Recent Changes in Temperature Extremes and Heat Stress over Eastern India

doi:10.21203/rs.3.rs-2127918/v1

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Recent Changes in Temperature Extremes and Heat Stress over Eastern India

https://doi.org/10.21203/rs.3.rs-2127918/v1

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published 10 May, 2024

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The rise of extreme heat events and increase in heat stress under changing climate has been a major concern in recent periods. The present study has been undertaken to analyze temperature extremes and heat stress patterns over Eastern India from the period 1981–2020 for April, May, and June (AMJ). Six indices namely Monthly maximum value of daily maximum temperature (TXx), Monthly maximum value of daily minimum temperature (TNx), Monthly minimum value of daily maximum temperature (TXn), Monthly minimum value of daily minimum temperature (TNn), Percentage of days when maximum temperature > 90th percentile (TX90p) and minimum temperature > 90th percentile (TN90p) has been utilized to assess changes in temperatures extremes. Furthermore, three heat stress indices (HSIs) namely, Humidex, Simplified Wet-Bulb Globe Temperature, and Apparent Temperature have been used to estimate and categorize heat stress levels. Mixed results with both increasing and decreasing trends over different parts during three months have been observed in the case of TXx, TXn, TNx, and TNn. During AMJ, there has been amplification in the frequency of TX90p over coastal regions of West Bengal and Odisha season in the latest decades. Increasing TN90p frequency has been found over Sikkim, Bihar, and North Bengal while it has decreased over Chhattisgarh and Odisha. The frequency of strong heat stress has intensified over Gangetic West Bengal and Bihar during recent decades. Spatial coverage of strong stress has also gradually extended and the area under no stress has decreased during AMJ season.

Temperature Extremes

Heat Stress

Climate Change

Eastern India

Around the globe, rising global temperatures have been the latest concern. During the recent years, annual surface air temperatures and minimum night-time temperatures have shown an increasing trend due to anthropogenic climate change, suggesting changes in mean value or variance in temperatures. These changes have led to the occurrence of extreme heat events worldwide. India has been suffering from extreme heat events during the summer season since the mid-20th century. Due to climate change the intensity and frequency of these events have been augmented to such a degree that India acknowledged them as natural disasters since 2016 after about 2500 heat related deaths were reported in 2015 (Guleria and Gupta, 2018; Kumar et al. 2022). India being a country with about 65% rural population, consequences of extreme heat are scarcity of drinking water, crop failures, power outages, wildfires, human illness as well as infrastructure disruption and damage (Garcia-Herrera, 2010). Some of the earliest studies over India have reported maximum temperatures exceeding the daily normal by 1°-1.2°C (Ramamurthy 1972) occurring mostly during March-June (Raghavan 1966, Subbaramayya and Rao 1976). Most of these studies were case-by-case over various parts of India, constrained by the non-availability of quality controlled daily temperature data and hence, inconsistent in their results. Recent studies (Pai et. al 2013, Ratnam et. al 2016) have indicated that the most extreme heat prone areas were the North, Northwest, Central and Northeast peninsula regions. The statistical analysis of high resolution gridded daily temperature data set using percentiles of maximum daily temperature as thresholds to classify extreme heat events in many studies (Anderson and Bell 2009, 2011, Rohini et. al 2016, Sharma and Majumdar 2017) have shown an increase in the frequency, intensity, and duration of the extreme heat events. However, these studies have mainly considered only one or two indices and also haven’t included extremes in minimum temperature. Present study has included the relevant maximum and minimum temperature related indices for more detailed assessment.

Several reports, (WHO 2006, 2008; NOAA Watch 2014) have stated that extreme heat events lead to heat stress and can negatively affect human health capacity and work productivity. Heat stress depends on environmental and physiological factors. Many heat stress indicators (HSIs) have been developed with varying complexity and different input parameters in combination with air temperature such as humidity, radiation, and wind speed (Schwingshackl et al., 2021). Some of the existing HSIs includes Daily Maximum Near-surface Air Temperature (Fischer & Schar, 2010; Sillmann et al., 2013; Shiogama et al., 2019), the NOAA Heat Index used by the US National Oceanic and Atmospheric Administration for issuing heat warnings (Burkart et al., 2011; Lin et al., 2012; Kent et al., 2014), the Universal Thermal Climate Index developed as a multimode model of human heat transfer (Di Napoli et al., 2018), Humidex used by Canadian meteorological services (Masterson & Richardson, 1979), Apparent Temperature (Anderson et al., 2013), Wet-Bulb Globe Temperature (Blazejczyk et al., 2012; Lemke & Kjellstrom, 2012), Simplified Wet-Bulb globe Temperature (Willett & Sherwood, 2010; Vaneckova et al., 2011; Lin et al., 2012; Fischer & Knutti, 2013; Zhao et al., 2015). In India, very few studies have focused on the analysis of heat stress. Bhadram et al. (2005) have used three heat indices, namely, Heat Index, Thom’s Discomfort Index, and Webbs Comfort Index to understand the severity of extreme heat effects over Andhra Pradesh state only. An analysis of heat stress and heatwaves in the four major metropolitan cities of India, i.e., Kolkata, Delhi, Chennai, and Mumbai have been done by Kumar et al., (2022). Grid wise detailed assessment of heat stress using state-of-the-art gridded data is still lacking over the Eastern Part of India. In present study, only temperature and humidity have been taken as the key factors for assessment and three HSIs have been chosen accordingly. Because higher temperature and higher humidity put more stress on human organism (Davis et al. 2016; Hanna and Tait 2015; McGregor and Vanos 2017). The impacts of hot extremes on physiology and health can be exacerbated by elevated ambient humidity (Chen et al., 2018; Mora et al., 2017, Brouillet and Joussaume,2019). Due to global warming, dangerous heat conditions exceeding human physiological limitations have been predicted by many studies (Diffenbaugh et al., 2007; Pal and Eltahir ,2016; Im et al. ,2017). This is especially of concern in most rural states of eastern India such as Bihar, Jharkhand, Chhattisgarh and Odisha where problems such as scarcity of drinking water and high consumption of electricity are most likely to arise. This study has been undertaken keeping these concerns in mind.

2.1 Study Area

This study has been focused over the Eastern India region distributed from 18^oN to 28^oN (by latitude) and 80^oE to 90^oE (by longitude) as shown in Fig. 1; the area mostly consists of the states of West Bengal (WB), Bihar, Jharkhand, Odisha and Chhattisgarh (CGR) along with parts of eastern Uttar Pradesh (UP) and Madhya Pradesh (MP). The geography of the study area includes the Indo-Gangetic Plain in West Bengal and Bihar, the Chhota Nagpur Plateau in Jharkhand, the Deccan Plateau and the Eastern Ghats in Odisha, and the Bay of Bengal in the south-east. April, May and June are three hottest months of the year over this area.

2.2 Data

For estimation of heat waves, gridded maximum temperature data (T_max) from India Meteorological Department (IMD) has been utilized in the present study. On the other hand, temperature and relative humidity data for calculating heat stress indices have been taken from National Centre for Medium Range Weather Forecasting (NCMRWF) reanalysis data.

2.2.1 IMD gridded temperature data

IMD gridded temperature data is freely available for the period 1951–2020 on IMD data portal: https://www.imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html. This data was developed using maximum and minimum temperature from 395 quality-controlled stations (Srivastava et al., 2009). These station data were interpolated 1° latitude × 1° longitude grids through a modified version of the Shepard’s angular distance weighting algorithm. The errors due to interpolation in preparing the gridded data over the plains were found to be about 0.5°C at the maximum. It was relatively higher in the hilly regions of Jammu & Kashmir and Uttarakhand, mostly due to sparse sources of observational weather data availability in those zones.

3.2.2 NCMRWF Reanalysis Data

A reanalysis is a high-quality climate-data product produced by assimilating long time series of observations with a consistent and state-of-the-art numerical weather prediction (NWP) model and data assimilation (DA) system to represent the best estimate of the state of the atmosphere. The Indian Monsoon Data Assimilation and Analysis reanalysis (IMDAA) is a regional atmospheric reanalysis over the Indian subcontinent. The IMDAA is the result of collaboration among the Met Office (MO), U.K, National Centre for Medium Range Weather Forecasting (NCMRWF), India and IMD with funding from the National Monsoon Mission (NMM) project of the Ministry of Earth Sciences, Government of India. The reanalysis runs are carried out using high resolution (12km) mesoscale version of the Met Office Unified Model (UM) since 1979, to span the era of modern meteorological satellites. This dataset is freely available on https://rds.ncmrwf.gov.in/.

2.3 Methodology

2.3.1 Extreme Temperature Indices

Six climate extreme indices related to temperature as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) has been used to assess temperature extremes in present study. The details of the indices have been provided below:

a) Monthly maximum value of daily maximum temperature (TXx)

Let TXx be the daily maximum temperatures in month k, period j. The maximum daily maximum temperature each month is then:

TXx_kj=max (TXx_kj)

b) Monthly maximum value of daily minimum temperature (TNx)

Let TNx be the daily minimum temperatures in month k, period j. The maximum daily minimum temperature each month is then:

TNx_kj=max (TNx_kj)

c) Monthly minimum value of daily maximum temperature (TXn)

Let TXn be the daily maximum temperatures in month k, period j. The minimum daily maximum temperature each month is then:

TXn_kj=min (TXn_kj)

d) Monthly minimum value of daily minimum temperature (TNn)

Let TNn be the daily minimum temperatures in month k, period j. The minimum daily minimum temperature each month is then:

TNn_kj=min (TNn_kj)

e) Percentage of days when TX > 90th percentile (TX90p)

Let TXij be the daily maximum temperature on day i in period j and let TXin90 be the calendar day 90th percentile centred on a 5-day window for the base period 1961–1990. The percentage of time for the base period is determined where:

TX_ij > TX_in90

f) Percentage of days when TN > 90th percentile (TN90p)

Let TNij be the daily minimum temperature on day i in period j and let TNin90 be the calendar day 90th percentile centred on a 5-day window for the base period 1961–1990. The percentage of time for the base period is determined where:

TN_ij > TN_in90

2.3.2 Heat Stress Indicators

Three different heat stress indicators (HSI) namely Humidex (HI), Simplified Wet-Bulb Globe Temperature (SWBGT) and Apparent Temperature (AT) have been utilized in present study. These HSIs are based on air temperature(T) and vapor pressure (VP) as input variables. The following formulae have been used to calculate VP, following Raudkivi (1979):

Saturation vapour pressure = 6.11 exp\(\left(\frac{17.27T}{T+237.3}\right)\)

Actual vapour pressure (VP) = \(\left(\frac{Relative Humidity}{100}\right)\) x Saturation vapour pressure.

To assess the severity of heat stress four classes i.e., ‘slight’, ‘moderate’, ‘strong’ and ‘extreme’ as per Zhao et al., (2015) have been used. Detailed information about HSIs have been provided in Table 1.

Table 1

Different heat stress indicators used in present study (as per Zhao et al., 2015)
Heat Stress Classes
Indicator	Formula	Slight	Moderate	Strong	Extreme
Humidex (HI)	HI = T + 5/9°C ⋅ (VP − 10)	35–40	40–45	45–54	> 54
Simplified Wet-Bulb Globe Temperature (SWBGT)	SWBGT = 0.567 T + 0.393 VP + 3.94	28–32	32–35	35–38	> 38
Apparent Temperature (AT)	AT = 0.92 T + 0.22 VP − 1.3	28–32	32–35	35–40	> 40
T and VP represents the temperature of air in° C, and the actual water vapour pressure in milibar respectively.

3.1 Changes in TXx, TXn, TNx and TNn during 1981–2020

Changes in monthly maximum of maximum temperature (TXx), monthly minimum of maximum temperature (TXn), monthly maximum of minimum temperature (TNx) and monthly minimum of minimum temperature (TNn) has been depicted in terms of linear trend (°C per 40 years) in Fig. 2–5 respectively. Trends significant at 0.1 level have been shaded in the figures.

From Fig. 2, the trend values of TXx lie in the range of -1.5 to 2.5°C per 40 years during April over the study region. Significant positive trends have been found over eastern UP, eastern MP and coastal Odisha. Non-significant negative trends have been observed over Eastern Bihar, Northern WB and southern CGR and Odisha. During May, the trend values have been in between − 1.5 to 2.5°C per 40 years. The pattern has been quite similar to that of April. However, the trends over coastal Odisha have become non-significant and the trends have been positive over southern CGR and Odisha unlike April. Trend values in the range − 1.5 to 2.5°C per 40 years have been found during June. Decreasing trends have been noticed over majority of the study area whereas positive trends have been observed over coastal WB and east coast of Odisha (non-significant), Sikkim and North-East WB (significant).

From Fig. 3, the trend values of TXn have been in between − 3 to 2.5°C per 40 years during April. Non-significant positive trends have been noticed over maximum grids. Trends have been considerably higher over Jharkhand and Northern Odisha. Significant decreasing trend has been over south-eastern WB which has been expanded to larger areas in May. Trend values in the range 3 to 2.5°C per 40 years have been found during May. Except CGR, cooling trends have been found over the study Area. In contrast, positive trends have been observed majority portions except a small region over Odisha. During June, the trend values lie in the range of -3 to 2.5°C per 40 years and non-significant. Trends have been considerably higher over Jharkhand and Bihar.

From Fig. 4, Trend values of TNx in the range − 2 to 2°C per 40 years have been found during April, May and June, respectively. Significant warming trends have been observed over 30% of region including WB, Bihar and Sikkim whereas non-significant cooling trends have been noticed over Odisha and CGR. During May, the non-significant Cooling trends have been amplified over Odisha and CGR. There has been significant increase in TNx over 16% area Sikkim, Northern WB, Eastern and Southern Bihar and Eastern MP. Significant decreasing trends have been found over Odisha and CGR whereas significant increasing trends have been observed over Sikkim, Northern WB and North-Eastern Bihar during June. Clearly, there has been a see-saw pattern in trends between North-Eastern and South-Western parts of the study area during all three months.

From Fig. 5, the trend values of TNn lie in the range of -1 to 3.5°C, -1 to 3.5°C and − 1 to 3.5°C per 40 years during April, May and June, respectively. Significant positive trends have been found over 57% region including Sikkim, Bihar, Jharkhand, Eastern UP, Eastern MP and Northern WB during April. Trend values have been significant over 23% grids during May that has extended to 39% in June. Significant warming trends have been found over WB, Bihar and Sikkim during both May and June and over Jharkhand and Eastern Odisha during June. Non-significant decreasing trends have been noticed over Odisha for all three months.

3.2 Changes in frequency of TX90p and TN90p during 1981–2020

Frequencies of daily maximum and minimum temperature exceeding 90th percentile threshold (TX90p and TN90p) during AMJ season for four time periods (1981–1990,1991–2000,2001–2010,2011–2020) have been shown in Figs. 6 and 7, respectively.

During 1981–1990, maximum frequency (12–15 days/ season) of TX90p has been observed over a patch region consisting parts of Bihar, Jharkhand and WB (Fig. 6). However, frequency over this region have gradually diminished over next two decades and slightly increased again during 2011–2020. In the following decade of 1991–2000, frequencies have increased over Eastern UP and MP whereas reduction has occurred over rest of the study areas. During 2001–2010, increases in frequency have been observed over CGR, Jharkhand, Southern WB, Eastern UP and MP. Large amplification of frequency up to 12–15 days/ season have been noticed over Odisha. During the last decade, coastal parts of WB and Odisha have been found to be most vulnerable zone to extreme heat with TX90p frequencies 15–18 days (Fig. 6).

From Fig. 7, higher TN90p frequency (9–12 days/ season) has been over western parts of Odisha during 1981–1990. However, the frequency has reduced over this region during the next three decades. During 1991–2000, frequencies have increased over Eastern UP, Bihar, Northern Jharkhand and majority parts of WB. Except a patch region over Odisha, increase of frequency has been observed throughout the study area during 2001–2010. During the last decade, reduction in frequency has been noticed in CGR and Odisha whereas large amplification (18–24 days/season) has been found over Sikkim, Bihar and Northern WB.

3.3 Monthly climatology of HSI during 1981–2020

Climatology of different HSIs for April, May and June during 1981–2020 has been depicted in Fig. 8 (a-c). As per humidex, moderate heat stress (< 45 & >40) has been mainly observed over coastal regions of WB and Odisha in April. Interior parts of Odisha, WB and CGR has faced slight heat stress (< 40 & >35) while no stress (< 35) has been observed over rest of the areas in April. Regions with moderate stress has extended in May covering whole WB, Bihar and Odisha. Slight stress has prevailed over rest of areas excluding Sikkim. Majority of areas has fallen under moderate stress during June. A patch region with strong stress has been observed over Bihar.

3.4 Changes in percentage frequencies of days for various HSI during 1981–2020

3.4.1 Changes in Humidex

Percentage of days in AMJ season (i.e., percentage frequency) falling under a certain category during four decades has been shown in Fig. 9 (a-d) [for Humidex], Fig. 10 (a-d) [for SWBGT] and Fig. 11(a-d) [ for AT]

During 1981–1990, higher percentage frequencies (10–30%) for strong stress have been observed over Eastern UP, Bihar, Gangetic WB and adjoining parts of Odisha (Fig. 9a). Similar Scenario has been noticed in next decade (1991–2000) with an increasing frequency over Gangetic WB. There has been further increment of strong stress frequencies over parts of Bihar, WB and Odisha during 2001–2010. For the last decade, percentage frequencies have increased throughout Bihar, Eastern UP and adjoining areas in WB-Jharkhand border.

Higher number of days (60–80%) with moderate stress have been found over coastal regions of WB and Odisha (Fig. 9b). Frequencies of moderate stress have increased slightly over parts of Bihar, CGR and interior parts of Odisha during next three decades. However, there has been a reduction of frequency over Eastern UP during 1991–2000 and 2011–2020. For later decade, it can be attributed to the increasing strong stress over that region.

Higher percentage frequencies (10–30%) for slight stress have been observed over Western Odisha and Southern CGR during 1981–1990 (Fig. 9c). A reduction in frequency over Eastern UP and Bihar has been noticed during next two decades which has again increased in 2011–2020. Higher number of days (60–80%) with no stress have been found over Eastern MP, Northern CGR and Western Jharkhand during 1981–1990 (Fig. 9d). Frequencies have gradually decreased over Eastern UP and Bihar during next three decades.

3.4.2 Changes in Simplified Wet Bulb Globe Temperature

During the first decade (1981–1990), higher percentage frequencies (10–30%) of strong stress have been observed over eastern UP, Bihar, WB-Jharkhand border, Gangetic WB and adjoining coastal regions of Odisha [Fig. 10a]. For the next ten years (1991–2000), the frequency over Gangetic WB and Odisha has increased, while that over Bihar has decreased slightly. From 2001–2010, there is a significant increase in frequency (30–40%) over WB, coastal Odisha, and Bihar (20–30%). This increment further grows in area during 2011–2020.

Higher percentage frequencies (60–80%) for moderate heat stress have been found consistently over coastal WB and Odisha for the first two decades (1981–2000) [Fig. 10b]. This area has decreased in over the last two decades (2001–2020) owing to the increase in strong stress. About 40–60% frequency has been observed over parts of Bihar, Gangetic WB, WB-Jharkhand border and interior parts of Odisha for all four decades with slight trivial changes in the latest years.

The highest frequencies (50–80%) has been restricted to southern Chhattisgarh and scattered areas over Odisha with little to no changes throughout the study period [Fig. 10c]. For the rest of the region, about 20–30% of the days have been under slight stress from 1981–2010 and increased to 30–50% from 2011–2020. The regions free from heat stress have mostly been eastern UP, eastern MP, majority of CGR and Jharkhand with 70–100% of the days being under no stress from 1981–2000 [Fig. 10d]. The frequency has fallen to 60–70% in central CGR during the last two decades (2001–2020).

3.4.2 Changes in Apparent Temperature

During 1981–1990, the highest percentage frequency (20–30%) has been observed over parts of UP, Gangetic WB and Odisha [Fig. 11a]. Small areas with higher percentage frequency (40–50%) of strong stress has appeared over WB and Odisha in the next decade. These areas have grown during 2001–2010 and has been prominently observed in scattered areas over Gangetic WB, Odisha and extreme south of CGR border while their surrounding areas as well as parts of UP and Bihar have a 10% increment (30–40%) in strong stress. However, there has been a decrease in frequency over WB, and Odisha in the last ten years.

In case of moderate heat stress, for the first decade, the highest percentage frequency (50–70%) has been observed following the coastal line of WB and Odisha [Fig. 11b]. This area has decreased in size in the next thirty years. The areas immediately adjacent to this area has been observed to have 40–50% frequency while towards the interior 20–40% frequency has been observed. The overall area covered by these percentages of frequency has remained almost the same throughout the study period.

Areas with the highest percentage (40–70%) frequency of slight stress has been observed to be over northern CGR and parts of Jharkhand and southern border of CGR-Odisha [Fig. 11c]. Over the span of forty years, the percentage has increased gradually in small increments in these areas. The remaining area has been under 20–40% stress constantly, except for Gangetic WB and parts of central Odisha where 0–10% stress has prevailed throughout the study period. The regions free from heat stress has been observed to be scattered over northern CGR, Jharkhand and southern Odisha [Fig. 11d]. This has remained constant throughout 1981–2020 with the rest of the area having 10–50% frequency of no stress overall.

3.4 Changes in spatial coverage for various HSI during 1981–2020

There has been a small but gradual increase of areas with strong heat stress in AMJ season during 1981–2020. As per humidex, 7.2% area has experienced strong stress in 2011–2020 compared to 2.4% in 1981–1990 (Fig. 12). There has not been much changes in moderate and slight stressed areas whereas coverage of areas without stress has decreased during 1981–2020. At monthly scale, increment of slight stress (27–37.7%) and decline of no stress (64.8–50.8%) have been observed in April. Moderate stress has increased (46.1–52.1%) and slight stress has decreased (46.5–40.1%) in May. During June, a rise of strong stress (7.1–19.8%) has been noticed whereas reduction in moderate (73.2–66.6%) and slight stress (17.7–11.9%) has been found. Similar scenario has also been observed for SWBGT with slight changes in percentages at both seasonal and monthly scale (Fig. 13). Unlike the other two HSIs, area under moderate and slight stress for AT has decreased from 46.7–44.1% and 32.2–29.7% respectively during AMJ (Fig. 14). An increment of moderate stress (19.6–23.5%) and decline of slight stress (69.7–67.8%) have been observed in April. Strong stress has been found to be increased (15.4–26% in May and 34–41% in June) while moderate stress has decreased (69.8–62.7% in May and 50.7–46.1% in June) during next two months.

Present study has assessed the changes in extreme temperature indices and heat stress indices over the eastern part of India during 1981–2020. Six ETCCDI indices and three HSIs namely have been utilized. The findings have been summarized below:

There has been an increase in TXx over the western part of the study area whereas decreasing trend has been observed over the eastern part during April and May. Cooling trend has been noticed over majority areas during June. On the other hand, TXn has in general increased during April and June but decreased during May.
A sea saw pattern has been noticed in case of TNx. There has been warming over the north-eastern and cooling over the south-western parts of this region during all three months. In case of TNn, increasing and decreasing trends have been found over northern and southern part of the region during April. But east-west contrast (warming over west and cooling over east) has been have been noticed during later months.
Frequency of TX90p has been found to be amplified over coastal regions of West Bengal and Odisha during AMJ season. On the other hand, TN90p frequency has increased over Sikkim, Bihar and North Bengal and decreased over Chhattisgarh and Odisha.
Climatologically, Gangetic West Bengal and Odisha has been the most heat stress prone area in April. The spatial coverage of heat stress has gradually increased in the next two months. During June, majority areas have experienced moderate to strong heat stress.
Frequency of strong stress in AMJ has intensified over Gangetic West Bengal and Bihar during recent decades. There has been an increase in moderate frequency over Chhattisgarh. Subsequently, no stress frequency has reduced over these regions. Spatial coverage of strong stress has also gradually increased and area under no stress has decreased during AMJ season (especially during June).

The regional assessment of heat extremes presented in this study would provide useful information to the policy and decision makers especially in the health sector. Future work would assess the changes in the heat extremes under different Shared Socioeconomic Pathways (SSP) from The Coupled Model Intercomparison Project phase 6 (CMIP6) simulations.

Acknowledgement

The corresponding author (J.A.) is grateful to University Grants Commission (UGC), India for sponsoring this research through the Dr. D.S. Kothari Post-Doctoral Fellowship Scheme. The Authors would like to thank India Meteorological Department (IMD) and National Centre for Medium Range Weather Forecasting (NCMRWF) for free access of the data utilized in the research. Authors are thankful to the developers of open-access packages like ‘esd’, ‘raster’, ‘fields’, ‘RColorBrewer’ in R for computation work.

Author Contributions: The authors have contributed as following- J.Akhter: Original concept, Experiment designing and Computation, Manuscript editing and reviewing, R. Biswas : Data preparation and analysis, First Draft Writing ; L.Das: Manuscript editing and reviewing ,S.K. Midya: Manuscript reviewing and supervision of the whole work. All the authors have discussed the results and contributed to the final manuscript.

Data Availability: All the data used in this study are open source and available in their respective websites.

Code Availability: The analyses have been carried out using open-source R packages platform.

Funding Statement: University Grants Commission (UGC), India have sponsored this research through providing Dr. D.S. Kothari Post-Doctoral Fellowship to the author Javed Akhter.

Ethics approval: The manuscript has not been submitted to more than one journal for simultaneous consideration. The manuscript has not been published elsewhere previously.

Conflict of interest: The authors declare no competing interests.

Consent to participate: Not Applicable

Consent for publication: Not Applicable

Anderson BG, Bell ML (2009) Weather-related mortality. Epidemiology, 20(2): 205–213. doi:10.1097/ede.0b013e318190ee08
Anderson GB, Bell ML (2011) Heat waves in the United States: Mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. communities. Environmental Health Perspectives, 119(2): 210–218. doi:10.1289/ehp.1002313
Anderson GB, Bell ML, Peng RD (2013). Methods to calculate the heat index as an exposure metric in environmental health research. Environmental Health Perspectives, 121(10): 1111–1119. doi:10.1289/ehp.1206273
Bhadram CV, Amatya BV, Pant GB, Kumar KK (2005). Heat waves over Andhra Pradesh: A case study of summer 2003. MAUSAM, 56(2): 385–394. doi:10.54302/mausam.v56i2.941
Blazejczyk K, Epstein Y, Jendritzky G, Staiger H, Tinz B (2011). Comparison of UTCI to selected thermal indices. International Journal of Biometeorology, 56(3): 515–535. doi:org/10.1007/s00484-011-0453-2
Brouillet A, Joussaume S (2019). Investigating the role of the relative humidity in the co- occurrence of temperature and heat stress extremes in CMIP5 projections. Geophysical Research Letters, 46. doi:10.1029/2019GL084156
Burkart K, Schneider A, Breitner S, Khan MH, Krämer A, Endlicher W (2011). The effect of atmospheric thermal conditions and urban thermal pollution on all-cause and cardiovascular mortality in Bangladesh. Environmental Pollution, 159(8–9): 2035–2043. doi:10.1016/j.envpol.2011.02.005
Chen Y, Moufouma-Okia W, Masson-Delmotte VÃ, Zhai P, Pirani A (2018). Recent progress and emerging topics on weather and climate extremes since the fifth assessment report of the intergovernmental panel on climate change. Annual Review of Environment and Resources, 43(1): 35–59. doi:10.1146/annurev-environ-102017-030052.
Davis RE, McGregor GR, Enfield KB (2016). Humidity: a review and primer on atmospheric moisture and human health. Environmental Research Letters, 144(PtA):106–116, PMID:26599589. doi:10.1016/j.envres.2015.10.014
Di Napoli C, Pappenberger F, Cloke HL (2018). Assessing heat-related health risk in Europe via the universal thermal climate index (UTCI). International Journal of Biometeorology, 62(7): 1155–1165. doi:10.1007/s00484-018-1518-2
Fischer EM, Knutti R (2012). Robust projections of combined humidity and temperature extremes. Nature Climate Change, 3(2), 126–130. doi:10.1038/nclimate1682
Fischer EM, Schär C (2010). Consistent geographical patterns of changes in high-impact European heatwaves. Nature Geoscience, 3(6), 398–403. doi:10.1038/ngeo866
Garcia-Herrera R, D_ıaz J, Trigo R M, Luterbacher J, Ficher E (2010). A review of the European summer heat wave of 2003. Crit. Rev. Environ. Sci. Technol, 40: 267–306. doi:10.1080/10643380802238137
Guleria S, Gupta AK, (2018). Heat Wave in India Documentation of State of Telangana and Odisha (2016). National Institute of Disaster Management, New Delhi, pp. 124. https://nidm.gov.in/PDF/pubs/heat_wave_18.pdf.
Hanna E, Tait PW (2015). Limitations to thermoregulation and acclimatization challenge human adaptation to global warming. International Journal of Environmental Research and Public Health, 12(7): 8034–8074, PMID:26184272, doi:10.3390/ijerph120708034.
Kent, S. T., McClure, L. A., Zaitchik, B. F., Smith, T. T., Gohlke, J. M. (2014). Heat waves and health outcomes in Alabama (USA): The importance of heat wave definition. Environmental Health Perspectives, 122(2): 151–158. doi:10.1289/ehp.1307262
Kjellstrom T, Lemke B, Otto M (2013). Mapping occupational heat exposure and effects in south-east Asia: Ongoing time trends 1980^|^ndash;2011 and future estimates to 2050. Industrial Health, 51(1): 56–67. doi:10.2486/indhealth.2012-0174
Kumar P, Rai A, Upadhyaya A, Chakraborty A (2022). Analysis of heat stress and heat wave in the four metropolitan cities of India in recent period. Science of The Total Environment, 818, 151788. doi:10.1016/j.scitotenv.2021.151788
Lin Y, Chang C, Li M, Wu Y, Wang Y (2012). High-temperature indices associated with mortality and outpatient visits: Characterizing the association with elevated temperature. Science of The Total Environment, 427–428, 41–49. doi:10.1016/j.scitotenv.2012.04.039
Masterson JM, Richardson FA, (1979). A Method of Quantifying Human Discomfort Due to Excessive Heat and Humidity. Downs View. Environment Canada, Ontario, p. 45.
McGregor GR, Vanos JK (2017). Heat: a primer for public health researchers. Public Health, 161:138–146, PMID:29290376. doi:10.1016/j.puhe.2017.11.005.
Mora C, Dousset B, Caldwell IR, Powell FE, Geronimo RC, Bielecki C et al. (2017). Global risk of deadly heat. Nature Climate Change,7(7): 501–506. doi:10.1038/nclimate3322.
Pai D, Nair S, Ramanathan A (2013). Long term climatology and trends of heat waves over India during the recent 50 years (1961–2010). MAUSAM, 64(4): 585–604. doi:10.54302/mausam.v64i4.742
Ramamurthy K (1972). A case study of a heat wave during July 1966. MAUSAM, 23(2): 165–172. doi:10.54302/mausam.v23i2.5272
Ratnam JV, Behera SK, Ratna SB, Rajeevan M, Yamagata T (2016). Anatomy of Indian heatwaves. Scientific Reports, 6(1). doi:10.1038/srep24395
Rohini P, Rajeevan M, Srivastava AK (2016). On the variability and increasing trends of heat waves over India. Scientific Reports, 6(1). doi:10.1038/srep26153
Schwingshackl C, Sillmann J, Vicedo-Cabrera AM, Sandstad M, Aunan K (2021). Heat stress indicators in CMIP6: Estimating future trends and Exceedances of impact‐relevant thresholds. Earth’s Future, 9(3). doi:10.1029/2020ef001885
Sharma S, Mujumdar P (2017). Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Scientific Reports, 7(1). doi:10.1038/s41598-017-15896-3
Shiogama H, Hasegawa T, Fujimori S, Murakami D, Takahashi K, Tanaka K, Emori S, Kubota I, Abe M, Imada Y, Watanabe M, Mitchell D, Schaller N, Sillmann J, Fischer EM, Scinocca JF, Bethke I, Lierhammer L, Takakura J, Schleussner C (2019). Limiting global warming to 1.5°C will lower increases in inequalities of four hazard indicators of climate change. Environmental Research Letters, 14(12): 124022. doi:10.1088/1748-9326/ab5256
Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013). Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. Journal of Geophysical Research: Atmospheres, 118(4): 1716–1733. doi:10.1002/jgrd.50203
Subbaramayya I, Rao DS (1976). Heat wave and cold wave days in different states of India. MAUSAM, 27(4): 436–440. doi:10.54302/mausam.v27i4.2631
Vaneckova P, Neville G, Tippett V, Aitken P, FitzGerald G, Tong S (2011). Do Biometeorological indices improve modeling outcomes of heat-related mortality? Journal of Applied Meteorology and Climatology, 50(6): 1165–1176. doi:10.1175/2011jamc2632.1
Willett KM, Sherwood S (2010). Exceedance of heat index thresholds for 15 regions under a warming climate using the wet-bulb globe temperature. International Journal of Climatology, 32(2): 161–177. doi:10.1002/joc.2257
Zhao Y, Ducharne A, Sultan B, Braconnot P, Vautard R (2015). Estimating heat stress from climate-based indicators: Present-day biases and future spreads in the CMIP5 global climate model ensemble. Environmental Research Letters, 10(8): 084013. doi:10.1088/1748- 9326/10/8/084013

No competing interests reported.

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Recent Changes in Temperature Extremes and Heat Stress over Eastern India

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Journal Publication

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Abstract

Figures

1 Introduction

2 Materials And Method

2.1 Study Area

2.2 Data

2.2.1 IMD gridded temperature data

3.2.2 NCMRWF Reanalysis Data

2.3 Methodology

2.3.1 Extreme Temperature Indices

2.3.2 Heat Stress Indicators

3 Results And Discussion

3.1 Changes in TXx, TXn, TNx and TNn during 1981–2020

3.2 Changes in frequency of TX90p and TN90p during 1981–2020

3.3 Monthly climatology of HSI during 1981–2020

3.4 Changes in percentage frequencies of days for various HSI during 1981–2020

3.4.1 Changes in Humidex

3.4.2 Changes in Simplified Wet Bulb Globe Temperature

3.4.2 Changes in Apparent Temperature

3.4 Changes in spatial coverage for various HSI during 1981–2020

4 Conclusions

Declarations

References

Additional Declarations

Status:

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