High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning

first_pagesettingsOrder Article Reprints Open AccessArticle High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning by Charles Galdies 1,*ORCID andRoberta Guerra 2,3ORCID 1 Institute of Earth Systems, University of Malta, MSD 2080 Msida, Malta 2 Department of Physics and Astronomy (DIFA), Alma Mater Studiorum—Università di Bologna, 40126 Bologna, Italy 3 Interdepartmental Research Centre for Environmental Sciences (CIRSA), University of Bologna, 48123 Ravenna, Italy * Author to whom correspondence should be addressed. Water 2023, 15(8), 1454; https://doi.org/10.3390/w15081454 Received: 25 February 2023 / Accepted: 3 April 2023 / Published: 7 April 2023 (This article belongs to the Topic Water Management in the Era of Climatic Change) Download Browse Figures Versions Notes Abstract This study combines measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), pH, earth observation (EO), and ocean model products with deep learning to provide a good step forward in detecting changes in the ocean carbonate system parameters at a high spatial and temporal resolution in the North Atlantic region (Long. −61.00° to −50.04° W; Lat. 24.99° to 34.96° N). The in situ reference dataset that was used for this study provided discrete underway measurements of DIC, TA, and pH collected by M/V Equinox in the North Atlantic Ocean. A unique list of co-temporal and co-located global daily environmental drivers derived from independent sources (using satellite remote sensing, model reanalyses, empirical algorithms, and depth soundings) were collected for this study at the highest possible spatial resolution (0.04° × 0.04°). The resulting ANN-estimated DIC, TA, and pH obtained by deep learning shows a high correspondence when verified against observations. This study demonstrates how a select number of geophysical information derived from EO and model reanalysis data can be used to estimate and understand the spatiotemporal variability of the oceanic carbonate system at a high spatiotemporal resolution. Further methodological improvements are being suggested.