| one publication added to basket [284441] | Observing and modelling phytoplankton community structure in the North Sea
Ford, D.A.; van der Molen, J.; Hyder, K.; Bacon, J.; Barciela, R.; Creach, V.; McEwan, R.; Ruardij, P.; Forster, R. (2017). Observing and modelling phytoplankton community structure in the North Sea. Biogeosciences 14(6): 1419-1444. https://dx.doi.org/10.5194/bg-14-1419-2017 In: Gattuso, J.P.; Kesselmeier, J. (Ed.) Biogeosciences. Copernicus Publications: Göttingen. ISSN 1726-4170; e-ISSN 1726-4189, more |   |
| Authors | | Top | - Ford, D.A.
- van der Molen, J., more
- Hyder, K.
| - Bacon, J.
- Barciela, R.
- Creach, V.
| - McEwan, R.
- Ruardij, P., more
- Forster, R.
|
| Abstract | Phytoplankton form the base of the marine foodchain, and knowledge of phytoplankton community structureis fundamental when assessing marine biodiversity. Policymakers and other users require information on marinebiodiversity and other aspects of the marine environmentfor the North Sea, a highly productive European shelf sea.This information must come from a combination of observationsand models, but currently the coastal ocean is greatlyunder-sampled for phytoplankton data, and outputs of phytoplanktoncommunity structure from models are thereforenot yet frequently validated. This study presents a novel setof in situ observations of phytoplankton community structurefor the North Sea using accessory pigment analysis. Theobservations allow a good understanding of the patterns ofsurface phytoplankton biomass and community structure inthe North Sea for the observed months of August 2010 and2011. Two physical–biogeochemical ocean models, the biogeochemicalcomponents of which are different variants ofthe widely used European Regional Seas Ecosystem Model(ERSEM), were then validated against these and other observations.Both models were a good match for sea surface temperatureobservations, and a reasonable match for remotelysensed ocean colour observations. However, the two modelsdisplayed very different phytoplankton community structures,with one better matching the in situ observations thanthe other. Nonetheless, both models shared some similaritieswith the observations in terms of spatial features and interannualvariability. An initial comparison of the formulationsand parameterizations of the two models suggests that diversitybetween the parameter settings of model phytoplanktonfunctional types, along with formulations which promote agreater sensitivity to changes in light and nutrients, is key tocapturing the observed phytoplankton community structure.These findings will help inform future model development,which should be coupled with detailed validation studies, inorder to help facilitate the wider application of marine biogeochemicalmodelling to user and policy needs. |
|
