Leveraging Modern Oceanic Sampling Technologies to Improve Climate Models

The exchange of carbon between the atmosphere and ocean is a fundamental control on global climate across timescales from years to centuries and beyond. Improving simulations and forecasts of this exchange is key to efforts to improve climate projections. However, it is poorly constrained by data, especially in crucial high-latitude regions where the exchange can be the strongest, but the remoteness and inhospitable conditions present significant challenges to observational programs.

Recent technological innovations are now enabling a new paradigm for sampling oceanic physical and biogeochemical variables. In addition to underwater floats and gliders, autonomous surface vehicles (ASVs) have the capability of directly measuring the rate of transfer of CO₂ between the atmosphere and the ocean in real-time with dynamic spatial capacity. If deployed at scale, this would significantly expand the available data set, offering the potential to constrain and validate model projections, with the model output fields used to optimize the sampling strategy.

This new sampling paradigm presents an opportunity to collect data, assimilate it into appropriately-constructed models, analyze model sensitivities to determine where additional data would best reduce uncertainties, and direct further data gathering accordingly. As such, it has the potential to transform the collection of appropriate surface flux data, the use of this data in model schemes, and the deployment of new methods for “ground-truthing” models to establish predictive skill. This poster presents the initial concepts and progress of a collaboration that has been initiated to develop and implement this approach.