2015
DOI: 10.1002/2015gl064540
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Phytoplankton biomass cycles in the North Atlantic subpolar gyre: A similar mechanism for two different blooms in the Labrador Sea

Abstract: An analysis of seasonal variations in climatological surface chlorophyll points to distinct biogeographical zones in the North Atlantic subpolar gyre. In particular, the Labrador Sea appears well delineated into two regions on either side of the 60°N parallel, with very different climatological phytoplankton biomass cycles. Indeed, north of 60°N, an early and short spring bloom occurs in late April, while south of 60°N, the bloom gradually develops 1 month later and significant biomass persists all summer long… Show more

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Cited by 39 publications
(30 citation statements)
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References 43 publications
(60 reference statements)
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“…the minimum irradiance required for net phytoplankton growth during spring) of 1.9 ± 0.3 mol quanta m −2 d −1 was proposed for Arctic waters (Tremblay et al 2006). This estimate is similar to the critical values of 1.3 and 2.5 mol quanta m −2 d −1 for the onset of spring bloom in North Atlantic waters (35-75°N) (Siegel et al 2002) and in the Labrador Sea (Lacour et al 2015), respectively. Large centric diatoms, such as Thalassiosira spp.…”
Section: Introductionsupporting
confidence: 72%
“…the minimum irradiance required for net phytoplankton growth during spring) of 1.9 ± 0.3 mol quanta m −2 d −1 was proposed for Arctic waters (Tremblay et al 2006). This estimate is similar to the critical values of 1.3 and 2.5 mol quanta m −2 d −1 for the onset of spring bloom in North Atlantic waters (35-75°N) (Siegel et al 2002) and in the Labrador Sea (Lacour et al 2015), respectively. Large centric diatoms, such as Thalassiosira spp.…”
Section: Introductionsupporting
confidence: 72%
“…The bioregions were defined here using a cluster K-means analysis (see the supporting information for more details), previously applied successfully in the Mediterranean Sea [D'Ortenzio and Ribera d'Alcalà, 2009;Mayot et al, 2016], in the North Atlantic [Lacour et al, 2015] and at the global scale [D'Ortenzio et al, 2012]. The analysis was performed on climatological and normalized annual chl a cycle, in order to statistically organize the GLOBcolour time series (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and to create clusters representing regions of similarity (i.e., annual chl a cycles).…”
Section: Clustering K-means Methodsmentioning
confidence: 99%
“…This occurs especially for radiometric quantities (Fig. 7) as a consequence of the decreasing stability of the water column associated with deteriorated sky and sea conditions (D'Ortenzio et al, 2005;Lacour et al, 2015). This high contribution of the Northern Hemisphere to the database is due to the first projects piloting the deployment of BGC-Argo floats that were mainly focused on the North Atlantic subpolar gyre (i.e., 48-65 • N; remOcean project) and the Mediterranean Sea (i.e., 31-44 • N; NAOS project).…”
Section: Bopad-prof: Spatiotemporal Distribution Of the Biogeochemicamentioning
confidence: 99%
“…In addition, the array provided measurements of the underwater light field (i.e., irradiance) and of the inherent optical properties (i.e., particulate optical beam attenuation and backscattering coefficients) of the oceans. All these measurements, and derived quantities, are useful for both biogeochemical and bio-optical studies, to address the variability in biological processes (e.g., phytoplankton phenology and primary production; Lacour et al, 2015) and linkages with physical drivers (Boss et al, 2008;Boss and Behrenfeld, 2010;Lacour et al, 2017;Mignot et al, 2017;Stanev et al, 2017), to estimate particulate organic carbon concentrations and export (e.g., Bishop et al, 2002;Dall'Olmo and Mork, 2014;, and to support satellite missions through validation of bio-optical products retrieved from ocean color remote sensing (e.g., chlorophyll concentration; Claustre et al, 2010b;IOCCG, 2011IOCCG, , 2015Gerbi et al, 2016;Haëntjens et al, 2017) or by identification of those regions with bio-optical behaviors departing from mean-statistical trends (i.e., bio-optical anomalies; Organelli et al, 2017).…”
Section: Introductionmentioning
confidence: 99%