Abstract
Understanding processes driving air-sea gas transfer and being able to model both its mean and variability are critical for studies of climate and carbon cycle. The air-sea gas transfer velocity (K (660)) is almost universally parameterized as a function of wind speed in large scale models-an oversimplification that buries the mechanisms controlling K (660) and neglects much natural variability. Sea state has long been speculated to affect gas transfer, but consistent relationships from in situ observations have been elusive. Here, applying a machine learning technique to an updated compilation of shipboard direct observations of the CO(2) transfer velocity (K (CO2,660)), we show that the inclusion of significant wave height improves the model simulation of K (CO2,660), while parameters such as wave age, wave steepness, and swell-wind directional difference have little influence on K (CO2,660). Wind history is found to be important, as in high seas K (CO2,660) during periods of falling winds exceed periods of rising winds by ∼20% in the mean. This hysteresis in K (CO2,660) is consistent with the development of waves and increase in whitecap coverage as the seas mature. A similar hysteresis is absent from the transfer of a more soluble gas, confirming that the sea state dependence in K (CO2,660) is primarily due to bubble-mediated gas transfer upon wave breaking. We propose a new parameterization of K (CO2,660) as a function of wind stress and significant wave height, which resemble observed K (CO2,660) both in the mean and on short timescales.