Satellite-based prediction of pCO2 in coastal waters of the eastern North Pacific

Continental margin carbon cycling is complex, highly variable over a range of space and time scales, and forced by multiple physical and biogeochemical drivers. Predictions of globally significant air-sea CO2 fluxes in these regions have been extrapolated based on very sparse data sets. We present here a method for predicting coastal surface-water pCO(2) from remote-sensing data, based on self organizing maps (SOMs) and a nonlinear semi-empirical model of surface water carbonate chemistry. The model used simple empirical relationships between carbonate chemistry (total dissolved carbon dioxide (T-CO2) and alkalinity (T-Alk)) and satellite data (sea surface temperature (SST) and chlorophyll (Chl)). Surface-water CO2 partial pressure (pCO(2)) was calculated from the empirically-predicted T-CO2 and T-Alk. This directly incorporated the inherent nonlinearities of the carbonate system, in a completely mechanistic manner. The model's empirical coefficients were determined for a target study area of the central North American Pacific continental margin (22-50 degrees N, within 370 km of the coastline), by optimally reproducing a set of historical observations paired with satellite data. The model-predicted pCO(2) agreed with the highly variable observations with a root mean squared (RMS) deviation of <20 mu atm, and with a correlation coefficient of >0.8 (r = 0.81; r(2) = 0.66). This level of accuracy is a significant improvement relative to that of simpler models that did not resolve the biogeochemical sub-regions or that relied on linear dependences on input parameters. Air-sea fluxes based on these pCO(2) predictions and satellite-based wind speed measurements suggest that the region is a similar to 14 Tg C yr(-1) sink for atmospheric CO2 over the 1997-2005 period, with an approximately equivalent uncertainty, compared with a 0.5 Tg C yr(-1) source predicted by a recent bin-averaging and interpolation-based estimate for the same area. (C) 2012 Elsevier Ltd. All rights reserved.