Constraining Southern Ocean Air-Sea-Ice Fluxes Through Enhanced Observations - Swart, Sebastiaan; Gille, Sarah T.; Delille, Bruno; Josey, Simon; Mazloff, Matthew; Newman, Louise; Thompson, Andrew F.; Thomson, Jim; Ward, Brian; du Plessis, Marcel D.; Kent, Elizabeth C.; Girton, James; et al. (Frontiers in Marine Science, 2019)
Air-sea and air-sea-ice fluxes in the Southern Ocean play a critical role in global climate through their impact on the overturning circulation and oceanic heat and carbon uptake. The challenging conditions in the Southern Ocean have led to sparse spatial and temporal coverage of observations. This has led to a ‘knowledge gap’ that increases uncertainty in atmosphere and ocean dynamics and boundary-layer thermodynamic processes, impeding improvements in weather and climate models. Improvements will require both process-based research to understand the mechanisms governing air-sea exchange and a significant expansion of the observing system. This will improve flux parameterizations and reduce uncertainty associated with bulk formulae and satellite observations. Improved estimates spanning the full Southern Ocean will need to take advantage of ships, surface moorings, and the growing capabilities of autonomous platforms with robust and miniaturized sensors. A key challenge is to identify observing system sampling requirements. This requires models, Observing System Simulation Experiments (OSSEs), and assessments of the specific spatial-temporal accuracy and resolution required for priority science and assessment of observational uncertainties of the mean state and direct flux measurements. Year-round, high-quality, quasi-continuous in situ flux measurements and observations of extreme events are needed to validate, improve and characterize uncertainties in blended reanalysis products and satellite data as well as to improve parameterizations. Building a robust observing system will require community consensus on observational methodologies, observational priorities, and effective strategies for data management and discovery.