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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. 

Central North America is the global hotspot for tornadoes, fueled by elevated terrain of the Rockies to the west and a source of warm, moist air from equatorward oceans. This conventional wisdom argues that central South America, with the Andes to the west and Amazon basin to the north, should have a “tornado alley” at least as active as central North America. Central South America has frequent severe thunderstorms yet relatively few tornadoes. Here, we show that conventional wisdom is missing an important ingredient specific to tornadoes: a smooth, flat ocean-like upstream surface. Using global climate model experiments, we show that central South American tornado potential substantially increases if its equatorward land surface is smoothed and flattened to be ocean-like. Similarly, we show that central North American tornado potential substantially decreases if its equatorward ocean surface is roughened to values comparable to forested land. A rough upstream surface suppresses the formation of tornadic environments principally by weakening the poleward low-level winds, characterized by a weakened low-level jet east of the mountain range. Results are shown to be robust for any midlatitude landmass using idealized experiments with a simplified continent and mountain range. Our findings indicate that large-scale upstream surface roughness is likely a first-order driver of the strong contrast in tornado potential between North and South America. 

Abstract Global Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales. 

Abstract The Community Earth System Model, version 2 (CESM2), has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Usingk-means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late twenty-first century in four shared socioeconomic pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects. 

Abstract Although societally important, extreme precipitation is difficult to represent in climate models. This study shows one robust aspect of extreme precipitation across models: extreme precipitation over tropical oceans is strengthened through a positive feedback with cloud-radiative effects. This connection is shown for a multi-model ensemble with experiments that make clouds transparent to longwave radiation. In all cases, tropical extreme precipitation reduces without cloud-radiative effects. Qualitatively similar results are presented for one model using the cloud-locking method to remove cloud feedbacks. The reduced extreme precipitation without cloud-radiative feedbacks does not arise from changes in the mean climate. Rather, evidence is presented that cloud-radiative feedbacks enhance organization of convection and most extreme precipitation over tropical oceans occurs within organized systems. This result suggests that climate models must correctly predict cloud structure and properties, as well as capture the essence of organized convection in order to accurately represent extreme rainfall. 

Abstract This work examines the effect of horizontal resolution and topography on the North American monsoon (NAM) in experiments with an atmospheric general circulation model. Observations are used to evaluate the fidelity of the representation of the monsoon in simulations from the Community Atmosphere Model version 5 (CAM5) with a standard 1.0° grid spacing and a high-resolution 0.25° grid spacing. The simulated monsoon has some realistic features, but both configurations also show precipitation biases. The default 1.0° grid spacing configuration simulates a monsoon with an annual cycle and intensity of precipitation within the observational range, but the monsoon begins and ends too gradually and does not reach far enough north. This study shows that the improved representation of topography in the high-resolution (0.25° grid spacing) configuration improves the regional circulation and therefore some aspects of the simulated monsoon compared to the 1.0° counterpart. At higher resolution, CAM5 simulates a stronger low pressure center over the American Southwest, with more realistic low-level wind flow than in the 1.0° configuration. As a result, the monsoon precipitation increases as does the amplitude of the annual cycle of precipitation. A moisture analysis sheds light on the monsoon dynamics, indicating that changes in the advection of enthalpy and moist static energy drive the differences between monsoon precipitation in CAM5 1.0° compared to the 0.25° configuration. Additional simulations confirm that these improvements are mainly due to the topographic influence on the low-level flow through the Gulf of California, and not only the increase in horizontal resolution.