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Numerical simulation and preliminary analysis of spectral slope and tail characteristics using nested WAM-SWAN in a shallow water application off Visakhapatnam
No full textOver the past several decades, understanding the nature and slope of high frequency tail in wind-wave modelling studies is extremely important, and its uncertainty amongst −4 and −5 frequency exponent representation was an area of major significance. Hence, an attempt has been made to estimate the slope of the high frequency tail of the wind-wave frequency spectrum off coastal Visakhapatnam using measured data for the period November 2011 to December 2015. The investigation reveals that the high frequency slope of the spectra varied seasonally in the range between −1.80 and −3.77. Annually 75.11% of wave conditions were dominated by swells and the rest 24.89% by wind-seas. Further, the validation of 1D wave spectra at the coastal location utilizing the nested WAM-SWAN setup was promising enough indicating a departure in capturing the peak energies. With the aid of spectral fitting method, the JONSWAP spectra was compared with the measured spectra; which demonstrated significant deviations from the measured spectra revealing high Scatter Index ranging from 0.24 to 1.73. The study aims to report on the uncertainty in the correct slope for the high frequency tail; and the concept of a unified slope at any coastal location remains unpredictable for the oceanographic community
SoVeAt: a tool for visualizing sound velocity data for Naval applications
Full text linkThis report discusses various functionalities of Sound Velocity Atlas (SoVeAT) tool developed for use by
Naval Operations Data Processing and Analysis Centre (NODPAC) a wing of Indian Navy. The subsurface
profile data of temperature and salinity (T/S) used in developing this tool is the Argo data which is
obtained from INCOIS Argo mirror archives which is obtained from two Global Data Assembly Centre’s
(GDAC) namely Coriolis in France and USGODAE in USA. These data sets are processed, quality
controlled and merged to form a unique data set for enhancing the Sound Velocity climatology of Indian
Ocean (30E - 120 E and 69 S - 30 N). With this sound velocity data derived from Argo T/S, Graphic User
Interface (GUI) based tool is built for visualizing parameters viz., Sound Velocity, Temperature, Salinity
and bathymetry. This tool has capability to generate climatology dynamically between any chosen
periods apart from visualizing various plots which are useful for Navy while at sea. Also provision for
adding newly observed T/S data is provided making this most robust sound velocity tool for use by the
Indian Navy
Unraveling the Mystery of Indian Summer Monsoon Prediction: Improved Estimate of Predictability Limit
No full textLarge socioeconomic impact of the Indian summer monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather low potential predictability (PP) of the seasonal ISM, contributed significantly by “internal,” interannual variability was considered insurmountable. Here we show that the internal variability contributed by the ISM subseasonal (synoptic + intraseasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g., El Niño and Southern Oscillation). This provides a scientific basis for predictability of the ISM rainfall beyond the conventional estimates of PP. We establish a much higher actual limit of PP (r∼0.82) through an extensive reforecast experiment (1,920 years of simulation) by improving two major physics in a global coupled climate model, which raises a hope for a very reliable dynamical seasonal ISM forecasting in the near futur
Signature of La Niña in interannual variations of the East India Coastal Current during spring
No full textWe have investigated interannual variations of the spring (February–April average) East India Coastal Current (EICC) magnitude between 2000 and 2018 using OSCAR (Ocean Surface Current Analysis Real-time) current and a linear, continuously stratified (LCS) model. Interannual variability of the EICC shows significant decrease in magnitude during spring of 2000, 2008 and 2011, the years when high negative ONI (Oceanic Niño Index for sea surface temperature) value has been observed due to dominance of strong La Niña events. Our LCS model also successfully simulates these interannual variability of the spring average EICC between 2000 and 2018. We carried out numerical experiments using LCS model related to local and remote forcing response on EICC. Dynamics of the EICC during spring are dominated by four different forcing processes; local wind along east coast of India, remote forcing response from the eastern and northern boundary of the BoB including islands, interior BoB and the Equatorial Indian Ocean (EIO). During El Niño and normal spring years, strong poleward interannual EICC are due to very weak negligible (order of 0–5 cm s −1 ) EICC from EIO remote response and in-phase poleward EICC formation using other three forcings. However, during La Niña spring years, weak (order of 0–10 cm s −1 ) poleward interannual EICC are formed due to destructive interference between equatorward current (order of 10–25 cm s −1 ) from EIO forcing and in-phase poleward current from other three forcings. We have also found propagation of interannual upwelling (downwelling) favorable Kelvin wave from EIO via eastern and western boundary of the BoB during spring in the El Niño (La Niña) years. The interannual variations in the propagation of EIO Kelvin wave are associated with the changes in the EIO zonal wind direction by climate mode like ENSO (El Niño–Southern Oscillation)
Understanding Iodine Chemistry Over the Northern and Equatorial Indian Ocean
No full textObservations of halogen oxides, ozone, meteorological parameters, and physical and biogeochemical water column measurements were made in the Indian Ocean and its marine boundary layer as a part of the Second International Indian Ocean Expedition (IIOE‐2). The expedition took place on board the oceanographic research vessel Sagar Nidhi during 4–22 December 2015 from Goa, India, to Port Louis, Mauritius. Observations of mixed layer depth, averaged temperature, salinity, and nitrate concentrations were used to calculate predicted iodide concentrations in the seawater. The inorganic iodine ocean‐atmosphere flux (hypoiodous acid [HOI] and molecular iodine [I2]) was computed using the predicted iodide concentrations, measured atmospheric ozone, and wind speed. Iodine oxide (IO) mixing ratios peaked at 0.47 ± 0.29 pptv (parts per trillion by volume) in the remote open ocean environment. The estimated iodide concentrations and HOI and I2 fluxes peaked at 200/500 nM, 410/680 nmol·m−2·day−1, and 20/80 nmol·m−2·day−1, respectively, depending on the parameterization used. The calculated fluxes for HOI and I2 were higher closer to the Indian subcontinent; however, atmospheric IO was only observed above the detection limit in the remote open ocean environment. We use NO2 observations to show that titration of IO by NO2 is the main reason for this result. These observations show that inorganic iodine fluxes and atmospheric IO show similar trends in the Indian Ocean marine boundary layer, but the impact of inorganic iodine emissions on iodine chemistry is buffered in elevated NOx environments, even though the estimated oceanic iodine fluxes are higher
A BGC-Argo guide: Planning, deployment, data handling and usage
Full text linkThe Biogeochemical-Argo program (BGC-Argo) is a new profiling-float-based, ocean wide, and distributed ocean monitoring program which is tightly linked to, and has benefited significantly from, the Argo program. The community has recommended for BGC-Argo the addition of six measurements in addition to pressure, temperature and salinity measured by Argo, to include oxygen, pH, nitrate, downwelling light, chlorophyll fluorescence and the optical backscattering coefficient. The purpose of this addition is to enable the monitoring of ocean biogeochemistry and health, and in particular, monitor major processes such as ocean deoxygenation, acidification and warming and their effect on phytoplankton, the main source of energy of marine ecosystems. Here we describe the salient issues associated with the operation of the BGC-Argo network, with information useful for those interested in deploying and using the data it produces. These include, float testing, deployment and increasingly, recovery. Aspect of data management, processing and quality control are covered as well as specific issues associated with each of the six BGC-Argo sensors. In particular, it is recommended that water samples be collected during float deployment to be used for validation of sensor output
Assessment of model-simulated upper ocean biogeochemical dynamics of the Bay of Bengal
No full textThe capability of a physical-biogeochemical model configured using Regional Ocean Modeling System (ROMS) in simulating upper ocean biogeochemical dynamics of the Bay of Bengal (BoB) is evaluated with available remote sensing and in situ observations. The accuracy of model-simulated and satellite (MODIS-Aqua) retrieved surface chlorophyll-a (chl-a) are individually assessed against in situ data, measured in surface waters of the western BoB, from March 2008 to November 2015. Model-simulated chl-a showed better correlation (R2 = 0.80) with the in situ observations as compared to that retrieved from satellite (R2 = 0.72). Although, the model underestimates chl-a (slope = 0.84), significant correlation proves its capability to reproduce the in situ trend. The root mean square error (RMSE) between model simulated and satellite retrieved chl-a against measured chl-a are 0.33 and 0.36, respectively. Additionally, a comparison with remote sensing time series data indicates that the model realistically simulates the seasonal variability of chl-a. Further, temperature, salinity, nitrate, chl-a and dissolved oxygen (DO) profiles obtained from two biogeochemical Argo floats deployed in the central BoB, are also compared with model-simulated profiles. In comparison, the model adequately simulates the observed subsurface variability of chl-a as well as persistent Deep Chlorophyll Maximum (DCM) at depths between 20 and 90 m having concentration 0.75–1.0 mg-m−3. The undulations in the subsurface spatial variability of chl-a are appreciably well captured by the model and comparable with the observations albeit the magnitude is overestimated in the model. It is noted that the temporal variability of the DCM and oxycline in the BoB is significantly influenced by the vertical movement of the thermocline
Basin-wide sea level coherency in the tropical Indian Ocean driven by Madden–Julian Oscillation
Full text linkChanges in sea level may be attributed either to barotropic (involving the entire water column) or baroclinic processes (governed by stratification). It has been widely accepted that barotropic sea level changes in the tropics are insignificant at intraseasonal time scales (periods of 30–80 days). Based on bottom pressure records, we present evidence for significant basin-wide barotropic sea level variability in the tropical Indian Ocean during December–April with standard deviations amounting to ∼30–60% of the standard deviation in total intraseasonal sea level variability. The origin of this variability is linked to a small patch of wind over the Eastern Indian Ocean, associated with boreal winter Madden–Julian Oscillations (MJO). These large fluctuations are likely to play a prominent role in the intraseasonal sea level and mass budgets. Because of their much faster propagation than baroclinic processes, they allow the basin to adjust to climatic perturbations much more rapidly than was previously thought
Spectral Modelling on the Characteristics of High Frequency Tail in Shallow Water Wave Spectra at Coastal Puducherry, East Coast of India
No full textAbout 8 years of measured wave spectra (June 2007–December 2014) off coastal Puducherry located in the east coast of India and bordering the Bay of Bengal were analyzed with an objective to understand the slope of the high frequency tail of the wave spectrum and to determine the occurrence of single peaked, double-peaked and multi-peaked spectra in varying sea states. The temporal and inter-annual variation of the spectral energy density over the years indicates marked variability and the study signifies that wave spectra were multi-peaked from June to October and predominantly double peaked during the rest of the year. Swell and wind sea components have been estimated from the wave spectra by separation frequency method. The analysis shows that swells dominate Puducherry coastal region not only during southwest monsoon (95%), but also during the post-monsoon (100%) and northeast monsoon season. The measured wave spectra were compared with numerical wave model outputs to attain a level of confidence with the buoy data. In addition, analysis on the slope of the high frequency tail of the wave energy spectra shows that its slope varied seasonally in the range of − 1.96 to − 3.27 at the coastal location. Further, the JONSWAP model fitted into measured wave spectra showed high discrepancy between the two, especially in the high frequency tail with Scatter Index ranging between 0.79 and 3.98. The correct slope for the high frequency or even whether a unique slope exists remains elusive for the ocean wave communit
The Eurasian Jet Streams as Conduits for East Asian Monsoon Variability
No full textPurpose of Review: This article gives a brief review on how the jet streams over the Eurasian continent influence the East Asian monsoon on intraseasonal to interdecadal time scales and discusses the seasonal predictability and change. Recent Findings: The wave train along the Eurasian jet streams is found to be crucial for East Asian monsoon variability. Interaction of the upper-level Rossby wave train with the Siberian High causes changes in winter monsoon climate over East Asia. In the case of summer, the Silk Road pattern, embedded in the Asian jet in association with western North Pacific circulation and the Pacific-Japan pattern, alters the strength and phase of the monsoon. Current coupled models showed limited skills in seasonal prediction of the Eurasian jet variations and their influences on the East Asian monsoon variability. Summary: The Eurasian jets as conduits for East Asian monsoon variability involve multiple feedbacks. Its interaction with low-level circulation mostly determines the degree of strength of variations in the monsoon climate. Global warming projections based on RCP 4.5 and 8.5 in the CMIP5 (the Coupled Model Intercomparison Project phase 5) models indicate that the mean Asian jet strengthens in future during winter, but no change is reported during summer