Seasonal Cycles of Phytoplankton Expressed by Sine Equations Using the Daily Climatology from Satellite-Retrieved Chlorophyll-a Concentration (1997–2019) Over Global Ocean

Mao, Zexi; Mao, Zhihua; Jamet, Cédric; Linderman, Marc; Wang, Yuntao; Chen, Xiaoyan

doi:10.3390/rs12162662

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Monthly mean MODIS values of Chl a from 2005-2016 were calculated, and refined curvilinear boundaries based on these data were hand-digitized to delineate the four Red Sea Phytoplankton Biomass Zones. Chlorophyll a data within these boundaries were fit with a sinusoidal function [28] Asin (ωt + ϕ) + m, where A is the fitted amplitude of Chl a (mg m −3 ), ω is frequency (2π/24), t is month (1 = January 12 = December), ϕ is phase shift (π) and m is the average fitted maximum value for Chl a (mg m −3 ). Accuracy of this model was determined by computing a root mean square error (RMSE).…”

Section: Long-term Red Sea Phytoplankton Biomass Mappingmentioning

confidence: 99%

Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants

Gomaa

Mulla

Galzki

et al. 2020

JMSE

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Harmful algal blooms (HABs) and the high biomass associated with them have afflicted marine desalination plants along coastal regions around the world. Few studies of HABs have been conducted in the Red Sea, where desalination plants along the Saudi Arabian Red Sea coast provide drinking water for millions of people. This study was conducted along the Saudi Arabian Red Sea coast from 2014 to 2015 to assess the potential for using Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing of chlorophyll a (Chl a) or fluorescence line height (FLH) to identify risks for biofouling at these desalination plants. Ship-based surveys of phytoplankton were conducted along the Saudi Arabian coastline offshore of desalination plants at Jeddah, Al Shoaibah and Al Qunfudhuh to assess the density of phytoplankton populations and identify any potential HAB species. Ship-based surveys showed low to moderate concentrations of phytoplankton, averaging from 1800–10,000 cells L−1 at Jeddah, 2000–11,000 cells L−1 at Al Shoaibah and 1000–20,500 cells L−1 at Al Qunfudhuh. Sixteen different species of potentially toxigenic HABs were identified through these surveys. There was a good relationship between ship-based total phytoplankton counts and monthly averaged coastal MODIS Chl a (R2 = 0.49, root mean square error (RMSE) = 0.27 mg m−3) or FLH (R2 = 0.47, RMSE = 0.04 mW m−2 µm−1 sr−1) values. Monthly average near shore Chl a concentrations obtained using MODIS satellite imagery were much higher in the Red Sea coastal areas at Al Qunfudhuh (maximum of about 1.3 mg m−3) than at Jeddah or Al Shoaibah (maximum of about 0.4 and 0.5 mg m−3, respectively). Chlorophyll a concentrations were generally highest from the months of December to March, producing higher risks of biofouling desalination plants than in other months. Concentrations decreased significantly, on average, from April to September. Long-term (2005–2016) monthly averaged MODIS Chl a values were used to delineate four statistically distinct zones of differing HAB biomass across the entire Red Sea. Sinusoidal functions representing monthly variability were fit to satellite Chl a values in each zone (RMSE values from 0.691 to 0.07 mg m−3, from Zone 1 to 4). December to January mean values and annual amplitudes for Chl a in these four sinusoidal functions decreased from Zones 1–4. In general, the greatest risk of HABs to desalination occurs during winter months in Zone 1 (Southern Red Sea), while HAB risks to desalination plants in winter months are low to moderate in Zone 2 (South Central Red Sea), and negligible in Zones 3 (North Central) and 4 (Northern).

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Section: Long-term Red Sea Phytoplankton Biomass Mappingmentioning

confidence: 99%

Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants

Gomaa

Mulla

Galzki

et al. 2020

JMSE

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“…Along with understanding the drivers of the tropical upwelling system, it is of great socioeconomic interest to predict their variability. The coastal tropical upwelling system in the Pacific and Atlantic Ocean exhibits a pronounced seasonal cycle, with important interannual variations superimposed (11)(12)(13). Note that state-of-the-art climate models have difficulties to realistically represent eastern boundary upwelling regions and their variability (14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

Coastal trapped waves and tidal mixing control primary production in the tropical Angolan upwelling system

Körner,

Brandt,

Illig

et al. 2024

Sci. Adv.

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Eastern boundary upwelling systems are hotspots of marine life and primary production. The strength and seasonality of upwelling in these systems are usually related to local wind forcing. However, in some tropical upwelling systems, seasonal maxima of productivity occur when upwelling favorable winds are weak. Here, we show that in the tropical Angolan upwelling system (tAUS), the seasonal productivity maximum is due to the combined effect of coastal trapped waves (CTWs) and elevated tidal mixing on the shelf. During austral winter, the passage of an upwelling CTW displaces the nitracline upward by more than 50 m. Thereby, nitrate-rich waters spread onto the shelf, where elevated vertical mixing causes a nitrate flux into the surface mixed layer. Interannual variability of the productivity maximum is strongly correlated to the amplitude of the upwelling CTW as seen in sea level data. Given that CTWs are connected to equatorial forcing, a predictability of the strength of the productivity maximum is suggested.

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“…Chlorophyll a (Chl-a) concentration is affected by the changes in intracellular pigment levels driven by light and nutrients. Recently, satellite-estimated phytoplankton carbon has been used as an indicator of phytoplankton biomass [1][2][3]. The modulation of phytoplankton blooms in spring promote the annual cycle of phytoplankton in large regions of the world's oceans, which has a dramatic effect on both high trophic levels and carbon export [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing and Argo Float Observations Reveal Physical Processes Initiating a Winter-Spring Phytoplankton Bloom South of the Kuroshio Current Near Shikoku

et al. 2020

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BIO-Argo float (chlorophyll a (Chl-a), temperature, and salinity profiles) and remote sensing data (Chl-a, photosynthetic available radiation (PAR), and wind) located south of the Kuroshio current near Shikoku from September 2018 to May 2019 were used to study phytoplankton bloom and their mechanisms of development in open oceans. Results show that higher (lower) Chl-a concentrations are correlated with a deeper (shallower) mixed layer (RPearson = 0.77, Rcrit = 0.12 (alpha = 0.05, n = 263)) compared to the average of Chl-a and mixed layer depth (0.13 mg/m3 and 105 m). The average net accumulation rates (r) of phytoplankton were close to 0.08 d−1. An increasing r corresponds to a gradually increasing surface Chl-a (S (Chl-a): 0–20 m average Chl-a) and integrated Chl-a inventory (I (Chl-a): integrated Chl-a from surface to euphotic depth). These phenomena indicate that the mechanism of winter-spring phytoplankton blooms is consistent with the dilution-recoupling hypotheses (DRH). During the bloom formation, winter deep mixing and eddy-wind Ekman pumping are enhanced by a strong winter monsoon. The enhancement may disturb predator–prey interactions and dilute zooplankton in deep mixed layers. Moreover, winter deep mixing and eddy-wind Ekman pumping can cause the nutrients to be transported into the euphotic layer, which can promote the growth of phytoplankton and increase grazing. During the bloom extinction, the stratification strengthens and the intensity of light increases; this increases grazing and nutrient consumption, and decreases the phytoplankton bloom significantly (S (Chl-a) and I (Chl-a) increase by 0.3 mg/m3 and 27 mg/m2, respectively). The output from a biogeochemistry model shows that nutrients are consistent with the temporal distribution of S (Chl-a) and I (Chl-a). Our results suggest that physical processes (deep winter mixing and eddy-wind Ekman pumping) under the DHR framework are critical factors for winter-spring blooms in open oceans with an anticyclone eddy.

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Seasonal Cycles of Phytoplankton Expressed by Sine Equations Using the Daily Climatology from Satellite-Retrieved Chlorophyll-a Concentration (1997–2019) Over Global Ocean

Cited by 8 publications

References 46 publications

Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants

Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants

Coastal trapped waves and tidal mixing control primary production in the tropical Angolan upwelling system

Remote Sensing and Argo Float Observations Reveal Physical Processes Initiating a Winter-Spring Phytoplankton Bloom South of the Kuroshio Current Near Shikoku

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