A seamless coastal to global ocean pCO2 and pH reconstruction at a 0.25-degree resolution with extrapolation to the near future

Abstract

<p>Observational networks monitoring marine carbon variables are established to meet the critical need to estimate ocean CO2 uptake, as well as assessing its consequences on ocean health through changes in carbonate chemistry (ocean acidification). Despite considerable efforts over the past decades, data coverage is still sparse over large ocean regions, prompting the implementation of mapping methods to gap-fill carbon datasets over the globe. Different statistical approaches have been proposed with the aim to generate reconstructions of the complete marine CO2 system at high spatial-temporal resolutions. Following this goal, we first introduce a global reconstruction of surface ocean partial pressure of CO2 (pCO2) at monthly and 0.25-degree resolutions over the period 1985-2021. This high-resolution pCO2 product is derived from ensemble neural network models interpolating monthly gridded observation-based data from Surface Ocean CO2 ATlas (SOCAT). We will assess the ability of the proposed pCO2 ensemble (1) to derive long-term time series of pCO2 and associated 1-sigma uncertainty per 0.25-degree grid cell for each month, (2) to reproduce temporal and horizontal gradients of coastal pCO2 observations in comparison with a coarser spatial resolution, (3) to estimate surface ocean pH and air-sea CO2 fluxes. Furthermore, we will present an extension of the ensemble neural network models, which is referred to as a new module extrapolating pCO2 to several years ahead. The extended ensemble-based approach will ultimately be used to project global ocean CO2 uptake and ocean acidification with low latency.</p>

Seasonal Variability of the Surface Ocean Carbon Cycle: A Synthesis

“…How is the ocean carbon cycle changing as a consequence of sustained increases in emissions of carbon to the atmosphere? Important steps toward answering this question over the last several decades have been provided via estimates of ocean carbon uptake from both interior hydrographic measurements (Gruber et al, 2019;Müller et al, 2023;Sabine et al, 2004), surface fluxes inferred from measurements of the sea surface partial pressure of CO 2 (pCO 2 ) (Chau et al, 2022(Chau et al, , 2023Gregor et al, 2019;Landschützer et al, 2014;Rödenbeck et al, 2015), global ocean biogeochemistry model simulations (Friedlingstein et al, 2022a(Friedlingstein et al, , 2022bHauck et al, 2020;Orr et al, 2001) and ocean inverse models (Gloor et al, 2003;Gruber et al, 2009). A first global synthesis was performed roughly a decade ago through the REgional Carbon Cycle Assessment and Processes (RECCAP) project (https://www.globalcarbonproject.org/reccap), highlighting inevitable forced carbon cycle changes, while also identifying sources of uncertainty.…”

Global Analysis of Surface Ocean CO2 Fugacity and Air‐Sea Fluxes With Low Latency

“…Correspondingly, we have found an anomalous reduction in CO 2 uptake setting this regional sink back to its magnitude in the 2000s. These results emphasize needs and open the possibility to derive monthly predictions for global surface ocean maps of numerous variables driven by f CO 2 , including air-sea fluxes, seawater pH, and dissolved inorganic carbon, as the reconstruction quality of f CO 2 drives that of the other variables (Chau et al, 2022a(Chau et al, , 2022b. The data sets for the year 2022 (January) to 2023 (August) are available via the LSCE data center (see Section Data availability) and are updated each month.…”

“…However, SOCATv2021 has more data before 2018, up to at least 1000 more in some years (e.g., 2011 and 2012) due to an erroneous flagging of some data (Bakker et al, 2021). Despite this feature, the two corresponding FFNN reconstructions do not exhibit large systematic offsets in their f CO 2 estimates (Chau et al, 2022a).…”

“…It consists of an ensemble of feed-forward neural network (FFNN) models. This ensemble approach was developed at LSCE in order to reconstruct surface ocean carbonate system variables and to support the operational distribution of such data sets by CMEMS since 2019 (Product identity: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, Chau et al, 2022a). The original CMEMS-LSCE-FFNN fields cover the global ocean at a resolution of 1°× 1°a nd for the period since the year 1985 at monthly resolution.…”

Differences in carbonate chemistry up-regulation of long-lived reef-building corals

“…The up-regulation of pH cf is one way for corals to compensate for the reduced metabolic carbon input from the algal symbiont and to maintain supersaturated conditions in a biologically controlled compartment with respect 28 . These maps were obtained from an ensemble-based forward feed neural network approach mapping in situ data for surface ocean fugacity (SOCAT data base 29 , https:// www.…”