Changes in patterns of extreme temperature distribution across different regions in India

Author(s):  
Angana Borah ◽  
Udit Bhatia

<p>Global studies of extreme temperature suggest that in recent times there has been an increase in frequency and intensity for hot temperature and decrease for cold temperatures  while a few others show an increase in both warm and cold extremes in the last decade of the twentieth century. Previous research on large scale climate projections show an amplified increase in the highest percentile of maxima and minima, with respect to the lowest percentiles of temperature extremes. The indices recommended by the Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI) to analyse trends of extreme climate are pertinent to policy and decision making with regard to impact and adaptation studies. While literature is abundant with large scale evaluation of trends in extreme temperatures, there is a want of studies in the regional patterns and distribution of temperature extremes in India. Although India is widely known as a tropical country, the diversities found in the topography of the regions from north to south and east to west, renders microclimate unique to each region leading to disparate inter-annual temperature ranges across the country. So, it is important to explore how regional trends in the different climatic zones of the Indian subcontinent correspond with each other in view of its unique climatic regimes. A comprehensive analysis of temperature extremes in the urban agglomerates and their suburban and rural counterparts is relatively unexplored for India. The results offer insights on the change in the percentile based indices recommended by the IPCC as well as summer and winter maximas and minimas for the entire India over the last several decades. The frequency and intensity of extreme temperatures characterised by number of days less than 10th percentile and more than 90th percentile, and minimum annual minimum temperature and maximum annual maximum temperature respectively, of the distribution over the last six decades have been assessed. The findings of this study suggests that warmer extremes follow an increasing trend, while the colder extremes exhibit no significant trend. However, the trends appear to be spatially coherent irrespective of the extent of urbanization.   Additionally, change in maximum and minimum percentiles of summer and winter temperatures are assessed between the first half of the last century and  the later half of the last century, for the entire country. It was found that change in highest percentiles in both summer and winter minima is more pronounced than lowest percentiles, while increase in highest percentile is more amplified for summer and winter maxima. </p><p> </p>

2021 ◽  
Author(s):  
Mastawesha Misganaw Engdaw ◽  
Andrew Ballinger ◽  
Gabriele Hegerl ◽  
Andrea Steiner

<p>In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.</p><p><strong>Keywords:</strong> climate change, temperature, extreme events, attribution, CMIP6</p><p> </p><p><strong>Acknowledgement:</strong> This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)</p>


2018 ◽  
Author(s):  
Kishore Pangaluru ◽  
Isabella Velicogna ◽  
Tyler C. Sutterley ◽  
Yara Mohajerani ◽  
Enrico Ciraci ◽  
...  

Abstract. Changes in extreme temperature and precipitation may give some of the largest significant societal and ecological impacts. For changes in the magnitude of extreme temperature and precipitation over India, we used a statistical model of generalized extreme value (GEV) distribution. The GEV statistical distribution is a time-dependent distribution with different time scales of variability bounded by a precipitation, maximum (Tmax), and minimum (Tmin) temperature extremes and also assessed their possibility changes are evaluated and quantified over India is presented. The GEV-based method is applied on both precipitation and temperature extremes over India during the 20th and 21st centuries using multiple coupled climate models taking an interest in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and observational datasets. The regional means of historical warm extreme temperatures are 34.89, 36.42, and 38.14 °C for three different (10, 20, and 50-year) periods, respectively; whereas the cold extreme mean temperatures are 7.75, 4.19, and −1.57 °C. It indicates that 20th century cold extreme temperatures have relatively larger variations than the warm extremes. As for the future, the CMIP5 models of warm extreme regional mean values increase from 0.33 to 0.75 °C in all return periods (10-, 20-, and 50-year periods), while in the case of cold extreme means values vary between 0.58 and 2.29 °C. In the future, cold extreme values have a larger increasing rate over the northwest, northeast, some parts of north-central, and Inter Peninsula regions. The CRU precipitation extremes are larger than the historical extreme precipitation in all three (10, 20, and 50-year) return-periods.


2021 ◽  
Vol 13 (22) ◽  
pp. 12462
Author(s):  
Wei-Xiong Yan ◽  
Jun-Fang Zhao ◽  
Jian-Ping Li ◽  
Yun-Xia Wang

Some studies have suggested that variations in the seasonal cycle of temperature and season onset could affect the efficiency in the use of radiation by plants, which would then affect yield. However, the study of the temporal variation in extreme climatic variables is not sufficient in China. Using seasonal trend analysis (STA), this article evaluates the distribution of extreme temperature seasonality trends in mainland China, describes the trends in the seasonal cycle, and detects changes in extreme temperature characterized by the number of hot days (HD) and frost days (FD), the frequency of warm days (TX90p), cold days (TX10p), warm nights (TN90p), and cold nights (TN10p). The results show a statistically significant positive trend in the annual average amplitudes of extreme temperatures. The amplitude and phase of the annual cycle experience less variation than that of the annual average amplitude for extreme temperatures. The phase of the annual cycle in maximum temperature mainly shows a significant negative trend, accounting for approximately 30% of the total area of China, which is distributed across the regions except for northeast and southwest. The amplitude of the annual cycle indicates that the minimum temperature underwent slightly greater variation than the maximum temperature, and its distribution has a spatial characteristic that is almost bounded by the 400 mm isohyet, increasing in the northwest and decreasing in the southeast. In terms of the extreme air temperature indices, HD, TX90p, and TN90p show an increasing trend, FD, TX10p, and TN10p show a decreasing trend. They are statistically significant (p < 0.05). This number of days also suggests that temperature has increased over mainland China in the past 42 years.


2020 ◽  
Vol 117 (16) ◽  
pp. 8757-8763 ◽  
Author(s):  
Ji Nie ◽  
Panxi Dai ◽  
Adam H. Sobel

Responses of extreme precipitation to global warming are of great importance to society and ecosystems. Although observations and climate projections indicate a general intensification of extreme precipitation with warming on global scale, there are significant variations on the regional scale, mainly due to changes in the vertical motion associated with extreme precipitation. Here, we apply quasigeostrophic diagnostics on climate-model simulations to understand the changes in vertical motion, quantifying the roles of dry (large-scale adiabatic flow) and moist (small-scale convection) dynamics in shaping the regional patterns of extreme precipitation sensitivity (EPS). The dry component weakens in the subtropics but strengthens in the middle and high latitudes; the moist component accounts for the positive centers of EPS in the low latitudes and also contributes to the negative centers in the subtropics. A theoretical model depicts a nonlinear relationship between the diabatic heating feedback (α) and precipitable water, indicating high sensitivity of α (thus, EPS) over climatological moist regions. The model also captures the change of α due to competing effects of increases in precipitable water and dry static stability under global warming. Thus, the dry/moist decomposition provides a quantitive and intuitive explanation of the main regional features of EPS.


2011 ◽  
Vol 11 (9) ◽  
pp. 2583-2603 ◽  
Author(s):  
A. El Kenawy ◽  
J. I. López-Moreno ◽  
S. M. Vicente-Serrano

Abstract. Spatial and temporal characteristics of extreme temperature events in northeastern Spain have been investigated. The analysis is based on long-term, high-quality, and homogenous daily maximum and minimum temperature of 128 observatories spanning the period from 1960 to 2006. A total of 21 indices were used to assess changes in both the cold and hot tails of the daily temperature distributions. The presence of trends in temperature extremes was assessed by means of the Mann-Kendall test. However, the autocorrelation function (ACF) and a bootstrap methodology were used to account for the influence of serial correlation and cross-correlation on the trend assessment. In general, the observed changes are more prevalent in hot extremes than in cold extremes. This finding can largely be linked to the increase found in the mean maximum temperature during the last few decades. The results indicate a significant increase in the frequency and intensity of most of the hot temperature extremes. An increase in warm nights (TN90p: 3.3 days decade−1), warm days (TX90p: 2.7 days decade−1), tropical nights (TR20: 0.6 days decade−1) and the annual high maximum temperature (TXx: 0.27 °C decade−1) was detected in the 47-yr period. In contrast, most of the indices related to cold temperature extremes (e.g. cold days (TX10p), cold nights (TN10p), very cold days (TN1p), and frost days (FD0)) demonstrated a decreasing but statistically insignificant trend. Although there is no evidence of a long-term trend in cold extremes, significant interdecadal variations were noted. Almost no significant trends in temperature variability indices (e.g. diurnal temperature range (DTR) and growing season length (GSL)) are detected. Spatially, the coastal areas along the Mediterranean Sea and the Cantabrian Sea experienced stronger warming compared with mainland areas. Given that only few earlier studies analyzed observed changes in temperature extremes at fine spatial resolution across the Iberian Peninsula, the results of this work can improve our understanding of climatology of temperature extremes. Also, these findings can have different hydrological, ecological and agricultural implications (e.g. crop yields, energy consumption, land use planning and water resources management).


2017 ◽  
Vol 30 (24) ◽  
pp. 9897-9914 ◽  
Author(s):  
Meng Gao ◽  
Christian L. E. Franzke

In this study, temporal trends and spatial patterns of extreme temperature change are investigated at 352 meteorological stations in China over the period 1956–2013. The temperature series are first examined for evidence of long-range dependence at daily and monthly time scales. At most stations there is evidence of significant long-range dependence. Noncrossing quantile regression has been used for trend analysis of temperature series. For low quantiles of daily mean temperature and monthly minimum value of daily minimum temperature (TNn) in January, there is an increasing trend at most stations. A decrease is also observed in a zone ranging from northeastern China to central China for higher quantiles of daily mean temperature and monthly maximum value of daily maximum temperature (TXx) in July. Changes of the large-scale atmospheric circulation partly explain the trends of temperature extremes. To reveal the spatial pattern of temperature changes, a density-based spatial clustering algorithm is used to cluster the quantile trends of daily temperature series for 19 quantile levels (0.05, 0.1, …, 0.95). Spatial cluster analysis identifies a few large clusters showing different warming patterns in different parts of China. Finally, quantile regression reveals the connections between temperature extremes and two large-scale climate patterns: El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO). The influence of ENSO on cold extremes is significant at most stations, but its influence on warm extremes is only weakly significant. The AO not only affects the cold extremes in northern and eastern China, but also affects warm extremes in northeastern and southern China.


2020 ◽  
Author(s):  
Baljinnyam Nyamjantsan ◽  
Changhyun Yoo

Abstract Employing the percentile-based indices, TN10p, TX10p, TN90p, and TX90p during 1961–2018, we evaluate temporal and spatial trends in extreme temperature at 54 stations over Mongolia. Statically significant changes in temperature extremes in the warm (TN90p and TX90p) and cool indices (TN10p and TX10p) are found. The rate of increase in the number of warm nights and days are respectively 1.5 and 1.9 days decade− 1, while the cool nights and days show a declining trend of -0.8 and − 1.5 days decade− 1, respectively. Despite the fact that the trends are most vigorous during June-July-August, seasonal variations can be seen. Also, spatial distributions of the trends reveal weakest magnitudes in Gobi Desert, while strongest in the west and north of Mongolia. The large-scale atmospheric circulations account for changes in the temperature extreme indices. The East Atlantic, East Atlantic/western Russian, and Scandinavian patterns, and the Arctic Oscillation is found to contribute the most to the interannual variation in the temperature extremes.


Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1171
Author(s):  
Junju Zhou ◽  
Jumei Huang ◽  
Xi Zhao ◽  
Li Lei ◽  
Wei Shi ◽  
...  

The increase in the frequency and intensity of extreme weather events around the world has led to the frequent occurrence of global disasters, which have had serious impacts on the society, economic and ecological environment, especially fragile arid areas. Based on the daily maximum temperature and daily minimum temperature data of four meteorological stations in Shiyang River Basin (SRB) from 1960 to 2015, the spatio-temporal variation characteristics of extreme temperature indices were analyzed by means of univariate linear regression analysis, Mann–Kendall test and correlation analysis. The results showed that the extreme temperatures warming indices and the minimum of daily maximum temperature (TXn) and the minimum of daily minimum temperature (TNn) of cold indices showed an increasing trend from 1960 to 2016, especially since the 1990s, where the growth rate was fast and the response to global warming was sensitive. Except TXn and TNn, other cold indices showed a decreasing trend, especially Diurnal temperature (DTR) range, which decreased rapidly, indicating that the increasing speed of daily min-temperature were greater than of daily max-temperature in SRB. In space, the change tendency rate of the warm index basically showed an obvious altitude gradient effect that decreased with the altitude, which was consistent with Frost day (FD0) and Cool nights (TN10p) in the cold index, while Ice days (ID0) and Cool days (TX10p) are opposite. The mutation of the cold indices occurred earlier than the warm indices, illustrating that the cold indices in SRB were more sensitive to global warming. The change in extreme temperatures that would have a significant impact on the vegetation and glacier permafrost in the basin was the result of the combined function of different atmospheric circulation systems, which included the Arctic polar vortex, Western Pacific subtropical high and Qinghai-tibet Plateau circulation.


2021 ◽  
Author(s):  
Farhan Aziz ◽  
Nadeem Tariq ◽  
Akif Rahim ◽  
Ambreen Mahmood

&lt;p&gt;In recent years, extreme events and their severe damage have become more common around the world. It is widely known that atmospheric greenhouse gases have contributed to global warming. &lt;br&gt;A set of appropriate indicators describing the extremes of climate change can be used to study the extent of climate change. This study reveals the trends of temperature extreme indices on the spatial scale in the western part of Northwest Himalayas.&amp;#160;The study is conducted&amp;#160;at&amp;#160;13 climate stations lies&amp;#160;at&amp;#160;a different altitude of the study area.The Daily maximum and minimum temperature data during 2000--2018 of stations obtained from the Pakistan Meteorological Department (PMD) and Water and Power Development Authority (WAPDA). The 12 extreme temperature indices (FD, SU, TXx , TXn., TNx, TNn, TN10p , TN90p, TX10p , TX90p, CSDI, WSDI) recommended by ETCCDI (Expert Team on Climate Change Detection and Indices) are used to study the variabilities in temperature extremes. These indices are characterized based on amplitude, persistence, and frequency. The analysis is performed by using R package of extremes &amp;#8220;RClimDEX&amp;#8221;. The analysis shows the frequency of summer days (Su) and warm spells (WSDI) have increasing trends in the Southwest region, whereas the frequency of cold spells and frost days have decreasing trends observed in the Northern region of the study areas. The maximum and minimum values of daily maximum temperature (TXX, TXN) increase in the foothill area of the region and decreasing trends in the high elevation region. The day and night get cool in the Northwest region, whereas the days and nights are showing warmer trends in low elevation regions of the study area. Overall, the study concludes that the Northwestern parts have cool trends while South West and South eastern parts have warm trends during the early 21st century.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key words:&lt;/strong&gt;&amp;#160; Temperature Extremes, Northwest Himalayas, Trends, R-Climdex, Climate Change&lt;/p&gt;


2020 ◽  
Author(s):  
David Sexton ◽  
Jason Lowe ◽  
James Murphy ◽  
Glen Harris ◽  
Elizabeth Kendon ◽  
...  

&lt;p&gt;UK Climate Projections 2018 (UKCP18) included land and marine projections and were published in 2018 to replace UKCP09. The land projections had three components, and all were designed to provide more information on future weather compared to UKCP09. The first component updated the UKCP09 probabilistic projections by including newer CMIP5 data and focussing on seasonal means from individual years rather than 30-year averages. The probabilistic projections represent the wider uncertainty. The second two components (global and regional projections) both had the aim of providing plausible examples of future climate, but at different resolutions.&lt;/p&gt;&lt;p&gt;The global projections were a combination of 13 CMIP5 models and a 15-member perturbed parameter ensemble (PPE) of coupled simulations for 1900-2100 using CMIP5 RCP8.5 from 2005 onwards. The PPE was provided at 60km atmosphere, quarter degree ocean and the large-scale conditions from twelve of the members were used to drive the regional model at both 12km and 2.2km resolution. These plausible examples are more useful for providing information about weather in a future climate to support a storyline approach for decision making.&lt;/p&gt;&lt;p&gt;The talk will present examples of new ways to use UKCP18 compared to UKCP09.&amp;#160; We will show how the global projections can be used to understand that the recent record winter daily maximum temperature in the UK in 2019 had a large contribution from internal variability and what this means for breaking the record in a warming climate. We also use an example from China to demonstrate one way to exploit information at different time scales, looking at how a circulation index, which is predictable and related to tropical cyclone landfall, changes in a future climate.&lt;/p&gt;&lt;p&gt;Finally, we show that while the enhanced resolution of the global and regional projections has improved our capability to provide climate information linked to the better representation of circulation, they lack diversity in some of the key drivers of future climate. Therefore, a key way forward will be to find an appropriate balance between the need for better diversity (e.g. multiple ensembles such as CMIP or PPEs) and the need for an appropriate resolution to retain this new capability.&lt;/p&gt;


Sign in / Sign up

Export Citation Format

Share Document