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Le Clainche, Yvonnick, Vézina, Alain F., Levasseur, Maurice, Cropp, Roger A., Gunson, Jim R., Vallina, Sergio M., Vogt, Meike, Lancelot, Christiane, Allen, J. Icarus, Archer, Stephen D., Bopp, Laurent, Deal, Clara, Elliott, Scott, Jin, Meibing, Malin, Gill 
ORCID: https://orcid.org/0000-0002-3639-9215, Schoemann, Véronique, Simó, Rafel, Six, Katharina D. and Stefels, Jacqueline
(2010)
A first appraisal of prognostic ocean DMS models and prospects for their use in climate models.
Global Biogeochemical Cycles, 24 (3).
ISSN 0886-6236
Abstract
Ocean dimethylsulfide (DMS) produced by marine biota is the largest natural source of atmospheric sulfur, playing a major role in the formation and evolution of aerosols, and consequently affecting climate. Several dynamic process-based DMS models have been developed over the last decade, and work is progressing integrating them into climate models. Here we report on the first international comparison exercise of both 1D and 3D prognostic ocean DMS models. Four global 3D models were compared to global sea surface chlorophyll and DMS concentrations. Three local 1D models were compared to three different oceanic stations (BATS, DYFAMED, OSP) where available time series data offer seasonal coverage of chlorophyll and DMS variability. Two other 1D models were run at one site only. The major point of divergence among models, both within 3D and 1D models, relates to their ability to reproduce the summer peak in surface DMS concentrations usually observed at low to mid- latitudes. This significantly affects estimates of global DMS emissions predicted by the models. The inability of most models to capture this summer DMS maximum appears to be constrained by the basic structure of prognostic DMS models: dynamics of DMS and dimethylsulfoniopropionate (DMSP), the precursor of DMS, are slaved to the parent ecosystem models. Only the models which include environmental effects on DMS fluxes independently of ecological dynamics can reproduce this summer mismatch between chlorophyll and DMS. A major conclusion of this exercise is that prognostic DMS models need to give more weight to the direct impact of environmental forcing (e.g., irradiance) on DMS dynamics to decouple them from ecological processes.
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