing vertical velocity over Kenya and increasing vertical velocity over the Maritime Continent. We demonstrate that models with a simulated IOWC which is close to reanalysis in historical runs project different changes to Kenyan rainfall than those which do not. In particular, the projected rainfall increase during the long rains is confined to a single month (April) in these models. We call for a renewed focus on the Walker Circulation as a way to constrain uncertain rainfall projections elsewhere in the tropics. Plain Language Summary: East Africa relies on agriculture to support its population and economy. Therefore, it is important to have reliable projections of how rainfall may change in future as a result of climate change. However, current projections of rainfall are uncertain because climate models (which are used to make projections) are not able to simulate important features of the regional climate. We find that models that produce too much or too little rainfall over Kenya in the present climate continue to do so in future. These wet/dry conditions in future projections are linked to model simulations of processes linked to the Indian Ocean Walker Circulation (IOWC). If we separate out projections by only choosing models which can simulate the IOWC well, we get different rainfall projections than in models which do not simulate it well. This has implications for which projections are more reliable, which is important because models disagree on future rainfall trends. Key Points: Coupled Model Intercomparison Project Phase 5 (CMIP5) rainfall biases over Kenya are associated with the representation of the Indian Ocean Walker CirculationModels with historical rainfall biases continue to be wet/dry in future projections, which are associated with Indian Ocean Walker Circulation (IOWC) trendsCMIP5 rainfall projections could be usefully constrained by applying IOWC metrics [ABSTRACT FROM AUTHOR]

Although volcanic forcing impacts on climate have gained much international attention during recent years, focus has largely been at hemispheric scale, and most particularly on the Northern Hemisphere (NH). Here we investigate the impact of major volcanic eruptions since 1883 on southern African climate, with a view to establishing potential sub‐regional differences across a variety of temporal scales. To this end, we use historical simulations from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) to study near‐surface temperature responses to four major eruptions (Krakatau, 1883; Santa Maria, 1902; Agung, 1963; Pinatubo, 1991) over six sub‐regions of southern Africa. Results show significant cooling across all sub‐regions after each eruption. However, we detect considerable sub‐regional differences in the amplitude, timing and duration of responses. For instance, stronger temperature departures occur in northern rather than southern sub‐regions where diurnal and annual temperature ranges are normally greater. While significant responses occur within the first year after each eruption, there is a more delayed response in three of the sub‐regions (southwestern coast and central southern Africa) following the Santa Maria eruption. Strongest responses follow the Krakatau eruption in all sub‐regions, while the weakest response follows the Santa Maria eruption in western sub‐regions and the Agung eruption in eastern sub‐regions. Most regions experience the strongest temperature departures during austral autumn and winter (MAM and JJA), and weakest during spring (SON). [ABSTRACT FROM AUTHOR]

This study presents results from an assessment of climate change projections over South America using fifth phase of the Coupled Model Intercomparison Project models. Change in near‐surface temperature, precipitation, evapotranspiration, integrated water vapour transport (IVT), sea level pressure (SLP), and wind at three pressure levels is quantified across the multi‐model suite. Additionally, model agreement for the sign and significance of projected change is assessed within the ensemble. Models are in strong agreement that the highest magnitude of projected warming will be over tropical regions. The CMIP5 models project a decrease in precipitation for all seasons over southern South America, especially along the northern portions of the present‐day mid‐latitude storm track. This is consistent with a robustly projected poleward shift of the Pacific extratropical high‐pressure system and mid‐latitude storm track indicated by a systematic increase in SLP and decrease in westerly wind magnitude over the region. Decreased precipitation for the months of September, October, and November is also projected, with strong model agreement, over portions of northern and northeastern Brazil, coincident with decreases in SLP and increases in evapotranspiration. IVT is broadly projected to decrease over southern South America, coincident with the projected poleward shift of the mid‐latitude storm track, with increases projected in the vicinity of the South Atlantic Convergence Zone in spring and summer. Results provide a comprehensive picture of climate change across South America and highlight where model consensus on change is most robust. [ABSTRACT FROM AUTHOR]

Climate models exhibit a broad range in the simulated properties of the climate system. In the early historical period, the absolute global mean surface air temperature in Coupled Model Intercomparison Project, Phase 5 (CMIP5) models spans a range of ∼12°C – 15°C. Other climate variables may be linked to global mean temperature, and so accurate representation of the baseline climate state is crucial for meaningful future climate projections. In CMIP5 baseline climate states, statistically significant intermodel correlations between Southern Ocean surface temperature, outgoing shortwave radiation, cloudiness, the position of the mid‐latitude eddy‐driven jet, and Antarctic sea ice area are found. The baseline temperature relationships extend to projected future changes in the same set of variables, impacting on the projected global mean surface temperature change. Models with initially cooler Southern Ocean tend to exhibit more global warming, and vice versa for initially warmer models. These relationships arise due to a "capacity for change". For example, cold‐biased models tend to have more cloud cover, sea ice, and equatorward jet initially, and thus a greater capacity to lose cloud cover and sea ice, and for the jet to shift poleward under global warming. A first look at emerging data from CMIP6 reveals a shift of the relationship from the Southern Ocean towards the Antarctic region, possibly due to reductions in Southern Ocean biases, such as in westerly wind representation. Plain Language Summary: Modern simulations of the Earth's climate system differ in some of their large‐scale features. For example, in models reported on by the Intergovernmental Panel on Climate Change in the Fifth Assessment Report, the global average temperature ranges between 12°C and 15°C. Global mean temperature is known to be linked to other features, such as wind, clouds, and rainfall. Accurately modeling the present‐day climate is important, so that we can have more confidence in the possible futures they simulate under different levels of anthropogenic greenhouse gas emissions. In this study, strong relationships are found between simulated Southern Ocean temperature and the amount of sea ice and clouds. In addition, it is found that the initial Southern Ocean temperature is also related to changes in sea ice and cloud simulated in the future. A model that is cooler initially, for example, tends to have more sea ice and cloud, but also loses more sea ice and cloud in the future, and simulates more global warming. Key Points: There are robust intermodel correlations across elements of the Southern Ocean climate system in historical Coupled Model Intercomparison Project, Phase 5 (CMIP5) simulationsThe baseline Southern Ocean temperature relationship extends to projected changes in radiation, cloudiness, the jet latitude and sea iceCMIP5 models with initially cooler Southern Ocean exhibit more global warming, likely due to greater capacity for change [ABSTRACT FROM AUTHOR]