Implications of a regional-scale process (the Lakshadweep low) on the mesozooplankton community structure of the Arabian Sea

2019 ◽  
Vol 70 (3) ◽  
pp. 345 ◽  
Author(s):  
K. K. Karati ◽  
G. Vineetha ◽  
T. V. Raveendran ◽  
P. K. Dineshkumar ◽  
K. R. Muraleedharan ◽  
...  
Keyword(s):  
Physical Process ◽  
Arabian Sea ◽  
Mixed Layer Depth ◽  
Regional Scale ◽  
Zooplankton Community ◽  
Ocean Basin ◽  
Zooplankton Biomass ◽  
Global Ocean ◽  
Mesozooplankton Community ◽  
Tropical Ocean

The Arabian Sea, a major tropical ocean basin in the northern Indian Ocean, is one of the most productive regions in the global ocean. Although the classical Arabian Sea ‘paradox’ describes the geographical and seasonal invariability in zooplankton biomass in this region, the effect of the Lakshadweep low (LL), a regional-scale physical process, on the zooplankton community has not yet been evaluated. The LL, characterised by low sea surface height and originating around the vicinity of the Lakshadweep islands during the mid-summer monsoon, is unique to the Arabian Sea. The present study investigated the effect of the LL on the zooplankton community. The LL clearly had a positive effect, with enhanced biomass and abundance in the mixed-layer depth of the LL region. Copepods and chaetognaths formed the dominant taxa, exhibiting strong affinity towards the physical process. Of the 67 copepod species observed, small copepods belonging to the families Paracalanidae, Clausocalanidae, Calanidae, Oncaeidae and Corycaeidae dominated the LL region. Phytoplankton biomass (chlorophyll-a) was the primary determinant influencing the higher preponderance of the copepod community in this region.

scholarly journals FFNN-LSCE: A two-step neural network model for the reconstruction of surface ocean pCO<sub>2</sub> over the Global Ocean

2018 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Marion Gehlen ◽  
Mathieu Vrac ◽  
Carlos Mejia
Keyword(s):  
Neural Network ◽  
Indian Ocean ◽  
Southern Oscillation ◽  
Mixed Layer Depth ◽  
Regional Scale ◽  
Sea Surface ◽  
Global Ocean ◽  
Co2 Fluxes ◽  
Surface Ocean ◽  
Data Coverage

Abstract. A new Feed-Forward Neural Network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) reconstruction of pCO2 climatology and (2) reconstruction of pCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity and temperature (SSS and SST), sea surface height (SSH), chlorophyll a (Chl), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step 2 augmented by their anomalies. During each step, the FFNN model reconstructs the non-linear relations between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1º x 1º grid for the period 2001–2016. Global ocean pCO2 was reconstructed with a satisfying accuracy compared to independent observational data from SOCAT. However, errors are larger in regions with poor data coverage (e.g. Indian Ocean, Southern Ocean, subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO2 with reasonable skills over the Equatorial Pacific associated with ENSO (El Niño Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variabilty between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern hemisphere and in particular, the Southern Pacific and the Indian Ocean, as these regions suffer from poor data-coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea-air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.

scholarly journals Characteristics of the seasonal cycle of surface layer salinity in the global ocean

2011 ◽  
Vol 8 (6) ◽  
pp. 2377-2415 ◽  
Author(s):  
F. M. Bingham ◽  
G. R. Foltz ◽  
M. J. McPhaden
Keyword(s):  
Surface Layer ◽  
Seasonal Variability ◽  
Seasonal Cycle ◽  
Arabian Sea ◽  
Mixed Layer Depth ◽  
Bimodal Distribution ◽  
Surface Fluxes ◽  
Global Ocean ◽  
Freshwater Flux ◽  
The North

Abstract. The seasonal variability of surface layer salinity (SLS), evaporation (E), precipitation (P) and E-P over the global ocean is examined using in situ salinity data and the National Center for Environmental Prediction's Climate System Forecast Reanalysis. Seasonal amplitudes and phases are calculated using harmonic analysis and presented in all areas of the open ocean between 60° S and 60° N. Areas with large amplitude SLS seasonal variations include: the intertropical convergence zone in the Atlantic, Pacific and Indian Oceans; western marginal seas of the Pacific; and the Arabian Sea. The median value in areas that have statistically significant seasonal cycles of SLS is 0.19. Between about 60° S and 60° N, 37 % of the ocean surface has a significant seasonal cycle of SLS and 75 % a seasonal cycle of E-P. Phases of SLS have a bimodal distribution, with most areas of the ocean peaking in SLS in either March/April or September/October. The same calculation is done with surface freshwater flux using a mixed-layer depth climatology. With the exception of an area near the western boundaries of the North Atlantic and North Pacific, seasonal variability is dominated by precipitation. Surface freshwater fluxes also have a bimodal distribution, with peaks in January and July, 1–2 months before the peaks of SLS. The amplitudes and phases of SLS and surface fluxes compare well in a qualitative sense, suggesting that much of the variability in SLS is due to E-P forcing. However, the amplitudes of SLS are somewhat larger than would be expected and the peak of SLS comes typically about one month earlier than expected. The differences of the amplitudes of the two quantities is largest in such areas as the Amazon River plume, the Arabian Sea, the ITCZ and the eastern equatorial Pacific and Atlantic, indicating that other processes such as ocean mixing and lateral transport must be important, especially in the tropics.

scholarly journals Characteristics of the seasonal cycle of surface layer salinity in the global ocean

Ocean Science ◽  
2012 ◽  
Vol 8 (5) ◽  
pp. 915-929 ◽  
Author(s):  
F. M. Bingham ◽  
G. R. Foltz ◽  
M. J. McPhaden
Keyword(s):  
Surface Layer ◽  
Mixed Layer ◽  
Seasonal Variability ◽  
Seasonal Cycle ◽  
Arabian Sea ◽  
Mixed Layer Depth ◽  
Bimodal Distribution ◽  
Global Ocean ◽  
Late Winter ◽  
Freshwater Forcing

Abstract. The seasonal variability of surface layer salinity (SLS), evaporation (E), precipitation (P), E-P, advection and vertical entrainment over the global ocean is examined using in situ salinity data, the National Centers for Environmental Prediction's Climate System Forecast Reanalysis and a number of other ancillary data. Seasonal amplitudes and phases are calculated using harmonic analysis and presented in all areas of the open ocean between 60° S and 60° N. Areas with large amplitude SLS seasonal variations include: the intertropical convergence zone (ITCZ) in the Atlantic, Pacific and Indian Oceans; western marginal seas of the Pacific; and the Arabian Sea. The median amplitude in areas that have statistically significant seasonal cycles of SLS is 0.19. Between about 60° S and 60° N, 37% of the ocean surface has a statistically significant seasonal cycle of SLS and 75% has a seasonal cycle of E-P. Phases of SLS have a bimodal distribution, with most areas in the Northern Hemisphere peaking in SLS in March/April and in the Southern Hemisphere in September/October. The seasonal cycle is also estimated for surface freshwater forcing using a mixed-layer depth climatology. With the exception of areas near the western boundaries of the North Atlantic and North Pacific, seasonal variability is dominated by precipitation. Surface freshwater forcing also has a bimodal distribution, with peaks in January and July, 1–2 months before the peaks of SLS. Seasonal amplitudes and phases calculated for horizontal advection show it to be important in the tropical oceans. Vertical entrainment, estimated from mixed-layer heaving, is largest in mid and high latitudes, with a seasonal cycle that peaks in late winter. The amplitudes and phases of SLS and surface fluxes compare well in a qualitative sense, suggesting that much of the variability in SLS is due to E-P. However, the amplitudes of SLS are somewhat different than would be expected and the peak of SLS comes typically about one month earlier than expected. The differences of the amplitudes of the two quantities is largest in such areas as the Amazon River plume, the Arabian Sea, the ITCZ and the eastern equatorial Pacific and Atlantic.

Zooplankton community responses to regional-scale weather variability: a synoptic climatology approach

2015 ◽  
Vol 62 (3) ◽  
pp. 189-198 ◽  
Author(s):  
AL Primo ◽  
DG Kimmel ◽  
SC Marques ◽  
F Martinho ◽  
UM Azeiteiro ◽  
...  
Keyword(s):  
Regional Scale ◽  
Zooplankton Community ◽  
Synoptic Climatology ◽  
Community Responses ◽  
Weather Variability

scholarly journals MAREDAT: towards a World Ocean Atlas of MARine Ecosystem DATa

2012 ◽  
Vol 5 (2) ◽  
pp. 1077-1106 ◽  
Author(s):  
E. T. Buitenhuis ◽  
M. Vogt ◽  
R. Moriarty ◽  
N. Bednaršek ◽  
S. C. Doney ◽  
...  
Keyword(s):  
Marine Ecosystem ◽  
World Ocean ◽  
Zooplankton Biomass ◽  
Global Ocean ◽  
Special Issue ◽  
Biomass Distribution ◽  
Phytoplankton Pigment ◽  
Autotrophic Biomass ◽  
Ocean Ecosystem ◽  
World Ocean Atlas

Abstract. We present a summary of biomass data for 11 Plankton Functional Types (PFTs) plus phytoplankton pigment data, compiled as part of the MARine Ecosystem biomass DATa (MAREDAT) initiative. The goal of the MAREDAT initiative is to provide global gridded data products with coverage of all biological components of the global ocean ecosystem. This special issue is the first step towards achieving this. The PFTs presented here include picophytoplankton, diazotrophs, coccolithophores, Phaeocystis, diatoms, picoheterotrophs, microzooplankton, foraminifers, mesozooplankton, pteropods and macrozooplankton. All variables have been gridded onto a World Ocean Atlas (WOA) grid (1° × 1° × 33 vertical levels × monthly climatologies). The data show that (1) the global total heterotrophic biomass (2.0–6.4 Pg C) is at least as high as the total autotrophic biomass (0.5–2.6 Pg C excluding nanophytoplankton and autotrophic dinoflagellates), (2) the biomass of zooplankton calcifiers (0.9–2.3 Pg C) is substantially higher than that of coccolithophores (0.01–0.14 Pg C), (3) patchiness of biomass distribution increases with organism size, and (4) although zooplankton biomass measurements below 200 m are rare, the limited measurements available suggest that Bacteria and Archaea are not the only heterotrophs in the deep sea. More data will be needed to characterize ocean ecosystem functioning and associated biogeochemistry in the Southern Hemisphere and below 200 m. Microzooplankton database: doi:10.1594/PANGAEA.779970.

scholarly journals Consistency of the current global ocean observing systems from an Argo perspective

Ocean Science ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 547-557 ◽  
Author(s):  
K. von Schuckmann ◽  
J.-B. Sallée ◽  
D. Chambers ◽  
P.-Y. Le Traon ◽  
C. Cabanes ◽  
...  
Keyword(s):  
Sea Level ◽  
Heat Content ◽  
Deep Ocean ◽  
Global Ocean ◽  
Regional Variability ◽  
Tropical Ocean ◽  
Steric Sea Level ◽  
Ocean Observing ◽  
Ocean Observing Systems ◽  
Observing Systems

Abstract. Variations in the world's ocean heat storage and its associated volume changes are a key factor to gauge global warming and to assess the earth's energy and sea level budget. Estimating global ocean heat content (GOHC) and global steric sea level (GSSL) with temperature/salinity data from the Argo network reveals a positive change of 0.5 ± 0.1 W m−2 (applied to the surface area of the ocean) and 0.5 ± 0.1 mm year−1 during the years 2005 to 2012, averaged between 60° S and 60° N and the 10–1500 m depth layer. In this study, we present an intercomparison of three global ocean observing systems: the Argo network, satellite gravimetry from GRACE and satellite altimetry. Their consistency is investigated from an Argo perspective at global and regional scales during the period 2005–2010. Although we can close the recent global ocean sea level budget within uncertainties, sampling inconsistencies need to be corrected for an accurate global budget due to systematic biases in GOHC and GSSL in the Tropical Ocean. Our findings show that the area around the Tropical Asian Archipelago (TAA) is important to closing the global sea level budget on interannual to decadal timescales, pointing out that the steric estimate from Argo is biased low, as the current mapping methods are insufficient to recover the steric signal in the TAA region. Both the large regional variability and the uncertainties in the current observing system prevent us from extracting indirect information regarding deep-ocean changes. This emphasizes the importance of continuing sustained effort in measuring the deep ocean from ship platforms and by beginning a much needed automated deep-Argo network.

scholarly journals Validation of an ocean shelf model for the prediction of mixed-layer properties in the Mediterranean Sea west of Sardinia

Ocean Science ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 235-257 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Boundary Conditions ◽  
Mixed Layer ◽  
Initial Conditions ◽  
Mixed Layer Depth ◽  
Vertical Mixing ◽  
Global Ocean ◽  
Wave Number ◽  
Surface Boundary ◽  
Regional Ocean Modeling System ◽  
The Mediterranean

Abstract. The Regional Ocean Modeling System (ROMS) has been employed to explore the sensitivity of the forecast skill of mixed-layer properties to initial conditions, boundary conditions, and vertical mixing parameterisations. The initial and lateral boundary conditions were provided by the Mediterranean Forecasting System (MFS) or by the MERCATOR global ocean circulation model via one-way nesting; the initial conditions were additionally updated through the assimilation of observations. Nowcasts and forecasts from the weather forecast models COSMO-ME and COSMO-IT, partly melded with observations, served as surface boundary conditions. The vertical mixing was parameterised by the GLS (generic length scale) scheme Umlauf and Burchard (2003) in four different set-ups. All ROMS forecasts were validated against the observations which were taken during the REP14-MED survey to the west of Sardinia. Nesting ROMS in MERCATOR and updating the initial conditions through data assimilation provided the best agreement of the predicted mixed-layer properties with the time series from a moored thermistor chain. Further improvement was obtained by the usage of COSMO-ME atmospheric forcing, which was melded with real observations, and by the application of the k-ω vertical mixing scheme with increased vertical eddy diffusivity. The predicted temporal variability of the mixed-layer temperature was reasonably well correlated with the observed variability, while the modelled variability of the mixed-layer depth exhibited only agreement with the observations near the diurnal frequency peak. For the forecasted horizontal variability, reasonable agreement was found with observations from a ScanFish section, but only for the mesoscale wave number band; the observed sub-mesoscale variability was not reproduced by ROMS.

scholarly journals Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

2020 ◽  
Author(s):  
Wei-Lei Wang ◽  
Guisheng Song ◽  
François Primeau ◽  
Eric S. Saltzman ◽  
Thomas G. Bell ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Mixed Layer ◽  
Dimethyl Sulfide ◽  
Mixed Layer Depth ◽  
Environmental Parameters ◽  
Global Scale ◽  
Global Ocean ◽  
Layer Depth

Abstract. Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. However, a knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to understand the factors controlling surface ocean DMS and its impact on climate. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 57 810 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of lat-lon coordinates, time-of-day, time-of-year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, silicate, and oxygen. Linear regressions of DMS against the environmental parameters show that on a global scale mixed layer depth and solar radiation are the strongest predictors of DMS, however, they capture 14 % and 12 % of the raw DMS data variance, respectively. The multi-linear regression can capture more (∼29 %) of the raw data variance, but strongly underestimates high DMS concentrations. In contrast, the ANN captures ~61 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in DMS concentration and sea-to-air flux. The highest concentrations (fluxes) occur in the high-latitude oceans during the summer. We estimate a lower global sea-to-air DMS flux (17.90 &amp;pm; 0.34 Tg S yr−1) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used.

scholarly journals Marine Heatwaves in the Arabian Sea

2021 ◽  
Author(s):  
Abhisek Chatterjee ◽  
Gouri Anil ◽  
Lakshmi R. Shenoy
Keyword(s):  
Indian Ocean ◽  
Arabian Sea ◽  
Heat Budget ◽  
Monsoon Season ◽  
Fold Increase ◽  
Recent Decade ◽  
Ocean Basin ◽  
Significant Heterogeneity ◽  
Indian Ocean Basin Mode ◽  
Budget Analysis

Abstract. Marine heatwaves (MHWs) are prolonged warm sea condition events that cause a destructive impact on marine ecosystems. The documentation of MHWs and assessment of their impacts are largely confined to a few regional seas or in global mean studies. The Indian Ocean received almost no attention in this regard despite the fact that this ocean basin, particularly the Arabian Sea, is warming at the most rapid pace among the other tropical basins in recent decades. This study shows the characteristics MHWs for the Arabian Sea during 1982–2019. Our analysis shows that the duration of MHWs exhibit a rapidly increasing trend of ~20 days/decade (1.5–2 count/decade) in the northern Arabian Sea and in the southeastern Arabian Sea close to the west coast of India; which is more than 15 fold increase in the MHW days from the early 80s'. At the same time increase in MHW frequency is ~1.5–2 count/decade i.e an increase of ~6 fold, indicating more frequent and much longer heatwave events in the recent decade. Notably, since the beginning of the satellite record, the year 2010 and 2016 saw the maximum number of heatwave days with more than 75 % of days of the pre-monsoon and summer monsoon season experienced heatwaves. The accelerated trend of the heatwave days is found to be driven by the rapid rise of the mean SST of the Arabian Sea in the recent decade. Moreover, longer heatwave days are also associated with the dominant climate modes and among them, Indian Ocean Basin mode via the decaying phase of the El-Niño is found to be the most influencing mode contributing in more than 70–80 % of observed heatwave days in this basin. Mixed layer heat budget analysis suggests significant heterogeneity in the dominant processes across the years; however, weakening of latent heat loss is in general one of the key mechanism in the genesis of most of the MHWs.

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