Deep neural networks (DNNs) are implemented in Super‐Parameterized Energy Exascale Earth System Model (SP‐E3SM) to imitate the shortwave and longwave radiative transfer calculations. These DNNs were able to emulate the radiation parameters with an accuracy of 90–95% at a cost of 8–10 times cheaper than the original radiation parameterization. A comparison of time‐averaged radiative fluxes and the prognostic variables manifested qualitative and quantitative similarity between the DNN emulation and the original parameterization. It has also been found that the differences between the DNN emulation and the original parameterization are comparable to the internal variability of the original parameterization. Although the DNNs developed in this investigation emulate the radiation parameters for a specific set of initial conditions, the results justify the need of further research to generalize the use of DNNs for the emulations of full model radiation and other parameterization for seasonal predictions and climate simulations.

Results from the new Department of Energy super-parameterized (SP) Energy ExascaleEarth System Model (SP-E3SM) are analyzed and compared to the traditionally parameterized E3SMv1 and previous studies using SP models. SP-E3SM is unique in that it utilizes Graphics Processing Unit hardware acceleration, cloud resolving model mean-state acceleration, and reduced radiation to dramatically increase the model throughput and allow decadal experiments at 100-km external resolution. It also differs from other SP models by using a spectral element dynamical core on a cubed-sphere grid and a finer vertical grid with a higher model top. Despite these differences, SP-E3SM generally reproduces the behavior of other SP models. Tropical wave variability is improved relative to E3SM, including the emergence of a Madden-Julian Oscillation and a realistic slowdown of Moist Kelvin Waves. However, the distribution of precipitation exhibits indicates an overly frequent occurrence of rain rates less than 1 mm day −1 , and while the timing of diurnal rainfall shows modest improvements the signal is not as coherent as observations. A notable grid imprinting bias is identified in the precipitation field and attributed to a unique feedback associated with the interactions between the explicit cloud resolving model convection and the spectral element grid structure. Spurious zonal mean column water tendencies due to grid imprinting are quantified-while negligible for the conventionally parameterized E3SM, they become large with super-parameterization, approaching 10% of the physical tendencies. The implication is that finding a remedy to grid imprinting will become especially important as spectral element dynamical cores begin to be combined with explicitly resolved convection.

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The porting of a key kernel in the tracer advection routines of the Community Atmosphere Model-Spectral Element (CAM-SE) to use Graphics Processing Units (GPUs) using Ope-nACC is considered in comparison to an existing CUDA FORTRAN port. The development of the OpenACC kernel for GPUs was substantially simpler than that of the CUDA port. Also, OpenACC performance was about 1.5x slower than the optimized CUDA version. Particular focus is given to compiler maturity regarding OpenACC implementation for modern fortran, and it is found that the Cray implementation is currently more mature than the PGI implementation. Still, for the case that ran successfully on PGI, the PGI OpenACC runtime was slightly faster than Cray. The results show encouraging performance for OpenACC implementation compared to CUDA while also exposing some issues that may be necessary before the implementations are suitable for porting all of CAM-SE. Most notable are that GPU shared memory should be used by future OpenACC implementations and that derived type support should be expanded.

Modern computer architectures reward added computation if it reduces algorithmic dependence, reduces data movement, increases accuracy/robustness, and improves memory accesses. The driving motive for this study is to develop a numerical algorithm that respects these constraints while improving accuracy and robustness. This study introduces the ADER‐DT (Arbitrary DERivatives in time and space‐differential transform) time discretization to positive‐definite, weighted essentially nonoscillatory (WENO)‐limited, finite volume transport on the cubed sphere in lieu of semidiscrete integrators. The cost of the ADER‐DT algorithm is significantly improved from previous implementations without affecting accuracy. A new function‐based WENO implementation is also detailed for use with the ADER‐DT time discretization. While ADER‐DT costs about 1.5 times more than a fourth‐order, five‐stage strong stability preserving Runge‐Kutta (SSPRK4) method, it is far more computationally dense (which is advantageous on accelerators such as graphics processing units), and it has a larger effective maximum stable time step. ADER‐DT errors converge more quickly with grid refinement than SSPRK4, giving 6.5 times less error in the L∞ norm than SSPRK4 at the highest refinement level for smooth data. For nonsmooth data, ADER‐DT resolves C0 discontinuities more sharply. For a complex flow field, ADER exhibits less phase error than SSPRK4. Improving both accuracy and robustness as well as better respecting modern computational efficiency requirements, we believe the method presented herein is competitive for efficiently transporting tracers over the sphere for applications targeting modern computing architectures.

We present a methodology for solution reproducibility for the Energy Exascale Earth System Model during its ongoing software infrastructure development to prepare for exascale computers. The nonlinear chaotic nature of climate system simulations precludes traditional model verification approaches since machine precision differences—resulting from code refactoring, changes in software environment, and so on—grow exponentially to a different weather state. Here, we leverage the nature of climate as a statistical description of the atmosphere in order to establish model reproducibility. We evaluate the degree to which two-sample equality of distribution tests can confidently detect the change in climate from minor tuning parameter changes on model output variables in order to establish the level of difference that indicates a new climate. To apply this (baselined test), we target a section of the model’s development cycle wherein no intentional science changes have been applied to its source code. We compare an ensemble of short simulations that were conducted using a verified model configuration against a new ensemble with the same configuration but with the latest software infrastructure (Common Infrastructure for Modeling the Earth, CIME5.0), compiler versions, and software libraries. We also compare these against ensemble simulations conducted using the original version of the software infrastructure (CIME4.0) of the earlier model configuration, but with the latest compilers and software libraries, to test the impact of new compilers and libraries in isolation from additional software infrastructure. The two-sample equality of distribution tests indicates that these ensembles indeed represent the same climate.