Comparisons between data assimilated HYCOM output and in situ Argo measurements in the Bay of Bengal

Friday, 19 December 2014
Earle Andre Wilson and Stephen Riser, University of Washington Seattle Campus, Seattle, WA, United States
This study evaluates the performance of data assimilated Hybrid Coordinate Ocean Model (HYCOM) output for the Bay of Bengal from September 2008 through July 2013. We find that while HYCOM assimilates Argo data, the model still suffers from significant temperature and salinity biases in this region. These biases are most severe in the northern Bay of Bengal, where the model tends to be too saline near the surface and too fresh at depth. The maximum magnitude of these biases is approximately 0.6 PSS. We also find that the model's salinity biases have a distinct seasonal cycle. The most problematic periods are the months following the summer monsoon (Oct-Jan). HYCOM's near surface temperature estimates compare more favorably with Argo, but significant errors exist at deeper levels. We argue that optimal interpolation will tend to induce positive salinity biases in the northern regions of the Bay. Further, we speculate that these biases are introduced when the model relaxes to climatology and assimilates real-time data.