The influence of surface layer salinity on wintertime convection in Wilkinson Basin, Gulf of Maine

A 1D ecosystem model, driven by surface heat and wind forcing and relaxed toward observed salinity profiles, was applied to simulate the interannual and decadal scale variability of phytoplankton blooms and plankton production from 1984 to 2007 in the Nova Scotian Shelf (NSS) and Gulf of Maine (GoM) region. The model captured the mean observed timing and magnitude of the spring (SPB) and fall phytoplankton bloom (FPB) in both systems, as well as observed interannual variations in SPB peak timing. Model simulations for both the GoM and NSS exhibited marked interannual variability in SPB and FPB timing (± 2 to 3 wk) and magnitude (up to ~1 mg chlorophyll m -3 ). Earlier SPBs and delayed FPBs are linked to enhanced water column stability generated by less saline surface water or sharper salinity gradients over the top 50 m of the water column. The modeled variation in annual primary productivity, mesozooplankton productivity, and particle export flux was modest (<10% of the mean). Years with high primary production were weakly associated with early SPBs (GoM: r = -0.205; NSS: r = -0.51), but there was no significant relationship with water column stability. This suggests that variation in annual productivity in the GoM and NSS reflects a combination of variation in light limitation (which is alleviated by increased water column stability) and nutrient limitation (which is exacerbated by increased water column stability) that offset and are of near equal importance when averaged over the year. Interannual variations in fisheries production due to changes in annual productivity are thus likely secondary to profound shifts in fisheries recruitment and production that have been linked to variations in SPB and FPB timing. KEY WORDS: Phytoplankton bloom · Interannual variability · Modeling · Nova Scotian Shelf · Gulf of Maine Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 426: [105][106][107][108][109][110][111][112][113][114][115][116][117][118] 2011 related environmental change might have caused significant decadal shifts in the state of the pelagic ecosystems in the NSS-GoM region (e.g. Durbin et al. 2003, Frank et al. 2005, Greene & Pershing 2007, Ji et al. 2007), presumably through a bottom-up process. Understanding phytoplankton bloom dynamics and its relationship with environmental factors has long been the focus of ecosystem studies in the Gulf of Maine and adjacent regions (e.g. Bigelow 1926, Bigelow et al. 1940, Cushing 1959, Townsend & Spinard 1986, Thomas et al. 2003, Ji et al. 2006. In recent years, interannual variations of phytoplankton bloom dynamics have been better detected with the help of field surveys (e.g. Durbin et al. 2003) and advances in technology such as remote sensing (e.g. Thomas et al. 2003, Ji et al. 2007) and continuous plankton recorders (e.g. Sameoto 2001, Greene & Pershing 2007. Most of these recent studies have focused on the spring phytoplankton bloom (SPB). There has been little analysis of the potential impacts of c...

Section: Discussionsupporting

confidence: 81%

Section: Factors Influencing Bloom Timing and Magnitudementioning

confidence: 99%

Interannual variability in phytoplankton blooms and plankton productivity over the Nova Scotian Shelf and in the Gulf of Maine

Song¹,

Ji²,

Stock³

et al. 2011

“…In a similar manner, the zooplankton community shift around 1990 may have originated in the GoM and been advected onto the Bank. The 1990s freshening greatly affected the upper layer stratification and winter mixing/convection in the western GoM (Taylor & Mountain 2009), which likely would have affected the timing of the spring phytoplankton bloom. Durbin et al (2003) attributed greater winter stratification associated with low salinity surface water observed in February 1999 to causing a strong, early phytoplankton bloom and increased productivity/ abundance of many of the smaller zooplankton species identified by Kane (2007) and Pershing et al (2005).…”

Section: Discussionmentioning

confidence: 99%

Major changes in the Georges Bank ecosystem, 1980s to the 1990s

Mountain¹,

Kane²

2010

A major change in surface layer salinity and in zooplankton community structure occurred on Georges Bank between the 1980s and 1990s. Around 1990, salinity began to significantly decrease and the zooplankton community shifted to a greater dominance by smaller sized species. Annual estimates of the number of cod Gadus morhua and haddock Melogrammus aeglefinus eggs hatched on Georges Bank for the years 1979 to 1987 and 1995 to 1999 allowed calculation of a firstyear survivorship index for both species by dividing the number of new recruits from a virtual population analysis by the hatched egg abundance. The survivorship index for each species also exhibited a major shift between the 2 decades, with cod survivorship declining by a factor of 2 from the 1980s to the 1990s, and haddock survivorship increasing by about the same amount. The strong trend in (or regime-shift nature of) each of the relatively short physical and biological time series makes establishing statistically significant relationships problematic. However, the co-occurrence of major shifts in all of the parameters suggests that a major change in the Georges Bank ecosystem occurred between the 1980s and the 1990s. Identifying the causes of that change and the mechanisms connecting the different components of the system during that change will require additional analyses and modeling.

“…According to Taylor & Mountain (2009), the interannual variability in surface salinity in the GoM can significantly affect the depth of vertical convective mixing in the GoM. Mupparapu & Brown (2002) compared the PWP model-simulated mixed-layer depths with measured mixed-layer depths and found that by excluding the role of convection, the PWP model underestimates the mixed-layer depth.…”

Section: Increasing Complexity and Bloom Variabilitymentioning

confidence: 99%

“…Compared to the Findlay et al (2006) results, our model results suggest that besides the rate of deepening of the mixed layer, re-stratification dynamics after the break down of the mixed layer (hurricane case) and gradual deepening of the mixed layer can also trigger the accumulation of phytoplankton in the euphotic layer. The interannual variability can be largely explained by the surface forcing via controlling the nitrate flux, and fall blooms in coastal regions are significantly affected by the intermittent disturbances of wind mixing, cooling events and re-stratification.According to Taylor & Mountain (2009), the interannual variability in surface salinity in the GoM can significantly affect the depth of vertical convective mixing in the GoM. Mupparapu & Brown (2002) compared the PWP model-simulated mixed-layer depths with measured mixed-layer depths and found that by excluding the role of convection, the PWP model underestimates the mixed-layer depth.…”

mentioning

confidence: 99%

Effects of surface forcing on interannual variability of the fall phytoplankton bloom in the Gulf of Maine revealed using a process-oriented model

Hu¹,

Chen²,

Ji³

et al. 2011

SeaWiFS (Sea-viewing Wide Field-of-view Sensor) chlorophyll data revealed strong interannual variability in fall phytoplankton dynamics in the Gulf of Maine, with 3 general features in any one year: (1) rapid chlorophyll increases in response to storm events in fall; (2) gradual chlorophyll increases in response to seasonal wind-and cooling-induced mixing that gradually deepens the mixed layer; and (3) the absence of any observable fall bloom. We applied a mixed-layer box model and a 1-dimensional physical-biological numerical model to examine the influence of physical forcing (surface wind, heat flux, and freshening) on the mixed-layer dynamics and its impact on the entrainment of deep-water nutrients and thus on the appearance of fall bloom. The model results suggest that during early fall, the surface mixed-layer depth is controlled by both wind-and coolinginduced mixing. Strong interannual variability in mixed-layer depth has a direct impact on short-and long-term vertical nutrient fluxes and thus the fall bloom. Phytoplankton concentrations over time are sensitive to initial pre-bloom profiles of nutrients. The strength of the initial stratification can affect the modeled phytoplankton concentration, while the timing of intermittent freshening events is related to the significant interannual variability of fall blooms. KEY WORDS: Fall phytoplankton bloom · Surface forcing · Freshening · Interannual variability · Gulf of Maine · Modeling Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 427: 2011 deep nutrients into the upper water column, where light is not limiting. While the fall bloom is mainly composed of the smaller-sized phytoplankton groups (dino flagellates) (O'Reilly & Busch 1984) rather than larger diatoms that are common in the spring bloom, the importance of the fall bloom to overall ecosystem structure and function can be significant. Greene & Pershing (2007) have speculated that climate changeinduced freshening at higher latitudes could enhance downstream phytoplankton blooms in the fall, which, in turn, could affect zooplankton dynamics in the region. The interannual variability of the fall bloom might also have important implications for populations at higher trophic level. For instance, Friedland et al. (2008) linked the fall bloom to haddock recruitment on Georges Bank, arguing that the intensity of the fall bloom influences maternal well-being and egg viability the following spring, although the detailed mechanisms remain to be further examined.Our key question here is: What are the mechanisms and physical-biological dynamics controlling the interannual variability of fall phytoplankton blooms? If, for example, the erosion of stratification and the concomitant nutrient flux are the triggers of the bloom, one would expect that both local forcing (such as wind and cooling) and remotely controlled surface freshening can result in interannual variations in phytoplankton development. This can be modified further by storm events that c...