DISTRIBUTION OF TEMPERATURE INDICES OF EXTREME IN THE TERRITORY OF THE EUROPEAN PART OF RUSSIA IN THE PERIOD 1980-2019

The spatio-temporal variability of the average values of temperature indices of climate extremity in the territory of the European part of Russia (ER) in 1980-2019 is presented. To calculate the extremeness indices, we used hourly data on the maximum and minimum temperatures obtained using the ERA5 reanalysis on a 1°´1° spatial grid. Statistical processing of the index values revealed an increase in the temperature indices TNX, TNN, TXN, TXX, associated with the minimum and maximum temperatures, with the exception of the north and southeast of the region. An increase in the number of sunny days and a decrease in the number of frosty days were also revealed.

Analysis of temperature variability and trends over Tripura

Attempts have been made to study the temporal variations and trends in monthly, seasonal and annual temperature over two stations - Agartala and Kailashahar in Tripura, India for the period 1969 to 2014. Analysis has been carried for four temperature indices, namely - minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean) and diurnal temperature range (DTR). Non-parametric Mann-Kendall (MK) test was used to detect the trends and the magnitude of the trends were determined with Sen’s estimator of slope. The analysis of the temperature data revealed significant increasing trends in Tmin, Tmax and Tmean variables at both the stations on annual scale, while decreasing trend in DTR was significant over Agartala only. On the seasonal scale, the increasing (decreasing) trends were observed in Tmin, Tmax and Tmean (DTR) temperature indices in all the four seasons - winter, pre-monsoon, monsoon and post-monsoon except non-significant decreasing trend in Tmax in winter season over Agatala. On the monthly scale, significant rising trends in Tmin, Tmax and Tmean were observed in the most of the months and significant decreasing trend in DTR was found only in the months of January and December.

Analysis and object-oriented generalization of meteorological data in solving the bioclimatic mapping problem

The focus of the paper is interrelation and character of changes in climate factors in Taimyr and the North of Evenki in the summer-autumn period between 1970 and 2020. Bioclimatic fields of the reindeer range as a form of object-oriented generalization of meteorological data have been constructed for the “average” and the most contrasting years in terms of temperature indices.

Assessment on Temporal and Spatial Variation Analysis of Extreme Temperature Indices: A Case Study of the Yangtze River Basin

Extreme temperature change is one of the most urgent challenges facing our society. In recent years, extreme temperature has exerted a considerable influence on society and the global ecosystem. The Yangtze River Basin is not only an important growth belt of China’s social and economic development, but also the main commodity grain base in China. The purpose of this study is to study the extreme temperature indices in the Yangtze River Basin. In this study, the Mann–Kendall nonparametric test and R/S analysis method are used to analyze the spatial and temporal variation characteristics of major extreme temperature indices in the Yangtze River Basin from 1970 to 2014. The main conclusions are drawn as follows: (1) The occurrence of cold days (TX10), cold nights (TN10), ice days (ID), and frost days (FD) decrease at a rate of −0.66–−2.5 d/10a, respectively, while the occurrence of warm days (TX90), warm nights (TN90), summer days (SU), and tropical nights (TR) show statistically significant increasing trends at a rate of 2.2–4.73 d/10a. (2) The trends of the coldest day (TXn), coldest night (TNn), warmest day (TXx), warmest night (TNx), and diurnal temperature range (DTR), range from −0.003 to 0.5 °C/10a. (3) Spatially, the main cold indices and warm indices increase and decrease the most in the upper and lower reaches of the Yangtze River Basin. (4) DTR and TN90 show no abrupt changes; the main cold indices changed abruptly in the 1980s and the main warm indices changed abruptly in the late 1990s and early 2000s. (5) The extreme temperature indices are affected by the atmospheric circulation and urban heat island effect in the Yangtze River Basin. Relative indices and absolute indices will continue to maintain the present trend in the future. In short, the main cold indices of extreme temperature indices show a decreasing trend, the main warm indices of extreme temperature indices show an increasing trend, and cold indices and warm indices will continue to maintain the present trend in the future in the Yangtze River Basin. Extreme temperature has an important impact on agriculture, social, and economic development. Therefore, extreme temperature prediction and monitoring must be strengthened to reduce losses caused by extreme temperature disasters and to promote the sustainable development in Yangtze River Basin.

Analysis of Spatiotemporal Variability in Extreme Climate and Potential Driving Factors on the Yunnan Plateau (Southwest China) during 1960–2019

Global warming is increasing the frequency and intensity of extreme weather events around the world. The extreme climate in plateau and mountainous areas is sensitive and fragile. Based on the software Rclimdex 1.0, the spatio-temporal variation characteristics of 27 extreme climate indices at 120 meteorological stations were calculated in Yunnan from 1960 to 2019. The results show that the extreme temperature is rising, and the warming rate at night is higher than that in the daytime. It showed a trend of warming and drying, and precipitation was concentrated into more intense bursts. Extreme temperature cold indices (TX10p, TN10p, FD0, ID0, and CSDI) were negatively correlated with extreme precipitation indices (R × 5day, PRCPTOT, R10 mm, R20 mm, and R25 mm). Extreme temperature warmth indices (TX90p and TN90p) were positively correlated with extreme precipitation indices (R × 5day, CWD, PRCPTOT, R10 mm, R20 mm, and R25 mm). The change rate of extreme temperature does not increase linearly with altitude. The increase in middle-altitude and high-altitude areas is higher than that in low-altitude areas. Compared with ENSO and AO, NAO is a vital circulation pattern affecting the extreme climate in Yunnan. The influence of NAO on Yunnan’s extreme climate indices is most significant in the current month and the second month that follows. NAO was negatively correlated with extreme temperature warm indices (TN90p, TX90p, SU25, and TR20). NAO positively correlates with the extreme cold temperature indices (TN10p and TX10p). Except that ENSO has a significant effect on CDD, the effect of the general circulation patterns on the extreme temperature indices was more significant than that on the extreme precipitation indices in Yunnan. The results of this study are helpful to further understand and predict the characteristics of extreme climatic events and the factors affecting their geographical locations and atmospheric circulation patterns in Yunnan.

Historical Variability And Future Changes In Seasonal Extreme Temperature Over Iran

The extreme temperature indices (ETI) are an important indicator of climate change, the detection of their changes over the next years can play an important role in the Climate Action Plan (CAP). In this study, four temperature indices (Mean of daily minimum temperature (TN), Mean of daily maximum temperature (TX), Cold-spell duration index (CSDI), and Warm-spell duration index (WSDI)) were defined by ETCCDI and two new indices of the Maximum number of consecutive frost days (CFD) and the Maximum number of consecutive summer days (CSU) were calculated to examine ETIs in Iran under climate change conditions. We used minimum and maximum daily temperature of five General circulation models (GCMs) including HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, and NorESM1-M from the set of CMIP5 Bias-Correction models. We investigated Two Representative Concentration Pathway (RCP) scenarios of RCP4.5 and RCP8.5 – during the historical (1965-2005) and future (2021-2060 and 2061-2100) periods. The performance of each model was evaluated using the Taylor diagram on a seasonal scale. Among models, GFDL-ESM2M and HadGEM2-ES models showed the highest, and NorESM1-M and IPSL-CM5A-LR models showed the lowest performance in Iran. Then an ensemble model was generated using Independence Weighted Mean (IWM) method. The results of multi-model ensembles (MME) showed a higher performance compared to individual CMIP5 models in all seasons. Also, the uncertainty value was significantly reduced, and the correlation value of the MME model reached 0.95 in all seasons. Additionally, it is found that WSDI and CSU indices showed positive anomalies in future periods and CSDI and CFD showed negative anomalies throughout Iran. Also, at the end of the 21st century, no cold spells are projected in almost every part of Iran. The CSU index showed that Iran's summer days are increasing sharply, according to the results of the RCP8.5 scenario in spring (MAM) and autumn (SON), the CSU will increase by 18.79 and 20.51 days, respectively at the end of the 21st century. It is projected that in the future, the spring and autumn seasons will be shorter and, summers, will be much longer than before.