LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean

• Published in: Geoscientific Model Development
• Main Authors: Denvil-Sommer, Anna, Gehlen, Marion, Vrac, Mathieu, Mejia, Carlos
• Format: Text
• Language: English
• Published: 2019
• Subjects: Indian; Pacific; Southern Ocean
• Online Access: https://doi.org/10.5194/gmd-12-2091-2019; https://gmd.copernicus.org/articles/12/2091/2019/

A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide ( p CO 2 ) over the global ocean. The model consists of two steps: (1) the reconstruction of p CO 2 climatology, and (2) the reconstruction of p CO 2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a ), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO 2 Atlas (SOCAT) provided the target. The same set of predictors was used during step (2) augmented by their anomalies. During each step, the FFNN model reconstructs the nonlinear relationships between p CO 2 and the ocean predictors. It provides monthly surface ocean p CO 2 distributions on a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M12" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">1</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">1</mn><msup><mi/><mo>∘</mo></msup></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="34pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="adb4f21844a1bb199b77c90adc5df69c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-12-2091-2019-ie00001.svg" width="34pt" height="11pt" src="gmd-12-2091-2019-ie00001.png"/></svg:svg> grid for the period from 2001 to 2016. Global ocean p CO 2 was reconstructed with satisfying accuracy compared with independent observational data from SOCAT. However, errors were larger in regions with poor data coverage (e.g., the Indian Ocean, the Southern Ocean and the subpolar Pacific). The model captured the strong interannual variability of surface ocean p CO 2 with reasonable skill over the equatorial Pacific associated with ENSO (the El Niño–Southern Oscillation). Our model was compared to three p CO 2 mapping methods that participated in the Surface Ocean p CO 2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variability between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern Hemisphere and, in particular, in the southern Pacific and the Indian Ocean, as these regions suffer from poor data coverage. Large regional uncertainties in reconstructed surface ocean p CO 2 and sea–air CO 2 fluxes have a strong influence on global estimates of CO 2 fluxes and trends.