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Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.

Climate simulations with more accurate process-level representation at finer resolutions ( 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 The Single Column Atmosphere Model (SCAM) is a single column model version of the Community Atmosphere Model (CAM). Here we describe the functionality and features of SCAM6, available as part of CAM6 in the Community Earth System Model, version 2 (CESM2). SCAM6 features a wide selection of standard cases, as well as the ability to easily configure a case specified by the user based on a particular point in a CAM 3‐D simulation. This work illustrates how SCAM6 reproduces CAM6 results for physical parameterizations, mostly of moisture and clouds. We demonstrate how SCAM6 can be used for model development through different physics selections, as well as with parameter sweep experiments to highlight the sensitivity of cloud properties to the specification of the vapor deposition process in the cloud microphysics. Furthermore, we use SCAM6 to illustrate the sensitivity of CAM6 cloud radiative properties and precipitation to variable drop number (cloud microphysics properties). Finally, we illustrate how SCAM6 can be used to explore critical emergent processes such as cloud feedbacks and show that CAM6 cloud responses to surface warming in stratus and stratocumulus regimes are similar to those in CAM5. CAM6 has a larger response in the shallow cumulus regime than CAM5. CAM6 cloud feedbacks in the shallow cumulus regime are sensitive to turbulence parameters. SCAM6 is thus a valuable tool for model development, evaluation, and scientific analy sis and an important part of the model hierarchy in Community Earth System Model, version 2.

Abstract It is the purpose of this paper to provide a comprehensive documentation of the new NCAR (National Center for Atmospheric Research) version of the spectral element (SE) dynamical core as part of the Community Earth System Model (CESM2.0) release. This version differs from previous releases of the SE dynamical core in several ways. Most notably the hybrid sigma vertical coordinate is based on dry air mass, the condensates are dynamically active in the thermodynamic and momentum equations (also referred to as condensate loading), and the continuous equations of motion conserve a more comprehensive total energy that includes condensates. Not related to the vertical coordinate change, the hyperviscosity operators and the vertical remapping algorithms have been modified. The code base has been significantly reduced, sped up, and cleaned up as part of integrating SE as a dynamical core in the CAM (Community Atmosphere Model) repository rather than importing the SE dynamical core from High‐Order Methods Modeling environment as an external code.

Abstract The spectral element (SE) variable‐resolution (VR) mesh dynamical core is tested in developmental versions of the Community Earth System Model version 2 (CESM2). The SE dynamical core is tested in baroclinic wave, aquaplanet and full physics configurations to evaluate variable‐resolution simulations against uniform high and uniform low‐resolution simulations. Different physical parameterization suites are also evaluated to gauge their sensitivity to resolution. Dry dynamical core variable‐resolution cases compare well to high‐resolution tests. More recent versions of the atmospheric physics, including cloud schemes for CESM2, are less sensitive to changes in horizontal resolution. Most of the sensitivity is due to sensitivity to time step and interactions between deep convection and large‐scale condensation, which is expected from the closure methods. The resulting full physics SE‐VR model produces a similar climate to the global low‐resolution mesh and similar high‐frequency statistics in the high‐resolution region. The SE‐VR simulations are able to reproduce uniform high‐resolution results, making them an effective tool for regional climate simulations at lower computational cost. Some biases are reduced (orographic precipitation in Western United States), but biases do not necessarily go away at high resolution (e.g., summertime surface temperatures). Variable‐resolution grids are a viable alternative to traditional nesting for regional climate studies and are available in CESM2.

This paper documents coupled simulations of two developmental versions of the Community Atmosphere Model (CAM) towards CAM6. The configuration called CAM5.4 introduces new microphysics, aerosol, and ice nucleation changes, among others to CAM. The CAM5.5 configuration represents a more radical departure, as it uses an assumed probability density function (PDF)-based unified cloud parameterization to replace the turbulence, shallow convection, and warm cloud macrophysics in CAM. This assumed PDF method has been widely used in the last decade in atmosphere-only climate simulations but has never been documented in coupled mode. Here, we compare the simulated coupled climates of CAM5.4 and CAM5.5 and compare them to the control coupled simulation produced by CAM5.3. We find that CAM5.5 has lower cloud forcing biases when compared to the control simulations. Improvements are also seen in the simulated amplitude of the Niño-3.4 index, an improved representation of the diurnal cycle of precipitation, subtropical surface wind stresses, and double Intertropical Convergence Zone biases. Degradations are seen in Amazon precipitation as well as slightly colder sea surface temperatures and thinner Arctic sea ice. Simulation of the 20th century results in a credible simulation that ends slightly colder than the control coupled simulation. The authors find this is due to aerosol indirect effects that are slightly stronger in the new version of the model and propose a solution to ameliorate this. Overall, in these early coupled simulations, CAM5.5 produces a credible climate that is appropriate for science applications and is ready for integration into the National Center for Atmospheric Research's (NCAR's) next-generation climate model.

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.

Climate simulation codes, such as the Community Earth System Model (CESM), are especially complex and continually evolving. Their ongoing state of development requires frequent software verification in the form of quality assurance to both preserve the quality of the code and instill model confidence. To formalize and simplify this previously subjective and computationally expensive aspect of the verification process, we have developed a new tool for evaluating climate consistency. Because an ensemble of simulations allows us to gauge the natural variability of the model's climate, our new tool uses an ensemble approach for consistency testing. In particular, an ensemble of CESM climate runs is created, from which we obtain a statistical distribution that can be used to determine whether a new climate run is statistically distinguishable from the original ensemble. The CESM ensemble consistency test, referred to as CESM-ECT, is objective in nature and accessible to CESM developers and users. The tool has proven its utility in detecting errors in software and hardware environments and providing rapid feedback to model developers.