Estimating temporal and spatial variation of ocean surface pCO(2) in the North Pacific using a self-organizing map neural network technique

作者：Nakaoka S^*; Telszewski M; Nojiri Y; Yasunaka S; Miyazaki C; Mukai H; Usui N

来源：Biogeosciences, 2013, 10(9): 6093-6106.

DOI：10.5194/bg-10-6093-2013

This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO(2)(sea)) in the North Pacific on a 0.25 degrees latitude x 0.25 degrees longitude grid from 2002 to 2008. The pCO(2)(sea) distribution was computed using a self-organizing map (SOM) originally utilized to map the pCO(2)(sea) in the North Atlantic. Four proxy parameters - sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) - are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO(2)(sea) distribution and biogeochemistry of the basin. The observed pCO(2)(sea) data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed pCO(2)(sea) values agreed well with the pCO(2)(sea) measurements, with the root-mean-square error ranging from 17.6 mu atm (for the NIES dataset used in the SOM) to 20.2 mu atm (for independent dataset). We confirmed that the pCO(2)(sea) estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO(2)(sea) that have tracked increases in atmospheric CO2. Estimated pCO(2)(sea) values accurately reproduced pCO(2)(sea) data at several time series locations in the North Pacific. The distributions of pCO(2)(sea) revealed by 7 yr averaged monthly pCO(2)(sea) maps were similar to Lamont-Doherty Earth Observatory pCO(2)(sea) climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO(2)(sea) anomalies over the North Pacific during the winter clearly showed regional contrasts between El Nino and La Nina years related to changes of SST and vertical mixing.

• 出版日期2013
• 单位中国气象科学研究院