This work documents the first version of the U.S. Department of Energy (DOE) new EnergyExascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully coupled physical model designed to address DOE mission-relevant water cycle questions. Its components include atmosphere and land (110-km grid spacing), ocean and sea ice (60 km in the midlatitudes and 30 km at the equator and poles), and river transport (55 km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima simulations consisting of a long preindustrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO 2 forcing simulations. The model performs well overall with biases typical of other CMIP-class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP-class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between preindustrial (1850) and present day, the trajectory of the warming diverges from observations in the Key Points: • This work documents E3SMv1, the first version of the U.S. DOE Energy Exascale Earth System Model • The performance of E3SMv1 is documented with a set of standard CMIP6 DECK and historical simulations comprising nearly 3,000 years • E3SMv1 has a high equilibrium climate sensitivity (5.3 K) and strong aerosol-related effective radiative forcing (-1.65 W/m 2 ) Correspondence to: Chris Golaz, golaz1@llnl.gov Citation: Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., et al. (2019). The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution. second half of the twentieth century with a period of delayed warming followed by an excessive warming trend. Using a two-layer energy balance model, we attribute this divergence to the model's strong aerosol-related effective radiative forcing (ERF ari+aci = −1.65 W/m 2 ) and high equilibrium climate sensitivity (ECS = 5.3 K). Plain Language Summary The U.S. Department of Energy funded the development of a new state-of-the-art Earth system model for research and applications relevant to its mission. The Energy Exascale Earth System Model version 1 (E3SMv1) consists of five interacting components for the global atmosphere, land surface, ocean, sea ice, and rivers. Three of these components (ocean, sea ice, and river) are new and have not been coupled into an Earth system model previously. The atmosphere and land surface components were created by extending existing components part of the Community Earth System Model, Version 1. E3SMv1's capabilities are demonstrated by performing a set of standardized simulation experiments described by...

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An ice ecosystem model was coupled to a global dynamic sea ice model to assess large‐scale variability of primary production and ice algal biomass within arctic sea ice. The component models are the Physical Ecosystem Model (PhEcoM) ice ecosystem model and the Los Alamos Sea Ice Model (CICE). Simulated annual arctic sea ice primary production was 15.1 Tg C; within the range of 9 to 73 Tg C estimated using in situ data. The amount of C fixed was >3 Tg C month−1 for March, April, and May. The Bering Sea, Arctic Ocean basins, and the Canadian Archipelago/Baffin Bay were the most productive regions on an annual basis, contributing approximately 24, 18, and another 18%, respectively. High production in the Bering Sea was due to high daily production rates, while the large sea ice coverage in the Canadian Archipelago/Baffin Bay and, in particular, the Arctic Ocean basins resulted in their considerable contribution to sea ice primary production. The simulated trends, patterns, and seasonality of ice algae agree reasonably well with very limited observations. In the model, ice growth rate controls the availability of nutrients to sea ice algae, such that ocean nutrient supply is of secondary importance to ice algal growth. The numerical model results suggest that ice melt rate, which determines the proportional rate of ice algal release, controls the termination of the bloom on large scales. The model described advances the role of sea ice algae in biogeochemical cycling within global climate models.

[1] A dynamic model is constructed for interactive silicon, nitrogen, sulfur processing in and below Arctic sea ice, by ecosystems residing in the lower few centimeters of the distributed pack. A biogeochemically active bottom layer supporting sources/sinks for the pennate diatoms is appended to thickness categories of a global sea ice code. Nutrients transfer from the ocean mixed layer to drive algal growth, while sulfur metabolites are reinjected from the ice interface. Freeze, flux, flush and melt processes are linked to multielement geocycling for the entire high-latitude regime. Major element kinetics are optimized initially to reproduce chlorophyll observations, which extend across the seasons. Principal influences on biomass are solute exchange velocity at the solid interface, optical averaging in active ice and cell retention against ablation. The sulfur mechanism encompasses open water features such as accumulation of particulate dimethyl sulfoniopropionate, grazing and other disruptive releases, plus bacterial/enzymatic conversion to volatile dimethyl sulfide. For baseline settings, the mixed layer trace gas distribution matches sparging measurements where they are available. However, concentrations rise to well over 10 nM in remote, unsampled locations. Peak contributions are supported by ice grazing, mortality and fractional melting. The model bottom layer adds substantially to a ring maximum of reduced sulfur chemistry that may be dominant across the marginal Arctic environment. Sensitivity tests on this scenario include variation of cell sulfur composition and remineralization, routings/chemical time scales, and the physical dimension of water layers. An alternate possibility that peripheral additions are small cannot be excluded from the outcomes. It is concluded that seagoing dimethyl sulfide data are far too sparse at the present time to distinguish sulfur-ice production levels.Citation: Elliott, S., C. Deal, G. Humphries, E. Hunke, N. Jeffery, M. Jin, M. Levasseur, and J. Stefels (2012), Pan-Arctic simulation of coupled nutrient-sulfur cycling due to sea ice biology: Preliminary results,