[1] 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.Citation: Le Clainche, Y., et al. (2010), A first appraisal of prognostic ocean DMS models and prospects for their use in climate models, Global Biogeochem. Cycles, 24, GB3021,

Abstract:The dimethylsulfide (DMS) production model NODEM (Northern Oceans DMS Emission Model) was coupled with the water column ocean model GOTM (General Ocean Turbulence Model) that includes a two-equation k-ε turbulence scheme. This coupled physical-biogeochemical ocean model represents a significant improvement over the previous uncoupled version of NODEM that was driven by a diagnostic vertical mixing scheme. Using the same set of biogeochemical parameters, the coupled model is used to simulate the annual cycles of 1992 and 1993 at Hydrostation S in the Sargasso Sea. The better reproduction of the turbulent mixing environment corrects some deficiencies in nitrogen cycling, especially in the seasonal evolution of the nutrient concentrations. Hence, the coupled model captures the late-winter chlorophyll-and DMS(P)-rich blooms. It is also more adept at reproducing the vertical distribution of chlorophyll and DMS(P) in summer. Moreover, the DMS pool becomes less dependent on parameters controlling the nitrogen cycle and relatively more sensitive to parameters related to the sulfur cycle. Finally, the coupled model reproduces some of the observed differences in DMS(P) pools between 1992 and 1993, the latter being an independent data set not used in calibrating the initial version of NODEM.Résumé : Le modèle NODEM (« Northern Oceans DMS Emission Model ») de production de dimethylsulfide (DMS) a été couplé avec le modèle 1-D d'océan GOTM (« General Ocean Turbulence Model »), qui inclue un schéma de la turbulence océanique du second ordre de type k-ε. Ce modèle couplé physique et biogéochimique constitue une amé-lioration significative de la précédente version non couplée de NODEM, qui s'appuyait sur un schéma diagnostique pour le mélange vertical. Reprenant les mêmes paramètres biogéochimiques, le modèle couplé a été utilizé pour simuler le cycle saisonnier des années 1992 et 1993 à la station S en mer des Sargasses. La meilleure prise en compte de l'aspect turbulent du mélange océanique corrige certains défauts dans la représentation du cycle de l'azote, en particulier l'évolution saisonnière des concentrations en nutriments. Le modèle couplé capture maintenant les floraisons phytoplanctoniques de fin d'hiver et les pics en chlorophylle et en DMS(P) qui les caractérisent. Il améliore également la représentation des distributions verticales de chlorophylle et de DMS(P) en été. De plus, le réservoir de DMS est moins dépendant des paramètres contrôlant le cycle de l'azote, et relativement plus sensible aux paramètres reliés au cycle du souffre. Finalement, le modèle couplé reproduit certaines des différences observées entre 1992 et 1993 dans les réservoirs de DMS(P), la dernière année constituant un jeu de données indépendant qui n'a pas été utilizé pour la calibration de la version initiale de NODEM.Le Clainche et al. 803

[1] The large-scale iron enrichment conducted in the NE Pacific during the Subarctic Ecosystem Response to Iron Enrichment Study (SERIES) triggered a phytoplankton bloom dominated successively by nanophytoplankton and large diatoms. During the first 14 days, surface dimethyl sulfide (DMS) levels increased both inside (up to 22 nmol L À1 ) and outside (up to 19 nmol L À1) the patch, with no consistent Fe effect. Later, DMS concentrations became sixfold lower inside the patch than outside. In this study, we used a DMS budget module embedded in a one-dimensional ocean turbulence model to investigate the contribution of the interacting physical, photochemical, and biological processes to this particular DMS response. Temporal variations in biological net DMS production were reconstructed using an inverse modeling approach. Our results show that short-term (days) variations in both the physical processes (i.e., turbulent mixing and ventilation) and the biological cycling of DMS are needed to explain the time evolution of DMS concentrations both outside and inside the Fe-enriched patch. The biological net DMS production was generally high (up to 0.35 nmol L À1 h À1 ) and comparable outside and inside the patch during the first 10 days, corresponding to the observed accumulation of DMS inside and outside the patch. Later, it became negative (net DMS biological consumption) inside the patch, suggesting a change in dimethylsulfoniopropionate bacterial metabolism. This study stresses the importance of short-term variations in biological processes and their sensitivity to the physical environment in shaping the DMS response to iron enrichment.Citation: Le Clainche, Y., et al. (2006), Modeling analysis of the effect of iron enrichment on dimethyl sulfide dynamics in the NE

The goal of this paper is to give a detailed description of the coupled physical-biogeochemical model of the Gulf of St. Lawrence that includes dissolved oxygen and carbonate system components, as well as a detailed analysis of the riverine contribution for different nitrogen and carbonate system components. A particular attention was paid to the representation of the microbial loop in order to maintain the appropriate level of the different biogeochemical components within the system over long term simulations. The skill of the model is demonstrated using in situ data, satellite data and estimated fluxes from different studies based on observational data. The model reproduces the main features of the system such as the phytoplankton bloom, hypoxic areas, pH and calcium carbonate saturation states. The model also reproduces well the estimated transport of nitrate from one region to the other. We revisited previous estimates of the riverine nutrient contribution to surface nitrate in the Lower St. Lawrence Estuary using the model. We also explain the mechanisms that lead to high ammonium concentrations, low dissolved oxygen, and undersaturated calcium carbonate conditions on the Magdalen Shallows.