scholarly journals Global distribution and variability of subsurface chlorophyll a concentration

2021 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Tsuneo Ono ◽  
Kosei Sasaoka ◽  
Kanako Sato

Abstract. Chlorophyll a (Chl-a) often retains its maximum concentration not at the surface but in the subsurface layer. The depth of the Chl-a maximum primarily depends on the balance between light penetration from the surface and nutrient supply from the deep ocean. However, a global map of subsurface Chl-a concentrations based on observations has not been presented yet. In this study, we integrate Chl-a concentration data not only from recent biogeochemical floats but also from historical ship-based and other observations, and present global maps of subsurface Chl-a concentration with related variables. The subsurface Chl-a maximum deeper than the mixed layer depth was stably observed in the subtropics and tropics (30° S to 30° N), only in summer in midlatitudes (30–40° N/S), and rarely at 45–60° S of the Southern Ocean and in the northern North Atlantic (north of 45° N). The depths of the subsurface Chl-a maxima are deeper than those of the euphotic layer in the subtropics and shallower in the tropics and midlatitudes. In the subtropics, seasonal oxygen increases below the mixed layer implied substantial biological new production, which corresponds to 10 % of the net primary production there. During El Niño, the subsurface Chl-a concentration in the equatorial Pacific is higher in the middle to the east and lower in the west than that during La Niña, which is opposite that on the surface. The spatiotemporal variability of the Chl-a concentration described here would be suggestive results not only for the biogeochemical cycle in the ocean but also for the thermal structure and the dynamics of the ocean via the absorption of shortwave radiation.

Author(s):  
TAKAHIRO OSAWA ◽  
CHAO FANG ZHAO ◽  
I WAYAN Nuarsa ◽  
I Ketut Swardika ◽  
YASUHIRO SUGIMORI

Ocean primary production is an important factor for determining the ocean's role in global carbon cycle. In recent years, much more chlorophyll-a concentration data in the euphotic layer were derived from the satellite ocean color sensors. The primary productivity algorithms have been proposed based on satellite chlorophyll measurements (Piatt, 1988; Morel, 1991) and other environmental parameters such as sea surface temperature or mixed layer depth (Behrenfeld and Falkowski, 1997; Esaias, 1996; Asanuma, 2002). In order to estimate integrated primary productivity in the whole water column, the vertical distribution of chlorophyll concentration below the sea surface should be reconstructed based on satellite data. In this paper, the vertical profile data of chlorophyll-a (Chl-a) measured around Japan Islands from 1974 to 1994 were reanalyzed based on the shifted-Gaussian shape proposed by Piatt et al (1988). Using this statistical model (neural network) and the photosynthesis irradiance parameters from Asanuma (2002), the distribution of primary productivity and its seasonal variation around Japan islands were estimated from SeaWiFS data, and the results were compared with in situ data and the other two models estimated from VGPM and mixed layer depth model. Keywords: ocean color, primary productivity, chlorophyll profile, artificial neural network


2020 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Wenlong Xu ◽  
Guifen Wang ◽  
Long Jiang ◽  
Xuhua Cheng ◽  
Wen Zhou ◽  
...  

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.


2011 ◽  
Vol 8 (8) ◽  
pp. 2391-2406 ◽  
Author(s):  
A. Mignot ◽  
H. Claustre ◽  
F. D'Ortenzio ◽  
X. Xing ◽  
A. Poteau ◽  
...  

Abstract. In vivo fluorescence of Chlorophyll-a (Chl-a) is a potentially useful property to study the vertical distribution of phytoplankton biomass. However the technique is presently not fully exploited as it should be, essentially because of the difficulties in converting the fluorescence signal into an accurate Chl-a concentration. These difficulties arise noticeably from natural variations in the Chl-a fluorescence relationship, which is under the control of community composition as well as of their nutrient and light status. As a consequence, although vertical profiles of fluorescence are likely the most recorded biological property in the open ocean, the corresponding large databases are underexploited. Here with the aim to convert a fluorescence profile into a Chl-a concentration profile, we test the hypothesis that the Chl-a concentration can be gathered from the sole knowledge of the shape of the fluorescence profile. We analyze a large dataset from 18 oceanographic cruises conducted in case-1 waters from the highly stratified hyperoligotrophic waters (surface Chl-a = 0.02 mg m−3) of the South Pacific Gyre to the eutrophic waters of the Benguela upwelling (surface Chl-a = 32 mg m−3) and including the very deep mixed waters in the North Atlantic (Mixed Layer Depth = 690 m). This dataset encompasses more than 700 vertical profiles of Chl-a fluorescence as well as accurate estimations of Chl-a by High Performance Liquid Chromatography (HPLC). Two typical fluorescence profiles are identified, the uniform profile, characterized by a homogeneous layer roughly corresponding to the mixed layer, and the non-uniform profile, characterized by the presence of a Deep Chlorophyll Maximum. Using appropriate mathematical parameterizations, a fluorescence profile is subsequently represented by 3 or 5 shape parameters for uniform or non-uniform profiles, respectively. For both situations, an empirical model is developed to predict the "true" Chl-a concentration from these shape parameters. This model is then used to calibrate a fluorescence profile in Chl-a units. The validation of the approach provides satisfactory results with a median absolute percent deviation of 33 % when comparing the HPLC Chl-a profiles to the Chl-a-calibrated fluorescence. The proposed approach thus opens the possibility to produce Chl-a climatologies from uncalibrated fluorescence profile databases that have been acquired in the past and to which numerous new profiles will be added, thanks to the recent availability of autonomous platforms (profiling floats, gliders and animals) instrumented with miniature fluorometers.


2020 ◽  
Vol 8 (12) ◽  
pp. 957
Author(s):  
Yanfeng Wang ◽  
Lijun Yao ◽  
Pimao Chen ◽  
Jing Yu ◽  
Qia’er Wu

The spatiotemporal distribution of fishing grounds in the Beibu Gulf and its relationship with marine environment were analyzed using the survey data of light falling-net vessels and satellite remote sensing data including sea surface temperature (SST), chlorophyll a concentration (Chl a) and net primary production (NPP), based on the generalized additive model (GAM) and the center of gravity (COG) of fishing grounds. The results showed that the total deviance explained by GAM for the catch per unit effort (CPUE) in the Beibu Gulf was 42.9%, in which SST was the most important influencing factor on CPUE, with a relative contribution of 40%; followed by latitude, Chl a, month and NPP, with relative contributions of 25.2%, 19%, 10.4% and 5.4%, respectively. Fishing grounds in the Beibu Gulf were mainly distributed in waters with SST of 27–29 °C, Chl a of 0.5–1.5 mg m−3 and NPP of 500–700 mg m−2 d−1. Light falling-net fishing grounds were concentrated in waters with latitude of 18.5° N and 20–20.25° N. There was a significant correlation between the mean latitude of optimum NPP and the latitudinal COG of CPUE, with the R2 being 0.91. These were connected with environmental factors such as the northeast monsoon that began in autumn and winter, warm pools near 19° N and local upwelling in the Beibu Gulf.


2009 ◽  
Vol 137 (11) ◽  
pp. 3744-3757 ◽  
Author(s):  
I-I. Lin ◽  
Iam-Fei Pun ◽  
Chun-Chieh Wu

Abstract Using new in situ ocean subsurface observations from the Argo floats, best-track typhoon data from the U.S. Joint Typhoon Warning Center, an ocean mixed layer model, and other supporting datasets, this work systematically explores the interrelationships between translation speed, the ocean’s subsurface condition [characterized by the depth of the 26°C isotherm (D26) and upper-ocean heat content (UOHC)], a cyclone’s self-induced ocean cooling negative feedback, and air–sea enthalpy fluxes for the intensification of the western North Pacific category 5 typhoons. Based on a 10-yr analysis, it is found that for intensification to category 5, in addition to the warm sea surface temperature generally around 29°C, the required subsurface D26 and UOHC depend greatly on a cyclone’s translation speed. It is observed that even over a relatively shallow subsurface warm layer of D26 ∼ 60–70 m and UOHC ∼ 65–70 kJ cm−2, it is still possible to have a sufficient enthalpy flux to intensify the storm to category 5, provided that the storm can be fast moving (typically Uh ∼ 7–8 m s−1). On the contrary, a much deeper subsurface layer is needed for slow-moving typhoons. For example at Uh ∼ 2–3 m s−1, D26 and UOHC are typically ∼115–140 m and ∼115–125 kJ cm−2, respectively. A new concept named the affordable minimum translation speed Uh_min is proposed. This is the minimum required speed a storm needs to travel for its intensification to category 5, given the observed D26 and UOHC. Using more than 3000 Argo in situ profiles, a series of mixed layer numerical experiments are conducted to quantify the relationship between D26, UOHC, and Uh_min. Clear negative linear relationships with correlation coefficients R = −0.87 (−0.71) are obtained as Uh_min = −0.065 × D26 + 11.1, and Uh_min = −0.05 × UOHC + 9.4, respectively. These relationships can thus be used as a guide to predict the minimum speed a storm has to travel at for intensification to category 5, given the observed D26 and UOHC.


2018 ◽  
Vol 15 (5) ◽  
pp. 1395-1414 ◽  
Author(s):  
Saleem Shalin ◽  
Annette Samuelsen ◽  
Anton Korosov ◽  
Nandini Menon ◽  
Björn C. Backeberg ◽  
...  

Abstract. The spatial and temporal variability of marine autotrophic abundance, expressed as chlorophyll concentration, is monitored from space and used to delineate the surface signature of marine ecosystem zones with distinct optical characteristics. An objective zoning method is presented and applied to satellite-derived Chlorophyll a (Chl a) data from the northern Arabian Sea (50–75∘ E and 15–30∘ N) during the winter months (November–March). Principal component analysis (PCA) and cluster analysis (CA) were used to statistically delineate the Chl a into zones with similar surface distribution patterns and temporal variability. The PCA identifies principal components of variability and the CA splits these into zones based on similar characteristics. Based on the temporal variability of the Chl a pattern within the study area, the statistical clustering revealed six distinct ecological zones. The obtained zones are related to the Longhurst provinces to evaluate how these compared to established ecological provinces. The Chl a variability within each zone was then compared with the variability of oceanic and atmospheric properties viz. mixed-layer depth (MLD), wind speed, sea-surface temperature (SST), photosynthetically active radiation (PAR), nitrate and dust optical thickness (DOT) as an indication of atmospheric input of iron to the ocean. The analysis showed that in all zones, peak values of Chl a coincided with low SST and deep MLD. The rate of decrease in SST and the deepening of MLD are observed to trigger the algae bloom events in the first four zones. Lagged cross-correlation analysis shows that peak Chl a follows peak MLD and SST minima. The MLD time lag is shorter than the SST lag by 8 days, indicating that the cool surface conditions might have enhanced mixing, leading to increased primary production in the study area. An analysis of monthly climatological nitrate values showed increased concentrations associated with the deepening of the mixed layer. The input of iron seems to be important in both the open-ocean and coastal areas of the northern and north-western parts of the northern Arabian Sea, where the seasonal variability of the Chl a pattern closely follows the variability of iron deposition.


2016 ◽  
Vol 144 (3) ◽  
pp. 877-896 ◽  
Author(s):  
Iam-Fei Pun ◽  
James F. Price ◽  
Steven R. Jayne

Abstract This paper describes a new model (method) called Satellite-derived North Atlantic Profiles (SNAP) that seeks to provide a high-resolution, near-real-time ocean thermal field to aid tropical cyclone (TC) forecasting. Using about 139 000 observed temperature profiles, a spatially dependent regression model is developed for the North Atlantic Ocean during hurricane season. A new step introduced in this work is that the daily mixed layer depth is derived from the output of a one-dimensional Price–Weller–Pinkel ocean mixed layer model with time-dependent surface forcing. The accuracy of SNAP is assessed by comparison to 19 076 independent Argo profiles from the hurricane seasons of 2011 and 2013. The rms differences of the SNAP-estimated isotherm depths are found to be 10–25 m for upper thermocline isotherms (29°–19°C), 35–55 m for middle isotherms (18°–7°C), and 60–100 m for lower isotherms (6°–4°C). The primary error sources include uncertainty of sea surface height anomaly (SSHA), high-frequency fluctuations of isotherm depths, salinity effects, and the barotropic component of SSHA. These account for roughly 29%, 25%, 19%, and 10% of the estimation error, respectively. The rms differences of TC-related ocean parameters, upper-ocean heat content, and averaged temperature of the upper 100 m, are ~10 kJ cm−2 and ~0.8°C, respectively, over the North Atlantic basin. These errors are typical also of the open ocean underlying the majority of TC tracks. Errors are somewhat larger over regions of greatest mesoscale variability (i.e., the Gulf Stream and the Loop Current within the Gulf of Mexico).


2021 ◽  
Vol 13 (9) ◽  
pp. 4649
Author(s):  
Ze-Lin Na ◽  
Huan-Mei Yao ◽  
Hua-Quan Chen ◽  
Yi-Ming Wei ◽  
Ke Wen ◽  
...  

Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 μg/L) observed near the shore and higher concentrations (6.70 μg/L) observed offshore, with a gradual decreasing trend over time (−0.8).


2016 ◽  
Vol 13 (2) ◽  
pp. 364 ◽  
Author(s):  
Tereza Jarníková ◽  
Philippe D. Tortell

Environmental context The trace gas dimethylsulfide (DMS) is emitted from surface ocean waters to the overlying atmosphere, where it forms aerosols that promote cloud formation and influence Earth’s climate. We present an updated climatology of DMS emissions from the vast Southern Ocean, demonstrating how the inclusion of new data yields higher regional sources compared with previously derived values. Our work provides an important step towards better quantifying the oceanic emissions of an important climate-active gas. Abstract The Southern Ocean is a dominant source of the climate-active gas dimethylsulfide (DMS) to the atmosphere. Despite significant improvements in data coverage over the past decade, the most recent global DMS climatology does not include a growing number of high-resolution surface measurements in Southern Ocean waters. Here, we incorporate these high resolution data (~700000 measurements) into an updated Southern Ocean climatology of summertime DMS concentrations and sea–air fluxes. Owing to sparse monthly data coverage, we derive a single summertime climatology based on December through February means. DMS frequency distributions and oceanographic properties (mixed-layer depth and chlorophyll-a) show good general coherence across these months, providing justification for the use of summertime mean values. The revised climatology shows notable differences with the existing global climatology. In particular, we find increased DMS concentrations and sea–air fluxes south of the Polar Frontal zone (between ~60 and 70°S), and increased sea–air fluxes in mid-latitude waters (40–50°S). These changes are attributable to both the inclusion of new data and the use of region-specific parameters (e.g. data cut-off thresholds and interpolation radius) in our objective analysis. DMS concentrations in the Southern Ocean exhibit weak though statistically significant correlations with several oceanographic variables, including ice cover, mixed-layer depth and chlorophyll-a, but no apparent relationship with satellite-derived measures of phytoplankton photophysiology or taxonomic group abundance. Our analysis highlights the importance of using regional parameters in constructing climatological DMS fields, and identifies regions where additional observations are most needed.


2008 ◽  
Vol 59 (1) ◽  
pp. 10 ◽  
Author(s):  
Choon Weng Lee ◽  
Chui Wei Bong

In the present study, the relationship between bacteria and phytoplankton in tropical coastal waters was investigated. The bacterial abundance, bacterial production, chlorophyll a concentration and net primary production were measured at several locations in the coastal waters of Peninsular Malaysia. Chlorophyll a concentration ranged from 0.40 to 32.81 μg L–1, whereas bacterial abundance ranged from 0.1 to 97.5 × 106 cells mL–1. Net primary production ranged from 8.49 to 55.95 μg C L–1 h–1, whereas bacterial production ranged from 0.17 to 70.66 μg C L–1 h–1. In the present study, the carbon conversion factor used to convert bacterial production (cells mL–1 h–1) into carbon units ranged from 10 to 32.8 fg C cell–1, and was estimated from the bacterial size distribution measured at each location. Both phototrophic and heterotrophic biomass (bacteria–chlorophyll a) and activity (bacterial production–net primary production) were significantly correlated, although their correlation coefficients (r2) were relatively low (r2 = 0.188 and r2 = 0.218 respectively). Linear regression analyses provided the following equations to represent the relationship between: bacteria and chlorophyll a (Chl a), log Bacteria = 0.413 log Chl a + 6.057 (P = 0.003); and between bacterial production (BP) and net primary production (NPP), log BP = 0.896 log NPP – 0.394 (P = 0.004), which fitted with published results well. Comparison of annual carbon fluxes confirmed the prevalence of net heterotrophy in these coastal waters, and together with the low correlation coefficients, suggested the role of allochthonous organic matter in supporting heterotrophic activity.


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