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(Formerly CSIR Centre for Mathematical Modelling and Computer Simulation)

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Modelling for science, for a better future - some recent outcomes

by Bishnupada Sethi, Krushna Chandra Gouda, Sarat Chandra Sahu, Manas Kumar Mallick, Amrutanshu Panigrahi & Satish Kumar Samal 

In the recent years, extreme temperature i.e. cold in winter and warm in summer has been observed in the coastal Odisha state in the east part of India. Hydro-meteorological disasters such as floods, droughts, heat waves and cold spells being experienced nowadays are unprecedented in living memory. In this work the climate variability over the coastal Odisha region is quantified considering the temperature parameter. A climate departure index (CDI) of three versions (simple, absolute and least-square) are calculated using monthly, annually and seasonally maximum, minimum as well as mean temperature form seven stations in the costal Odisha for the period 1969 to 2018 for assessing the degree of normal/unusual/extreme of a particular year in compared to the long-term average for the specific coast under consideration. Temperature time series data for the period 1969 to 2018 are homogenized by using the relative comparison between station observations and composite reference series by calculating CDI. Results indicates that CDI values of least-square on all cases are comparatively greater than simple and absolute CDIs. Least-square method is the appropriate method for determining the category of a year or a month or a season in terms of the temperature variation. Analysis based on mean temperature infers that only twenty extreme seasons are observed in monsoon season while three normal are observed during the month of October i.e., in post-monsoon season over the coastal Odisha. The relationship of climatic event including El Nino or La Nina feature of a particular year with respect to CDI is also investigated. The climatic variability is very high on some of the years based on analysis of index values of annual average of the temperatures (maximum, minimum and mean) over a region.

Source: https://doi.org/10.1007/s00024-024-03465-5

 

by Iranna Gogeri & Krushna Chandra Gouda

The road transport sector in India is a significant contributor to the country’s GHG emissions inventories, so it is essential to develop a comprehensive database of emission inventories to inform the design and implementation of suitable technologies and policies for appropriate mitigation measures. The development of an inventory using all available data can be useful in developing policies and technologies for reduced GHG emissions from the road sector. The paper seeks to develop an inventory of GHG emissions from the India’s road transport sector and analyse the fuel consumptions usage during the recent decade period, i.e. 2011 to 2021. In this study, IPCC guideline Tier-I and Tier-II were adopted for the emission estimation. Country-specific emission factor (EFCS) was developed based on carbon content and corresponding net calorific value (NCV) of country-specific fuel usage. The CO2eq emissions are also measured and compared with the corresponding global warming potentials (GWP). It appears that emissions of CO2 equivalent emission (CO2eq) from multiple greenhouse gases have significantly increased from 2011 to 2021. Estimation based on carbon country-specific emission factor shows nearly 165.09 Mt in 2011, increasing to about 241.20 Mt in 2021. Hence, it has been observed nearly 4.39% of reduction in CO2eq emissions using EFCS in the year 2021. Similar decreasing trends have also been observed for other gases like CH4 and N2O. The development of a comprehensive national GHG inventory for the road transport sector in India is a critical step towards achieving the country’s climate change goals and ensuring a sustainable future for generations to come.

Source: https://doi.org/10.1007/s12647-024-00738-1

 

by Rameshan Kallummal

Current perspectives on lower frequency variations and secular warming have predominantly been shaped by traditional anomalies that assume an annual cycle (AC) with a time-invariant amplitude. However, this anomaly framework falls short in capturing the complexity of multiple periodic modes with intricate waveforms and time dependent amplitude—the traits, in general, shared by externally forced responses of complex dynamical systems. By allowing interannual amplitude modulation of ACs, we show that the monotonic amplitude increases of the first AC of Sea Surface Temperature (SST) are manifested as the basin-wide secular warming of the ocean surface. Notably, the first SST-AC exhibits significant interannual variances and the largest linear warming rates in the Pacific Warm Pool. While the linear warming pattern mirrors that of a long-term mean SST, it depicts an entirely different warming pattern on the surface of the tropical Pacific Ocean compared to those reported so far. Moreover, all interannual warm (El Niño) and cold (La Niña) events in tropical Pacific regions are abnormal interannual modulations in the third and fourth ACs of SST, respectively. Specifically, a strong El Niño event occurs when a positive amplitude modulation leads to the overlap of two consecutive positive phases of the third AC of SST. Conversely, the absence of such overlaps during negative amplitude modulations significantly contributes to the positive skewness of SST anomalies. No systematic decadal changes in the zonal propagation characteristics of SST in the eastern Pacific (EP) and central Pacific (CP) regions were detected. These findings underscore that the secular warming and low-frequency events in EP and CP are intrinsic to three distinct ACs.

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