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Many climate models exhibit a dry and warm bias over the central United States during the summer months, including the Energy Exascale Earth System Model (E3SM) and its Multiscale Modeling Framework (MMF) configuration. Understanding the causes of this bias is important to shine a light on this common model error and reduce the uncertainty in future projections. In this study, we use E3SMv2 and E3SM-MMF to assess how parameterized and resolved convection affect temperature and precipitation biases over the Southern Great Plains site of the Atmospheric Radiation Measurement program. Both configurations overestimate near-surface temperature and underestimate precipitation at the ARM SGP site. The bias is associated with a lack of low-level clouds during days without precipitation and too much incoming solar radiation causing the surface to warm. Low-level cloud fraction in E3SM-MMF during the nonprecipitating days is lower in comparison to E3SMv2 and observation, consistent with the larger warm bias. We also find that the underestimated precipitation can be characterized as "too frequent, too weak" in E3SMv2 and "too rare, too intense" in E3SM-MMF. These deficiencies conspire to sustain the warm and dry bias over the central United States. [ABSTRACT FROM AUTHOR]

Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation. [ABSTRACT FROM AUTHOR]

Recent studies examine the potential for large urban fires ignited in a hypothetical nuclear exchange of one hundred 15 kt weapons between India and Pakistan to alter the climate (e.g., Mills et al., 2014, https://doi.org/10.1002/2013EF000205, and Reisner et al., 2018, https://doi.org/10.1002/2017JD027331). In this study, the global climate forcing and response is predicted by combining two atmospheric models, which together span the micro‐scale to global scale processes involved. Individual fire plumes are modeled using the Weather Research and Forecasting (WRF) model, and the climate response is predicted by injecting the WRF‐simulated black carbon (BC) emissions into the Energy Exascale Earth System Model (E3SM) atmosphere model Version 1 (EAMv1). Consistent with previous studies, the radiative forcing depends on smoke quantity and injection height, examined here as functions of fuel loading and atmospheric conditions. If the fuel burned is 1 g cm−2, BC is quickly removed from the troposphere, causing no global mean climate forcing. If the fuel burned is 16 g cm−2 and 100 such fires occurred simultaneously with characteristics similar to historical large urban firestorms, BC reaches the stratosphere, reducing solar radiation and causing cooling at the Earth's surface. Uncertainties in smoke composition and aerosol representation cause large uncertainties in the magnitude of the radiative forcing and cooling. The approximately 4 yr duration of the radiative forcing is shorter than the 8 to 15 yr that has previously been simulated. Uncertainties point to the need for further development of potential nuclear exchange scenarios, quantification of fuel loading, and improved understanding of fire propagation and aerosol modeling. Plain Language Summary: If the detonation of nuclear weapons causes large fires, the smoke emissions could block sunlight and affect the global climate. A commonly studied scenario is the climate impact that would be caused by the detonation of one hundred 15 kt nuclear weapons in a "regional nuclear exchange" between India and Pakistan (Mills et al., 2014, https://doi.org/10.1002/2013EF000205, Reisner et al., 2018, https://doi.org/10.1002/2017JD027331). We simulate the global climate impacts of this scenario using new models for predicting the fire plume and climate and find that, when smoke from the fires remains in the lower troposphere, it is quickly removed and the climate impact is minimal. Conversely, when fires inject smoke into the upper troposphere or higher, more smoke is transported to the stratosphere where enough light is blocked to cause global surface cooling. Our simulations show that the smoke from 100 simultaneous firestorms would block sunlight for about 4 yr, instead of the 8 to 15 yr predicted in other models. Climate impacts are also shown to be sensitive to assumptions about the composition of the smoke. Additionally, we show that the global effects of the fires are sensitive to fuel availability and consumption, factors which are uncertain for cities in India and Pakistan. Key Points: Fire plume and climate modeling of a regional nuclear exchange finds potential global cooling, shorter in duration than previously assessedAerosol emissions from fires and their representation in a climate model contribute uncertainty to the climate responseA wide range of climate impacts are simulated depending on fuel availability at the detonation sites [ABSTRACT FROM AUTHOR]

This study provides an overview of the coupled high‐resolution Version 1 of the Energy Exascale Earth System Model (E3SMv1) and documents the characteristics of a 50‐year‐long high‐resolution control simulation with time‐invariant 1950 forcings following the HighResMIP protocol. In terms of global root‐mean‐squared error metrics, this high‐resolution simulation is generally superior to results from the low‐resolution configuration of E3SMv1 (due to resolution, tuning changes, and possibly initialization procedure) and compares favorably to models in the CMIP5 ensemble. Ocean and sea ice simulation is particularly improved, due to better resolution of bathymetry, the ability to capture more variability and extremes in winds and currents, and the ability to resolve mesoscale ocean eddies. The largest improvement in this regard is an ice‐free Labrador Sea, which is a major problem at low resolution. Interestingly, several features found to improve with resolution in previous studies are insensitive to resolution or even degrade in E3SMv1. Most notable in this regard are warm bias and associated stratocumulus deficiency in eastern subtropical oceans and lack of improvement in El Niño. Another major finding of this study is that resolution increase had negligible impact on climate sensitivity (measured by net feedback determined through uniform +4K prescribed sea surface temperature increase) and aerosol sensitivity. Cloud response to resolution increase consisted of very minor decrease at all levels. Large‐scale patterns of precipitation bias were also relatively unaffected by grid spacing. Plain Language Summary: The Energy Exascale Earth System Model (E3SM) is a relatively new fully coupled Earth system and climate model used in major international model simulation projects and mission‐defined efforts for the U.S. Department of Energy. This paper describes the first simulation of the model in its high‐resolution configuration. This higher‐resolution version is able to capture the most energetic motions in the ocean, which are poorly represented in standard resolution coupled climate models, as well as the largest of storms in the atmosphere. Evaluation of this simulation confirms the benefits of high resolution found by other models with a few notable exceptions. These discrepancies with other studies are interesting because they provide a richer understanding of how and why resolution affects model bias. Another key finding is that climate and aerosol sensitivity in E3SM is unaffected by resolution change. This affirms the usefulness of coarser‐resolution models for understanding global‐scale climate change. This study also confirms the benefits of increased resolution for studying fine‐scale features such as hurricanes and orographic precipitation. Finally, the high‐resolution version of E3SM is shown to compare favorably to its low‐resolution counterpart and to the models participating in Phase 5 of the Coupled Model Intercomparison Project. Key Points: The high‐resolution E3SMv1 model was run for 50 years using 1950 forcing data according to the HighResMIP protocolHigher resolution and associated retuning improved bias relative to coarser versions of E3SMv1, particularly in ocean and sea ice metricsAerosol and climate sensitivity were relatively unaffected by resolution change; resolution‐related tuning had a larger impact [ABSTRACT FROM AUTHOR]

Climate simulations with more accurate process-level representation at finer resolutions (<100 km) are a pressing need in order to provide more detailed actionable information to policy makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building and running high-resolution (HR, here 0.25 ∘ average grid spacing at the Equator) models (HRMs). A more affordable path to HRMs is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low resolution (LR, 1 ∘). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces the precipitation metrics of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behavior of physical parameterizations, especially for variables influencing sub-grid-scale physical processes. RRMs can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a test bed and as such represents an efficient and more robust physics test bed capability. Our results provide additional confirmatory evidence that the RRM is an efficient and effective test bed for HRM development. [ABSTRACT FROM AUTHOR]

Climate simulation with more accurate process-level representation at finer resolutions is a pressing need in order to provide actionable information to policy-makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building, and running high-resolution (HR, here 0.25° average grid spacing at the equator) models (HRM). A more affordable path to HRM is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low-resolution (LR, 1°). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations, as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces both well- and poorly simulated behaviours of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behaviour of physical parameterizations, especially for variables influencing sub-grid scale physical processes. RRM can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a testbed, and as such represents an efficient and more robust physics testbed capability. Our results provide additional confirmatory evidence that the RRM is an efficient and effective approach for HRM development and hydrologic research. [ABSTRACT FROM AUTHOR]

Model calibration (or "tuning") is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets, and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target. [ABSTRACT FROM AUTHOR]

An ensemble of simulations covering the present day observational period using forced sea surface temperatures and prescribed sea-ice extent is configured with an 85 truncation resolution spectral transform dynamical core (T85) within the Community Atmosphere Model (CAM), version 4 and is evaluated relative to observed and model derived data sets and the one degree finite volume (FV) dynamical core. The spectral option provides a well-known base within the climate model community to assess climate behavior and statistics, and its relative computational efficiency for smaller computing platforms allows it to be extended to perform high-resolution climate length simulations. Overall, the quality of the T85 ensemble is similar to FV. Analyzing specific features of the T85 simulations show notable improvements to the representation of wintertime Arctic sea level pressure and summer precipitation over the Western Indian subcontinent. The mean and spatial patterns of the land surface temperature trends over the AMIP period are generally well simulated with the T85 ensemble relative to observations, however the model is not able to capture the extent nor magnitude of changes in temperature extremes over the boreal summer, where the changes are most dramatic. Biases in the wintertime Arctic surface temperature and annual mean surface stress fields persist with T85 as with the CAM3 version of T85, as compared to FV. An experiment to identify the source of differences between dycores has revealed that the longwave cloud forcing is sensitive to the choice of dycore, which has implications for tuning strategies of the physics parameter settings. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) model version 3.0.1 is used in both short-range (days) and long-range (years) simulations to explore the California wintertime model wet bias. California is divided into four regions (the coast, central valley, mountains, and Southern California) for validation. Three sets of gridded surface observations are used to evaluate the impact of measurement uncertainty on the model wet bias. Short-range simulations are driven by the North American Regional Reanalysis (NARR) data and designed to test the sensitivity of model physics and grid resolution to the wet bias using eight winter storms chosen from four major types of large-scale conditions: the Pineapple Express, El Niño, La Niña, and synoptic cyclones. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to assess the robustness of microphysics and cumulus parameterizations to resolution changes. Additionally, long-range simulations driven by both NARR and general circulation model (GCM) data are performed at low resolution to gauge the impact of the GCM forcing on the model wet bias. These short- and long-range simulations show that low-resolution runs tend to underpredict precipitation in the coast region and overpredict it elsewhere in California. The sensitivity test of WRF physics in short-range simulations indicates that model precipitation depends most strongly on the microphysics scheme, though convective parameterization is also important, particularly near the coast. In contrast, high-resolution (2 km) simulation increases model precipitation in all regions. As a result, it improves the forecast bias in the coast region while it downgrades the model performance in the other regions. It is also found that the choice of validation dataset has a significant impact on the model wet bias of both short- and long-range simulations. However, this impact in long-range simulations appears to be a secondary contribution as compared to its counterpart from the GCM forcing. [ABSTRACT FROM AUTHOR]