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Abstract/Summary
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<jats:p>Abstract. Ocean surface pCO2 estimates are of great interest for the calculation of air–sea CO2 fluxes, oceanic uptake of anthropogenic CO2, and eventually the Global Carbon Budget. They are accessible from direct observations, which are discrete in space and time and thus always sparse, or from biogeochemical models, which only approximate reality. Here, a combined method for the extrapolation of pCO2 observations is presented that uses (1) model-based patterns of variability from an empirical orthogonal function (EOF) analysis of variability with (2) observational data to constrain EOF pattern amplitudes in (3) an ensemble approach, which locally adjusts the spatial scale of the mapping to the density of the observations. Thus, data-constrained, gap- and discontinuity-free mapped fields including local error estimates are obtained without the need for or dependence on ancillary data (e.g. satellite sea surface temperature maps). This extrapolation approach is generic in that it can be applied to any oceanic or coastal region covered by a suitable model and observations. It is used here to establish a regional pCO2 climatology of the Baltic Sea (Bittig et al., 2023: https://doi.org/10.1594/PANGAEA.961119), largely based on ICOS-DE ship of opportunity (SOOP) Finnmaid surface pCO2 observations between Lübeck-Travemünde (Germany) and Helsinki (Finland). The climatology can serve as improved input for atmosphere–ocean CO2 flux estimation in this coastal environment.
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Hinweis
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DeepGreen-ID: 586b784845ca495190373b5fce4f8ec3 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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