3/10/2023 0 Comments National research council![]() ![]() The amplitude, onset, and duration of the DMS annual cycle vary significantly across different regions, as revealed by the k-means++ clustering. Notably, DMS regional patterns are associated with the spatial distribution of phytoplankton biomass and the thickness of the ocean mixed layer, displaying high DMS concentrations above 50°N from June to August. ![]() The proposed GPR outperforms the other methods for predicting DMS, displaying the highest coefficient of determination (R2) value of 0.71 and the least root mean square error (RMSE) of 0.21. Further comparison was made with the previously employed machine learning methods (i.e., artificial neural network and random forest regression) and the existing empirical DMS algorithms. The model was built using DMS observations from cruises, combined with satellite-derived oceanographic data and Copernicus-modelled data. In this study, we propose a machine learning predictive algorithm based on Gaussian process regression (GPR) to model the distribution of daily DMS concentrations in the North Atlantic waters over 24 years (1998–2021) at 0.25° × 0.25° spatial resolution. As the most ubiquitous natural source of sulfur in the atmosphere, dimethylsulfide (DMS) promotes aerosol formation in marine environments, impacting cloud radiative forcing and precipitation, eventually influencing regional and global climate.
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