This work documents version two of the Department of Energy’s Energy Exascale Earth System Model (E3SM). E3SM version 2 (E3SMv2) is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid-latitudes and 30 km at the equator and poles. The model performance is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima (DECK) simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate is generally realistic, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Programme (WCRP) assessment. However, E3SMv2 significantly underestimates the global mean surface temperature in the second half of the historical record. An analysis of single-forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol-related forcing.

The accurate simulation of oceanic turbulence is crucial to understanding global ocean circulation, impacting accurate prediction of global warming effects and national security problems. Nonetheless, high fidelity simulations are difficult due to the ocean’s complex physics and broad range of spatial and temporal scales. These range from the smallest dissipative scales with typical lengths of millimeters, to the largest energetic mesoscale eddies with characteristic wavelengths exceeding tens of kilometers. Whereas the accurate parameterization of all flow scales with the Reynolds-averaged Navier-Stokes equations (RANS) is nearly impossible, resolving all scales of turbulence through a direct numerical simulation (DNS) is beyond the capabilities of the most powerful supercomputers in the foreseeable future. Hence, efficient parameterizations are needed. In this work, we extend the bridging partially-averaged Navier-Stokes equations (PANS) model to ocean flows. This parameterization operates between RANS and DNS, and aims only to resolve the scales not amenable to modeling in order to increase the efficiency of ocean computations. We also propose a PANS scale-aware closure to model the unresolved scales. The initial validation tests of the new PANS model include two representative ocean channel flows. The results confirm the potential of the new parameterization to predict oceanographically relevant turbulence efficiently.

We present a new data assimilation algorithm known as the Continuous Data Assimilation (CDA) algorithm that has been tested extensively in the mathematical literature and, most recently, in a downscaling simulation in the atmospheric literature. Unlike more common data assimilation methods, the CDA algorithm has an exponential convergence rate and is computationally efficient. This work is the first attempt to demonstrate the viability of the data assimilation algorithm in large-scale ocean models. We implement the CDA algorithm in the Model for Prediction Across Scales - Ocean in an idealized mesoscale eddy test case, demonstrating the ability of the data assimilation algorithm to capture the net effects of unresolved processes in low-resolution models.