Modeling the biogeochemical cycle of dimethylsulfide in the upper ocean: A review

An important focus of climate-change research is the understanding of the role of ecosystems in shaping climate. Central to this aim is the identification of any feedbacks by which ecosystems may moderate anthropogenic forcing of climate. One possible ecosystem feedback involves the marine food-web and the biogenic sulfur compound dimethylsulfide (DMS). DMS is produced by algae containing the precursor compound dimethylsulfoniopropionate (DMSP), and once ventilated to the atmosphere can be transformed to sulfate aerosols and global climate. It was hypothesized that an increase in biogenically produced sulfate aerosols leading to formation of more cloud condensation nuclei (CCN), and brighter clouds, could stabilize the climate against perturbations due to greenhouse warming. Although a large database of DMS seawater measurements exist, attempts to statistically correlate DMS concentrations with other biological parameters, such as chlorophyll a or nutrients, have failed. This underscores the complex and dynamic nature of the DMS cycle, and means that simple regression-type predictive models are unlikely to be useful, except at local scales. Regional-scale simulations of the DMS cycle have involved multi-parameter, deterministic formulations based on ecological food-web approaches but with the added challenge of properly simulating the behavior of coupled sulfur and nitrogen (or carbon) cycles. Here we review the current DMS modeling approaches, outline the parameterization of key processes, and identify areas where our knowledge is poor and improvements should be made. Model skill can only be assessed against detailed regional and global data sets, however data have not always been collected in a form suitable for model parameter estimation or model calibration/validation. DMS time series, which are essential for calibration of seasonal or multi-annual simulations, are rare. We discuss the minimum requirements for a successful future integration of observational and theoretical efforts.