Using Satellite Ocean Color Data to Derive an Empirical Model for the Penetration Depth of Solar Radiation (Hp) in the Tropical Pacific Ocean

Satellite-based ocean color measurements indicate clear evidence for bioclimate interactions in the tropical Pacific associated with El Niño–Southern Oscillation (ENSO). Recent modeling studies have demonstrated that ocean biology can potentially affect the climate through the penetration depth of solar radiation in the upper ocean (Hp), a primary parameter in coupling biology to physics in the ocean. At present, interannual variability in Hp and its related bioclimate feedback effects have not been adequately represented in coupled ocean–atmosphere models. In this work, chlorophyll (Chl) concentration data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), available since 1997, are used to characterize interannual Hp variability in the tropical Pacific and to quantify its relationships with physical fields, including sea surface temperature (SST) and sea level (SL). It is found that interannual Hp variability is dominated by ENSO signals, with the largest variability located in the central basin near the date line and a coherent relationship with SST. A singular value decomposition (SVD) analysis is adopted to extract interannual covariability patterns between Hp and SST during the period 1997–2007. Their close relationships are then utilized to construct an empirical anomaly model for Hp, allowing for its prognostic estimate in terms of SST anomalies without explicit involvement of a marine ecosystem model. Validation and sensitivity experiments indicate that the empirical model can reasonably well capture interannual Hp responses to SST anomalies in association with ENSO. The derived empirical Hp model offers a simple and an effective way to parameterize and represent the effects of Chl containing biomass on penetrative solar radiation in the tropical Pacific, demonstrating the dynamical implication of remotely sensed Chl data for bioclimate coupling studies. Further improvements and applications of the empirical Hp model to climate modeling are discussed.