Statistical Downscaling of Seasonal Forecasts of Sea Level Anomalies for US Coasts

Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scales. In this study, we explore statistical downscaling of monthly hindcasts from six current seasonal prediction systems to provide a high-resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally invariant downscaling operator, constructing by linearly regressing high-resolution (1/12 degrees) ocean reanalysis data against its coarse-grained (1 degrees) counterpart, to each hindcast ensemble member for the period 1982-2011. The resulting high-resolution coastal hindcasts have significantly more deterministic skill than the original hindcasts interpolated onto the high-resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable patterns while damping the less predictable patterns.

Automated summary

In this study, we used statistical downscaling to provide high-resolution predictions of sea level anomalies along the North American coast. By applying a seasonally invariant downscaling operator to monthly hindcasts from six seasonal prediction systems, we achieved significantly improved deterministic skill compared to the original interpolated hindcasts. This improvement was most pronounced in the summer and fall seasons.