NABOS Project
Nansen and Amundsen Basins Observational System
NABOS Project
Nansen and Amundsen Basins Observational System
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Nansen and Amundsen Basins Observational System
Research Summary
Research Summary
The Arctic is undergoing significant changes, and the extensive modification of stratification that is associated with freshwater distribution is among the most prominent shifts that are taking place in the Arctic. To better understand these changes, our study investigates the impacts of a strong freshening event on oceanic stratification, heat fluxes, and currents in the eastern Eurasian Basin of the Arctic Ocean in 2015–2017. Our analysis of mooring observations revealed an excess of freshwater content by about 0.5m in the upper ~170m ocean. Tracer analysis showed that the main cause of this extraordinary freshening was the influx of riverine water from the Kara Sea through the Vilkitsky Straight (and, to a lesser extent, the Laptev Sea shelf) and reduced advection of saltier waters from St. Anna Trough. This freshening resulted in increased upper ocean stratification and weakened ventilation of the halocline. Furthermore, there was a three-to four-fold suppression of oceanic heat fluxes, which resulted in slower winter sea ice formation rates and lower summer sea ice melting rates in 2016 and 2017. One of the important ramifications of these changes was the weakening of total and near-inertial currents and shear in the upper ocean. My findings highlight the important role that extreme events may play in the Arctic's shift to a new climate state.
MAQWS 2019 Project
Metropolitan Air Quality and Weather Services - SAFAR
MAQWS 2019 Project
Metropolitan Air Quality and Weather Services - SAFAR
Research Summary
Research Summary
During the pandemic lockdowns, significant reductions in emissions provided an opportunity to assess their impact on air quality and ozone (O3) production across various locations. Analyzing pollutants such as PM10, PM2.5, NOx, CO, and O3 at eight distinct stations (urban, rural, and coastal) revealed diverse outcomes based on geography and lockdown effectiveness.
For instance, Delhi (Chandni chowk), the most polluted pre-lockdown, saw substantial reductions in PM2.5, NO2, and CO during the first lockdown phase, though O3 levels remained nearly stable. Conversely, Mahabaleshwar, the least polluted, experienced minimal reductions in PM2.5 and NO2, but a notable increase in O3. Rural areas with extensive vegetation saw O3 as the primary pollutant due to biogenic VOC emissions, whereas in urban and coastal stations, particulate matter largely influenced the Air Quality Index (AQI).
Refer to publications.
During the pandemic lockdowns, significant reductions in emissions provided an opportunity to assess their impact on air quality and ozone (O3) production across various locations. Analyzing pollutants such as PM10, PM2.5, NOx, CO, and O3 at eight distinct stations (urban, rural, and coastal) revealed diverse outcomes based on geography and lockdown effectiveness.
For instance, Delhi (Chandni chowk), the most polluted pre-lockdown, saw substantial reductions in PM2.5, NO2, and CO during the first lockdown phase, though O3 levels remained nearly stable. Conversely, Mahabaleshwar, the least polluted, experienced minimal reductions in PM2.5 and NO2, but a notable increase in O3. Rural areas with extensive vegetation saw O3 as the primary pollutant due to biogenic VOC emissions, whereas in urban and coastal stations, particulate matter largely influenced the Air Quality Index (AQI).
Refer to publications.
Pollutant concentration during lockdowns in Metro Cities
Pollutant concentration during lockdowns in Metro Cities
Location of MAPAN stations across India
Location of MAPAN stations across India
Study of Mortality in Ahmedabad City: Summer of 2018
Study of Mortality in Ahmedabad City: Summer of 2018
• Conducted a study of mortality data across eight stations in Ahmedabad during the summer of 2018.
• Investigating heat island effects using maximum temperature and PM2.5 correlations during the summer of 2018.
Study of Atmospheric Gaseous Mercury in Metropolitan Areas: 2015-2017
Study of Atmospheric Gaseous Mercury in Metropolitan Areas: 2015-2017
• Studied the variation of atmospheric total gaseous mercury (TGM) in Pune over three years (2015–2017) and its relationship with meteorological parameters.
• Performed air mass trajectory analysis using HYSPLIT back trajectory to identify potential sources of mercury emissions.
IITM Master's Thesis Project
Sensitivity of Bay of Bengal Sea Surface Temperature to the Forcing: An Ocean Model study
IITM Master's Thesis Project
Sensitivity of Bay of Bengal Sea Surface Temperature to the Forcing: An Ocean Model study
Research Summary
Research Summary
The Bay of Bengal (BoB) is a tropical ocean basin influenced by heavy precipitation and riverine freshwater. It exhibits strong stratification and significant sea surface temperature (SST) variations, which are critical for atmospheric convection and intraseasonal oscillations (ISOs).
The Bay of Bengal (BoB) is a tropical ocean basin influenced by heavy precipitation and riverine freshwater. It exhibits strong stratification and significant sea surface temperature (SST) variations, which are critical for atmospheric convection and intraseasonal oscillations (ISOs).
This study used the Modular Ocean Model-5 (MOM5) to compare a Control Experiment (CTRL) with traditional forcing and an Experimental Setup (EXP) incorporating advanced flux datasets (OA Flux, ERA-5, GPCP) over 1998–2009. Key outcomes include:
This study used the Modular Ocean Model-5 (MOM5) to compare a Control Experiment (CTRL) with traditional forcing and an Experimental Setup (EXP) incorporating advanced flux datasets (OA Flux, ERA-5, GPCP) over 1998–2009. Key outcomes include:
- Improved SST Simulation: EXP reduced SST errors by 30–40%, with RMSE dropping from 0.9°C to <0.6°C in central BoB.
- Better Heat Flux Representation: EXP decreased latent heat flux (LHF) and net heat flux (NHF) RMSE by 5–20 W/m², particularly in northern and central BoB.
- Increased Correlation with Observations: SST correlation rose from 0.85 to 0.93 at southern buoy locations in EXP.
While EXP improved SST and heat flux simulations, ISO variability was still underestimated in central and southern BoB due to limitations in ocean physics representation, highlighting the need for further model refinements.
While EXP improved SST and heat flux simulations, ISO variability was still underestimated in central and southern BoB due to limitations in ocean physics representation, highlighting the need for further model refinements.
Sea surface temperature of Bay of Bengal for CTRL, EXP, and TropFlux
Sea surface temperature of Bay of Bengal for CTRL, EXP, and TropFlux
Timeseries of SST for CTRL, EXP and TRMM
Timeseries of SST for CTRL, EXP and TRMM
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