Satellite-measured phytoplankton variability in the Gulf of Maine

Abstract

The first 4 years of SeaWiFS ocean color data (September 1997-August 2001 provide the first synoptic quantification of seasonal and interannual phytoplankton chlorophyll variability in the Gulf of Maine. Climatological monthly means show spatial patterns associated with the annual cycle. Concentrations are elevated throughout the year in coastal regions and over shallow banks (Georges Bank, Nantucket Shoals and Browns Bank) with a spring and fall bloom superimposed. Over deeper basins and the Scotia Shelf, a canonical North Atlantic seasonal cycle is present with low (o 1 mg m À3 ) winter (December-February) concentrations, an annual maximum in March-April (>2 mg m À3 ), reduced concentrations in summer and a fall bloom beginning as early as September in Jordan Basin but in OctoberNovember over other regions. Strong interannual variability over the 4-year time series shows the climatological seasonal features are often a biased picture of both timing and magnitude. The clearest interannual signal is of reduced chlorophyll concentrations throughout 1998, including weak spring and fall blooms. A connection between low concentrations in 1998 and local wind forcing is not evident. However, the low concentrations are coincident with negative anomalies in satellite surface temperature fields and follow the intrusion of relatively cold, low salinity slope water into the Northeast Channel which previous authors have argued is linked to changes in Labrador slope water transport induced by the North Atlantic Oscillation. Reduced concentrations in 1998 are consistent with both lower nitrate/nitrite concentrations in the intruding water as well as reduced subsurface stratification which would delay or reduce the onset of the spring bloom. r

Spring bloom dynamics and zooplankton biomass response on the US Northeast Continental Shelf

“…This variability likely reflects both enduring climatological differences within regions and transient, localized processes, such as storms, mesoscale eddies, horizontal advection of distinct water masses, tidal mixing and bathymetry (Greene et al, 2012;Mahadevan et al, 2012;McGillicuddy et al, 2007). Finally, our analysis compares well to the seasonal averages described for the Gulf of Maine by Thomas et al (2003) and differ from the descriptions of bloom initiation in Ji et al (2007) and Song et al (2010) because their study domains extended further north and eastward over the shallower areas of the Scotian Shelf.…”

“…The timing of the Northeast Shelf spring bloom was first examined in remote sensing data in an effort to develop a baseline understanding of the production cycle of the Gulf of Maine (Thomas et al, 2003). As the remote sensing datasets matured, more complete analyses were accomplished.…”

Determination of Potential Fishing Grounds of Rastrelliger kanagurta Using Satellite Remote Sensing and GIS Technique

“…There was a positive correlation between SST and chl-a (Figure 3b), with coefficient of correlation at R=0.3, which means increase in SST resulting in the increase of chl-a concentration. This is in contrast with the finding from Barnard et al (1997), Choudhury et al (2007), Georgakarakos and Kitsiou (2008), , Solanki et al (2001), Thomas et al (2003) and Yoder et al (2002) which found inverse correlation between SST and chl-a. This inverse correlation was reported in areas with occurrence of upwelling.…”

Interannual variability of alongshore spring bloom dynamics in a coastal sea caused by the differential influence of hydrodynamics and light climate

“…Despite its recurrence, timing, spatial extent, and duration of the first seasonal peak in algal concentration show considerable interannual variation. While in deep waters the onset of a bloom typically follows stratification in spring, interannual variability and spatial heterogeneity are particularly strong in shallow coastal seas (Thomas et al, 2003;Cloern, 1996). Only the magnitude of the bloom seems to be predictable as a function of winter nutrient concentration (Loebl et al, 2009;Muylaert et al, 2006;Cloern, 1996).…”

“…In the last two decades, however, satellite imagery added a wealth of data on surface water properties. It greatly facilitated the assessment of CHL-a variability (Thomas et al, 2003) and largescale productivity estimates (Behrenfeld et al, 2005;Behrenfeld and Falkowski, 1997) and even proved useful to better understand trophic links in pelagic ecosystems (Platt et al, 2003). In temperate coastal seas and especially in the North Sea, however, high cloudiness strongly restricts the availability of data and often prevents the use of satellite imagery to detect fast biological dynamics on a scale of only a few days.…”