Variability Of Chl Research Articles

Pulsed wind-driven control of phytoplankton biomass at a groundwater-enriched nearshore environment

Along some Mediterranean coastal areas and other world regions, nutrient and chlorophyll concentrations often show gradient increases of up to one order of magnitude perpendicular to the coast. This nearshore stripe, extending a few hundred meters from the coast, is enriched by submarine groundwater discharges (SGD) containing elevated nutrient concentrations that may eventually sustain high biomass phytoplankton blooms. During a survey carried out in the summer of 2018, we examined the short-term (hours) variability of the phytoplankton biomass (measured as chlorophyll; Chl) in response to environmental changes associated with SGD and wind forcing in the nearshore waters of Palma Beach (Mediterranean Sea). Continuous CTD records revealed a general salinity decline indicative of SGD along the shoreline. Large and pulsed salinity fluctuations (i.e. 2–3 psu variations, 1–4 h) were observed each day that were consistent with offshore advection episodes of the lower salinity water retained in the nearshore (peak crosshore velocity 5–6 cm s−1). Chl near the shoreline was markedly higher than offshore (3.55 ± 1.29 and 0.68 ± 0.27 mg m−3 respectively) but recurrently fluctuated in the afternoon to up to >7 mg m−3. Primary production estimations showed that despite the higher production in the nearshore (50.29 ± 10.98 μmol O2 L−1 d−1, 4-fold offshore values) productivity per unit chlorophyll did not significantly vary (p > 0.01) therefore suggesting that, at this time scales, high biomass episodes in the nearshore are driven by an accumulation mechanism. Statistical analysis (CCA) demonstrates that Chl variability is largely explained (93 %) by variations in wind and current velocity. Our results provide evidence that the dynamics of this nearshore environment are modulated by the interplay between the shoreward wind-induced flow and the offshore directed density flow. This mechanism could explain the occurrence and episodic nature of high biomass blooms in the nearshore, as well as be an important factor influencing the microbial community structure at the coastal zone.

Seasonal variations in chlorophyll–a and sea surface temperature in the exclusive economic zone of Sri Lanka

Oceanic phytoplankton plays a crucial role in regulating the biogeochemical cycle in marine ecosystems. Light harvesting pigments such as chlorophyll–a (chl–a) can be used to understand marine primary productivity, especially phytoplankton abundance. Sea surface temperature is closely linked with phytoplankton abundance and climatic changes. The objective of the current study is to determine spatial and temporal variations of chl–a and sea surface temperature and their co-variation, over 12 years using statistical methodologies and Remote Sensing, Geographic Information System (GIS) and Cloud Computing Approach. In this study, the available sea surface temperature and chl–a data acquired from a Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor were proceeded through Google Earth Engine and Earth Science Data System. Data visualization was carried out using ArcMap™ software. Results show a weak negative linear relationship (−0.40, P < 0.05) between sea surface temperature and chl–a. Therefore, additional favorable environmental factors such as coastal upwelling, and nutrient discharge through river run-off can control the primary productivity of phytoplankton. We also illustrated spatial and temporal maps for chl–a and sea surface temperature distribution in the exclusive economic zone of Sri Lanka. Chlorophyll–a concentration is enhanced during the southwest monsoon, and the lowest concentration is observed during the first inter-monsoon period. Next, the highest sea surface temperature is recorded during the first inter-monsoon period, and the lowest sea surface temperature is observed during the northeast monsoon. Seasonal behavior of chl–a concentration and sea surface temperature are changed as chl–a (SWM) > chl–a (NEM) > chl–a (SIM) > chl–a (FIM) and SST (FIM) > SST (SIM) > SST (SWM) > SST (NEM), respectively. In addition, both trend lines for each variable show a positive gradient, which may be attributed to global warming. Finally, we observed the alteration of chl–a and sea surface temperature that linked to changes in ocean chemistry during the global COVID–19 pandemic.

Variability of chlorophyll a concentration in surface waters of the open Baltic Sea

In situ, satellite and reanalysis data from numerical models were used to study the characteristic features of Chl variability in the Baltic Sea. The analysis is focused on the years 2003–2020 when regular observations of ocean color with the MODIS AQUA are available. In the Baltic Sea, there is a pronounced annual cycle in physical conditions in the water column, driven by seasonal cycles in atmospheric forcing. The seasonal cycle of Chl concentration does not conform to the picture known from classical models, with low phytoplankton concentration when nutrients are low. In contrast, in the Baltic Sea, the concentration of Chl is high even during the summer months when nutrients are depleted. This can be explained by a continuous supply of nutrients by runoff from land, as well as by a significant contribution to primary production by phytoplankton able to survive in environment poor in dissolved nutrients. There is also a considerable interannual variability in Chl. There are many possible cause/effect interactions involved, but the data series are still too short to make clear which of them are the most important. The most striking event was a spring bloom in 2008.

Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects

Accurately estimating leaf chlorophyll content (Chl) at large spatial scales is crucial for monitoring agricultural production and plant photosynthesis. Sentinel-2 and Landsat-7/8 offer the potential to assess Chl with high spatial resolutions using various physically-based, empirical, and hybrid methods, but they still present challenges due to the confounding effects of canopy structure and soil background. The near-infrared reflectance of vegetation (NIRV) has been proposed to characterize canopy structural and soil background effects in sun-induced chlorophyll fluorescence. Therefore, in this study, we developed a novel strategy that incorporates NIRV to minimize canopy structural and soil background effects on Chl retrieval, using bidirectional reflectance factor (BRF) from Sentinel-2 and Landsat-7/8 imagery. We compared Chl estimation using empirical random forest (RF), the physically-based PROSAIL-5B inversion, and a hybrid method using RF to train simulated datasets from PROSAIL-5B. We validated Chl retrieval performance with measured datasets from the National Ecological Observatory Network, covering seven vegetation types. We also compared spatiotemporal Chl variations from Sentinel-2, Landsat-7/8, and MODIS. The results showed that NIRV effectively characterized the canopy structural effect on Chl estimation, associated with leaf area index, average leaf angle, and canopy fraction of senescent leaves. Compared to BRF, correcting BRF by NIRV improved Chl retrieval with RMSE values reduced by 12.2%–27.0% for Sentinel-2, 7.4%–16.4% for Landsat-7, and 6.9%–13.7% for Landsat-8, respectively. This also resulted in a significant increase in R2 values for Sentinel-2 (0.13–0.46), Landsat-7 (0.08–0.19), and Landsat-8 (0.12–0.2), respectively. Sentinel-2 outperformed Landsat-7/8 in Chl estimation, due to the contribution of red edge bands and a higher spatial resolution. Furthermore, the hybrid method performed best for Chl estimation from Sentinel-2 and Landsat-7/8 with relative RMSE values of 17.33%, 20.22%, and 19.23%, respectively. Our hybrid method demonstrated lower model uncertainty with smaller variations of RMSE values across varying biochemical and structural traits and solar and viewing angles, and exhibited more consistent spatiotemporal variations of Chl from Sentinel-2 with phenology compared to Landsat-7/8 and the published MODIS Chl data. Our hybrid method demonstrates significant potential in mitigating canopy structural effects, enhancing Chl retrieval, and facilitating the development of Chl data from various satellite imagery sources.

Analysis of Spatiotemporal Variation of Oceanographic Parameters in the North Atlantic of Morocco

Analysis of the spatiotemporal evolution of oceanographic parameters such as sea surface temperature (SST) and chlorophyll concentration (CHL) in the North Atlantic between 2018 and 2022 revealed interesting seasonal and interannual trends. Seasonal variations in SST and CHL are associated with factors such as sunlight, winds, and ocean currents. These two parameters showed an inverse relationship caused by several factors, of which the coastal upwelling is the most important. Interannual variations are influenced by weather conditions, extreme climatic events, and changes in ocean currents. The study of the spatiotemporal evolution of these two parameters is essential to understand environmental changes, assess the state of health of marine ecosystems and predict future changes. It also contributes to the establishment of appropriate management measures for the conservation of ocean ecosystems and to assess the impact of environmental disturbances.

A new vegetation index combination for leaf carotenoid-to-chlorophyll ratio: minimizing the effect of their correlation

ABSTRACT The ratio of leaf carotenoid to chlorophyll (Car/Chl) is an indicator of vegetation photosynthesis, development and responses to stress. However, the correlation between Car and Chl, and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio. This study aims to investigate combinations of vegetation indices (VIs) to minimize the influence of Car-Chl correlation, thus being more sensitive to the variability in the ratio across vegetation species and sites. VIs sensitive to Car and Chl variability were combined into four candidates of combinations, using a simulated dataset from the PROSPECT model. The VI combinations were then tested using six simulated datasets with different Car-Chl correlations, and evaluated against four independent datasets. The ratio of the carotenoid triangle ratio index (CTRI) with the red-edge chlorophyll index (CIred-edge) was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability. Compared with published VIs and two machine learning algorithms, CTRI/CIred-edge also showed the optimal performance in the four field datasets. This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio, applicable for assessing vegetation physiology, phenology, and response to environmental stress. Trial registration: Clinical Trials Registry India identifier: CTRI/..

Fortnightly variability of Chl a in the Indonesian seas

Abstract. Twenty years of daily MODIS-Aqua ocean color observations (2002–2022) are used to identify periodic variability of near-surface chlorophyll (Chl a) in the Indonesian seas. The frequency spectrum of Chl a is dominated by the mean and low-frequency monsoonal variability; however, a prominent peak around the fortnightly tidal period, MSf, is present. Harmonic analysis is used to quantify and map the fortnightly Chl a signal, which is discovered to be significant along the continental shelves of NW Australia and at several sites associated with narrow passages between the Lesser Sunda Islands, within the Sulu Archipelago, and at a few other sites in the Philippines Archipelago. Fortnightly variability at the shallow coastal sites is attributed to the spring–neap cycle of barotropic ocean currents, while we hypothesize that the variability in deeper water near the island passages is due to the modulation of vertical nutrient fluxes by baroclinic tidal mixing. The results document the significance of tidal mixing and highlight the heterogeneous character of biophysical processes within the Indonesian seas.

Dominant timescales of variability in global satellite chlorophyll and SST revealed with a MOving Standard deviation Saturation (MOSS) approach

Satellite-derived sea surface temperature (SST) and chlorophyll (Chl) datasets have been invaluable for estimating the oceanic primary production, air-sea heat exchange, and the spatial and seasonal patterns in their variability. However, data gaps, resulting from clouds and other factors, reduce coverage unevenly (to just about 20%) and make it difficult to analyze the temporal variability of Chl and SST on sub-seasonal time scales. Here, we present a MOving Standard deviation Saturation (MOSS) method to enable the analysis of sparse time series (with as little as 10% of the data). We apply the method to identify the dominating (sub-annual) timescales of variability, τd, for SST and Chl in every region. We find that τd values for Chl and SST are not consistent or correlated with each other over large areas, and in general, SST varies on longer timescales than Chl, i.e. τd(SST) >τd(Chl). There is a threefold variability in τd for SST and Chl even within regions that are traditionally considered to be biogeographically homogeneous. The largest τd for Chl is generally found on the equatorial side of the trade wind belts, whereas the smallest τd are found in the tropical Pacific and near coasts, especially where upwelling is common. If the temporal variability in Chl and SST were driven largely by ocean dynamics or advection by the flow, regional patterns of τd for SST and Chl should co-vary. This is seen in coastal upwelling zones, but more broadly, the lack of coherence between τd(Chl) and τd(SST) suggests that biological processes, such as phytoplankton growth and loss, decouple the timescales of Chl variability from those of SST and generate shorter term variability in Chl.

Variability of Chl a Concentration of Priority Marine Regions of the Northwest of Mexico

Priority Marine Regions (PMR) are important areas for biodiversity conservation in the Northwest Pacific Ocean in Mexico. The oceanographic dynamics of these regions are very important to understand their variability, generate analyses, and predict climate change trends by generating an adequate management of marine resources and their ecological characterization. Chlorophyll a (Chl a) is important to quantify phytoplankton biomass, consider the main basis of the trophic web in marine ecosystems, and determine the primary productivity levels and trends of change. The objective of this research is to analyze the oceanographic variability of 24 PMR through monthly 1-km satellite image resolution Chl a data from September 1997 to October 2018. A cluster analysis of Chl a data yielded 18 regions with clear seasonal variability in the Chl a concentration in the South-Californian Pacific (maximum values in spring-summer and minimum ones in autumn-winter) and Gulf of California (maximum values in winter-spring and minimum ones in summer-autumn). Significant differences (p &lt; 0.05) were observed in Chl a concentration analyses for each one of the regions when climate patterns—El Niño/La Niña Southern Oscillation (ENSO) and normal events—were compared for all the seasons of the year (spring, summer, autumn, and winter).

Differences in pigment circadian rhythmicity in green- and red-leafed tree species in the sun and shade

Light flux and quality are crucial factor for setting endogenous plant circadian rhythms. Evaluating the daily rhythmicity of leaf chlorophyll content is an effective method to monitor the plant physiological endogenous clock in response to environmental signals such as light availability/quality. Here, we used a leaf-clip sensor to monitor diurnal rhythms in the content of chlorophyll and flavonoids such as flavonols and anthocyanins in three green- (Ailanthus altissima, Tilia platyphyllos and Platanus × acerifolia) and two red-leafed (Acer platanoides cv. Crimson King and Prunus cerasifera var. pissardii) tree species, adapted to sun (L) or shade (S). Significant differences in chlorophyll content (Chl) and its variations during the day were observed among treatments in all the analyzed species. S-plants had more Chl than L-plants irrespective of leaf color, and Chl variations were more distinct during the day than in L-plants. In particular, contents were lowest in the morning (9:00) and in the middle of the day (at 12:00 and 15:00), and the highest at dusk (21:00). The less evident trends in Chl variation in L-plants were attributed to a decrease in Chl content in high light, which likely masked any increases in the shaded counterparts during the afternoon. Daily flavonol levels did not vary no notably during the day. In sun-exposed red leaves, anthocyanins partially screened mesophyll cells from incident light, and its levels were similar to the Chl dynamics in the shaded counterparts. This study provides new bases for further work on endogenous rhythms of plant pigments and improves our understanding of plant physiology in the context of day/night rhythmicity.

Plant Evolution History Overwhelms Current Environment Gradients in Affecting Leaf Chlorophyll Across the Tibetan Plateau.

AimsLeaf chlorophyll (Chl) is a fundamental component and good proxy for plant photosynthesis. However, we know little about the large-scale patterns of leaf Chl and the relative roles of current environment changes vs. plant evolution in driving leaf Chl variations.LocationsThe east to west grassland transect of the Tibetan Plateau.MethodsWe performed a grassland transect over 1,600 km across the Tibetan Plateau, measuring leaf Chl among 677 site-species.ResultsLeaf Chl showed a significantly spatial pattern across the grasslands in the Tibetan Plateau, decreasing with latitude but increasing with longitude. Along with environmental gradient, leaf Chl decreased with photosynthetically active radiation (PAR), but increased with water availability and soil nitrogen availability. Furthermore, leaf Chl also showed significant differences among functional groups (C4 > C3 species; legumes < non-legume species), but no difference between annual and perennial species. However, we surprisingly found that plant evolution played a dominant role in shaping leaf Chl variations when comparing the sum and individual effects of all the environmental factors above. Moreover, we revealed that leaf Chl non-linearly decreased with plant evolutionary divergence time. This well-matches the non-linearly increasing trend in PAR or decreasing trend in temperature during the geological time-scale uplift of the Tibetan Plateau.Main ConclusionThis study highlights the dominant role of plant evolution in determining leaf Chl variations across the Tibetan Plateau. Given the fundamental role of Chl for photosynthesis, these results provide new insights into reconsidering photosynthesis capacity in alpine plants and the carbon cycle in an evolutionary view.

CHANGES IN PHOTOSYNTHETIC PIGMENT CONTENTS AND SPAD INDEX IN RELATION TO WATERLOGGING IN SUGARCANE

Waterlogging is a major abiotic factor in tropics and subtropics India affecting plant productivity by creating anoxia, aerial rooting, mineral deficiency and toxicity, physiological and biochemical alterations. In present study, an effort was made to evaluate changes in photosynthetic pigments, chlorophyll a (Chl a), chlorophyll b (Chl b), total Chl (Chl a+Chl b), carotenoids (CAR), the ratio of Chl a and Chl b (Chl a/b) and total Chl/CAR ratio and Soil plant analysis development (SPAD) index in plants grown under waterlogged conditions (WL) along with untreated control (C) using four sugarcane cultivars; CoLk 94184, BO 91, CoS 767 and CoJ 64. The results obtained indicated variation in Chl a, b, total Chl contents (mg/g fresh wt.) among different cultivars and relatively less in waterlogged plants. Chl ranged between 1.277-2.300 (mean: 1.949 in control) and 1.057- 2.150 (mean: 1.716 in waterlogged), Chl b, 0.319- 0.607 (mean: 0.518) in control and 0.290- 0.571 (mean: 0.453) in waterlogged plants, respectively. Chl a/b ratio is slightly higher in waterlogged (3.640-3.957, mean: 3.794) as compared to control (3.610-3.997, mean: 3.789) plants except in CoJ 64, while total Chl/ CAR ratio was comparatively lesser in waterlogged plants as compared to untreated control. SPAD index was relatively higher in control (30.1 - 35.3, mean: 34.0) than waterlogged plants (22.4-34.1, mean: 29.6) similar to destructive analysis of chlorophyll contents. Carotenoids contents ranged from 0.477- 0.787 in control and 0.467-0.797 mg/g fresh wt in waterlogging treatment; BO91 and CoS767 cultivars showed slightly higher value in waterlogged. Correlation among different attributes indicated significant positive relationship except chlorophyll a/b ratio showing negative association. Cultivar CoJ 64 showed highest decrease in Chl a/b, total Chl/CAR ratio and CAR contents reflecting highly sensitive to waterlogging stress as compared to other cultivars studied.

Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors.

Monitoring leaf Chlorophyll (Chl) in-situ is labor-intensive, limiting representative sampling for detailed mapping of Chl variability at field scales across time. Unmanned aeria-l vehicles (UAV) and hyperspectral cameras provide flexible platforms for observing agricultural systems, overcoming this spatio-temporal sampling constraint. Here, we evaluate a customized machine learning (ML) workflow to retrieve multi-temporal leaf-Chl levels, combining sub-centimeter resolution UAV-hyperspectral imagery (400–1,000 nm) with leaf-level reflectance spectra and SPAD measurements, capturing temporal correlations, selecting relevant predictors, and retrieving accurate results under different conditions. The study is performed within a phenotyping experiment to monitor wild tomato plants’ development. Several analyses were conducted to evaluate multiple ML strategies, including: (1) exploring sequential versus retraining learning; (2) comparing insights gained from using 272 spectral bands versus 60 pigment-based vegetation indices (VIs); and (3) assessing six regression methods (linear, partial-least-square regression; PLSR, decision trees, support vector, ensemble trees, and Gaussian process; GPR). Goodness-of-fit (R2) and accuracy metrics (MAE, RMSE) were determined using training/testing and validation data subsets to assess the models’ performance. Overall, while equally good performance was obtained using either PLSR, GPR, or random forest, results show: (1) the retraining strategy improved the ability of most of the approaches to model SPAD-based Chl dynamics; (2) comparative analysis between retrievals and validation data distributions informed the models’ ability to capture Chl dynamics through SPAD levels; (3) VI predictors slightly improved R2 (e.g., from 0.59 to 0.74 units for GPR) and accuracy (e.g., MAE and RMSE differences of up to 2 SPAD units) in specific algorithms; (4) feature importance examined through these methods, revealed strong overlaps between relevant bands and VI predictors, highlighting a few decisive spectral ranges and indices useful for retrieving leaf-Chl levels. The proposed ML framework allows the retrieval of high-quality spatially distributed and multi-temporal SPAD-based chlorophyll maps at an ultra-high pixel resolution (e.g., 7 mm).

Phytoplankton biomass dynamics in the Arabian Sea from VIIRS observations

Using chlorophyll-a (Chl-a) as an indicator for phytoplankton biomass, observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2019 in combination with wind, various ocean hydrographic data, and nutrient data are used to conduct a comprehensive study to characterize and quantify the phytoplankton biomass dynamics in the Arabian Sea, and assess the mechanisms that drive the summer and winter phytoplankton blooms. Phytoplankton biomass dynamics in the Arabian Sea are driven by the seasonal reversal of the monsoon wind. The summer phytoplankton bloom located mainly in the western and central Arabian Sea is stronger than the winter phytoplankton bloom in the northern Arabian Sea in terms of both Chl - a values and the extent of phytoplankton bloom coverage. Interannual variability of Chl - a is less significant in comparison to the seasonal variability. Chl-a data measured by the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) from 1997 to 2010 are also used. The comparison of Ch-a climatology data between SeaWiFS and VIIRS shows no long-term trend of Chl-a change in the Arabian Sea. This study provides an improved understanding (with new knowledge) of (1) phytoplankton biomass dynamics, (2) physical, biological, and biogeochemical processes, and (3) nutrient dynamics in the Arabian Sea. The hydrographic and nutrient data reveal two different driving mechanisms for phytoplankton blooms in the summer and winter monsoons. During the summer monsoon, the cold high-nutrient waters are brought to the surface from the thermocline depth through strong coastal upwelling and Ekman pumping, leading to increased Chl-a at the surface-layer. The vertical velocity reaches ~1 m/day at 70 m depth. In comparison, the winter phytoplankton bloom is driven by the moderate boost of the nutrient level due to the mixed-layer deepening and strong vertical mixing caused by the surface cooling in the northern Arabian Sea. The nutrient dynamics in the Arabian Sea also suggests that the nitrate concentration is the major nutrient driver for its seasonal phytoplankton biomass dynamics. • The phytoplankton biomass dynamics was characterized and quantified. • There is no long-term trend of Chl-a change in the Arabian Sea in the last two decades. • Two different driving mechanisms for phytoplankton blooms in the summer and winter

A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices

This study investigated the potential to predict primary production in benthic ecosystems using meteorological variables and spectral indices. In situ production experiments were carried out during the vegetation season of 2020, wherein the primary production and spectral reflectance of different communities of submerged aquatic vegetation (SAV) were measured and chlorophyll (Chl a+b) concentration was quantified in the laboratory. The reflectance of SAV was measured both in air and underwater. First, in situ reflectance spectra of each SAV class were used to calculate different spectral indices, and then the indices were correlated with Chl a+b. Indices using red and blue band combinations such as 650/450 and 650/480 nm explained the largest part of variability in Chl a+b for datasets measured in air and underwater. Subsequently, the best-performing indices were used in boosted regression trees (BRT) models, together with meteorological data to predict the community photosynthesis of different SAV classes. The predictive power (R2) of production models were very high, estimated at the range of 0.82–0.87. The variable contributing the most to the model description was SAV class, followed in most cases by the water temperature. Nevertheless, the inclusion of spectral indices significantly improved BRT models, often by over 20%, and surprisingly their contribution mostly exceeded that of photosynthetically active radiation.

Seasonal and interannual variabilities of chlorophyll across the eastern equatorial Indian Ocean and Bay of Bengal

Seasonal and interannual variabilities of surface and subsurface chlorophyll (Chl) in the eastern equatorial Indian Ocean (IO) and Bay of Bengal were examined based on vertical Chl inferred from satellite data. Four biotic zones were identified by Empirical orthogonal function analysis, which represented different types of mixed layer processes. The areas south of Sri Lanka indicated the most significant Chl increase in the surface and subsurface during summer due to coastal upwelling. Surface Chl highs in the Sri Lanka Dome occurred every summer during the time series. Chl increases were also recorded in the southwestern Bay of Bengal during fall–winter in response to wind-induced divergence and convective mixing. The other two zones in south IO basin between 5 and 15°S exhibited seasonal surface Chl highs in summer and subsurface Chl maximum during fall–winter. Wind-induced divergence plays a major role in the maintenance of this upwelling zone year-round; however, stratification inhibited the surface Chl increase in winter and results in enhanced Chl in the subsurface. The Chl interannual variability was related with Indian Ocean dipole (IOD) associated physical field oscillations. In positive IOD, wind divergence increased significantly along the eastern equatorial IO coast that contributed to Chl enhancement there, it decreased in Bay of Bengal and South IO and acted to reduce the Chl in these regions. In negative IOD, reversals in the forcing anomalies drove increasing Chl in Bay of Bengal and South IO and decreasing of that in the eastern equator. This resulted in significant correlations between IOD index and Chl confined in these regions. There are pronounced variance of phytoplankton carbon biomass in the Bay of Bengal in IOD events, however, less changes of that were seen over the South IO during IOD. Our findings imply that physiological changes in cellular pigmentation are the dominant cause of satellite-observed interannual variations in Chl in the South IO. Our results highlight the role of physiological processes in regulating Chl distributions, and support the use of Chl-based indices as indicators of climatic feedbacks.

Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition

Chlorophyll (Chl) is widely taken as a proxy for phytoplankton biomass, despite well-known variations in Chl:C:biomass ratios as an acclimative response to changing environmental conditions. For the sake of simplicity and computational efficiency, many large scale biogeochemical models ignore this flexibility, compromising their ability to capture phytoplankton dynamics. Here we evaluate modelling approaches of differing complexity for phytoplankton growth response: fixed stoichiometry, fixed stoichiometry with photoacclimation, classical variable-composition with photoacclimation, and Instantaneous Acclimation with optimal resource allocation. Model performance is evaluated against biogeochemical observations from time-series sites BATS and ALOHA, where phytoplankton composition varies substantially. We analyse the sensitivity of each model variant to the affinity parameters for light and nutrient, respectively. Models with fixed stoichiometry are more sensitive to parameter perturbations, but the inclusion of photoacclimation in the fixed-stoichiometry model generally captures Chl observations better than other variants when individually tuned for each site and when using similar parameter sets for both sites. Compared to the fixed stoichiometry model including photoacclimation, models with variable C:N ratio perform better in cross-validation experiments using model-specific parameter sets tuned for the other site; i.e., they are more portable. Compared to typical variable composition approaches, instantaneous acclimation, which requires fewer state variables, generally yields better performance but somewhat lower portability than the fully dynamic variant. Further assessments using objective optimisation and more contrasting stations are suggested.

Comparative study on the influene factors of chlorophyll-a and nutrient structure between winter and summer in the central Bohai Sea

Based on the data of chlorophyll a (Chl a), nutrients, and hydrological environment parameters of the central Bohai Sea in January(winter) and June(summer) of 2016, we studied spatial and temporal variations of Chl a, nutrient structure, analyzed the influence factors for the phytoplankton growth and the changing trend of the phytoplankton community structure over long-time scale. The results showed that nutrient structure and Chl a had significant seasonal variation characteristics (<italic>P</italic><0.01). In winter, the concentrations of dissolved inorganic nitrogen (DIN), active phosphate (PO^3-₄-P) and silicate (SiO^2-₃-Si) were (10.51±4.52)μmol·L^-1, (0.28±0.14)μmol·L^-1, and (10.63±2.09)μmol·L^-1, respectively, and NO^-₃-N (92%) was the main form of DIN, and all nutrient contents were above the threshold, meanwhile the nutrient ratios of N/P, Si/P, and Si/N were 69.30±86.80, 64.50±68.80, and 1.20±0.50, respectively, the N and Si limitations were not obvious except that some stations showed P potential limitations because of input of the Northern Yellow Sea cold flow, the sediment resuspension, and the release by the mineralization of organic matters. Affected by seawater temperature (<italic>T</italic><8℃), the phytoplankton reproduction and growth were restricted and the content of Chl a (0.99±0.25) mg·m^-3 was low. In summer, influenced by the Yellow River dilute water, the Northern Yellow Sea cold flow, and phytoplankton uptake. The concentrations of DIN, PO^3-₄-P and SiO^2-₃-Si were (4.72±4.20) μmol·L^-1, (0.04±0.04) μmol·L^-1, and (3.81±1.72) μmol·L^-1, and decreased by 55.0%, 85.7% and 64.1%, respectively, NH⁴⁺-N (48%) and NO^-₃-N (45%) became the main forms of DIN, compared to those in the winter. The nutrient ratios of N/P, Si/P and Si/N were 274.19±328.06, 202.18±182.07 and 1.39±1.00, respectively, increased significantly compared with that of winter. The nutrient limitations showed obvious P limitation, and the contents were below the threshold of DIN and Si in some stations, which would limit the growth of phytoplankton, especially the diatoms. The content of Chl a was (4.58±5.51) mg·m^-3 with the seawater temperature of (22.1±1.88)℃, 4.6 times higher than that in winter, there were some stations in the Yellow River Estuary, the content was beyond 10 mg·m^-3 and showed an outbreak risk of alage bloom.

Temporal variability of economic fish assemblage of the Northern Bay of Bengal in relation to its environment

The Northern Bay of Bengal (NBoB) contributes largely towards the fish production of India and its neighboring countries. A pioneering attempt was made to understand temporal variability of the fish assemblage (exclusively to those of fisheries interest) of the NBoB (20°20ʹ–21°30ʹN and 87°30ʹ–89°E) in relation to sea surface temperature (SST) (°C), sea surface salinity (SSS), and chlorophyll-a (Chl- a ) (mg/l) changes. Between 2006 and 2015, the inter-annual variations of SST, SSS, and Chl- a were minor. Seasonal variability of SST, SSS, and Chl- a was observed and a yearly bi-modal peak of Chl- a production was evident. Inter-annual variability of the species dominance (D) was most plausibly explained by the SST*Chl- a (Δ AIC = 0) model. Variation of evenness index (J) was explained most plausibility by the Chl- a (Δ AIC = 0) model. Seasonal variability of SST and Chl- a levels, possibly influence the fish community of the NBoB, therefore, a regular monitoring programme is suggested for understanding the long-term changes of the fish assemblage of the NBoB.

Eddy‐Induced Near‐Surface Chlorophyll Anomalies in the Subtropical Gyres: Biomass or Physiology?

AbstractSatellite observations show higher near‐surface phytoplankton chlorophyll (Chl) in anticyclonic eddies (AEs) than in cyclonic eddies (CEs) in the subtropical gyres, which is opposite to the prevailing features in midlatitude oceans. Recent studies have attributed this anomalous effect solely to changes in phytoplankton biomass, therefore reached a conclusion that AEs are more productive than CEs, contrary to the conventional eddy pumping paradigm. Here, we show that the Chl variations are due mainly to physiological adjustments instead. The high Chl in AEs is mostly explained by photoacclimation to low‐light environment caused by deepened mixing and more rapid light attenuation, and vice versa for CEs. Such a scenario indicates that phytoplankton are likely more cellular pigmented, rather than more abundant, in the AEs than in the CEs. These findings invite us to rethink the effects of eddies on oceanic productivity and carbon cycling in the subtropical gyres.