Seasonal Forecasts of Bottom Temperatures along the North American West Coast

Bottom Temperature (BT) along the North American West Coast strongly influences benthic and demersal marine species. However, since high-resolution coastal-wide data and prediction systems are lacking, seasonal BT forecast efforts have been limited and sources of BT predictability largely undiagnosed. Here, an empirical model called a Linear Inverse Model (LIM), constructed from a high-resolution ocean reanalysis, is used to predict BTs along the North American West Coast and to identify “forecasts of opportunity” when BTs may be especially predictable. The LIM is considerably more skillful than persistence, particularly when targeting winter. As identified a priori through analysis of the LIM’s dynamics, elevated forecast skill is linked to El Niño-Southern Oscillation (ENSO), which drives a predicted BT response whose peak occurs later with increasing latitude. Also, forecast skill is maximized for locations where bathymetry depths are around 50-150m, which is anticipated from the LIM’s forecast signal-to-noise ratio.