CONICET | Buscador de Institutos y Recursos Humanos

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 pCO2 from remote-sensing data, based on self organizing maps (SOMs) and a non-linear semi-empirical model of surface water carbonate chemistry. The model used simple empirical relationships between carbonate chemistry (total dissolved carbon dioxide (TCO2) and alkalinity (TAlk)) and satellite data (sea surface temperature (SST) and chlorophyll (Chl)). Surface-water CO2 partial pressure (pCO2) was calculated from the empirically-predicted TCO2 and TAlk. This directly incorporated the inherent nonlinearities of the carbonate system, in a completely mechanistic manner. The models empirical coefficients were determined for a target study area of the central North American Pacific continental margin (22-50°N, within 370 km of the coastline), by optimally reproducing a set of historical observations paired with satellite data. The model-predicted pCO2 agreed with the highly variable observations with a root mean squared (RMS) deviation of < 20 μatm, and with a correlation coefficient of > 0.8 (r = 0.81; r2 = 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 pCO2 predictions and satellite-based wind speed measurements suggest that the region is a ∼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.