Heat waves are increasing in frequency and exhibit high spatial variability in their distribution over India. There are limited studies focused on thermal indices over India due to the nonavailability of high-resolution (HR) climate data. Here we develop dynamically downscaled HR (4 × 4 km) daily climate information for the months of April to June during 2001–2016 using a regional climate model called Weather Research and Forecasting (WRF) Model, which are validated with station observations. The thermal comfort, heat stress, and its spatiotemporal variability and change over India are quantified in terms of indices like excessive heat factor (EHF), the heat index (HI), humidex, apparent temperature (AT), and wet bulb globe temperature (WBGT). The results show that there is an increasing trend in annual heat waves coverage (22,240 km2/year), annual frequency (0.07 days/year), and average intensity (0.04 °C/year) during 2001–2016. The spatial distribution of indices exhibits high spatial and temporal variability. The days with the severe threshold of indices are significantly increasing over north India at the rate of EHF (15.9%), HI (14.9%), humidex (15.9%), AT (13.4%), and WBGT (13.8%). The heat waves’ most vulnerable hotspots are on the parts of Rajasthan, Uttar Pradesh, Madhya Pradesh, and the coastal regions of Andhra Pradesh and Odisha. During heat waves, prolonged exposure under the sun will lead to adverse health impacts, and it is mostly observed over severe heat wave zone. These findings stress the need for developing suitable mitigation strategies for a sustainable ecosystem with minimum impact.
by Adithya Samanth, V Rakesh, Smrati Purwar, S M Gavaskar, B Jagadeesha Pai & G N Mohapatra
Karnataka, a state in south India with nearly 80% of the cultivated land under rainfed farming, is very much dependent on rainfall for agricultural productivity. The spatio-temporal variability in observed rainfall over Karnataka is investigated using various data analytical techniques such as parametric and non-parametric methods, rotated empirical orthogonal function (REOF), clustering and spectral analysis. The observed data used for studying rainfall variability is the daily taluk-wise telemetric rain gauge data for a period of 1960–2016. A similar pattern in trend is observed in annual and south-west monsoon (SWM) rainfall over Karnataka such that taluks in the western and northern parts showed a decreasing trend, whereas the south interior part showed an increasing trend. A significant increasing trend in rainfall was found during pre-monsoon seasons whereas the northeast monsoon (NEM) rainfall showed a decreasing trend. The REOF analysis also indicated an upward (downward) trend in SWM and annual over the northern (southern) Karnataka and a weakening trend in the NEM rainfall. Using the hierarchical clustering method, six homogeneous rainfall clusters were identified over Karnataka based on distribution and variability of rainfall. The spectral analysis over different clusters showed significant oscillations in the annual and SWM rainfall in the 1970s and recent decades except the Western Ghat region where oscillations were much weaker during recent decades. The pre-monsoon and NEM rainfall also showed strong variability with a periodicity of 2–4 years in recent decades. The findings of this study can have implications while designing water resource management strategies across various sectors in Karnataka.
The COVID-19 pandemic has created a major threat to human beings and huge losses over the globe. In order to control the pandemic spread, almost all parts of the world imposed lockdown. The imposed lockdown drastically impacted on reduction in the atmospheric pollutions and also resulted in net decrease in aerosol optical depth (AOD) in the atmosphere. In this study, the reduction in the AOD during the COVID-19 lockdown over the Indian subcontinent is being assessed using the moderate resolution imaging spectroradiometer (MODIS) satellite data available in Giovanni version 4.34 developed by NASA. The long-term mean analysis is computed considering 20 years (i.e., 2000–2019) data on Terra platform with a temporal resolution of daily and monthly and spatial resolution of 1 degree. The dataset of AOD with a temporal resolution of monthly was used for investigation of AOD anomaly for March, April and May 2020, and the seasonal variation (March to May 2020) is also assessed. Similarly, the daily scale dataset was used to investigate the percentage change in AOD during pre-lockdown and lockdown period with respect to long-term mean. The key findings in the present study show that reduction in AOD level over Indian subcontinent is approximately 14.75% during the lockdown period with spatial variation in the magnitude from region to region. The level of AOD is greatly reduced in the northern part of India (~ 22.53%), whereas changes in the southern part of India are much less (~ -0.31%); this may be due to ongoing anthropogenic activities during the lockdown period in this region. Furthermore, a positive AOD anomaly was observed in the eastern and central regions of India (i.e., over the states of Odisha, Chhattisgarh, Telangana, Jharkhand, West Bengal, Part of Maharashtra and Karnataka). However, negative AOD anomaly was observed in the north and northwest regions of India, whereas not much change in the AOD anomaly in other parts of the country. The overall assessment of the AOD level shows a net decrease over the Indian subcontinent during the lockdown period, i.e., March to May 2020. This kind of assessment study will surely help the government for the sustainable policy decisions for atmospheric pollution control by implementing proper lockdown procedures over various parts of the country.
The spatio-temporal characteristics of the observed warming of the Indian Ocean (IO) surface temperature and its statistical link to the IO Warm Pool (IOWP) are still unclear. This study discriminates the basin-wide monotonic warming mode of the IO surface from the internally and remotely forced variability. The trend pattern reported in this study reveals a radically different warming signal. It shows that the monotonic warming in the observed sea surface temperature (SST) has the spatial pattern of summer-mean SST and the Warm Pool as its most conspicuous feature. The highest warming (0.17∘C per decade) is in IOWP and not in the other IO regions identified in the previous studies. By 2070, IOWP will cover about 80% of tropical IO at the current latitudinal expansion rates. The mean states of equatorial SST, wind, and surface pressure are shifting towards an endless summer. Irrespective of the season, SST near Indonesia would remain above 31∘C by 2080 and beyond (i.e., more than 2.2∘C rise since 1950). This would substantially increase local rainfall intensity and frequency. Thus, IO is poised to play a more significant role in climate change. We also argue that the basin-wide warming is due to anthropogenic forces’ rectification by coupled processes in ocean-atmosphere mixed layers.
by Smrutishree Lenka, Rani Devi, Chennemkeril Mathew Joseph & Krushna Chandra Gouda
There are several important large-scale oceanic and atmospheric processes like El Niño-Southern Oscillation (ENSO), Madden–Julian Oscillation (MJO), and Indian Ocean Dipole (IOD) which have significant impact on global weather and climate system. This article reviews the mechanism and dynamics of ENSO, MJO, and IOD processes and their impact on global and regional weather and climate particularly the Indian summer monsoon rainfall. Generally, these processes are coupled ocean–atmosphere phenomenon and associated dynamics control the global weather and climate system. Sea surface temperature (SST) anomaly in the central and equatorial pacific region respectively results the warm (El Niño) and cold (La Niña) events and it has strong impacts globally. Similarly, MJO is a dominant phase of intra-seasonal variability in the tropical region and also has significant impacts on the global system like strong wind, convective waves, extreme rainfall, cyclones, and ENSO. The IOD is often termed as the counterpart of pacific El Niño and La Niña in Indian Ocean which mainly measures the SST gradient between Arabian Sea and the eastern Indian Ocean. IOD also linked to ENSO and the shifting warm/cool pool results in the summer monsoon rainfall variability in the India Ocean as well as continental Indian region. All these phenomena have direct impact on the Indian monsoon circulation system, so in this work, these impacts are quantified using the India Meteorological Department (IMD) observed rainfall data over Indian subcontinent as a case study. This work also provides the review of the studies using observation and modelling to understand the dynamics of all the three processes. This review and analysis work will help in understanding the process feedback on the regional rainfall distribution and there is a need of near-future modelling research on these processes and their impacts on weather and climate system and associated sectors.
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