Wind-driven upwelling and surface nutrient delivery in a semi-enclosed coastal sea

Wind-driven upwelling is an important control on surface nutrients and water properties in stratified lakes and seas. In this study, a high-resolution biophysical coupled model is used to investigate upwelling in the Strait of Georgia on the Canadian Pacific coast. The model is forced with surface winds from a high-resolution atmospheric forecast and has been tuned in previous studies to reproduce extensive observations of water level, temperature, salinity, nutrients and chlorophyll with competitive skill relative to similar models of the study region. A total of 5 years of hourly surface nitrate and temperature fields are analyzed in order to characterize the dominant upwelling patterns of the basin. A prevailing along-axis wind pattern steered by mountainous topography produces episodic upwelling along the western shore during the spring and fall southeasterlies and along the eastern shore during the summer northwesterlies, as indicated by positive nitrate anomalies. Principal component analysis reveals that these cross-axis upwelling patterns account for nearly one-third of the surface nitrate variance during the summer productive season. By contrast, nearly half of the surface temperature variance over the same period is dominated by a single, combined mixing and diurnal heating–cooling pattern. The principal components associated with these patterns correlate with along-axis wind stress in a manner consistent with these physical interpretations. The cross-axis upwelling response to wind is similar to other dynamically wide basins where the baroclinic Rossby deformation radius is smaller than the basin width. However, the nitrate anomaly during upwelling along the eastern shore is stronger in the northern basin, which may be indicative of an along-axis pycnocline tilt or an effect of the background along-axis stratification gradient due to the Fraser River. Our findings highlight an important spatiotemporal consideration for future ecosystem monitoring.