A 20-year (1998–2017) global sea surface dimethyl sulfide gridded dataset with daily resolution
Abstract. The oceanic emission of dimethyl sulfide (DMS) plays a vital role in the Earth's climate system and is a significant source of uncertainty in aerosol radiative forcing. Currently, the widely used monthly climatology of sea surface DMS concentration cannot meet the requirement for accurately simulating DMS-derived aerosols by chemical transport models. Thus, there is an urgent need to construct a global sea surface DMS dataset with high time resolution spanning multiple years. Here we develop an artificial neural network ensemble model based on 9 environmental factors, which demonstrate high accuracy and generalization in predicting DMS concentrations. Subsequently, a global sea surface DMS concentration and flux dataset (1°×1°) with daily resolution covering the period from 1998 to 2017 is established. According to this dataset, the global annual average concentration was ~1.82 nM, and the annual total emission was ~17.9 TgS yr–1, with ~60 % originating from the southern hemisphere. While overall seasonal variations are consistent with previous DMS climatologies, notable differences exist in regional-scale spatial distributions. The new dataset enables further investigation of daily and decadal variations. During 1998–2017, the global annual average concentration exhibited a slight decrease, while total emissions showed no significant trend. Benefiting from the incorporation of daily and interannual variation information, the DMS flux from our dataset showed a much stronger correlation with observed atmospheric methanesulfonic acid concentration compared to those from previous monthly climatologies. As a result, it can serve as an improved emission inventory of oceanic DMS and has the potential to enhance the simulation of DMS-derived aerosols and associated radiative effects. The new DMS gridded products are available at https://zenodo.org/record/7898187 (Zhou et al., 2023).
Status: final response (author comments only)
A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution https://doi.org/10.5281/zenodo.10279659
Model code and software
An artificial neural network ensemble model for sea surface DMS simulation https://doi.org/10.5281/zenodo.8077751
Viewed (geographical distribution)