Remote sensing, modeling, and synthesis: On the development of a global ocean wind/wave climatology and its application to sensitive climate parameters

In this study, data from TOPEX satellite altimetry is combined with ERA40 (ECMWF 40-year reanalysis) and non-data-assimilating WaveWatch3 model output to develop a comprehensive 2.5°x2.5° monthly global climatology of wind and wave properties useful in determining the global extent of Langmuir mixing. The climatology is forged from data covering the years 1994 - 2001. The variables mapped include: significant wave height, mean wave period, 10-meter atmospheric wind speed, skin friction velocity, wind direction, and wave direction. Further computation of surface Stokes drift and Langmuir number from these parameters exhibits sensitivity to data from the climatology, demonstrating its applicability and limitations for use as Langmuir turbulence forcing.;Agreement among the three data sources in the climatology is better than 90% for most basic wind/wave variables in the climatology, with wave period and wave direction showing the most disagreement. However, small disagreements in simple wave parameters lead to large discrepancies (approaching 50-100%) in estimates of Stokes drift and Langmuir number.;The average Langmuir number worldwide was found to be near 0.3 in regions of aligned wind and waves, but significantly less in trade wind regions. Scatter between the three sources in the average worldwide Langmuir number is 0.28 -- 0.40, with the best-guess world average being ∼0.35. Further study of the resulting Langmuir number climatology reveals that the choice of Langmuir number definition has an impact on the statistics of the result by skewing the resulting Langmuir number histogram. Because of this, care should be taken to ensure proper use of means, medians, and standard deviations.;This study shows that comparing data assimilating and non-assimilating models illuminates the magnitude of missing model physics, and provides a check on the usefulness of model data versus empirical data. Context is gained by comparing multiple data sources rather than using just one.