Modelling the contribution of deep chlorophyll maxima to annual primary production in the North Sea View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-03-17

AUTHORS

Sonja M. van Leeuwen, Johan van der Molen, Piet Ruardij, Liam Fernand, Tim Jickells

ABSTRACT

Seasonally stratified areas in temperate shelf seas are usually characterized by a strong spring bloom, followed by limited production within the surface mixed layer as nutrients are depleted in the post-bloom period. The bottom mixed layer remains nutrient-rich, due to regeneration processes. When the thermocline is within the euphotic zone, primary production at thermocline depth is possible as organisms can access nutrients from the bottom layer. Ship-based observations indicate that production within these deep chlorophyll maxima (DCM) can form a substantial part of the annual production, but this process is not captured by satellite observations. The spatial and temporal behaviour of DCM-related production is therefore less well known. To study the extent of DCM occurrence we applied a fully three dimensional, hydro-biogeochemical model to the North Sea. The simulation was used to quantify sub-surface production in space and time to determine the importance of production within the DCM layer with respect to annual and spring bloom primary production in the North Sea. The results showed that locally, in thermally stratified areas in the North Sea, up to 30% of annual net primary production occurred below 15 m depth. In a 21-year average, sub-surface production contributed 10% to the total annual net primary production in the entire North Sea. These values should be interpreted as lower estimates, as the model was unable to represent observed hot-spots of very high chlorophyll a concentrations at depth. The importance of DCM-related production may increase under future climate scenarios when the length of the stratified period may increase. More... »

PAGES

137-152

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10533-012-9704-5

DOI

http://dx.doi.org/10.1007/s10533-012-9704-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035364237


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