Statistical Downscaling of Seasonal Forecast of Sea Level Anomalies for US Coasts | CU Experts

Increasing coastal inundation risk in a warming climate will require; accurate and reliable seasonal forecasts of sea level anomalies at fine; spatial scale. In this study, we explore statistical downscaling of; monthly hindcasts from six current seasonal prediction systems to; provide 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 (1/12º) ocean; reanalysis data against its coarse-grained (1º) counterpart, to each; hindcast ensemble member for the period 1982-2011. The resulting high; resolution coastal hindcasts are significantly more skillful 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 pattern while damping the less predictable pattern.

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