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

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau. Plain Language Summary: Compound climate extremes, such as the events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. Can climate model simulate the historical compound extremes? If yes, how the global compound extremes will change in the future? In this study, we found that the global climate model performs well in simulating temporal changes of warm/wet and warm/dry extremes during the period 1955–2014. With greenhouse gas emissions continuing to increase in the future, compound warm/dry and warm/wet extremes show a continuous increase in frequency in the next few decades, while compound cold/dry and cold/wet extremes are projected to occur less frequently. Key Points: A multi‐model ensemble of CMIP6 models performs well in simulating temporal changes of warm/wet extremesThe inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremesThe projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 [ABSTRACT FROM AUTHOR]

The impact of global warming on lightning flash rates remains relatively unknown. In this study, the South China Sea (SCS) and the surrounding areas within Southeast Asia were selected to examine the long‐term trend and future projection of lightning activity based on the currently longest satellite‐based lightning data set available and climate models. Our study revealed a reduction in the observed lightning flash rates around the SCS, with a linear trend of −0.11 fl km−2 yr−2 during 1996–2013. In contrast, the precipitation around the SCS exhibited an increasing trend and was negatively correlated with the local lightning flash rate. The sea surface temperature gradient over equatorial Pacific Ocean, latent heat flux over the equatorial Indian Ocean, local convective available potential energy, precipitation and aerosol changes collectively accounted for 82% of the variance in the lightning fluctuations over the SCS and Southeast Asia. Multiple linear regression proxies of lightning flash rates were constructed and applied to the climate models. The models indicated that lightning activity around the SCS is projected to intensify by 10% and 12% by the end of the 21st century under SSP245 and SSP370, respectively. Plain Language Summary: The projection of future changes in lightning activity is uncertain owing to various factors, and different regions exhibit distinct trajectories. In this study, the focus was on the South China Sea (SCS) and Southeast Asia, and past variations in lightning activity was examined and used to predict future variations in lightning activity. Employing a multiple linear regression approach, we identified the large‐scale thermodynamic drivers and local environmental factors that influence lightning activity around the SCS. The research findings indicated a reduction in lightning activity over the past few decades over the SCS and Southeast Asia region. The simulation results indicate that in the future under ongoing global warming, regional lightning activity will intensify. Key Points: Lightning activity and precipitation show opposing regional trends around the South China Sea (SCS)Remote thermodynamic and local environmental factors both contribute to the variability in lightning around the SCSUnder future climate change scenarios, lightning activity around the SCS is projected to gradually increase [ABSTRACT FROM AUTHOR]

While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the "best" CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time. Plain Language Summary: The rate of Arctic warming varied in time, while the rate of global warming was nearly constant over the period 1960–2020. This led to a variable Arctic Amplification (AA) defined as a ratio of the Arctic warming trend to mean global warming trend. In addition, the Arctic experienced sudden changes in the rate of warming with step‐like changes in annual mean Arctic Amplification around the years 1986 and 1999. Climate models do not capture the second observed sharp increase of the AA. We suggest that the first AA increase around 1986 is primarily due to external forcing (increasing concentration of carbon dioxide) while the second sharp increase around 1999 is dominated by internal climate variability which current models cannot capture accurately within the time scale of the physical phenomena. Key Points: Annual mean Arctic Amplification (AA) within the period 1970–2020 changed in steep steps around 1986 and 1999. It reached values over 4.0Even those CMIP6 models best at reproducing the AA did not reproduce the second sudden increase near 1999We conjecture that the first AA sharp increase is due to external forcing, while the second is due to internal climate variability [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]

Tropical coral reefs are among the most sensitive ecosystems to climate change and will benefit from the more ambitious aims of the United Nations Framework Convention on Climate Change's Paris Agreement, which proposed to limit global warming to 1.5° rather than 2°C above pre‐industrial levels. Only in the latest Intergovernmental Panel on Climate Change focussed assessment, the Coupled Model Intercomparison Project phase 6 (CMIP6), have climate models been used to investigate the 1.5° warming scenario directly. Here, we combine the most recent model updates from CMIP6 with a semi‐dynamic downscaling to evaluate the difference between the 1.5 and 2°C global warming targets on coral thermal stress metrics for the Great Barrier Reef (GBR). By ~2080, severe bleaching events are expected to occur annually under intensifying emissions (shared socioeconomic pathway SSP5‐8.5). Adherence to 2° warming (SSP1‐2.6) halves this frequency but the main benefit of confining warming to 1.5° (SSP1‐1.9) is that bleaching events are reduced further to 3 events per decade. Attaining low emissions of 1.5° is also paramount to prevent the mean magnitude of thermal stress from stabilizing close to a critical thermal threshold (8 Degree Heating Weeks). Thermal stress under the more pessimistic pathways SSP3‐7.0 and SSP5‐8.5 is three to fourfold higher than the present day, with grave implications for future reef ecosystem health. As global warming continues, our projections also indicate more regional warming in the central and southern GBR than the far north and northern GBR. [ABSTRACT FROM AUTHOR]

We analyze projected changes in climate extremes (extreme temperatures and heavy precipitation) in the multimodel ensembles of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). The results reveal close similarity between both ensembles in the regional climate sensitivity of the projected multimodel mean changes in climate extremes, that is, their projected changes as a function of global warming. This stands in contrast to widely reported divergences in global (transient and equilibrium) climate sensitivity in the two multimodel ensembles. Some exceptions include higher warming in the South America monsoon region, lower warming in Southern Asia and Central Africa, and higher increases in heavy precipitation in Western Africa and the Sahel region in the CMIP6 ensemble. The multimodel spread in regional climate sensitivity is found to be large in both ensembles. In particular, it contributes more to intermodel spread in projected regional climate extremes compared with the intermodel spread in global climate sensitivity in CMIP6. Our results highlight the need to consider regional climate sensitivity as a distinct feature of Earth system models and a key determinant of projected regional impacts, which is largely independent of the models' response in global climate sensitivity. Plain Language Summary: Many articles analyze and compare global climate sensitivity in climate models, that is, how their global warming differs at a given level of CO2 concentrations. However, global warming is only one quantity affecting impacts. To assess human‐ and ecosystem‐relevant impacts, it is essential to evaluate the regional climate sensitivity of climate models, that is, how their regional climate features differ at a given level of global warming. We analyze here regional climate sensitivity in the new multimodel ensemble that will underlie the conclusions of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This ensemble of model projections is called the "Sixth Coupled Model Intercomparison Project" or CMIP6. We find that differences in regional climate sensitivity between models in CMIP6 often contribute more to the uncertainty of regional extremes projections than the uncertainty in global mean warming between models. Overall, the regional climate sensitivity features in the CMIP6 models' projections ensemble are very similar to those of the prior ensemble (CMIP5), although the model ensembles have been highlighted to differ in their global climate sensitivity over the 21st century. Key Points: Changes in climate extremes as a function of global warming are quasilinear and determine a "regional climate sensitivity" in CMIP5 and CMIP6The regional climate sensitivity of climate extremes is found to be very similar in CMIP5 and CMIP6, unlike global climate sensitivityModel spread in regional climate sensitivity in CMIP6 contributes more to uncertainty of projected extremes than global climate sensitivity [ABSTRACT FROM AUTHOR]