Feasibility of reconstructing the summer basin-scale sea surface partial pressure of carbon dioxide from sparse in situ observations over the South China Sea

摘要

Sea surface partial pressure of CO 2 ( p CO 2 ) data with a high spatiotemporal resolution are important in studying the global carbon cycle and assessing the oceanic carbon uptake. However, the observed sea surface p CO 2 data are usually limited in spatial and temporal coverage, especially in marginal seas. This study provides an approach to reconstruct the complete sea surface p CO 2 field in the South China Sea (SCS) with a grid resolution of 0.5 ° × 0.5 ° over the period of 2000–2017 using both remote-sensing-derived p CO 2 and observed underway p CO 2 , among which the gridded underway p CO 2 data in 2004, 2005, and 2006 are presented for the first time. Empirical orthogonal functions (EOFs) were computed from the remote-sensing-derived p CO 2 . Then, a multilinear regression was applied to the observed p CO 2 as the response variable with the EOFs as the explanatory variables. EOF1 explains the general spatial pattern of p CO 2 in the SCS. EOF2 shows the pattern influenced by the Pearl River plume on the northern shelf and slope. EOF3 is consistent with the pattern influenced by coastal upwelling along the northern coast of the SCS. When p CO 2 observations cover a sufficiently large area, the reconstructed fields successfully display a pattern of relatively high p CO 2 in the mid and southern basin. The rate of sea surface p CO 2 increase in the SCS is 2.4±0.8 ? μatm?yr ?1 based on the spatial average of the reconstructed p CO 2 over the period of 2000–2017. This is consistent with the temporal trends at Station SEATS (SouthEast Asia Time-series Study; 18 ° ?N, 116 ° ?E) in the northern basin of the SCS and at Station ALOHA (A Long-Term Oligotrophic Habitat Assessment; 22 ° 45 ′ ?N, 158 ° ?W) in the North Pacific. We validated our reconstruction with a leave-one-out cross-validation approach, which yields the root-mean-square error (RMSE) in the range of 2.4–5.2? μatm , smaller than the spatial standard deviation of our reconstructed data and much smaller than the spatial standard deviation of the observed underway data. The RMSE between the reconstructed summer p CO 2 and the observed underway p CO 2 is no larger than 31.7? μatm , in contrast to (a) the RMSE from?12.8 to?89.0? μatm between the remote-sensing-derived p CO 2 and the underway data and (b) the RMSE from?32.6 to?44.5? μatm between the neural-network-produced p CO 2 and the underway data. The difference between the reconstructed p CO 2 and those calculated from observations at Station SEATS is in the range from ?7 to 10? μatm . These comparison results indicate the reliability of our reconstruction method and output. All the data for this paper are openly and freely available at PANGAEA under the link https://doi.org/10.1594/PANGAEA.921210 (Wang et al., 2020).