Eddy-driven stratification initiates North Atlantic spring phytoplankton blooms.
ABSTRACT Springtime phytoplankton blooms photosynthetically fix carbon and export it from the surface ocean at globally important rates. These blooms are triggered by increased light exposure of the phytoplankton due to both seasonal light increase and the development of a near-surface vertical density gradient (stratification) that inhibits vertical mixing of the phytoplankton. Classically and in current climate models, that stratification is ascribed to a springtime warming of the sea surface. Here, using observations from the subpolar North Atlantic and a three-dimensional biophysical model, we show that the initial stratification and resulting bloom are instead caused by eddy-driven slumping of the basin-scale north-south density gradient, resulting in a patchy bloom beginning 20 to 30 days earlier than would occur by warming.
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ABSTRACT: Changes in the net heat flux (NHF) into the ocean have profound impacts on global climate. We analyse a long-term plankton time-series and show that the NHF is a critical indicator of ecosystem dynamics. We show that phytoplankton abundance and diversity patterns are tightly bounded by the switches between negative and positive NHF over an annual cycle. Zooplankton increase before the transition to positive NHF in the spring but are constrained by the negative NHF switch in autumn. By contrast bacterial diversity is decoupled from either NHF switch, but is inversely correlated (r = 20.920) with the magnitude of the NHF. We show that the NHF is a robust mechanistic tool for predicting climate change indicators such as spring phytoplankton bloom timing and length of the growing season.PLoS ONE 06/2014; 9(6). · 3.73 Impact Factor
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ABSTRACT: Regular grid (“lawnmower”) survey is a classical strategy for synoptic sampling of the ocean. Is it possible to achieve a more effective use of available resources if one takes into account a-priori knowledge about variability in magnitudes of uncertainty and decorrelation scales? In this article, we develop and compare the performance of several path-planning algorithms: optimized “lawnmower”, a graph-search algorithm (A*), and a fully non-linear Genetic Algorithm. We use the machinery of the best linear unbiased estimator (BLUE) to quantify the ability of a vehicle fleet to synoptically map distribution of phytoplankton off the central California coast. We used satellite and in-situ data to specify covariance information required by the BLUE estimator. Computational experiments showed that two types of sampling strategies are possible: a sub-optimal space-filling design (produced by the “lawnmower” and the A* algorithms) and an optimal uncertainty-aware design (produced by the Genetic Algorithm). Unlike the space-filling designs that attempted to cover the entire survey area, the optimal design focused on revisiting areas of high uncertainty. Results of the multi-vehicle experiments showed that fleet performance predictors, such as cumulative speed or the weight of the fleet, predicted the performance of a homogeneous fleet well; however, these were poor predictors for comparing the performance of different platforms.Journal of Geophysical Research: Oceans. 07/2014;
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ABSTRACT: Subpolar phytoplankton blooms have traditionally been attributed to changes in the depth of the ocean's seasonal thermocline: as the upper ocean warms and stratifies in the spring, phytoplankton reside within increasingly shallow depths where they experience higher light levels, and, as a result, begin to bloom. Recent studies have challenged this explanation, proposing instead that bloom initiation is driven either by the onset of positive heat fluxes, decreases in wind strength, decreases in grazing pressure, or by eddy-induced stratification. We compare traditional and recent ideas of bloom initiation and present a new argument that attributes the initiation to a decrease in the dominant mixing length scales in the upper ocean. From an examination of data across the subpolar North Atlantic, we find that decreases in this length scale are a better predictor of bloom initiation than current theories, thus providing a new explanation of bloom dynamics in a one-dimensional framework.Geophysical Research Letters. 04/2014;