Monitoring marine phytoplankton seasonality from space

Remote Sensing of Environment, 2013

Photosynthetic production of organic matter by microscopic oceanic phytoplankton fuels ocean ecosystems and contributes roughly half of the Earth's net primary production. For 13 years, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission provided the first consistent, synoptic observations of global ocean ecosystems. Changes in the surface chlorophyll concentration, the primary biological property retrieved from SeaWiFS, have traditionally been used as a metric for phytoplankton abundance and its distribution largely reflects patterns in vertical nutrient transport. On regional to global scales, chlorophyll concentrations covary with sea surface temperature (SST) because SST changes reflect light and nutrient conditions. However, the ocean may be too complex to be well characterized using a single index such as the chlorophyll concentration. A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans. The SeaWiFS data set demonstrates complexity in the interpretation of changes in regional to global phytoplankton distributions and illustrates limitations for the assessment of phytoplankton dynamics using chlorophyll retrievals alone.

Global Biogeochemical Cycles, 2008

Phytoplankton plays an important role in the global carbon cycle via the fixation of inorganic carbon during photosynthesis. However, the efficiency of this ''biological pump of carbon'' strongly depends on the nature of the phytoplankton. Monitoring spatial and temporal variations of the distribution of dominant phytoplankton groups at the global scale is thus of critical importance. Recently, an algorithm has been developed to detect the major dominant phytoplankton groups from anomalies of the marine signal measured by ocean color satellites. This method, called PHYSAT, allows to identify nanoeucaryotes, Prochlorococcus, Synechococcus and diatoms. In this paper, PHYSAT has been improved to detect an additional group, named phaeocystis-like, by analyzing specific signal anomalies in the Southern Ocean during winter months. This new version of PHYSAT was then used to process daily global SeaWiFS GAC data between 1998 and 2006. The global distribution of major phytoplankton groups is presented in this study as a monthly climatology of the most frequent phytoplankton group. The contribution of nanoeucaryotes-dominated waters to the global ocean varies from 45 to 70% depending on the season, whereas both diatoms and phaeocystis-like contributions exhibit a stronger seasonal variability mostly due to the large blooms that occur during winter in the Southern Ocean. Three regions of particular interest are also studied in more details: the Southern Ocean, the North Atlantic, and the Equatorial Pacific. The North Atlantic diatom bloom shows a large interannual variability. Large blooms of both diatoms and phaeocystis-like are observed during winter in the Southern Ocean, with a larger contribution from diatoms. Their respective geographical distribution is shown to be tightly related to the depth of the mixed-layer, with diatoms prevailing in stratified waters. Synechococcus and Prochloroccocus prevail in the Equatorial Pacific, but our data show also sporadic diatoms contributions in this region during La Niña. The observed seasonal cycle and interannual variability of phytoplankton groups in the global ocean suggest that the PHYSAT archive is suitable to study the impact of climate variability on the structure of marine ecosystems.

Journal of Plankton Research, 2016

In most north temperate lakes, phytoplankton biomass oscillates on an annual scale. While phytoplankton seasonal succession within a year has been described for many lakes, much less is known about variability in seasonal succession over multiple years. Here, we describe how continuous wavelet transforms can be used to identify variation in the periodicity in phytoplankton time series at multiple timescales. To demonstrate our approach, we analyzed 16 years of biweekly phytoplankton data from eutrophic Lake Mendota, USA, that coincided with substantial variability in climate and nutrient loading. Throughout the time series, the wavelet transforms identified the annual scale as the dominant scale of variation in aggregated phytoplankton, except for a 3-year period when there was no significant dominant scale. This period coincided with drought and decreased nutrient loading. During this time, phytoplankton biomass was markedly lower, and the phytoplankton community exhibited a unimodal, not bimodal, pattern of seasonal succession. Our results highlight the utility of wavelet techniques for identifying changes in seasonal succession in long-term phytoplankton records, which are becoming more available for many lakes. As aquatic ecosystems increasingly experience exogenous forcings at multiple timescales, wavelet analyses provide a powerful tool for determining how phytoplankton communities may respond.

Advances in Space Research, 2002

The information obtained from the modem Earth-observing satellites, enables comprehensive studies of seasonal dynamics of ecosystems in different regions of the World Ocean. The properties of physical environment (sea surface temperature, measured by AVI-IRR radiometers; currents estimated from altimetry data; wind and precipitation measured by meteorological satellites) combined with biological data derived from ocean optics, reveal principle features of phytoplankton seasonal cycles in diverse ocean environments. The peculiarities of these cycles depend on local bottom topography, hydrological regime, wind forcing, etc; in coastal regions river discharge is of great importance. The examples of different types of seasonal cycles of phytoplankton are presented, and the mechanisms influencing the phytoplankton growth are discussed. The peculiar region is the Northwest Atlantic near Newfoundland Rise, where abrupt environmental contrasts (the cold Labrador Current flowing from the north and the warm Gulf Stream from the southwest) result in different types of seasonal cycles over rather narrow area. Mother illustrative example is the Black Sea where extremely high water stratification results in a seasonal cycle similar to subtropical regions rather than to temperate ones. The general patterns of seasonal phytoplankton cycles depend on physical factors influencing water stratification.

In the Ocean, the seasonal cycle is the mode that couples climate forcing to ecosystem response in production, diversity and carbon export. A better characterisation of the ecosystem's seasonal cycle therefore addresses an important gap in our ability to estimate the sensitivity of the biological pump to climate change. In this study, the regional characteristics of the seasonal cycle of phytoplankton biomass in the Southern Ocean are examined in terms of the timing of the bloom initiation, its amplitude, regional scale variability and the importance of the climatological seasonal cycle in explaining the overall variance. The seasonal cycle was consequently defined into four broad zonal regions; the subtropical zone (STZ), the transition zone (TZ), the Antarctic circumpolar zone (ACZ) and the marginal ice zone (MIZ). Defining the Southern Ocean according to the characteristics of its seasonal cycle provides a more dynamic understanding of ocean productivity based on underlying physical drivers rather than climatological biomass. The response of the biology to the underlying physics of the different seasonal zones resulted in an additional classification of four regions based on the extent of inter-annual seasonal phase locking and the magnitude of the integrated seasonal biomass. This regionalisation contributes towards an improved understanding of the regional differences in the sensitivity of the Southern Oceans ecosystem to climate forcing, potentially allowing more robust predictions of the effects of long term climate trends.

Biogeosciences, 2011

In the Ocean, the seasonal cycle is the mode that couples climate forcing to ecosystem response in production, diversity and carbon export. A better characterisation of the ecosystem's seasonal cycle therefore addresses an important gap in our ability to estimate the sensitivity of the biological pump to climate change. In this study, the regional characteristics of the seasonal cycle of phytoplankton biomass in the Southern Ocean are examined in terms of the timing of the bloom initiation, its amplitude, regional scale variability and the importance of the climatological seasonal cycle in explaining the overall variance. The seasonal cycle was consequently defined into four broad zonal regions; the subtropical zone (STZ), the transition zone (TZ), the Antarctic circumpolar zone (ACZ) and the marginal ice zone (MIZ). Defining the Southern Ocean according to the characteristics of its seasonal cycle provides a more dynamic understanding of ocean productivity based on underlying physical drivers rather than climatological biomass. The response of the biology to the underlying physics of the different seasonal zones resulted in an additional classification of four regions based on the extent of inter-annual seasonal phase locking and the magnitude of the integrated seasonal biomass. This regionalisation contributes towards an improved understanding of the regional differences in the sensitivity of the Southern Oceans ecosystem to climate forcing, potentially allowing more robust predictions of the effects of long term climate trends.

Ecological Indicators, 2012

In recent years, phytoplankton phenology has been proposed as an indicator to monitor systematically the state of the pelagic ecosystem and to detect changes triggered by perturbation of the environmental conditions. Here we describe the phenology of phytoplankton growth for the world ocean using remotesensing ocean colour data, and analyse its variability between 1998 and 2007. Generally, the tropics and subtropics present long growing period (≈15-20 weeks) of low amplitude (<0.5 mg Chl m -3 ), whereas the high-latitudes show short growing period (<10 weeks) of high amplitude (up to 7 mg Chl m -3 ). Statistical analyses suggest a close coupling between the development of the growing period and the seasonal increase in insolation in the North Atlantic and Southern Ocean. In the tropics and subtropics, variability in light is low, and the growing period is controlled by nutrient supply occurring when mixing increases. Large interannual variability in the duration of the growing period is observed over the decade 1998-2007, with positive anomalies following the major 1997-1998 El Ni ño-La Ni ña events, and generally negative anomalies from 2003 to 2007. Warmer Sea-Surface Temperature (SST) over the duration of the growing period is associated with longer duration at high-latitudes indicating an extension of the growing period over summer months. The opposite is observed in the tropics and subtropics, where the duration is shorter when the SST is warmer, indicating increased stratification. Positive phases of North Atlantic Oscillation and Southern Annular Mode and negative phases of Multivariate El Ni ño-Southern Oscillation index (El Ni ño conditions), associated with enhanced water mixing and nutrients supply, generally sustain longer growth. On the basis of the results, perspectives are drawn on the utility of phenology as an organising principle for the analysis of pelagic ecosystem.

Global Biogeochemical Cycles, 2012

1] We investigated the phenology of oceanic phytoplankton at large scales over two 5-year time periods: 1979-1983 and 1998-2002. Two ocean-color satellite data archives (Coastal Zone Color Scanner (CZCS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS)) were used to investigate changes in seasonal patterns of concentrationnormalized chlorophyll. The geographic coverage was constrained by the CZCS data distribution. It was best for the Northern Hemisphere and also encompassed large areas of the Indian, South Pacific, and Equatorial Atlantic regions. For each 2 pixel, monthly climatologies were developed for satellite-derived chlorophyll, and the resulting seasonal cycles were statistically grouped using cluster analysis. Five distinct groups of mean seasonal cycles were identified for each half-decade period. Four types were common to both time periods and correspond to previously identified phytoplankton regimes: Bloom, Tropical, Subtropical North, and Subtropical South. Two other mean seasonal cycles, one in each of the two compared 5-year periods, were related to transitional or intermediate states (Transitional Tropical and Transitional Bloom). Five mean seasonal cycles (Bloom, Tropical, Subtropical North, and Subtropical South, Transitional Bloom) were further confirmed when the whole SeaWiFS data set (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) was analyzed. For 35% of the pixels analyzed, characteristic seasonal cycles of the 1979-1983 years differed little from those of the 1998-2002 period. For 65% of the pixels, however, phytoplankton seasonality patterns changed markedly, especially in the Northern Hemisphere. Subtropical regions of the North Pacific and Atlantic experienced a widespread expansion of the Transitional Bloom regime, which appeared further enhanced in the climatology based on the full SeaWiFS record (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010), and, as showed by a more detailed analysis, is associated to La Niña years. This spatial pattern of Transitional Bloom regime reflects a general smoothing of seasonality at macroscale, coming into an apparent greater temporal synchrony of the Northern Hemisphere. The Transitional Bloom regime is also the result of a higher variability, both in space and time. The observed change in phytoplankton dynamics may be related not only to biological interactions but also to large-scale changes in the coupled atmosphere-ocean system. Some connections are indeed found with climate indices. Changes were observed among years belonging to opposite phases of ENSO, though discernible from the change among the two periods and within the SeaWiFS era (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). These linkages are considered preliminary at present and are worthy of further investigation. Citation: D'Ortenzio, F., D. Antoine, E. Martinez, and M. Ribera d'Alcalà (2012), Phenological changes of oceanic phytoplankton in the 1980s and 2000s as revealed by remotely sensed ocean-color observations, Global Biogeochem. Cycles, 26, GB4003,

Journal of Geophysical Research, 2010

[1] Global time series of satellite-derived winds and surface chlorophyll concentration 6 (Chl-a) show patterns of coherent areas with either positive or negative correlations. The 7 correlation between Chl-a and wind speed is generally negative in areas with deep 8 mixed layers and positive in areas with shallow mixed layers. These patterns are 9 interpreted in terms of the main limiting factors that control phytoplankton growth, i.e., 10 either nutrients that control phytoplankton biomass in areas with positive correlation 11 between Chl-a and wind speed or light that controls phytoplankton biomass in areas 12 with negative correlation between Chl-a and wind speed. More complex patterns are 13 observed in the equatorial regions due to regional specificities in physical-biological 14 interactions. These correlation patterns can be used to map out the biogeochemical 15 provinces of the world ocean in an objective way. 1. Introduction 19 [2] Phytoplankton productivity and biomass in the world 20 ocean are limited by nutrient (N, P, Si, Fe) concentrations 21 and/or the mean light level, 22 which is modulated by vertical mixing and seasonal vari-23 ability in daily insolation [Siegel et al., 2002]. Phytoplank-24 ton productivity drives the oceanic biological pump and 25 therefore has the potential to affect global atmospheric 26 CO 2 levels [Sarmiento and Orr, 1991]. Changes in atmo-27 spheric CO 2 and the associated climate forcing can in turn 28 impact phytoplankton productivity by changing ocean 29 stratification, circulation, and pH. A number of authors [Platt 30 and Sathyendranath, 1988; Longhurst et al., 1995] have 31 proposed the definition and use of quasi-stable biogeo-32 chemical provinces as a means of assessing basin scale 33 oceanic productivity and biogeochemical characteristics. 34 These provinces were traditionally based on measurements 35 from ship-based platforms with the obvious consequence 36 that the observed properties were dramatically undersampled 37 in both space and time and the resulting boundaries were 38 not well defined. Global time series of satellite measure-39 ments provide a significant amount of data to classify ocean 40 environments as different biogeochemical provinces and to 41 monitor the interannual and long-term changes in province 42 boundaries. A number of different methods have been 43 proposed to differentiate between biogeochemical provinces. 44 These include the annual variability in phytoplankton pig-45 ment concentration [Esaias et al., 2000]; remotely sensed 46 chlorophyll concentration, sea surface temperature, and the 47 fixed boundaries of Longhurst's provinces [Devred et al., 48 2007]; a bioinformatic clustering algorithm using water-49 leaving radiance at two wavelengths and the sea surface 50 temperature [Oliver and Irwin, 2008]; and a fuzzy logic 51 classification of ocean bio-optical signatures [Moore et al., 52 2009]. Ocean ecosystems are governed by physical forcing, 53 including winds, and studies of the relationships between 54 winds and ocean biology have a long history [e.g., Denman, 55 1973]. However, global, high-resolution data sets of winds 56 and phytoplankton data have not been available until 57 recently. Here we use the correlation between time series of 58 satellite-derived winds and surface chlorophyll-a concen-59 tration (Chl-a, mg m −3 ) to map the main biogeochemical 60 provinces in the world ocean based on the dominant 61 mechanisms responsible for the variability in phytoplankton 62 biomass. 63 2. Data and Methods 64 [3] Chlorophyll-a concentrations (Chl-a, mg m −3 ) were 65 obtained from NASA's Ocean Color Web site [McClain, 66 2009] (see for Web links and references) and from 67 the European Space Agency's GlobColour project. For this 68 analysis, we used remotely sensed level 3 (i.e., binned and 69 mapped) monthly and daily Chl-a data sets that were derived 70 using standard case 1 water algorithms [O'Reilly et al., 1998; 71 Morel and Maritorena, 2001]. Any single ocean color sensor 72 has a limited daily coverage resulting from gaps between 73 the swaths, Sun glint, and cloud cover. Merging data from 74 multiple sensors, if data from more than one sensor are 75 available, will increase the coverage due to the combination 76 of patchy and uneven daily coverage from sensors viewing 77 the ocean at slightly different times and geometries. Com-78 pared to data from individual sensors, the merged products 79 from three sensors (SeaWiFS, MERIS, and MODIS-Aqua) 80 have approximately twice the mean global coverage and 81 lower uncertainties in the retrieved variables [Maritorena 82 et al., 2010]

Frontiers in Marine Science, 2017

Phytoplankton functional diversity plays a key role in structuring the ocean carbon cycle and can be estimated using measurements of phytoplankton functional type (PFT) groupings. Concentrations of 18 phytoplankton pigments were calculated using a linear matrix inversion algorithm, with an average r 2 value of 0.70 for all pigments with p-values below the statistical threshold of 0.05. The inversion algorithm was then used with a chlorophyll-based absorption spectra model and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) chlorophyll observations to calculate phytoplankton pigment concentrations in an area of the Atlantic Ocean off the United States east coast. Pigment distributions were analyzed to assess the distribution of PFTs. Five unique PFTs were found and delineated into three distinct offshore, transition, and open ocean groups. Group 1 (Diatoms) had highest abundance along the coast. Group 2 (prymnesiophytes, prokaryotes, and green algae) was a year-round stable offshore community that extended at reduced levels into the coast. Group 3 (dinoflagellates) dominated offshore between the Groups 1 and 2. Phytoplankton communities were delineated into coastal and offshore populations, with Group 2 having a dampened seasonal cycle, relative to the coastal populations. Shannon Diversity Indices (H) for the PFTs showed both spatial and temporal variability and had a clear non-linear relationship with chlorophyll. Diversity levels varied seasonally with changes in chlorophyll a levels. Peak PFT H was observed on the shelf where frontal features dominate, with diversity levels declining nearshore and offshore. This region marks an ecotone for phytoplankton in the study domain, and is associated with the coastal-side boundary of dinoflagellate dominance. Highest levels of diversity were observed in the tidally well-mixed regions of the Gulf of Maine and along a band that ran along the shelf region of the study area that was narrowest in the summer periods and broadened during the winter. These peak diversity zones were associated with moderate levels (∼0.8 mg m −3) of chlorophyll a. While the sign in the linear trends in chlorophyll between 2002 and 2016 varied depending on the region, the trends in the PFT H values were nearly all negative due to the non-linear relationship between chlorophyll levels and H.