Comparison of global chlorophyll concentrations using MODIS data (original) (raw)
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Global Biogeochemical Cycles, 2015
In this study we analyze large-scale satellite-derived data using generalized additive models to characterize the global correlation patterns between environmental forcing and marine phytoplankton biomass. We found systematic differences in the relationships between key environmental drivers (temperature, light, and wind) and ocean chlorophyll in the subtropical/tropical and temperate oceans. For the subtropical/tropical and equatorial oceans, the chlorophyll generally declined with increasing temperature and light, while in temperate oceans, chlorophyll was best explained by bell-shaped or positive functions of temperature and light. The relationship between chlorophyll and wind speed is generally positive in low-latitude oceans and bell shaped in temperate oceans. Our analyses also demonstrated strong and geographically consistent positive autoregressive effects of chlorophyll from 1 month to the next and negative autoregressive effects for measurements 2 months apart. These findings imply possibly different regional phytoplankton responses to environmental forcing, suggesting that future environmental change could affect the tropical and temperate upper ocean chlorophyll levels differently. Being unicellular, fast growing, and short lived, phytoplankton responds rapidly to external ambient drivers and environmental change [Hays et al., 2005]. Environmental forcings such as temperature, light, and wind speed are recognized as important physical factors affecting the dynamics of marine phytoplankton both directly and indirectly (e.g., via mixing regimes, nutrient dynamics, and grazing) [Chavez et al., 2011; Mauri et al., 2007], and these environmental drivers will vary among regions. For example, it has been suggested that there is a negative correlation between sea surface temperature (SST) and phytoplankton abundance in warm regions of Northeast Atlantic [Richardson and Schoeman, 2004] as well as Atlantic and Pacific gyres [Gregg et al., 2005]. Positive or nonlinear effects of temperature on phytoplankton have also been found in the North Sea [Llope et al., 2009] and cold regions of North Atlantic [Irwin and Finkel, 2008; Raitsos et al., 2006]. Sarmiento et al. [2004] evaluated environmental effects on annual chlorophyll using a linear regression model and found that these coefficients varied across ocean regions. Both positive and negative correlations have been reported between wind speed and chlorophyll concentration in different ocean regions [Kahru et al., 2010]. Given the possibly complex relationships between different environmental factors and ocean chlorophyll in different regions, it is a challenge to outline the general correlation patterns between them on a global scale.
Journal Of Geophysical Research: Oceans, 2014
Quantifying change in ocean biology using satellites is a major scientific objective. We document trends globally for the period 1998-2012 by integrating three diverse methodologies: ocean color data from multiple satellites, bias correction methods based on in situ data, and data assimilation to provide a consistent and complete global representation free of sampling biases. The results indicated no significant trend in global pelagic ocean chlorophyll over the 15 year data record. These results were consistent with previous findings that were based on the first 6 years and first 10 years of the SeaWiFS mission. However, all of the Northern Hemisphere basins (north of 10 latitude), as well as the Equatorial Indian basin, exhibited significant declines in chlorophyll. Trend maps showed the local trends and their change in percent per year. These trend maps were compared with several other previous efforts using only a single sensor (SeaWiFS) and more limited time series, showing remarkable consistency. These results suggested the present effort provides a path forward to quantifying global ocean trends using multiple satellite missions, which is essential if we are to understand the state, variability, and possible changes in the global oceans over longer time scales.
Trends in Ocean Colour and Chlorophyll Concentration from 1889 to 2000, Worldwide
PLoS ONE, 2013
Marine primary productivity is an important agent in the global cycling of carbon dioxide, a major 'greenhouse gas', and variations in the concentration of the ocean's phytoplankton biomass can therefore explain trends in the global carbon budget. Since the launch of satellite-mounted sensors globe-wide monitoring of chlorophyll, a phytoplankton biomass proxy, became feasible. Just as satellites, the Forel-Ule (FU) scale record (a hardly explored database of ocean colour) has covered all seas and oceans -but already since 1889. We provide evidence that changes of ocean surface chlorophyll can be reconstructed with confidence from this record. The EcoLight radiative transfer numerical model indicates that the FU index is closely related to chlorophyll concentrations in open ocean regions. The most complete FU record is that of the North Atlantic in terms of coverage over space and in time; this dataset has been used to test the validity of colour changes that can be translated to chlorophyll. The FU and FU-derived chlorophyll data were analysed for monotonously increasing or decreasing trends with the non-parametric Mann-Kendall test, a method to establish the presence of a consistent trend. Our analysis has not revealed a globe-wide trend of increase or decrease in chlorophyll concentration during the past century; ocean regions have apparently responded differentially to changes in meteorological, hydrological and biological conditions at the surface, including potential long-term trends related to global warming. Since 1889, chlorophyll concentrations have decreased in the Indian Ocean and in the Pacific; increased in the Atlantic Ocean, the Mediterranean, the Chinese Sea, and in the seas west and north-west of Japan. This suggests that explanations of chlorophyll changes over long periods should focus on hydrographical and biological characteristics typical of single ocean regions, not on those of 'the' ocean. Citation: Wernand MR, van der Woerd HJ, Gieskes WWC (2013) Trends in Ocean Colour and Chlorophyll Concentration from 1889 to 2000, Worldwide. PLoS ONE 8(6): e63766.
Phenology of marine phytoplankton from satellite ocean color measurements
Geophysical Research Letters, 2009
Climate change is expected to affect the timing and magnitude of numerous environmental conditions, including temperature, wind, and precipitation. Amongst other repercussions, such alterations will engender a response in marine ecosystem productivity manifested by changes in the timing and magnitude of phytoplankton biomass and primary productivity. Several investigations have examined the change in magnitude in chlorophyll concentration in relation to changing environmental conditions, but little has been done to examine the change in the timing of the annual cycle of phytoplankton biomass. In order to establish a baseline from which to assess any future changes in the phenology of phytoplankton biomass, we constructed nine-year climatologies of phytoplankton bloom onset, maturity, start of bloom decay, and termination in the central North Atlantic. This was accomplished by extracting annual values of these phenological markers from Generalized Linear Models fit to pentad (five-day) estimates of SeaWiFS chlorophyll concentrations dating from 1998 to 2006. This novel modeling approach, which produced results consistent with known patterns of phytoplankton bloom dynamics in this region, provides a statistically robust approach to detect and account for changes in the annual cycle of phytoplankton biomass.
Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission
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.
Three Improved Satellite Chlorophyll Algorithms for the Southern Ocean
Remote sensing of Southern Ocean chlorophyll concentrations is the most effective way to detect large-scale changes in phytoplankton biomass driven by seasonality and climate change. However, the current algorithms for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, algorithm OC4v6), the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua, algorithm OC3M) and GlobColour significantly underestimate chlorophyll concentrations at high latitudes. Here we use a long-term dataset from the Southern Ocean (20 - 160E) to develop more accurate algorithms for all three of these products in southern high latitude regions. These new algorithms improve in situ versus satellite chlorophyll coefficients of determination (r^2) from 0.27 to 0.46, 0.26 to 0.51 and 0.25 to 0.27, for OC4v6, OC3M and GlobColour, respectively, while addressing the underestimation problem. This study also revealed that pigment composition, which reflects species composition and physiology, is key to understanding the reasons for satellite chlorophyll underestimation in this region. These significantly improved algorithms will permit more accurate estimates of standing stocks and more sensitive detection of spatial and temporal changes in those stocks, with consequences for derived products such as primary production and carbon cycling.
Mining a Sea of Data: Deducing the Environmental Controls of Ocean Chlorophyll
Chlorophyll biomass in the surface ocean is regulated by a complex interaction of physiological, oceanographic, and ecological factors and in turn regulates the rates of primary production and export of organic carbon to the deep ocean. Mechanistic models of phytoplankton responses to climate change require the parameterization of many processes of which we have limited knowledge. We develop a statistical approach to estimate the response of remote-sensed ocean chlorophyll to a variety of physical and chemical variables. Irradiance over the mixed layer depth, surface nitrate, sea-surface temperature, and latitude and longitude together can predict 83% of the variation in log chlorophyll in the North Atlantic. Light and nitrate regulate biomass through an empirically determined minimum function explaining nearly 50% of the variation in log chlorophyll by themselves and confirming that either light or macronutrients are often limiting and that much of the variation in chlorophyll concentration is determined by bottom-up mechanisms. Assuming the dynamics of the future ocean are governed by the same processes at work today, we should be able to apply these response functions to future climate change scenarios, with changes in temperature, nutrient distributions, irradiance, and ocean physics.
Estuarine, Coastal and Shelf Science, 2021
The trends of sea surface chlorophyll-a (Chl-a) concentrations in the Bohai and Yellow Seas of China (BYS) were analysed based on the satellite-derived Chl-a dataset from August 2002 to December 2018. The result of linear trend analysis based on the seasonal Mann-Kendall test indicates a significant positive Chl-a trend during this period, with an average trend of ~1.15% year − 1 (Slope: ~0.011 mg year − 1). However, the linear trends of Chl-a varied seasonally, with strong and significant increases in spring and summer (about 2% year − 1), and weak and non-significant increases in winter (lower than 1% year − 1). The results of the ensemble empirical mode decomposition (EEMD) analysis revealed highly nonlinear and time-varying trends of Chl-a in the BYS, with gradually increased Chl-a during 2002-2011 and decreased Chl-a from 2012 to 2018. The instantaneous rate of Chl-a change was continuously reduced from 2002 to 2018, from a positive value of ~2.0% year − 1 around the beginning year (2002) to a negative value of approximately − 2.0% year − 1 around the recent year (2018). The temporal evolution of the Chl-a trend was well in accordance with the changes in nutrient enrichment, suggesting that the status of eutrophication might be the primary driver of the long-term trends in Chl-a. The increase (decrease) in nutrient levels could alleviate (aggravate) the nutrient limitation for phytoplankton growth in spring and summer, thus regulating the changes in Chl-a. In contrast, the Chl-a trend seems to be unrelated to the trend of light intensity in this area. This is the first study aimed to discern and compare the linear and evolutionary nonlinear Chl-a trends in the BYS and provides a baseline against which future changes can be monitored.
Global correlations between winds and ocean chlorophyll
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]
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,