Global sea surface dimethyl sulfide dataset simulated by artificial neural network

This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 2005 to 2014 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes; (3) the simulated global monthly sea surface concentrations of DMS ranging from1850 to 2100 by ANN model and CMIP6 ensemble and the calculated Kt and sea-to-air fluxes; (4) the yearly mean DMS concentration of each grid in different sensitivity experiments exploring the roles different variables play in driving DMS historical and future changes. The input variables of this ANN model include chlorophyll a, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF) and sea surface salinity (SSS). The future projection (2015-2100) are subjected into two Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L–1, m s–1, and μmol S m–2 d –1, respectively.Questions and requests can be addressed to: sqzhou17@fudan.edu.cn