Abstract
Abstract. A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) the reconstruction of pCO2 climatology, and (2) the reconstruction of pCO2 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 CO2 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 pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1∘×1∘ grid for the period from 2001 to 2016. Global ocean pCO2 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 pCO2 with reasonable skill over the equatorial Pacific associated with ENSO (the El Niño–Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 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 pCO2 and sea–air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.
Highlights
The global ocean is a major sink of excess CO2 that has been emitted to the atmosphere since the beginning of the industrial revolution
These results were comparable to those obtained by Landschützer et al (2013) for the assessment of a surface ocean pCO2 reconstruction based on an alternative neural network-based approach
Results of the LSCE-feed-forward neural network (FFNN) mapping model were compared to three published mapping methods which participated in the “Surface Ocean pCO2 Mapping Intercomparison” (SOCOM) exercise presented in Rödenbeck et al (2015)
Summary
The global ocean is a major sink of excess CO2 that has been emitted to the atmosphere since the beginning of the industrial revolution. Between 2000 and 2009, the yearly average ocean Cant uptake was 2.3 ± 0.7 PgC yr−1 (Ciais et al, 2013). These global estimates hide substantial regional and interannual fluctuations (Rödenbeck et al, 2015), which need to be quantified in order to track the evolution of the Earth’s carbon budget (e.g., Le Quéré et al, 2018). Most estimates of interannual sea–air CO2 flux variability were based on atmospheric inversions (Peylin et al, 2005, 2013; Rödenbeck et al, 2005) or global ocean circulation models (Orr et al, 2001; Aumont and Bopp, 2006; Le Quéré et al, 2010). Models tend to underestimate the variability of sea–air CO2 fluxes (Le Quéré et al, 2003), whereas atmospheric inversions suffer from a sparse network of atmospheric CO2 measurements
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
