Abstract The Himalayan ecosystem is highly sensitive to rapidly intensifying temperature extremes, where complex topography and climate sensitivity amplify the severity of warming. As a Himalayan state, Sikkim is also highly exposed to temperature extremes, posing a great threat to environmental and social processes. Hence, this study explores trends and variations in the severity of warming using IMD gridded data from 1980 to 2023 and examines the contributions of ocean-atmosphere phenomena, regional circulation, and anthropogenic emissions in shaping these variations. The findings exhibit escalating warming tendencies for tropical nights (TR; Sen’s Slope = +1.04 yr−1), warm nights (TN90p; +0.83% yr−1), hot days (TX90p; +0.27% yr−1), and warm spell duration index (WSDI; +0.245 yr−1), and declining cool nights (TN10p; –0.33 yr−1) and cold spell duration index (CSDI). The pacific decadal oscillation emerges as a major driver of minimum temperature extremes, negatively affecting TR and TN90p and positively influencing TN10p. Alongside, the Monsoon Hadley Circulation explains 20–30% variability in nighttime temperature (TR = 29%, TN10p = 22%, and TN90p = 20%). Additionally, there is a substantial contribution of the air pollutants (PM2.5, BC, SO2) and greenhouse gases (H2O, CO2, CH4) to warming amplification. The findings clearly underscore a striking warming shift in Sikkim, with pronounced nighttime warming and declining cold events. These suggest the urgency of large and regional-scale climate modelling and forecasting, and integrating air pollution mitigation for better climate adaptation policy and heat risk management strategies in the Sikkim Himalaya. Highlights <UnorderedList Mark="Bullet"> <ItemContent> All the extreme temperature indices except summer days (SU) and cool days (TX10p) show a significant upward or downward trend over the study period. Tropical nights (TR), TX90p, TN90p, and warm spell duration index (WSDI) are increasing, while TN10p and cold spell duration index (CSDI) are decreasing. This finding suggests an intensifying warming trend in Sikkim. </ItemContent> <ItemContent> The minimum temperature extremes, including TR, TN10p, TN90p, and CSDI, started being affected earlier compared to the maximum temperature extremes. These minimum temperature extremes started changing in the 1990s, whereas maximum temperature indices, including TX90p and WSDI, show their changing point in 2005 and 2015, respectively. </ItemContent> <ItemContent> The minimum temperature extremes, including tropical nights, warm nights, cool nights, and cold spells, are significantly influenced by the large-scale climate modes, viz. Niño 4 and pacific decadal oscillation (PDO). The multiple linear regression results show TR and TN90p are positively associated with Niño 4 and negatively associated with the positive phase of PDO. On the other hand, TN10p and CSDI are negatively related to Niño 4, and TN10p is positively related to PDO. </ItemContent> <ItemContent> Wavelet transform coherence (WTC) analysis mainly exhibits an anti-phase relationship between TR, TN90p, and Niño 4, and an in-phase relationship between TN10p and CSDI; besides, large cycles of anti-phase association can be detected between TR, TN90p, and the positive phase of PDO, and in-phase association between TN10p and PDO. This outcome indicates a more persistent influence of PDO on temperature extremes, being multidecadal and of a large regional extent compared to Niño 4. </ItemContent> <ItemContent> Similar to the large-scale climate modes, the monsoon Hadley circulation index (MHCI) also illustrates a significant influence on temperature patterns. It reveals an almost linear increasing trend with TR and TN90p, and a non-linear declining trend with TN10p and CSDI. A wiggling pattern can be detected over TX10p. These findings exert a dominant control of MHCI mostly on minimum temperature extremes in Sikkim. </ItemContent> <ItemContent> Along with Niño 4, PDO, and MHCI, anthropogenic causes also impact the temperature patterns. H2O and CO2 have a significant impact on increasing warm nights and declining cold spells. Besides, PM2.5 (particulate matter) negatively impacts the occurrences of summer days and positively impacts the cool days. Black carbon (BC) increases the hot days and warm night extremes and negatively impacts the occurrences of cold spells. Hence, these results show a considerable effect of air pollutants and GHGs on temperature extreme patterns. </ItemContent> </UnorderedList>

Increasing temperature extremes in Sikkim Himalaya: impact of large-scale climate modes, monsoon dynamics and anthropogenic factors

期刊论文
发表日期：

2026年7月25日

查看全文

Jyotirmayee Sarkar,
Yash Nayan,
Amrita Dwivedi

摘要

Abstract

The Himalayan ecosystem is highly sensitive to rapidly intensifying temperature extremes, where complex topography and climate sensitivity amplify the severity of warming. As a Himalayan state, Sikkim is also highly exposed to temperature extremes, posing a great threat to environmental and social processes. Hence, this study explores trends and variations in the severity of warming using IMD gridded data from 1980 to 2023 and examines the contributions of ocean-atmosphere phenomena, regional circulation, and anthropogenic emissions in shaping these variations. The findings exhibit escalating warming tendencies for tropical nights (TR; Sen’s Slope = +1.04 yr⁻¹), warm nights (TN90p; +0.83% yr⁻¹), hot days (TX90p; +0.27% yr⁻¹), and warm spell duration index (WSDI; +0.245 yr⁻¹), and declining cool nights (TN10p; –0.33 yr⁻¹) and cold spell duration index (CSDI). The pacific decadal oscillation emerges as a major driver of minimum temperature extremes, negatively affecting TR and TN90p and positively influencing TN10p. Alongside, the Monsoon Hadley Circulation explains 20–30% variability in nighttime temperature (TR = 29%, TN10p = 22%, and TN90p = 20%). Additionally, there is a substantial contribution of the air pollutants (PM2.5, BC, SO₂) and greenhouse gases (H₂O, CO₂, CH₄) to warming amplification. The findings clearly underscore a striking warming shift in Sikkim, with pronounced nighttime warming and declining cold events. These suggest the urgency of large and regional-scale climate modelling and forecasting, and integrating air pollution mitigation for better climate adaptation policy and heat risk management strategies in the Sikkim Himalaya.

Highlights

All the extreme temperature indices except summer days (SU) and cool days (TX10p) show a significant upward or downward trend over the study period. Tropical nights (TR), TX90p, TN90p, and warm spell duration index (WSDI) are increasing, while TN10p and cold spell duration index (CSDI) are decreasing. This finding suggests an intensifying warming trend in Sikkim.

The minimum temperature extremes, including TR, TN10p, TN90p, and CSDI, started being affected earlier compared to the maximum temperature extremes. These minimum temperature extremes started changing in the 1990s, whereas maximum temperature indices, including TX90p and WSDI, show their changing point in 2005 and 2015, respectively.

The minimum temperature extremes, including tropical nights, warm nights, cool nights, and cold spells, are significantly influenced by the large-scale climate modes, viz. Niño 4 and pacific decadal oscillation (PDO). The multiple linear regression results show TR and TN90p are positively associated with Niño 4 and negatively associated with the positive phase of PDO. On the other hand, TN10p and CSDI are negatively related to Niño 4, and TN10p is positively related to PDO.

Wavelet transform coherence (WTC) analysis mainly exhibits an anti-phase relationship between TR, TN90p, and Niño 4, and an in-phase relationship between TN10p and CSDI; besides, large cycles of anti-phase association can be detected between TR, TN90p, and the positive phase of PDO, and in-phase association between TN10p and PDO. This outcome indicates a more persistent influence of PDO on temperature extremes, being multidecadal and of a large regional extent compared to Niño 4.

Similar to the large-scale climate modes, the monsoon Hadley circulation index (MHCI) also illustrates a significant influence on temperature patterns. It reveals an almost linear increasing trend with TR and TN90p, and a non-linear declining trend with TN10p and CSDI. A wiggling pattern can be detected over TX10p. These findings exert a dominant control of MHCI mostly on minimum temperature extremes in Sikkim.

Along with Niño 4, PDO, and MHCI, anthropogenic causes also impact the temperature patterns. H₂O and CO₂ have a significant impact on increasing warm nights and declining cold spells. Besides, PM2.5 (particulate matter) negatively impacts the occurrences of summer days and positively impacts the cool days. Black carbon (BC) increases the hot days and warm night extremes and negatively impacts the occurrences of cold spells. Hence, these results show a considerable effect of air pollutants and GHGs on temperature extreme patterns.