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Abstract Simulating the Earth system is crucial for studying Earth's climate and how it changes. Modeling approaches that simplify the Earth system while retaining key characteristics are important tools to advance understanding. The simplicity and flexibility of idealized models enables imaginative science and makes them powerful educational tools. Evolving scientific community needs and increasing model complexity, however, makes it challenging to maintain and support idealized configurations in cutting‐edge Earth system modeling frameworks. We call on the scientific community to re‐emphasize model hierarchies within these frameworks to aid in understanding the Earth system, advancing model development, and developing the future workforce. 

Summer temperature extremes can have large impacts on humans and the biosphere, and an increase in heat extremes is one of the most visible symptoms of climate change. Multiple mechanisms have been proposed that would predict faster warming of heat extremes than typical summer days, but it is unclear whether this is occurring. Here, we show that, in both observations and historical climate model simulations, the hottest summer days have warmed at the same pace as the median globally, in each hemisphere, and in the tropics from 1959 to 2023. In contrast, the coldest summer days have warmed more slowly than the median in the global average, a signal that is not simulated in any of 262 simulations across 28 CMIP6 models. The observed stretching of the cold tail indicates that observed summertime temperatures have become more variable despite the lack of hot day amplification. The interannual variability and trend in the warming of both hot and cold extremes compared to the median can be explained from a surface energy balance perspective based on changes in net surface radiation and evaporative fraction. Tropical hot day amplification is projected to emerge in the future (2024–2099, SSP3-7.0 scenario), while Northern Hemisphere heat extremes are expected to continue to follow the median. 

Anthropogenically forced climate change signals are emerging from the noise of internal variability in observations, and the impacts on society are growing. For decades, Climate or Earth System Models have been predicting how these climate change signals will unfold. While challenges remain, given the growing forced trends and the lengthening observational record, the climate science community is now in a position to confront the signals, as represented by historical trends, in models with observations. This review covers the state of the science on the ability of models to represent historical trends in the climate system. It also outlines robust procedures that should be used when comparing modeled and observed trends and how to move beyond quantification into understanding. Finally, this review discusses cutting-edge methods for identifying sources of discrepancies and the importance of future confrontations. 

Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas–driven hydroclimate change. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here, we present a concerning discrepancy between observed and model-based historical hydroclimate trends. Over the arid/semi-arid regions of the world, the predominant signal in all model simulations is an increase in atmospheric water vapor, on average, over the last four decades, in association with the increased water vapor–holding capacity of a warmer atmosphere. In observations, this increase in atmospheric water vapor has not happened, suggesting that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid/semi-arid regions. This discrepancy is most clear in locations that are arid/semi-arid year round, but it is also apparent in more humid regions during the most arid months of the year. It indicates a major gap in our understanding and modeling capabilities which could have severe implications for hydroclimate projections, including fire hazard, moving forward. 

Abstract. Climate simulation uncertainties arise from internal variability, model structure, and external forcings. Model intercomparisons (such as the Coupled Model Intercomparison Project; CMIP) and single-model large ensembles have provided insight into uncertainty sources. Under the Community Earth System Model (CESM) project, large ensembles have been performed for CESM2 (a CMIP6-era model) and CESM1 (a CMIP5-era model). We refer to these as CESM2-LE and CESM1-LE. The external forcing used in these simulations has changed to be consistent with their CMIP generation. As a result, differences between CESM2-LE and CESM1-LE ensemble means arise from changes in both model structure and forcing. Here we present new ensemble simulations which allow us to separate the influences of these model structural and forcing differences. Our new CESM2 simulations are run with CMIP5 forcings equivalent to those used in the CESM1-LE. We find a strong influence of historical forcing uncertainty due to aerosol effects on simulated climate. For the historical period, forcing drives reduced global warming and ocean heat uptake in CESM2-LE relative to CESM1-LE that is counteracted by the influence of model structure. The influence of the model structure and forcing vary across the globe, and the Arctic exhibits a distinct signal that contrasts with the global mean. For the 21st century, the importance of scenario forcing differences (SSP3–7.0 for CESM2-LE and RCP8.5 for CESM1-LE) is evident. The new simulations presented here allow us to diagnose the influence of model structure on 21st century change, despite large scenario forcing differences, revealing that differences in the meridional distribution of warming are caused by model structure. Feedback analysis reveals that clouds and their impact on shortwave radiation explain many of these structural differences between CESM2 and CESM1. In the Arctic, albedo changes control transient climate evolution differences due to structural differences between CESM2 and CESM1. 

Abstract Extreme heat events are a threat to human health, productivity, and food supply, so understanding their drivers is critical to adaptation and resilience. Anticyclonic circulation and certain quasi-stationary Rossby wave patterns are well known to coincide with heatwaves, and soil moisture deficits amplify extreme heat in some regions. However, the relative roles of these two factors in causing heatwaves is still unclear. Here we use constructed circulation analogs to estimate the contribution of atmospheric circulation to heatwaves in the United States in the Community Earth System Model version 1 (CESM1) preindustrial control simulations. After accounting for the component of the heatwaves explained by circulation, we explore the relationship between the residual temperature anomalies and soil moisture. We find that circulation explains over 85% of heatwave temperature anomalies in the eastern and western United States but only 75%–85% in the central United States. In this region, there is a significant negative correlation between soil moisture the week before the heatwave and the strength of the heatwave that explains additional variance. Further, for the hottest central U.S. heatwaves, positive temperature anomalies and negative soil moisture anomalies are evident over a month before heatwave onset. These results provide evidence that positive land–atmosphere feedbacks may be amplifying heatwaves in the central United States and demonstrate the geographic heterogeneity in the relative importance of the land and atmosphere for heatwave development. Analysis of future circulation and soil moisture in the CESM1 Large Ensemble indicates that, over parts of the United States, both may be trending toward greater heatwave likelihood. 

Abstract Understanding the roles of land surface conditions and atmospheric circulation on continental daily temperature variance is key to improving predictions of temperature extremes. Evaporative resistance ( r s , hereafter), a function of the land cover type, reflects the ease with which water can be evaporated or transpired and is a strong control on land-atmosphere interactions. This study explores the effects of r s perturbations on summer daily temperature variance using the Simple Land Interface Model (SLIM) by mimicking, for r s only, a global land cover conversion from forest to crop/grassland. Decreasing r s causes a global cooling. The cooling is larger in wetter areas and weaker in drier areas, and primarily results from perturbations in shortwave radiation (SW) and latent heat flux (LH). Decreasing r s enhances cloud cover due to greater land surface evaporation and thus reduces incoming SW over most land areas. When r s decreases, wetter areas experience strong evaporative cooling, while drier areas become more moisture-limited and thus experience less cooling. Thermal advection further shapes the temperature response by damping the combined impacts of SWand LH. Temperature variance increases in drier areas and decreases in wetter areas as r s decreases. The temperature variance changes can be largely explained from changes in the combined variance of SW and LH, including an important contribution of changes in the covariance of SW and LH. In contrast, the effects of changes in thermal advection variance mainly affect the Northern Hemisphere mid-latitudes. 

Abstract By summer 2021 moderate to exceptional drought impacted 28% of North America, focused west of the Mississippi, with serious impacts on fire, water resources, and agriculture. Here, using reanalyses and SST-forced climate models, we examine the onset and development of this southwestern drought from its inception in summer 2020 through winter and spring 2020/21. The drought severity in summer 2021 resulted from four consecutive prior seasons in which precipitation in the southwest United States was the lowest on record or, at least, extremely dry. The dry conditions in summer 2020 arose from internal atmospheric variability but are beyond the range of what the studied atmosphere models simulate for that season. From winter 2020 through spring 2021 the worsening drought conditions were guided by the development of a La Niña in the tropical Pacific Ocean. Decadal variability in the Pacific Ocean aided drought in the southern part of the region by driving the cool season to be drier during the last two decades. There is also evidence that the southern part of the region in spring is drying due to human-driven climate change. In sum the drought onset was driven by a combination of internal atmospheric variability and interannual climate variability and aided by natural decadal variability and human-driven climate change.