Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission

Siegel, David A.; Behrenfeld, Michael J.; Maritorena, Stéphane; McClain, Charles R.; Antoine, David; Bailey, Sean W.; Bontempi, Paula S; Boss, Emmanuel; Dierssen, Heidi M.; Doney, Scott C.; Eplee, Robert E.; Evans, Robert H.; Feldman, Gene C.; Fields, Erik; Franz, Bryan A.; Kuring, Norman; Mengelt, Claudia; Nelson, Norman B.; Patt, Frederick S.; Robinson, Wayne D.; Sarmiento, Jorge L.; Swan, C.; Werdell, P. Jeremy; Westberry, T. K.; Wilding, J.; Yoder, James A.

doi:10.1016/j.rse.2013.03.025

Cited by 276 publications

(245 citation statements)

References 94 publications

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“…12). The correlation patterns displayed in the tropics, subtropics and high latitudes are consistent with previous studies, which have been carried out for different time periods and different satellite sensors (e.g., Wilson and Coles 2005;Brewin et al 2012Brewin et al , 2014Siegel et al 2013). This consistency suggests that the patterns are independent of the time period selected for the analysis and of differences in chlorophyll-retrieval and atmospheric correction algorithms used (Brewin et al 2014; note also that OC-CCI product includes data from three sensors-SeaWiFS, MODIS and MERIS-which have been processed using SeaDASFu et al 1998-and POLYMER atmospheric correction algorithms-Steinmetz et al 2011 show a significant increase in data coverage (Sathyendranath and Krasemann 2014;Racault et al 2015;Sathyendranath et al 2016).…”

Section: Correlation Analyses Based On CCI Datasetssupporting

confidence: 91%

Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993–2011

et al. 2016

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We analyse the regional variability in observed sea surface height (SSH), sea surface temperature (SST) and ocean colour (OC) from the ESA Climate Change Initiative datasets over the period [1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011]. The analysis focuses on the signature of the ocean large-scale climate fluctuations driven by the atmospheric forcing and do not address the mesoscale variability. We use the ECCO version 4 ocean reanalysis to unravel the role of ocean transport and surface buoyancy fluxes in the observed SSH, SST and OC variability. We show that the SSH regional variability is dominated by the steric effect (except at high latitude) and is mainly shaped by ocean heat transport divergences with some contributions from the surface heat fluxes forcing that can be significant regionally (confirming earlier results). This is in contrast with the SST regional variability, which is the result of the compensation of surface heat fluxes by ocean heat transport in the mixed layer and arises from small departures around this background balance. Bringing together the results of SSH and SST analyses, we show that SSH and SST bear some common variability. This is because both SSH and SST variability show significant contributions from the surface heat fluxes forcing. It is evidenced by the high correlation between SST and buoyancy-forced SSH almost everywhere in the ocean except at high latitude. OC, which is determined by phytoplankton biomass, is governed by the availability of light and nutrients that essentially depend on climate fluctuations. For this reason, OC shows significant correlation with SST and SSH. We show that the correlation with SST displays the same pattern as the correlation with SSH with a negative correlation in the tropics and subtropics and a positive correlation at high latitude. We discuss the reasons for this pattern.

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Section: Correlation Analyses Based On CCI Datasetssupporting

confidence: 91%

Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993–2011

et al. 2016

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“…However, SeaWiFS-derived TCHLa indicated significant declines in NATL and TRA, not observed in the AMT data. Furthermore, whereas recent longterm analysis (N 10 years) of SeaWiFS-derived TCHLa indicates a decrease over the entire NATL province, regions of both increase and decrease have been observed in the TRA and SATL (Siegel et al, 2013;Vantrepotte & Mélin, 2009), suggesting variability in trends within these two provinces that may not be captured on a specific AMT transect route. Confidence in the satellite estimates of SeaWiFS TCHLa can be taken from the fact that they compare favourably with weekly and daily AMT match-ups (Aiken et al, 2009;Brewin et al, 2010).…”

Section: Trends In Total Chlorophyll-amentioning

confidence: 80%

“…Multi-annual trends were evaluated, for each province, using linear regression analysis on log 10 -transformed TCHLa (Siegel et al, 2013), but not log 10 -transformed phytoplankton size fractions. The rate of TCHLa change over the years was calculated by transforming log-scale to linear scale.…”

Section: Statisticsmentioning

confidence: 99%

Temporal changes in total and size-fractioned chlorophyll-a in surface waters of three provinces in the Atlantic Ocean (September to November) between 2003 and 2010

Ağırbaş

Martinez‐Vicente

Brewin

et al. 2015

Journal of Marine Systems

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Phytoplankton total chlorophyll concentration (TCHLa) and phytoplankton size structure are two important ecological indicators in biological oceanography. Using high performance liquid chromatography (HPLC) pigment data, collected from surface waters along the Atlantic Meridional Transect (AMT), we examine temporal changes in TCHLa and phytoplankton size class (PSC: micro-, nano-and pico-phytoplankton) between 2003 and 2010 (September to November cruises only), in three ecological provinces of the Atlantic Ocean. The HPLC data indicate no significant change in TCHLa in northern and equatorial provinces, and an increase in the southern province. These changes were not significantly different to changes in TCHLa derived using satellite oceancolour data over the same study period. Despite no change in AMT TCHLa in northern and equatorial provinces, significant differences in PSC were observed, related to changes in key diagnostic pigments (fucoxanthin, peridinin, 19′-hexanoyloxyfucoxanthin and zeaxanthin), with an increase in small cells (nano-and picophytoplankton) and a decrease in larger cells (micro-phytoplankton). When fitting a three-component model of phytoplankton size structure -designed to quantify the relationship between PSC and TCHLa to each AMT cruise, model parameters varied over the study period. Changes in the relationship between PSC and TCHLa have wide implications in ecology and marine biogeochemistry, and provide key information for the development and use of empirical ocean-colour algorithms. Results illustrate the importance of maintaining a timeseries of in-situ observations in remote regions of the ocean, such as that acquired in the AMT programme.

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“…It is presumed that perceived color of seawater in the NWES is strongly driven by CDOM and SPM although chl-a plays a seasonal and local role for example in the Central North Sea [32][33][34][35]. In situ station (blue dots) sampling was done in the German Bight (1)(2)(3)(4)(5)(6)(7)(8), North Sea (9-26), Inner Seas (27)(28)(29)(30)(31)(32)(33)(34), Irish Sea (35)(36)(37)(38)(39)(40)(41)(42)(43), and Celtic Sea (44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60). Region naming is based on maps from The Germany Federal Maritime and Hydrographic Agency [34] and International Hydrographic Organization [35].…”

Section: Study Areamentioning

confidence: 99%

Physical, Bio-Optical State and Correlations in North–Western European Shelf Seas

2014

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Abstract:Color of seawater has become an integral tool in understanding surface marine ecosystems and processes. In this paper we seek to assess the correlations and consequently the potential of using shipborne remote sensing products to infer marine environmental parameters. Typical seawater parameters are chlorophyll-a (chl-a), colored dissolved organic material (CDOM), suspended particulate material (SPM), Secchi-disk depth (SDD), temperature, and salinity. These parameters and radiometric quantities were observed from a total of 60 stations covering German Bight, North Sea, Inner Seas, Irish Sea, and Celtic Sea. Bio-optical models developed in this study were used to predict the in situ measured parameters, with low mean unbiased percent differences and absolute percent difference less than 35%. Our investigations show that the use of ocean color products namely the Forel-Ule Index to infer seawater parameters is encouraging. The constrained spatial and temporal span of measured in situ parameters does limit the accuracy of our models. Absorption coefficients of the main color producing agents CDOM, chl-a, and inorganic fraction of SPM (iSPM) were determined to estimate absorption budgets. During the field campaign, iSPM was the primary light absorber over the spectral range (400-700 nm) although variabilities were observed in the regional seas.

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Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission

Cited by 276 publications

References 94 publications

Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993–2011

Causes of the Regional Variability in Observed Sea Level, Sea Surface Temperature and Ocean Colour Over the Period 1993–2011

Temporal changes in total and size-fractioned chlorophyll-a in surface waters of three provinces in the Atlantic Ocean (September to November) between 2003 and 2010

Physical, Bio-Optical State and Correlations in North–Western European Shelf Seas

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