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Abstract The ocean removes man-made (anthropogenic) carbon from the atmosphere and thereby mitigates climate change. Observations from global hydrographic surveys reveal the spatial and temporal evolution of the ocean inventory of anthropogenic carbon and suggest substantial decadal variability in historical storage rates. Here, we use a 100-member ensemble of an Earth system model to investigate the influence of external forcing and internal climate variability on historical changes in ocean anthropogenic carbon storage over 1994 to 2014. Our findings reveal that the externally forced, decadal changes in storage are largest in the Atlantic (2–4 mmol m⁻³decade⁻¹) and positive nearly everywhere. Internal climate variability modulates regional ocean anthropogenic carbon storage trends by up to 10 mmol m⁻³decade⁻¹. The influence of internal climate variability on decadal storage changes is most prominent at depths of ∼300 m and at the edges of the subtropical gyres. Internal variability in anthropogenic carbon in the extratropics has high spectral power on decadal to multi-decadal timescales, indicating that the approximately decadal repetitions of hydrographic surveys may produce storage change estimates that are heavily influenced by internal climate variability. 

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 Pinatubo erupted during the first decadal survey of ocean biogeochemistry, embedding its climate fingerprint into foundational ocean biogeochemical observations and complicating the interpretation of long‐term biogeochemical change. Here, we quantify the influence of the Pinatubo climate perturbation on externally forced decadal and multi‐decadal changes in key ocean biogeochemical quantities using a large ensemble simulation of the Community Earth System Model designed to isolate the effects of Pinatubo, which cleanly captures the ocean biogeochemical response to the eruption. We find increased uptake of apparent oxygen utilization and preindustrial carbon over 1993–2003. Nearly 100% of the forced response in these quantities are attributable to Pinatubo. The eruption caused enhanced ventilation of the North Atlantic, as evidenced by deep ocean chlorofluorocarbon changes that appear 10–15 years after the eruption. Our results help contextualize observed change and contribute to improved constraints on uncertainty in the global carbon budget and ocean deoxygenation. 

Abstract Anthropogenic carbon emissions and associated climate change are driving rapid warming, acidification, and deoxygenation in the ocean, which increasingly stress marine ecosystems. On top of long‐term trends, short term variability of marine stressors can have major implications for marine ecosystems and their management. As such, there is a growing need for predictions of marine ecosystem stressors on monthly, seasonal, and multi‐month timescales. Previous studies have demonstrated the ability to make reliable predictions of the surface ocean physical and biogeochemical state months to years in advance, but few studies have investigated forecast skill of multiple stressors simultaneously or assessed the forecast skill below the surface. Here, we use the Community Earth System Model (CESM) Seasonal to Multiyear Large Ensemble (SMYLE) along with novel observation‐based biogeochemical and physical products to quantify the predictive skill of dissolved inorganic carbon (DIC), dissolved oxygen, and temperature in the surface and subsurface ocean. CESM SMYLE demonstrates high physical and biogeochemical predictive skill multiple months in advance in key oceanic regions and frequently outperforms persistence forecasts. We find up to 10 months of skillful forecasts, with particularly high skill in the Northeast Pacific (Gulf of Alaska and California Current Large Marine Ecosystems) for temperature, surface DIC, and subsurface oxygen. Our findings suggest that dynamical marine ecosystem prediction could support actionable advice for decision making. 

Abstract The decline in global emissions of carbon dioxide due to the COVID‐19 pandemic provides a unique opportunity to investigate the sensitivity of the global carbon cycle and climate system to emissions reductions. Recent efforts to study the response to these emissions declines has not addressed their impact on the ocean, yet ocean carbon absorption is particularly susceptible to changing atmospheric carbon concentrations. Here, we use ensembles of simulations conducted with an Earth system model to explore the potential detection of COVID‐related emissions reductions in the partial pressure difference in carbon dioxide between the surface ocean and overlying atmosphere (ΔpCO₂), a quantity that is regularly measured. We find a unique fingerprint in global‐scale ΔpCO₂that is attributable to COVID, though the fingerprint is difficult to detect in individual model realizations unless we force the model with a scenario that has four times the observed emissions reduction.