Improving Estimates of Dynamic Global Marine DMS and Implications for Aerosol Radiative Effect

Dimethyl sulfide (DMS) is the predominant natural sulfur source and plays a pivotal role in regulating global climate. However, the current method for estimating seawater DMS concentrations has limitations, and the existing DMS‐induced radiative effect heavily relies on bottom‐up DMS climatologies. This study aims to improve the method for estimating seawater DMS concentrations as well as to evaluate its induced aerosol direct radiative effect (DRE) and indirect radiative effect (IRE) using a state‐of‐the‐art aerosol microphysics scheme integrated with a chemical transport model. The predicted seawater DMS concentrations based on data‐driven methods were verified with multi‐year in situ measurements, revealing a marked reduction in mean bias by over 80%. Results show that our estimates generally indicate lower seawater DMS concentrations (1.48–1.88 μmol/m3) compared to previous seawater DMS climatologies, with differences ranging from −37% to 11%, and that interannual variability in DMS concentrations is varies significantly, particularly in polar regions. The DRE and cloud‐albedo IRE induced by DMS were −0.06 and −0.19 W/m2, respectively, representing a cooling effect on radiative effect that was weaker by 31.4% and 27.0% of those derived from the commonly used bottom‐up DMS climatology. The comprehensive evaluation of the model's performance of atmospheric DMS prediction based on global‐scale observations shows a significant improvement after using our estimates. Thus, we conclude that the global DMS fluxes provided in the past are overestimated, including its resulting DMS radiative effect, which highlights the need for refining the estimation of global aerosol radiative effect to enhance the accuracy of assessing aerosol‐induced climate impacts. Plain Language Summary Over the past decade, there has been a significant decrease in global anthropogenic sulfur emissions. This decrease sheds light on the importance of natural sulfur sources, specifically dimethyl sulfide (DMS), the largest among them, and its potential impact on climate. Several modeling studies have assessed the radiative effects induced by DMS. However, these models depend on data that combines DMS measurements from a global database, using smoothing and interpolation methods to create a map of DMS concentrations in sea surfaces. This introduces uncertainties due to the methods used. Our study aims to improve the method for estimating seawater DMS concentration and evaluate its induced r