Phenology of marine phytoplankton from satellite ocean color measurements

Vargas, Marco; Brown, Christopher W.; Sapiano, Mathew R. P.

doi:10.1029/2008gl036006

Cited by 43 publications

(43 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternative methods of reconstructing the chlorophyll data, such as the Gamma Generalized Linear Model method in Vargas et al . [] and Sapiano et al . [] or the method of filtering coefficients using a Lanczos filter [ Duchon , ] could be tested in the future to determine whether they produce different ROC BSDs, and which smoothing technique best reconstructs the chlorophyll time series.…”

Section: Discussionmentioning

confidence: 99%

A comparison of methods to determine phytoplankton bloom initiation

2013

View full text Add to dashboard Cite

[1] Phytoplankton bloom phenology has important consequences for marine ecosystems and fisheries. Recent studies have used remotely sensed ocean color data to calculate metrics associated with the phenological cycle, such as the phytoplankton bloom initiation date, on regional and global scales. These metrics are often linked to physical or biological forcings. Most studies choose one of several common methods for calculating bloom initiation, leading to questions about whether bloom initiation dates calculated with different methods yield comparable results. Here we compare three methods for finding the date of phytoplankton bloom initiation in the North Atlantic: a biomass-based threshold method, a rate of change method, and a cumulative biomass-based threshold method. We use these methods to examine whether the onset of positive ocean-atmosphere heat fluxes coincides with subpolar bloom initiation. In several coherent locations, we find differences in the patterns of bloom initiation created by each method and differences in the synchrony between bloom initiation and positive heat fluxes, which likely indicate various physical processes at play in the study region. We also assess the effect of missing data on the chosen methods.Citation: Brody, S. R., M. S. Lozier, and J. P. Dunne (2013), A comparison of methods to determine phytoplankton bloom initiation,

show abstract

Section: Discussionmentioning

confidence: 99%

A comparison of methods to determine phytoplankton bloom initiation

2013

View full text Add to dashboard Cite

show abstract

“…Both approaches assume that a bloom has occurred and thus, that chl a data contain enough information to characterize it. Here, we fitted a model with enough flexibility to characterize different types of seasonal cycles of chl a concentration (see Vargas et al, 2008;Sapiano et al, 2012). Seasonal chl a data were previously subset based on sea surface temperature time series.…”

Section: Discussionmentioning

confidence: 99%

“…by annual and semi-annual harmonics (x = 1/365; see Vargas et al, 2008;1 Sapiano et al, 2012), yielding the equation:…”

Section: Data Sources and Data Preparationmentioning

confidence: 99%

Seasonality of North Atlantic phytoplankton from space: impact of environmental forcing on a changing phenology (1998–2012)

Taboada

Anadón

2014

Global Change Biology

View full text Add to dashboard Cite

Seasonal pulses of phytoplankton drive seasonal cycles of carbon fixation and particle sedimentation, and might condition recruitment success in many exploited species. Taking advantage of long-term series of remotely sensed chlorophyll a (1998-2012), we analysed changes in phytoplankton seasonality in the North Atlantic Ocean. Phytoplankton phenology was analysed based on a probabilistic characterization of bloom incidence. This approach allowed us to detect changes in the prevalence of different seasonal cycles and, at the same time, to estimate bloom timing and magnitude taking into account uncertainty in bloom detection. Deviations between different sensors stressed the importance of a prolonged overlap between successive missions to ensure a correct assessment of phenological changes, as well as the advantage of semi-analytical chlorophyll algorithms over empirical ones to reduce biases. Earlier and more intense blooms were detected in the subpolar Atlantic, while advanced blooms of less magnitude were common in the Subtropical gyre. In the temperate North Atlantic, spring blooms advanced their timing and decreased in magnitude, whereas fall blooms delayed and increased their intensity. At the same time, the prevalence of locations with a single autumn/winter bloom or with a bimodal seasonal cycle increased, in consonance with a poleward expansion of subtropical conditions. Changes in bloom timing and magnitude presented a clear signature of environmental factors, especially wind forcing, although changes on incident photosynthetically active radiation and sea surface temperature were also important depending on latitude. Trends in bloom magnitude matched changes in mean chlorophyll a during the study period, suggesting that seasonal peaks drive long-term trends in chlorophyll a concentration. Our results link changes in North Atlantic climate with recent trends in the phenology of phytoplankton, suggesting an intensification of these impacts in the near future.

show abstract

“…Blooms last until nutrients are depleted, after which they crash (Levy, 2015), or are terminated by overgrazing. How rapidly the bloom develops will be determined by physical conditions, the physiological status of the phytoplankton population and by loses (Banse and English, 1994), resulting in diverse patterns of seasonal cycles of phytoplankton biomass, as evident in remotely sensed records of chlorophyll concentration (Vargas et al, 2009;Racault et al, 2012). In the model, the physiological status is described by the photosynthesis irradiance function, whereas the physical conditions are presented by the mixed layer depth, surface irradiance, and the attenuation coefficient.…”

Section: Time-dependent Mixed-layer Biomassmentioning

confidence: 99%

Extended Formulations and Analytic Solutions for Watercolumn Production Integrals

Kovač

Platt

Antunović³

et al. 2017

Front. Mar. Sci.

View full text Add to dashboard Cite

The effect of biomass dynamics on the estimation of watercolumn primary production is analyzed, by coupling a primary production model to a simple growth equation for phytoplankton. The production model is formulated with depth-and time-resolved biomass, and placed in the context of earlier models, with emphasis on the canonical solution for watercolumn production. A relation between the canonical solution and the general solution for the case of an arbitrary depth-dependent biomass profile was derived, together with an analytical solution for watercolumn production in case of a depth dependent biomass profile described with the shifted Gaussian function. The analysis was further extended to the case of a time-dependent, mixed-layer biomass, and two additional analytical solutions to this problem were derived, the first in case of increasing mixed-layer biomass and the second in case of declining biomass. The solutions were tested with Hawaii Ocean Time-series data. The canonical solution for mixed-layer production has proven to be a good model for this data set. The shifted Gaussian function was demonstrated to be an accurate model for the measured biomass profiles and the shifted Gaussian parameters extracted from the measured profiles were further used in the analytical solution for watercolumn production and results compared with data. The influence of time-dependent biomass on mixed-layer production was studied through analytical solutions. Re-examining the Critical Depth Hypothesis we derived an expression for the daily increase in mixed-layer biomass. Finally, the work was placed in a remote sensing context and the time-dependent model for biomass related to the remotely sensed-biomass.

show abstract

Phenology of marine phytoplankton from satellite ocean color measurements

Cited by 43 publications

References 24 publications

A comparison of methods to determine phytoplankton bloom initiation

A comparison of methods to determine phytoplankton bloom initiation

Seasonality of North Atlantic phytoplankton from space: impact of environmental forcing on a changing phenology (1998–2012)

Extended Formulations and Analytic Solutions for Watercolumn Production Integrals

Contact Info

Product

Resources

About