Abstract
Abstract. This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM) originally utilized to map the pCO2sea 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 pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea 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 pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.
Highlights
The ocean plays an important role as a major carbon reservoir for CO2 emitted to the atmosphere from fossil fuel burning, cement production, and biomass burning
Our results indicate that the correlation between pCOs2ea and mixed layer depth (MLD) was not apparent when the MLD was deeper than 200 m, a result reported for the North Atlantic by Telszewski et al (2009)
Keeping in mind that only data obtained by the NIES Volunteer Observing Ship (VOS) program were used in the self-organizing map (SOM) labeling process, these results suggest that the labeling process allows for variations labeled SOM from pCOs2ea neurons to effectively learn pCOs2ea values observed in other subtropical areas
Summary
The ocean plays an important role as a major carbon reservoir for CO2 emitted to the atmosphere from fossil fuel burning, cement production, and biomass burning. Most recently, Telszewski et al (2009) successfully applied a self-organizingmap (SOM) based NN technique to reconstruct pCOs2ea distribution in the North Atlantic (10.5 to 75.5◦ N, 9.5◦ E to 75.5◦ W) for three years (2004 to 2006) by examining nonlinear/discontinuous relationship between pCOs2ea and ocean parameters of sea surface temperature (SST), mixed layer depth (MLD), and chlorophyll a concentration (CHL). Two areas of frequent shipboard observations of pCOs2ea other than time-series stations are the eastern and western equatorial Pacific (e.g., Feely et al, 2006; Ishii et al, 2009), where the observed interannual pCOs2ea variations are associated with the El Niño–Southern Oscillation (ENSO) Another place where there have been frequent shipboard pCOs2ea observations in the North Pacific is the 137◦ E repeat line (Midorikawa et al, 2006), where a weak but signoibfiscearvnetdr.elAatiboanssihni-pwibdeetwaeneanlypsiCs Oofs2eoa basnedrvEedNpSOCOhs2aesa been variability (including the analysis of the interannual signal) has not yet been successfully performed. We presented the change of the pCOs2ea distribution in response to the ENSO events
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