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Abstract The Southern Ocean is an important region for both heat and carbon uptake, due in large part to wind-driven circulation. This region also continually experiences strong winds associated with the passage of synoptic storms, which influence the upper ocean through strong fluxes of momentum, heat, freshwater, and gases. While studies have found that storms can induce strong carbon outgassing, their role in the combined heat and carbon uptake remains unknown. In this work, we explore the climatological impact of storms on the Southern Ocean combined heat and carbon uptake through two preindustrial coupled climate model runs with contrasting seasonal carbon fluxes. We use a feature tracking system to identify storms and create composites for storm-following and post-storm anomalous fluxes of heat and carbon. Storms induce a net anomalous release of heat and carbon from the ocean throughout the year, with clear seasonality in the magnitude of the fluxes that coincide with the background seasonal cycles. We find a strong model dependency for the storm-driven anomalous carbon fluxes, both in terms of the seasonal range and timing of maximum outgassing. Storm-induced anomalous fluxes are dampened on the order of days after the storm passes, with a small continued release of heat that is most persistent in the winter. Our study underlines the high uncertainty about the seasonal nature of storm impacts on the ocean and suggests that evolving atmospheric and oceanic conditions could impose opposing shifts in the future seasonality of storm impacts. 

Abstract Southern Ocean (SO) phytoplankton chlorophyll is highly variable on sub‐seasonal time scales. Although the SO is the windiest ocean basin globally, it is not conclusively understood how storms impact SO phytoplankton dynamics. Much of our existing knowledge stems from satellites, but biases due to data gaps from cloud cover and low solar angles remain unquantified. Here, we use ocean–sea‐ice simulations with the Community Earth System Model to quantify the climatological 1997–2018 imprint of storms on chlorophyll and phytoplankton dynamics in the ice‐free SO. Additionally, by comparing the full‐field model output to synthetic satellite observations, we quantify sampling biases in satellite‐derived estimates. We find that both the sign and the magnitude of the average surface chlorophyll imprint vary substantially across storms but last for at least 4 days after the storm passing. Based on our analysis, more than one third of the storms explain the majority of local non‐seasonal chlorophyll variability, but satellite‐derived storm imprints are often too large in magnitude. On the day of the storm passing, changes in vertical mixing predominantly cause surface chlorophyll anomalies, and reduced light availability due to enhanced cloud cover outweighs the enhanced nutrient availability due to entrainment. Interestingly, storms imprint differently on total net primary production than on surface chlorophyll, demonstrating the difficulty to derive carbon‐cycle impacts from a surface‐chlorophyll assessment. With SO future storm activity projected to increase, complementing satellite observations with other observing technologies, for example, profiling floats, is necessary to better constrain how storms impact biological carbon cycling in the SO. 

Abstract. The ocean carbon store plays a vital role in setting the carbon response to emissions and variability in the carbon cycle. However, due to the ocean's strong regional and temporal variability, sparse carbon observations limit our understanding of historical carbon changes.Ocean temperature and salinity profiles are more widespread and rapidly expanding due to autonomous programmes, and so we explore how temperature and salinity profiles can provide information to reconstruct ocean carbon inventories with ensemble optimal interpolation. Here, ensemble optimal interpolation is used to reconstruct ocean carbon using synthetic Argo temperature and salinity observations, with examples for both the top 100 m and top 2000 m carbon inventories.When considering reconstructions of the top 100 m carbon inventory, coherent relationships between upper-ocean carbon, temperature, salinity, and atmospheric CO2 result in optimal solutions that reflect the controls of undersaturation, solubility, and alkalinity.Out-of-sample reconstructions of the top 100 m show that, in most regions, the trend in ocean carbon and over 60 % of detrended variability can be reconstructed using local temperature and salinity measurements, with only small changes when considering synthetic profiles consistent with irregular Argo sampling.Extending the method to reconstruct the upper 2000 m reveals that model uncertainties at depth limit the reconstruction skill.The impact of these uncertainties on reconstructing the carbon inventory over the upper 2000 m is small, and full reconstructions with historical Argo locations show that the method can reconstruct regional inter-annual and decadal variability.Hence, optimal interpolation based on model relationships combined with hydrographic measurements can provide valuable information about global ocean carbon inventory changes.