Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Estimating Sea Surface pCO2 in the North Atlantic based on CatBoost

Version 1 : Received: 31 March 2021 / Approved: 2 April 2021 / Online: 2 April 2021 (13:58:50 CEST)

How to cite: Sun, H.; Chen, Y.; Li, L.; Chen, Y. Estimating Sea Surface pCO2 in the North Atlantic based on CatBoost. Preprints 2021, 2021040065. https://doi.org/10.20944/preprints202104.0065.v1 Sun, H.; Chen, Y.; Li, L.; Chen, Y. Estimating Sea Surface pCO2 in the North Atlantic based on CatBoost. Preprints 2021, 2021040065. https://doi.org/10.20944/preprints202104.0065.v1

Abstract

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we use chlorophyll-a concentration (Chla), sea surface temperature (SST), absorption due to dissolved and particulate detrital matter (Adg), diffuse attenuation coefficient of downwelling irradiance at 490nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results show that the root mean square error (RMSE) is 8.25μatm, the mean bias error (MAE) is 4.92μatm and the coefficient of determination (R2) can reach 0.946 in the validation set, which mean that the CatBoost model makes an improvement compared to other models in the published studies. In the further analysis of the spatial and temporal distribution of the sea surface pCO2 in the North Atlantic, it can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, and influenced by biological activities in some sub-regions.

Keywords

sea surface pCO2; ocean color remote sensing; CatBoost algorithm; temporal and spatial distribution; influencing factors

Subject

Environmental and Earth Sciences, Atmospheric Science and Meteorology

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