Preprints
https://doi.org/10.5194/essd-2021-328
https://doi.org/10.5194/essd-2021-328

  20 Oct 2021

20 Oct 2021

Review status: this preprint is currently under review for the journal ESSD.

A monthly surface pCO2 product for the California Current Large Marine Ecosystem

Jonathan D. Sharp1,2, Andrea J. Fassbender2, Brendan R. Carter1,2, Paige D. Lavin3,4, and Adrienne J. Sutton2 Jonathan D. Sharp et al.
  • 1Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES), University of Washington, Seattle, WA, 98195, U.S.A.
  • 2NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA, 98115, U.S.A.
  • 3Cooperative Institute for Satellite Earth System Studies/Earth System Science Interdisciplinary Center (CISESS/ESSIC), University of Maryland, College Park, MD, 20740, U.S.A.
  • 4NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, 20740, U.S.A.

Abstract. To calculate the direction and rate of carbon dioxide gas (CO2) exchange between the ocean and atmosphere, it is critical to know the partial pressure of CO2 in surface seawater (pCO2(sw)). Over the last decade, a variety of data products of global monthly pCO2(sw) have been produced, primarily for the open ocean on 1° latitude by 1° longitude grids. More recently, monthly products of pCO2(sw) that are more finely spatially resolved in the coastal ocean have been made available. A remaining challenge in the development of pCO2(sw) products is the robust characterization of seasonal variability, especially in nearshore coastal environments. Here we present a monthly data product of pCO2(sw) at 0.25° latitude by 0.25° longitude resolution in the Northeast Pacific Ocean, centered around the California Current System (CCS). The data product (RFR-CCS; Sharp et al., 2021; https://doi.org/10.5281/zenodo.5523389) was created using the most recent (2021) version of the Surface Ocean CO2 Atlas (Bakker et al., 2016) from which pCO2(sw) observations were extracted and fit against a variety of satellite- and model-derived surface variables using a random forest regression (RFR) model. We validate RFR-CCS in multiple ways, including direct comparisons with observations from moored autonomous surface platforms, and find that the data product effectively captures seasonal pCO2(sw) cycles at nearshore mooring sites. This result is notable because alternative global products for the coastal ocean do not capture local variability effectively in this region. We briefly review the physical and biological processes — acting across a variety of spatial and temporal scales — that are responsible for the latitudinal and nearshore-to-offshore pCO2(sw) gradients seen in RFR-CCS reconstructions of pCO2(sw).

Jonathan D. Sharp et al.

Status: open (until 15 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of Sharp et al „A monthly surface pCO2 product for the California Current Large Marine Ecosystem”', Anonymous Referee #1, 15 Nov 2021 reply

Jonathan D. Sharp et al.

Data sets

RFR-CCS: A monthly surface pCO2 product for the California Current Large Marine Ecosystem Jonathan D. Sharp, Andrea J. Fassbender, Brendan R. Carter, Paige D. Lavin, Adrienne J. Sutton https://doi.org/10.5281/zenodo.5523389

Model code and software

RFR-CCS Jonathan D. Sharp https://github.com/jonathansharp/RFR-CCS

Jonathan D. Sharp et al.

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Short summary
Oceanographers calculate the exchange of carbon between the ocean and atmosphere by comparing partial pressures of carbon dioxide (pCO2). Because seawater pCO2 is not measured everywhere at all times, interpolation schemes are required to fill observational gaps. We describe a monthly gap-filled dataset of pCO2 in the northeast Pacific Ocean off the west coast of North America, created by machine learning interpolation. This dataset is unique in its robust representation of coastal seasonality.