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Abstract Phytoplankton in the Arctic Ocean and sub‐Arctic seas support a rich marine food web that sustains Indigenous communities as well as some of the world's largest fisheries. As sea ice retreat leads to further expansion of these fisheries, there is growing need for predictions of phytoplankton net primary production (NPP), which will likely allow better management of food resources in the region. Here, we use perfect model simulations of the Community Earth System Model version 2 (CESM2) to quantify short‐term (month to 2 years) predictability of Arctic Ocean NPP. Our results indicate that NPP is potentially predictable during the most productive summer months for at least 2 years, largely due to the highly predictable Arctic shelves where fisheries in the Arctic are projected to expand. Sea surface temperatures, which are an important limitation on phytoplankton growth and also are predictable for multiple years, are the most important physical driver of this predictability. Finally, we find that the predictability of NPP in the 2030s is enhanced relative to the 2010s, indicating that the utility of these predictions may increase in the near future. This work indicates that operational forecasts using Earth system models may provide moderately skillful predictions of NPP in the Arctic, possibly aiding in the management of Arctic marine resources. 

Abstract To fulfill their conservation potential and provide safeguards for biodiversity, marine protected areas (MPAs) need coordinated research and monitoring for informed management through effective evaluation of ecosystem dynamics. However, coordination is challenging, often due to knowledge gaps caused by inadequate access to data and resources, compounded by insufficient communication between scientists and managers. We propose to use the world's largest MPA in the Ross Sea, Antarctica as a model system to create a comprehensive framework for an interdisciplinary network supporting research and monitoring that could be implemented in other remote large‐scale international MPAs. Our proposed framework has three key components: (i) policy engagement, including delineation of policy needs and ecosystem metrics to assess MPA effectiveness; (ii) community partner engagement to elevate diverse voices, build trust, and share resources; and (iii) integrated science comprising three themes. These themes are: advancement of data science and cyberinfrastructure to facilitate data synthesis and sharing; biophysical modeling towards understanding ecosystem changes and uncertainties; and execution of observational and process studies to address uncertainties and evaluate ecosystem metrics. This proposed framework can improve MPA implementation by generating policy‐relevant science through this coordinated network, which can in turn improve MPA effectiveness in the Ross Sea and beyond. 

The Arctic is undergoing a pronounced and rapid transformation in response to changing greenhouse gasses, including reduction in sea ice extent and thickness. There are also projected increases in near‐surface Arctic wind. This study addresses how the winds trends may be driven by changing surface roughness and/or stability in different Arctic regions and seasons, something that has not yet been thoroughly investigated. We analyze 50 experiments from the Community Earth System Model Version 2 (CESM2) Large Ensemble and five experiments using CESM2 with an artificially decreased sea ice roughness to match that of the open ocean. We find that with a smoother surface there are higher mean wind speeds and slower mean ice speeds in the autumn, winter, and spring. The artificially reduced surface roughness also strongly impacts the wind speed trends in autumn and winter, and we find that atmospheric stability changes are also important contributors to driving wind trends in both experiments. In contrast to the clear impacts on winds, the sea ice mean state and trends are statistically indistinguishable, suggesting that near‐surface winds are not major drivers of Arctic sea ice loss. Two major results of this work are: (a) the near‐surface wind trends are driven by changes in both surface roughness and near‐surface atmospheric stability that are themselves changing from sea ice loss, and (b) the sea ice mean state and trends are driven by the overall warming trend due to increasing greenhouse gas emissions and not significantly impacted by coupled feedbacks with the surface winds. 

We assess Antarctic sea ice climatology and variability in version 2 of the Community Earth System Model (CESM2), and compare it to that in the older CESM1 and (where appropriate) real-world observations. In CESM2, Antarctic sea ice is thinner and less extensive than in CESM1, though sea ice area is still approximately 1 million km2 greater in CESM2 than in present-day observations. Though there is less Antarctic sea ice in CESM2, the annual cycle of ice growth and melt is more vigorous in CESM2 than in CESM1. A new mushy-layer thermodynamics formulation implemented in the latest version of the Community Ice Code (CICE) in CESM2 accounts for both greater frazil ice forma- tion in coastal polynyas and more snow-to-ice conversion near the edge of the ice pack in the new model. Greater winter ice divergence in CESM2 (relative to CESM1) is due to stronger stationary wave activity and greater wind stress curl over the ice pack. Greater wind stress curl, in turn, drives more warm water upwelling under the ice pack, thinning it and decreasing its extent. Overall, differences between Antarctic sea ice in CESM2 and CESM1 arise due to both differences in their sea ice thermodynamics formulations, and differences in their coupled atmosphere-ocean states. 

Abstract. In recent decades, Arctic sea ice has shifted toward ayounger, thinner, seasonal ice regime. Studying and understanding this“new” Arctic will be the focus of a year-long ship campaign beginning inautumn 2019. Lagrangian tracking of sea ice floes in the Community EarthSystem Model Large Ensemble (CESM-LE) during representative “perennial”and “seasonal” time periods allows for understanding of the conditionsthat a floe could experience throughout the calendar year. These modeltracks, put into context a single year of observations, provide guidance onhow observations can optimally shape model development, and how climatemodels could be used in future campaign planning. The modeled floe tracksshow a range of possible trajectories, though a Transpolar Drift trajectoryis most likely. There is also a small but emerging possibility of high-risktracks, including possible melt of the floe before the end of a calendaryear. We find that a Lagrangian approach is essential in order to correctlycompare the seasonal cycle of sea ice conditions between point-basedobservations and a model. Because of high variability in the melt season seaice conditions, we recommend in situ sampling over a large range of ice conditionsfor a more complete understanding of how ice type and surface conditionsaffect the observed processes. We find that sea ice predictability emergesrapidly during the autumn freeze-up and anticipate that process-basedobservations during this period may help elucidate the processes leading tothis change in predictability.