Variation in Extreme Temperature and Its Instability in China

In this study, the instability of extreme temperatures is defined as the degree of perturbation of the spatial and temporal distribution of extreme temperatures, which is to show the uncertainty of the intensity and occurrence of extreme temperatures in China. Based on identifying the extreme temperatures and by analyzing their variability, we refer to the entropy value in the entropy weight method to study the instability of extreme temperatures. The results show that TXx (annual maximum value of daily maximum temperature) and TNn (annual minimum value of daily minimum temperature) in China increased at 0.18 °C/10 year and 0.52 °C/10 year, respectively, from 1966 to 2015. The interannual data of TXx’ occurrence (CTXx) and TNn’ occurrence (CTNn), which are used to identify the timing of extreme temperatures, advance at 0.538 d/10 year and 1.02 d/10 year, respectively. In summary, extreme low-temperature changes are more sensitive to global warming. The results of extreme temperature instability show that the relative instability region of TXx is located in the middle and lower reaches of the Yangtze River basin, and the relative instability region of TNn is concentrated in the Yangtze River, Yellow River, Langtang River source area and parts of Tibet. The relative instability region of CTXx instability is distributed between 105° E and 120° E south of the 30° N latitude line, while the distribution of CTNn instability region is more scattered; the TXx’s instability intensity is higher than TNn’s, and CTXx’s instability intensity is higher than CTNn’s. We further investigate the factors affecting extreme climate instability. We also find that the increase in mean temperature and the change in the intensity of the El Niño phenomenon has significant effects on extreme temperature instability.

GRNN Prediction Model for Temperature-Induced Deformation of CRTS II Unballasted Slab Track

The General Regression Neural Network (GRNN) is one of the algorithms of artificial neural networks (ANN) that receives much attention in prediction applications. This research used the GRNN to predict the temperatureinduced deformation of unballasted track structures based on experimental data considering external weather conditions, such as sunshine duration, rain conditions, daily maximum temperature, daily minimum temperature, and daily average wind speed. The GRNN network predicts the average absolute error of the prediction results (0.0318 ℃), the maximum absolute error (1.7729 ℃), and the GRNN prediction sample mean squared error (0.070701). The average relative error is 0.32%. The finding of this study shows that the GRNN prediction method has good accuracy and robustness. Furthermore, it can promote the research of unballasted track temperature fields that are related to concrete structures.

Spatiotemporal Changes in Temperature and Precipitation in West Africa. Part I: Analysis with the CMIP6 Historical Dataset

Climate variability and change constitute major challenges for Africa, especially West Africa (WA), where an important increase in extreme climate events has been noticed. Therefore, it appears essential to analyze characteristics and trends of some key climatological parameters. Thus, this study addressed spatiotemporal variabilities and trends in regard to temperature and precipitation extremes by using 21 models of the Coupled Model Intercomparison Project version 6 (CMIP6) and 24 extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). First, the CMIP6 variables were evaluated with observations (CHIRPS, CHIRTS, and CRU) of the period 1983–2014; then, the extreme indices from 1950 to 2014 were computed. The innovative trend analysis (ITA), Sen’s slope, and Mann–Kendall tests were utilized to track down trends in the computed extreme climate indices. Increasing trends were observed for the maxima of daily maximum temperature (TXX) and daily minimum temperature (TXN) as well as the maximum and minimum of the minimum temperature (TNX and TNN). This upward trend of daily maximum temperature (Tmax) and daily minimum temperature (Tmin) was enhanced with a significant increase in warm days/nights (TX90p/TN90p) and a significantly decreasing trend in cool days/nights (TX10p/TN10p). The precipitation was widely variable over WA, with more than 85% of the total annual water in the study domain collected during the monsoon period. An upward trend in consecutive dry days (CDD) and a downward trend in consecutive wet days (CWD) influenced the annual total precipitation on wet days (PRCPTOT). The results also depicted an upward trend in SDII and R30mm, which, additionally to the trends of CDD and CWD, could be responsible for localized flood-like situations along the coastal areas. The study identified the 1970s dryness as well as the slight recovery of the 1990s, which it indicated occurred in 1992 over West Africa.

Evaluation of historical CMIP6 model simulations and future projections of temperature over the Pan-Third Pole region

The Pan-Third Pole (PTP) region, which encompasses the Eurasian highlands and their surroundings, has experienced unprecedented, accelerated warming during the past decades. This study evaluates the performance of historical simulation runs of the Coupled Model Intercomparison Project (CMIP6) in capturing spatial patterns and temporal variations observed over the PTP region for mean and extreme temperatures. In addition, projected changes in temperatures under four Shared Socioeconomic Pathway (SSP) scenarios (SSP1‐2.6, SSP2‐4.5, SSP3-7.0, and SSP5‐8.5) are also reported. Four indices were used to characterize changes in temperature extremes: the annual maximum value of daily maximum temperature (TXx), the annual minimum value of daily minimum temperature (TNn), and indices for the percentage of warm days (TX90p) and warm nights (TN90p). Results indicate that most CMIP6 models generally capture the characteristics of the observed mean and extreme temperatures over the PTP region, but there still are slight cold biases in the Tibetan Plateau. Future changes of mean and extreme temperatures demonstrate that a strong increase will occur for the entire PTP region during the twenty-first century under all four SSP scenarios. Between 2015 and 2099, ensemble area-averaged annual mean temperatures are projected to increase by 1.24 °C/100 year, 3.28 °C/100 year, 5.57 °C/100 year, and 7.40 °C/100 year for the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. For TXx and TNn, the most intense warming is projected in Central Asia. The greatest number of projected TX90p and TN90p will occur in the Southeast Asia and Tibetan Plateau, respectively.

LightGBM Low-Temperature Prediction Model Based on LassoCV Feature Selection

Icing disasters on power grid transmission lines can easily lead to major accidents, such as wire breakage and tower overturning, that endanger the safe operation of the power grid. Short-term prediction of transmission line icing relies to a large extent on accurate prediction of daily minimum temperature. This study therefore proposes a LightGBM low-temperature prediction model based on LassoCV feature selection. A data set comprising four meteorological variables was established, and time series autocorrelation coefficients were first used to determine the hysteresis characteristics in relation to the daily minimum temperature. Subsequently, the LassoCV feature selection method was used to select the meteorological elements that are highly related to minimum temperature, with their lag characteristics, as input variables, to eliminate noise in the original meteorological data set and reduce the complexity of the model. On this basis, the LightGBM low-temperature prediction model is established. The model was optimized through grid search and crossvalidation and validated using daily minimum surface temperature data from Yongshan County (station number 56489), Zhaotong City, Yunnan Province. The root mean square error, MAE, and MAPE of the model minimum temperature prediction after feature selection are shown to be 1.305, 0.999, and 0.112, respectively. These results indicate that the LightGBM prediction model is effective at predicting low temperatures and can be used to support short-term icing prediction.

Effects of ambient temperature during the nestling stage on a stress indicator in nestling pied flycatchers Ficedula hypoleuca

Long-term and short-term changes in ambient temperature can cause stress in birds, leading to changes in the level of hematological parameters. The H:L ratio (heterophil-to-lymphocyte ratio) is a hematological index that allows for the assessment of the stress induced by environmental changes, including weather conditions. In this paper, we examined the influence of temperatures and the sum of precipitation on the health of nestling pied flycatchers (Ficedula hypoleuca) by using the H:L ratio reflecting the body’s response to stress. All examined temperature indicators influenced the H:L ratio, yet the average value of daily minimum temperature during the first 12 days of nestling life had the strongest influence, maximum temperature had the weakest effect, while precipitation had no significant influence. Our research indicates that even a small increase in temperature caused a stress reaction in nestling pied flycatchers, which was reflected by an increase in the H:L ratio. The increase in the stress index (H:L ratio) was probably a result of poor weather conditions (precipitation, low temperature), which prevented the adult birds from actively foraging and properly feeding the nestlings.

Diverse Responses of Phenology in Multi-Grassland to Environmental Factors on Qinghai-Tibetan Plateau in China

Revealing grassland growing season spatial patterns and their climatic controls is crucial for the understanding of the productivity change mechanism in regional terrestrial ecosystem. However, the multi-grassland phenological factors are different, which has not been well studied. In this paper, the spatio-temporal patterns of the grassland start of the growing season (SOS) and the end of growing season (EOS) were investigated using MODIS Normalized Difference Vegetation Index (NDVI) on the Qinghai-Tibetan Plateau (QTP) during 2000 to 2019. At the same time, we analyzed the factors (including extreme and mean climate, drought, solar radiation, etc.) regulating grassland phenology under ongoing climate change. The results showed that the SOS appeared first in mountain meadow, shrub-tussock, temperature steppe and desert, then in alpine steppe and alpine meadow, showed a significant advancing tendency in all types. The EOS appeared first in temperature steppe, alpine steppe and alpine meadow, then in mountain meadow, shrub-tussock and desert. Further analysis indicated that the decrease of yearly minimum value of daily minimum temperature (TNN), yearly maximum value of daily minimum temperature (TNX), Temperature vegetation dryness index (TVDI) and the increase of yearly maximum consecutive five-day precipitation (RX5day) advance the grassland spring phenology, whereas the increase of solar radiation (SR) delay the grassland spring phenology. Meanwhile, SOS and its change rate showed the trend of significant delay and decline with the increase of altitude, respectively. We also found that the decrease of TVDI, TNN and the increase of yearly mean value of temperature (MAT_MEAN), yearly mean value of daily maximum temperature (MAT_MAX) and yearly mean value of daily minimum temperature (MAT_MIN) advanced the autumn phenology. The EOS and its change rate advance and increase with increasing altitude, respectively.

Radial growth responses of post oak (Quercus stellata) to climate variability and management in southeastern Oklahoma, USA

We investigated radial growth of post oak (Quercus stellata) growing in a range of stand structures (forest to savanna) created in 1984 by different harvesting and thinning treatments followed by different prescribed fire intervals. We related ring width index (RWI) to monthly and seasonal climate variables and time since fire to assess impacts of climate variability and interactions with management on radial growth. RWI of all treatments was positively correlated to minimum daily temperature the previous September and precipitation late spring/early summer the current-year, and negatively correlated to maximum daily temperatures and drought index late spring/early summer. June weather was most strongly correlated in four of five treatments. While stand structure affected absolute diameter growth, RWI of savanna and forest stands responded similarly to climate variability, and low intensity prescribed fire did not influence RWI. On average, 100 mm reduction in June precipitation decreased RWI by 8%, 1oC increase in previous-year September daily minimum temperature increased RWI by 3.5%, and 1oC increase in June maximum daily temperature decreased RWI by 3.7%. Therefore, negative effects of drought and warmer spring/summer temperatures may be reduced by longer growing seasons under warmer climate scenarios. However, management did not appear to influence RWI.

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.