Nitrogen dioxide is present in both the troposphere and the stratosphere. It is formed when nitrogen oxide(NO) and other nitrogen oxides (NOx) react with other chemicals in the air. The main anthropogenic source of nitrogen dioxide is the combustion of fossil fuels (coal, gas and oil). It is also produced from refining of petrol and metals, commercial manufacturing and food manufacturing. Its natural sources are volcanoes and bacteria. Its life span in the atmosphere is very small with a period of a few hours. Due to this small life span, the sharp reduction in NO2 concentration in the atmosphere over India at the time of lockdown is clearly depicted in the image. During the lockdown only major activity for the NO2 production in the Indian region is coal production. The NO2 column concentration is obtained from the satellite by measuring the backscattering of solar radiation in the 405-605 nm range. Presence of high concentration of NO2 in the atmosphere is hazardous to human health.
Figure shows Sentinel-5P (TROPOMI) satellite observations of columnar CO (XCO) over the Indian domain for the days of November 8 and 25. Enhanced concentrations of XCO observed over Indo-Gangetic plain (IGP) during November 8 (characterized with burning activity) indicates the role of biomass emissions on columnar concentration, while November 25 (with no or less burning activities) was observed with lesser concentration of XCO over IGP. The satellite based figures clearly demonstrates the influence of biomass burning emissions on columnar CO concentration over those burning hotspot regions. Credits: Ashique V
Figure shows the Monthly XCH4 concentration (in PPB) over India for the months of April, July and October. The 0.1o x 0.1o gridded product is generated using Column averaged CH4 observations from Sentinel 5P Satellite. Months of April and October (Pre and Post monsoon) show elevated concentration of methane in Indo-Gangetic plain. Month of July shows the effect of monsoon on satellite observations. Large data gap is seen all over the Indian region due to cloud cover. Credits: Monish V Deshpande
Figure shows the contrast in CO2 concentration of CarbonTracker CT2017 products and CONTRAIL observations over Indian domain during the year 2013. CONTRAIL is an ongoing project that measures atmospheric trace gases using aircraft of Japan Airlines (JAL). Summer months are exhibiting more differences in CO2 concentrations among model and observations. While winter months shows moderate correlation (r2 ~ 0.5) between observation and model, summer months displays null correlation. Differences may be arising from the inability of global climate models to simulate complex atmospheric processes during the summer. Non availability of observations on the Indian domain constrains the proper validation of the models. Credits:Vishnu Thilakan M
Figure shows the comparison in CO2 molefraction between two models (CarbonTracker(CT) & TM3) with observation from Cape Rama, India (CRI- 15.07oN,73.83oE) and Mauna Loa, United states (MLO- 19.53oN,155.57oW) for Cape Rama region . Even though models are following a pattern almost similar to observation, it is clearly visible that the global models have failed to capture the regional fluctuations in CRI. Compared to TM3, CT is much more close to CRI in showing CO2 variability. Since MLO observation data is used as one of the input for TM3 simulations, TM3 is closely following MLO pattern. Credits: Aparnna Ravi P
Cape Rama(CRI) was a point observation site in India with observational data availability from 1993-2012 . Over, the 10 year monthly average data over CRI, a seasonal pattern in CO2 mole fraction is observed, with increase in concentration during pre monsoon (MAM) and post monsoon (JFM) months and noticeable decrease in concentration during the monsoon months (JJAS). Credits: Aparnna Ravi P
Atmospheric carbon dioxide (CO2) and methane (CH4) are the most important anthropogenic greenhouse gases (GHG) responsible for the global warming. Hence a particular focus of any climate policy initiatives is to implement efficient strategies for limiting the GHG emissions to a level that minimizes the adverse anthropogenic effect on the climate system. An essential component to achieve this objective is the accurate quantification of sources and sinks of these gases.
Our research approach is to effectively utilize the atmospheric GHG measurements to derive their regional sources and sinks at spatial scales relevant for policy-makers. This requires the use of realistic atmospheric transport models in combination with biospheric models and an inverse modeling technique. The above-mentioned measurements are carried out around the world by a number of different observation platforms. In recent years, satellite instruments have been effectively enhanced with their spatiotemporal sampling and precision strategies.
This group is established with an aim of applying the numerical modeling tools and utilizing the observations in order to understand the biosphere-atmosphere exchange of carbon. The group is actively involved in implementing and developing modeling tools and techniques to extract much information from the atmospheric observations at the required spatiotemporal scales.
The group focuses are:
One of the important aspects of our research activities is to implement and further develop a high-resolution inversion framework to quantify GHG (mainly CO2 and CH4) sources and sinks that are consistent with atmospheric observations. Towards this, both Eulerian and Lagrangian models are being used as the core components of the modeling framework. The current work is focussed on the Indian subcontinent, a critically important but under-sampled region.
Presently the group is working on generating high-resolution simulations of GHG over India, which will be used for a wide range of climate-related studies. These data products are further used for designing optimum measurement network over India.
We are also working on validating the forward simulations with an objective to improve the atmospheric transport models and characterize their uncertainties. A key step towards these efforts is to identify and utilize the observational capabilities of these gases. A wide range of data streams (including remote sensing data) and their analysis are involved in this process.
The above-said validations would enable these simulations to meet the standards that can be used with high degree of certainty for applications across a broad range of sectors including regional carbon budgeting. In an effort to derive regional distribution of sources and sinks of important GHGs, the group is also involved in developing significant strategies for incorporating these observations in an Bayesian inverse modeling framework, taking into account measurement and model errors.
Prof. Dr. Martin Heimann
(MPI-BGC, Jena, Germany)
Dr. Christoph Gerbig
(MPI-BGC, Jena, Germany)
Dr. Julia Marshall
(MPI-BGC, Jena, Germany)
Dr. P Swathi
Dr. C S Jha
(NRSC ISRO, Hyderabad)
Prof. Anand Karipot
Dr. Manish Naja