Seasonal and Inter-Annual Variability of Sea Surface Temperature and Its Correlation with Maximum Sustained Wind Speed in Bay of Bengal

2021 ◽  
pp. 253-265
Author(s):  
Jiya Albert ◽  
Prasad K. Bhaskaran
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bay Of Bengal ◽  
Sea Surface ◽  
Annual Variability ◽  
Maximum Sustained Wind Speed

scholarly journals Seasonal and inter-annual variability of the sea surface temperature and mixed layer depth in the southern Bay of Bengal

2013 ◽  
Vol 4 (1) ◽  
pp. 70
Author(s):  
R. Ranith ◽  
L. Senthilnathan ◽  
M. Machendiranathan ◽  
T. Thangaradjou ◽  
A. Saravanakumar
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Bay Of Bengal ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Climate Change Scenarios ◽  
Layer Depth ◽  
South West Monsoon

Argo float data supplemented with satellite measurements was used to study the seasonal and inter-annual variation in wind speed, sea surface temperature (SST) and mixed layer depth (MLD) of the southern Bay of Bengal from 2003 through 2010. Due to persistence of wind, clear sky and high insolation an increase in SST by about 2°C is evident during summer months (March-May) and is followed by shallowed MLD with a minimum depth of 9.3 m during summer 2004. MLD reached the maximum depth during monsoon season (November-December) and often extends to post monsoon (February) owing to strong monsoon wind, cloudy sky and SST plummeted by 3°C. During the inter-monsoon period (August-October) the MLD shallowed and maintained a depth of 20–30 m all through the study period. High wind accompanied with moderate temperature (SST) due to the south west monsoon leads to decreased MLD with an average depth of 44 m in July. Analysis of wind speed, SST and MLD suggested that out of various meteorological parameters wind speed and induced mixing are highly influential in MLD formation. Reduced occurrence and amplitude of MLD deepening noticed in recent years can be attributed to the evident climate change scenarios. Large scale upper ocean variability observed from the present study has innumerable antagonistic consequences on the marine ecosystem which is evident from various events of seagrass burns and coral bleaching which have occurred in the last decade.

Seasonal and inter-annual variability of the sea surface temperature and mixed layer depth in the southern Bay of Bengal

2013 ◽  
Vol 4 (1) ◽  
pp. 70-81
Author(s):  
R. Ranith ◽  
L. Senthilnathan ◽  
M. Machendiranathan ◽  
T. Thangaradjou ◽  
A. Saravanakumar
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Bay Of Bengal ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Layer Depth ◽  
Annual Variability

Seasonal and Interannual Sea Surface Temperature Variability in the Coastal Cities of Arabian Sea and Bay of Bengal

2004 ◽  
Vol 31 (2) ◽  
pp. 549-560 ◽  
Author(s):  
Tariq Masood Ali Khan ◽  
Dewan Abdul Quadir ◽  
Tad S. Murty ◽  
Majajul Alam Sarker
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Temperature Variability ◽  
Sea Surface ◽  
Coastal Cities

The Behavior of the Bulk – Skin Sea Surface Temperature Difference under Varying Wind Speed and Heat Flux

1996 ◽  
Vol 26 (10) ◽  
pp. 1969-1988 ◽  
Author(s):  
Gary A. Wick ◽  
William J. Emery ◽  
Lakshmi H. Kantha ◽  
Peter Schlüssel
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Difference ◽  
Sea Surface

Modeling the Atmospheric Boundary Layer Wind Response to Mesoscale Sea Surface Temperature Perturbations

2014 ◽  
Vol 142 (11) ◽  
pp. 4284-4307 ◽  
Author(s):  
Natalie Perlin ◽  
Simon P. de Szoeke ◽  
Dudley B. Chelton ◽  
Roger M. Samelson ◽  
Eric D. Skyllingstad ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coupling Coefficient ◽  
Eddy Viscosity ◽  
Sea Surface ◽  
Coupling Coefficients ◽  
Wind Response ◽  
Speed Response

Abstract The wind speed response to mesoscale SST variability is investigated over the Agulhas Return Current region of the Southern Ocean using the Weather Research and Forecasting (WRF) Model and the U.S. Navy Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model. The SST-induced wind response is assessed from eight simulations with different subgrid-scale vertical mixing parameterizations, validated using Quick Scatterometer (QuikSCAT) winds and satellite-based sea surface temperature (SST) observations on 0.25° grids. The satellite data produce a coupling coefficient of sU = 0.42 m s−1 °C−1 for wind to mesoscale SST perturbations. The eight model configurations produce coupling coefficients varying from 0.31 to 0.56 m s−1 °C−1. Most closely matching QuikSCAT are a WRF simulation with the Grenier–Bretherton–McCaa (GBM) boundary layer mixing scheme (sU = 0.40 m s−1 °C−1), and a COAMPS simulation with a form of Mellor–Yamada parameterization (sU = 0.38 m s−1 °C−1). Model rankings based on coupling coefficients for wind stress, or for curl and divergence of vector winds and wind stress, are similar to that based on sU. In all simulations, the atmospheric potential temperature response to local SST variations decreases gradually with height throughout the boundary layer (0–1.5 km). In contrast, the wind speed response to local SST perturbations decreases rapidly with height to near zero at 150–300 m. The simulated wind speed coupling coefficient is found to correlate well with the height-averaged turbulent eddy viscosity coefficient. The details of the vertical structure of the eddy viscosity depend on both the absolute magnitude of local SST perturbations, and the orientation of the surface wind to the SST gradient.

Atmospheric Cold Pools and Their Influence on Sea Surface Temperature in the Bay of Bengal

2021 ◽  
Vol 126 (9) ◽  
Author(s):  
M. S. Girishkumar ◽  
Jofia Joseph ◽  
M. J. McPhaden ◽  
E. Pattabhi Ram Rao
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bay Of Bengal ◽  
Sea Surface ◽  
Cold Pools

scholarly journals Sea surface wind perturbations over the Kashevarov Bank of the Okhotsk Sea: a satellite study

2011 ◽  
Vol 29 (2) ◽  
pp. 393-399
Author(s):  
T. I. Tarkhova ◽  
M. S. Permyakov ◽  
E. Yu. Potalova ◽  
V. I. Semykin
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Surface Wind ◽  
Sea Surface ◽  
Cold Spot ◽  
Okhotsk Sea ◽  
Autumn Period ◽  
Sea Surface Wind ◽  
Spot Center

Abstract. Sea surface wind perturbations over sea surface temperature (SST) cold anomalies over the Kashevarov Bank (KB) of the Okhotsk Sea are analyzed using satellite (AMSR-E and QuikSCAT) data during the summer-autumn period of 2006–2009. It is shown, that frequency of cases of wind speed decreasing over a cold spot in August–September reaches up to 67%. In the cold spot center SST cold anomalies reached 10.5 °C and wind speed lowered down to ~7 m s−1 relative its value on the periphery. The wind difference between a periphery and a centre of the cold spot is proportional to SST difference with the correlations 0.5 for daily satellite passes data, 0.66 for 3-day mean data and 0.9 for monthly ones. For all types of data the coefficient of proportionality consists of ~0.3 m s−1 on 1 °C.

scholarly journals Impact of a Narrow Coastal Bay of Bengal Sea Surface Temperature Front on an Indian Summer Monsoon Simulation

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Dhrubajyoti Samanta ◽  
Saji N. Hameed ◽  
Dachao Jin ◽  
Vishnu Thilakan ◽  
Malay Ganai ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Bay Of Bengal ◽  
Sea Surface ◽  
Temperature Front ◽  
Coastal Bay

scholarly journals CO2 flux behavior in the maritorium of San Andres Islands on 2019

Respuestas ◽  
2020 ◽  
Vol 25 (3) ◽  
Author(s):  
Juan Guillermo Popayán-Hernández ◽  
Orlando Zúñiga-Escobar
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Images ◽  
Co2 Flux ◽  
Sea Surface ◽  
Remote Sensors ◽  
Partial Pressures ◽  
Thermodynamic Relationship ◽  
San Andres

This document estimated the behavior of the CO2 flux in the San Andrés Islas maritime for the first half of 2019. This behavior was established based on the thermodynamic relationship between the sea surface temperature, the partial pressures of CO2 in the atmosphere and the water column, this from data derived from remote sensors. The satellite data were derived from the MODIS aqua sensors and the MERRA model for sea surface temperature and wind speed respectively. Satellite images were obtained from NASA databases, subsequently processed and specialized in ArcGis 10.1. Finally, the behavior of the CO2 flux is shown for the San Andrés Islas maritime, finding that it does not have a tendency to capture CO2, so acidification processes are discarded for the selected study period.

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