Water vapour characteristics and radiative effects at high-altitude Himalayan sites
by U.C Dumka, D.G Kaskaoutis, Pradeep Khatri, Shantikumar S. Ningombam, Rahul Sheoran, Sridevi Jade, T. S. Shrungeshwara, Maheswar Rupakheti
We analyze long-term aerosol and precipitable water vapour (PWV) properties at two high-altitude sites (Nainital and Hanle) over the central Himalayan and western Trans-Himalayan region from 2008 to 2018. First-time assessment of the seasonality and variation in combined aerosol and water vapour radiative effects are also attempted, aiming to investigate the atmospheric effect on solar radiation over the Himalayan range that is especially important for the regional climate. A synergy of ground-based measurements from sun photometers, GPS (Global Positioning Systems) observations, radiosondes, along with satellite and reanalysis data was used to examine inter-annual and seasonal variability of PWV and specific humidity over both sites. The PWV is highest in monsoon and much lower during the dry winter season with slightly higher values at Nainital compared to Hanle. This is due to the lower altitude (∼2 km amsl) of Nainital, which is also directly affected by the Indian summer monsoon, compared to the Trans-Himalayan region. The vertical profiles of PWV from satellite and reanalysis data reveal a great consistency on a seasonal basis. The PWV is considered as one of the main greenhouse gases that exhibits a positive radiative effect at the Top of the Atmosphere (TOA) in the order of about 10 W m−2 at Nainital and 7.4 W m−2 at Hanle. The atmospheric radiative effect due to water vapour is about 3–4 times higher compared to aerosols, resulting in atmospheric heating rates of 0.94 and 0.96 K Day−1 at Nainital and Hanle, respectively. The results highlight the importance of water vapour and aerosol radiative effects in the climate sensitive Himalayan range.
Temperature-duration-frequency analysis over Delhi and Bengaluru city in India
by Rani Devi, K. C. Gouda & S. Lenka
The extreme temperature events are a concern in recent years due to climate variability particularly in India as there is an increase in the temperature intensity, frequency, and duration. This study represents stationary temperature-duration-frequency (TDF) analysis over two mega cities in India Delhi (north) and Bengaluru (south) using the daily maximum temperatures at meteorological stations for the period 1969–2016 observed by India Meteorological Department (IMD).The interannual variability of maximum temperature and the maximum daily recorded value indicates the increasing trend in both the cities. The study investigates the extreme analysis of the maximum temperature using two distributions, i.e., Gumbel’s Extreme Value Type 1 (GEVT) and Log Pearson Type III (LPT), for return periods 2, 5, 10, 25, 50, and 100 years at both the locations and the positive temporal trend is observed. The TDF curves were build using annual maximum temperature values for total 8 durations (different days) of 48 years analyzed and results show the increasing trend of maximum temperature at lower duration and high return period values. The TDF is also used for prediction of the maximum temperature for the 2 hottest years in India, i.e., 2012 and 2015, and it is comparable with the observed maximum temperature. Similarly, the predictions for 11 years, i.e. 2006 to 2016, over both the cities are simulated using both the GEVT-I and LPT-III and the models have better potential skill in predicting the extreme maximum temperature. These results can be useful for the sectors like health, energy, agriculture, urban management, and ecology management and can help the policy decision makers and disaster managers in the mitigation and adoption steps to face the extreme temperature disaster at city scale.
Sinking Slab Stress and Seismo-Tectonics of the Indo-Burmese Arc: A Reappraisal
by Anil Earnest, T. C. Sunilkumar and K. Silpa
Differing views on the north-eastern Indian plate subduction and its seismic potential place the subduction process as relict, ceased, creeping, or fully locked, challenging the seismic hazard uncertainty estimates of Indo-Burmese Arc (IBA) at either extreme. To clear some of these contentions, we re-examine the state of the IBA stress field using available earthquake faulting mechanism solutions. Our stress inversion results underline the tectonic complexity and emphasize that the stress of this subduction is primarily driven by the northward oblique motion of the Indian Plate. Models highlight the presence of the least compressive stresses as the margin-normal component, all along the shallow sinking slab. Furthermore, solutions link the shallow strike-slip earthquakes with the lateral slab shear due to oblique slip-partitioning. Spatial variations in the Indian slab stress field indicate higher margin parallel compression at the Naga versus the Chin and Rakhine-Arakan segments, implying resistance to the northward motion of India, illuminating the buttressing effects on the IBA. Models also depict active shortening across the Kabaw Fault. Inversion solutions bring out a higher compressional environment toward the south, along the Sagaing Fault. Finally, based on the overall stress field variations, we speculate that the Rakhine-Bangladesh Megathrust could be a low-stress forearc, as across the arc interacting stress fields are weak, capable of generating megathrust ruptures. Being weak, the stress replenishment times could be longer than the mechanically strong subduction zones. Due to the spatial stress field variations and buttressing effects, the interface may exhibit a variable rupture mode.
Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques
by Gyanendranath Mohapatra, V. Rakesh, Smrati Purwar & A. P. Dimri
Understanding and quantifying long-term rainfall variability at regional scale is important for a country like India where economic growth is very much dependent on agricultural production which in turn is closely linked to rainfall distribution. Using machine learning techniques viz., cluster analysis (CA) and principal component analysis (PCA), the spatial and temporal rainfall patterns over the meteorological subdivisions in India are examined. Monthly rainfall data of 117 years (1901–2017) from India Meteorological Department over 36 meteorological subdivisions in India is used in this study. Using hierarchical clustering method, six homogeneous rainfall clusters were identified in India. Among the rainfall clusters, Group 1 had 30% dissimilarity with Groups 2, 3, and 4 while Group 5 and Group 6 are highly dissimilar (more than 90% dissimilarity) with the rest of the groups. Rainfall seasons in each group were further classified into dry, wet, and transition periods. The duration of dry period is smaller in group which consists of subdivisions from southern part of the country. The transition period between dry and wet period was found to be smaller for subdivisions in the coastal region. Both CA and PCA showed high rainfall variability in Groups 5 and 6, which comprise subdivisions from north east, Kerala, Konkan, and costal Karnataka and low rainfall variability in Groups 1 and 2 which comprise subdivisions from east, north, and central part of the country. Strong negative trend in annual and Indian summer monsoon rainfall is seen in northeast India and Kerala while positive trend is observed over costal Karnataka and Konkan region. The negative trend in post monsoon rainfall particularly over the peninsular and northeast India indicates weakening of northeast monsoon rainfall in the country.
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