Observed and projected Sea Surface Temperature seasonal changes in the Western English Channel from satellite data and CMIP5 multi-model ensemble.

Seasonal Sea Surface Temperature (SST) changes in the Western English Channel have been estimated for the previous decades from high-resolution satellite data. Coastal seas, well separated from offshore waters by intense frontal structures, show colder SST by 1 to 2°C in summer. A significant warming trend is observed in the autumn season. This positive trend is stronger offshore, with an annual mean SST increase of 0.32°C/decade, but weaker in coastal waters (0.23°C/decade), where strong vertical mixing induced by tides and winds acts to reduce surface warming. The performance of an ensemble of CMIP5 climate model in simulating recent seasonal changes of SST in the region is estimated. The median of CMIP5 models reproduces very well the observed SST mean seasonal cycle in offshore waters but is less proficient in the coastal sector due to the coarse resolution of the models and the absence of tidal forcing and related processes. In the Iroise Sea, a region of intense biological activity located off the western tip of Brittany, the trend of the annual mean SST is relatively well simulated, albeit somewhat underestimated (0.20°/decade) and evenly distributed throughout the year. Here, the increase in annual mean SST in CMIP5 future scenarios simulations ranges from 0.5°C (RCP2.6) to 2.5°C (RCP8.5) by year 2100, with a seasonal modulation leading to a more intense warming in summer than in winter. This increase in SST may strongly affect marine biology, particularly phytoplankton phenology, macro-algae biomass and benthic fauna, including exploited shellfish, in the Western English Channel.

Keyword(s)

SST, Climate change, Western English Channel, CMIP5, regional study

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L'Heveder Blandine, Speich Sabrina, Ragueneau Olivier, Gohin Francis, Bryere Philippe (2017). Observed and projected Sea Surface Temperature seasonal changes in the Western English Channel from satellite data and CMIP5 multi-model ensemble. International Journal Of Climatology. 37 (6). 2831-2849. https://doi.org/10.1002/joc.4882, https://archimer.ifremer.fr/doc/00350/46163/

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