Can we trust simple marine DMS parameterisations within complex climate models?

Dimethylsulphide (DMS) is a globally important aerosol precurser. In 1987 Charlson and others proposed that an increase in DMS production by certain phytoplankton species in response to a warming climate could stimulate increased aerosol formation, increasing the lower-atmosphere's albedo, and promoting cooling. Despite two decades of research, the global significance of this negative climate feedback remains contentious. It is therefore imperative that schemes are developed and tested, which allow for the realistic incorporation of phytoplankton DMS production into Earth System models. Using these models we can investigate the DMS-climate feedback and reduce uncertainty surrounding projections of future climate. Here we examine two empirical DMS parameterisations within the context of an Earth System model and find them to perform marginally better than the standard DMS climatology at predicting observations from an independent global dataset. We then question whether parameterisations based on our present understanding of DMS production by phytoplankton, and simple enough to incorporate into global climate models, can be shown to enhance the future predictive capacity of those models. This is an important question to ask now, as results from increasingly complex Earth System models lead us into the 5th assessment of climate science by the Intergovernmental Panel on Climate Change. Comparing observed and predicted inter-annual variability, we suggest that future climate projections may underestimate the magnitude of surface ocean DMS change. Unfortunately this conclusion relies on a relatively small dataset, in which observed inter-annual variability may be exaggerated by biases in sample collection. We therefore encourage the observational community to make repeat measurements of sea-surface DMS concentrations an important focus, and highlight areas of apparent high inter-annual variability where sampling might be carried out. Finally, we assess future projections from two similarly valid empirical DMS schemes, and demonstrate contrasting results. We therefore conclude that the use of empirical DMS parameterisations within simulations of future climate should be undertaken only with careful appreciation of the caveats discussed.

Complex coastal oceanographic fields can be described by universal multifractals

Characterization of chlorophyll and sea surface temperature (SST) structural heterogeneity using their scaling properties can provide a useful tool to estimate the relative importance of key physical and biological drivers. Seasonal, annual, and also instantaneous spatial distributions of chlorophyll and SST, determined from satellite measurements, in seven different coastal and shelf-sea regions around the UK have been studied. It is shown that multifractals provide a very good approximation to the scaling properties of the data: in fact, the multifractal scaling function is well approximated by universal multifractal theory. The consequence is that all of the statistical information about data structure can be reduced to being described by two parameters. It is further shown that also bathymetry scales in the studied regions as multifractal. The SST and chlorophyll multifractal structures are then explained as an effect of bathymetry and turbulence.