Extreme Temperature Indices Research Articles

Modeling the influence of daily temperature and precipitation extreme indices on vegetation dynamics in Katsina State using statistical downscaling model (SDM)

Climate change significantly impacts local, regional, and global vegetation changes. These changes have continued to threaten ecosystems, especially in dryland areas where moisture is scarce, and the livelihoods of rural communities are at risk because of such changes, as well as their capacity to provide outputs and sustain the livelihoods of rural communities. This study aims to derive a simulation model of vegetation conditions concerning daily temperature and extreme precipitation indices in Katsina State, Nigeria. This study uses remote sensing and Geographic Information System (GIS)-based analysis, time series analysis of precipitation, maximum and minimum temperature, and twenty-four indices defined by the Expert Team on Climate Change Detection to evaluate the influence of Temp and precipitation extreme indices on vegetation dynamics/NDVI. The results indicate that the statistical downscaling model (SDSM) is performing satisfactorily in predicting maximum and minimum temperatures, along with precipitation, for the time horizon of the simulation. Mean precipitation of 1.98 mm (RCP2.6), 2.03 mm (RCP4.5), and 2.07 mm (RCP8.5) was revealed in the study area. Tmax and Tmin were projected under low emissions at 20.1 0C and 14.25 0C (RCP2.6), 20.13 °C and 14.26 0C (RCP4.5), and 20.15 0C and 14.27 0C (RCP8.5) respectively. All the scenarios present increasing minimum and maximum temperatures and decreasing precipitation, except for RCP8.5, which predicted a more adverse trend. However, in Katsina State, the Normalized Difference Vegetation Index (NDVI), precipitation, Tmax, and Tmin, extreme temperature and precipitation indices, and drought indices successfully demonstrated spatial and temporal variability.

Trends and Variability in Temperature and Related Extreme Indices in Rwanda during the Past Four Decades

Analysis of the trends and variability of climate variables and extreme climate events is important for climate change detection in space and time. In this study, the trends and variabilities of minimum, maximum, and mean temperatures, as well as five extreme temperature indices, are analyzed over Rwanda for the period of 1983 to 2022. The Modified Mann–Kendall test and the Theil–Sen estimator are used for the analysis of, respectively, the trend and the slope. The standard deviation is used for the analysis of the temporal variability. It is found, on average, over the country, a statistically significant (α = 0.05) positive trend of 0.17 °C/decade and 0.20 °C/decade in minimum temperature, respectively, for the long dry season and short rain season. Statistically significant (α = 0.05) positive trends are observed for spatially averaged cold days (0.84 days/decade), warm nights (0.62 days/decade), and warm days (1.28 days/decade). In general, maximum temperature represents higher variability compared to the minimum temperature. In all seasons except the long dry season, statistically significant (α = 0.05) high standard deviations (1.4–1.6 °C) are observed over the eastern and north-western highlands for the maximum temperature. Cold nights show more variability, with a standard deviation ranging between 5 and 7 days, than the cold days, warm nights, and warm days, having, respectively, standard deviations ranging between 2 and 3, 4 and 5 days, and 3 and 4, and, especially in the area covering the central, south-western, south-central, and northwestern parts of Rwanda. Temperature increase and its variability have an impact on agriculture, health, water resources, infrastructure, and energy. The results obtained from this study are important since they can serve as the baseline for future projections. These can help policy decision making take objective measures for mitigation and adaptation to climate change impacts.

Prediction of extreme climate on the Tibetan Plateau based on NEX-GDDP-CMIP6

As a climate-sensitive and fragile region, how the Tibetan Plateau (TP) responds to global warming would have significant implications for its ecological environment and sustainable socio-economic development. A crucial tool for assessing fine-scale climate impacts is the NEX-GDDP-CMIP6 dataset. This study utilized the NEX-GDDP-CMIP6 dataset to determine changes in extreme temperature and precipitation indices under different Shared Socioeconomic Pathways (SSP) relative to the baseline warming by 1.5 °C, 2 °C, and 3 °C. By using the Theil-Sen median trend and Mann-Kendall test methods, the research concluded that warming trends and increased humidity were on an upward trend. These changes led to amplified fluctuations in the frequency, intensity, and duration of multiple extreme indices.

Impacts of Extreme-High-Temperature Events on Vegetation in North China

Understanding the response of vegetation to temperature extremes is crucial for investigating vegetation growth and guiding ecosystem conservation. North China is a vital hub for China’s economy and food supplies, and its vegetation is highly vulnerable to complex heatwaves. In this study, based on remote sensing data, i.e., the normalized difference vegetation index (NDVI), spatio-temporal variations in vegetation and extreme high temperatures are investigated by using the methods of trend analysis, linear detrending, Pearson correlation and ridge regression. The impacts of extreme-high-temperature events on different vegetation types in North China from 1982 to 2015 are explored on multiple time scales. The results indicate that the NDVI in North China exhibits an overall increasing trend on both annual and monthly scales, with the highest values for forest vegetation and the fastest growth trend for cropland. Meanwhile, extreme-high-temperature events in North China also display an increasing trend. Before detrending, the correlations between the NDVI and certain extreme-high-temperature indices are not significant, while significant negative correlations are observed after detrending. On an annual scale, the NDVI is negatively correlated with extreme temperature indices, except for the number of warm nights, whereas, on a monthly scale, these negative correlations are only found from June to September. Grassland vegetation shows relatively strong correlations with all extreme temperature indices, while forests show nonsignificant correlations with the indices. This study offers new insight into vegetation dynamic variations and their responses to climate in North China.

Evaluation of WRF Model Boundary Layer Schemes in Simulating Temperature and Heat Extremes over the Middle East–North Africa (MENA) Region

Abstract We assess the sensitivity of the Weather Research and Forecasting (WRF) Model to the use of different planetary boundary layer (PBL) parameterizations focusing on air temperature and extreme heat conditions. This work aims to evaluate the performance of the WRF Model in simulating temperatures across the Middle East–North Africa (MENA) domain, explain the model biases resulting from the choice of different PBL schemes, and identify the best-performing configuration for the MENA region. Three different PBL schemes are used to downscale the ECMWF ERA-Interim climate over the MENA region at a horizontal resolution of 24 km, for the period 2000–10. These are the Mellor–Yamada–Janjić (MYJ), Yonsei University (YSU), and the asymmetric convective model, version 2 (ACM2). For the evaluation of the WRF runs, we used related meteorological variables from the ERA5 reanalysis, including summer maximum and minimum 2-m air temperature and heat extreme indices. Our results indicate that simulations tend to overestimate maximum temperatures and underestimate minimum temperatures, and we find that model errors are very dependent on the geographic location. The possible physical causes of model biases are investigated through the analysis of additional variables (such as boundary layer height, moisture, and heat fluxes). It is shown that differences among the PBL schemes are associated with differences in vertical mixing strength, which alters the magnitude of the entrainment of free-tropospheric air into the PBL. The YSU is found to be the best-performing scheme, and it is recommended in WRF climate simulations for the MENA region.

Analyzing Extreme Temperature Patterns in Subtropical Highlands Climates: Implications for Disaster Risk Reduction Strategies

This study utilized hot and cold indices to evaluate the changes in extreme temperature events that occurred in subtropical highland climates from 1991 to 2020. The modified Mann–Kendall (MMK) test and the Theil–Sen (TS) slope estimator were used to analyze the linear trends in the time series of the extreme temperature indices. The northern highlands of Pakistan (NHP) were considered as a case study region. The results showed that the annual maximum temperature had a slightly increasing tendency (at the rate of 0.14 °C/decade), while the annual minimum temperature had a slightly decreasing tendency (at the rate of −0.02 °C/decade). However, these trends were not significant at the 5% significance level. The decadal averages of the hot indices were the highest in the second decade (2000s), while they were the lowest in the subsequent decade (2010s). In comparison, all the cold indices except the annual minimum value of the maximum temperature (TXn) showed a persistent decline in their decadal averages throughout the 2000s and 2010s. Overall, the frequency of hot days significantly increased in the NHP during the study period. This study found that the hot days and coldest days increased over the past three decades in the NHP. However, there was a decreasing trend in the cold spell duration, cold nights, and the coldest nights over the past three decades, as demonstrated by the trends of the cold spell duration index (CSDI), the temperature of cold nights (TN10p), and the annual minimum value of the minimum temperature (TNn) indices. These changes may impact the environment, human health, and agricultural operations. The findings provide useful insights into the shifting patterns of extreme temperature events in northern Pakistan and have crucial implications for the climate-change-adaptation and resilience-building initiatives being undertaken in the region. It is suggested that the continuous monitoring of extreme temperature events is necessary to comprehend their effects on the region and devise strategies for sustainable development.

Projected climate extremes over agro-climatic zones of Ganga River Basin under 1.5, 2, and 3° global warming levels.

Recurring floods, droughts, heatwaves, and other hydro-meteorological extreme events are likely to be increased under the climate change scenarios. The increased risk of these extreme events might have more exposure to the population; thus, it is important to discuss such extreme events and their projected behavior under a changing climate scenario. In the present study, we have computed the extreme precipitation and temperature indices over the 10 agro-climatic zones falling under the Ganga River Basin (GRB)utilizing a high-resolution daily gridded temperature and precipitation multi-model ensembled CMIP6 dataset (0.25° × 0.25°) under global warming levels of 1.5°C, 2°C, and 3°C. We found that the annual daily minimum temperature (TNN) showed a higher rise of about 67% than the maximum temperature (TXX) of 48% in GRB. The basin also experiences a greater increase in the frequency of warm nights (TN90P) of about 67.71% compared to warm days (TX90P) of 29.1% for the 3°C global warming level. Along with extreme indices, the population exposed due to the impact of the extreme maximum temperature has also been analyzed for progressive warming levels. Population exposure to extreme temperature event (TXX) has been analyzed with 20-year return period using GEV distribution method. The study concludes that the exposed population to extreme temperature event experienced an increase from 46.99 to 52.16% for the whole Ganga Basin. Consecutive dry days (CDD) and consecutive wet days (CWD) both show a significant increasing trend, but CWD has a significant increase in the majority of the zones, while CDD shows a significant decreasing trend for some of the zones for three warming levels periods. Extreme climate indices help to understand the frequency and intensity of extreme weather events such as heavy rainfall, droughts, and heatwaves to develop early warning systems and adaptation strategies to mitigate such events.

Vegetation Dynamics and Its Response to Extreme Climate on the Inner Mongolian Plateau during 1982–2020

The impact of extreme climate change on terrestrial ecosystems continues to intensify. This study was conducted to understand extreme climate–vegetation interactions under exacerbated frequency, severity, and duration of extreme climatic events. The Inner Mongolian Plateau (IMP) was selected due to its sensitive natural location, which is particularly vulnerable to climate change. Based on the Normalized Difference Vegetation Index (NDVI) and daily meteorological station data from 1982 to 2020, changes in the patterns of vegetation and extreme climate in the three ecological zones (forest, steppe, and desert steppe) of the IMP were identified. Furthermore, the effects of extreme climate on vegetation were quantified using correlation analysis and a geographical detector. The results showed that the annual NDVI of 95.1%, 50.6%, and 19.5% of the area increased significantly in the forest, steppe, and desert steppe, respectively. The Tx90p (warm days) and Tn90p (warm nights) increased significantly at the rate of 0.21 and 0.235 day·yr−1, respectively, while the Tx10p (cold days) and Tn10p (cold nights) showed a significantly decreasing trend at the rate of −0.105 and −0.117 day·yr−1. An extreme warming phenomenon was observed in all extreme temperature indices on the IMP. The results of both the correlation analysis and factor detector indicated that extreme temperature intensity and frequency greatly affected forest vegetation. In contrast, extreme precipitation intensity and frequency were relatively more important to the vegetation of the desert steppe. The lag in NDVI response to extreme temperature intensity was not less than three months in the IMP; however, extreme precipitation intensity exhibited a two-month time lag in the NDVI. This study can improve our understanding of extreme climate–vegetation interactions, provide theoretical support for disaster mitigation, and aid in understanding the ecological environment of the IMP.

Projection on Antarctic Temperature Extremes from the CMIP6 Multimodel Ensemble under Different Scenarios

Abstract Global warming has been accelerating the frequency and intensity of climate extremes, and has had an immense influence on the economy and society, but attention is seldom paid to future Antarctic temperature extremes. This study investigates five surface extreme temperature indices derived from the multimodel ensemble mean (MMEM) based on 14 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathways (SSPs) of SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. In Antarctica, the variations in extreme temperature indices exhibit regional and seasonal differences. The diurnal temperature range (DTR) usually illustrates a downward trend, particularly for the Antarctic Peninsula and Antarctic coast, and the strongest change occurs in austral summer. In all cases, the annual highest minimum/maximum temperature (TNx/TXx) increases faster in inland Antarctica. Antarctic amplification of extreme temperature indices is detected and is strongest at the lowest maximum temperature (TXn). At the Antarctic Peninsula, TXx amplification only appears in winter. Great DTR amplification appears along the Antarctic coast and is strongest in summer and weakest in winter. The changes in extreme temperature indices indicate the accelerated Antarctic warming in future scenarios.

Potential benefits of limiting global warming for the mitigation of temperature extremes in China

In this study, we attempt to quantify the potential impacts of two global warming levels (i.e., 1.5 °C and 2.0 °C) on extreme temperature indices across China. The CMIP6 dataset is first evaluated against the CN05.1 observation for the historical period of 1995–2014. Then, future spatiotemporal patterns of changes in extreme temperature at two global warming levels under two shared socio-economic pathway scenarios (SSP245 and SSP585) are further analyzed. Overall, China will experience more frequent and intense high temperature events, such as summer days (SU), tropical nights (TR), warm days (TX90p) and nights (TN90p). On the other hand, under the SSP585, the number of icing days and frost days is projected to decrease at two global warming levels, with the maximal days of decrease (exceeding 20 days) seen in the west of China. Our results suggest that limiting global warming to 1.5 °C rather than 2.0 °C is beneficial to reduce extreme temperature risks. As temperature increases to 1.5 °C and then 2.0 °C above preindustrial levels, the most extreme temperature indices are expected to increase proportionately more during the final 0.5° than during the first 1.5° across most regions of China. For some warm indices, such as the warmest day (TXx), summer days (SU), and warm days (TX90p), the largest incremental changes (from 1.5° to 2.0°) tend to be found in the southwest. Under the SSP585, the incremental changes are similar to the change in the SSP245, but smaller magnitude and spatial extent.

Crop production in northwestern Ethiopian highlands under changing climate

AbstractClimate change is the most serious threat to agricultural productivity. Ethiopia's agricultural sector is affected by climate change. The northern part of Ethiopia is among the drought prone and topographically diversified areas. The purpose of this study was to examine the impact of climate change on crop yields, assess the pattern of rainfall maximum and lowest temperatures and assess the trajectory of selected crop yields. Historical weather station and National Aeronautics and Space Administration (NASA) power data were collected from 1981 to 2020. The Mann–Kendall (MK) trend test and Sen's slope (SS) method estimator were employed to estimate the trends and the direction of the trend. RClimDex package is used for climate extreme analysis. Nine rainfall and 11 temperature indices from a list of 27 extreme rainfall and temperature indices were used. The study found that the rainfall showed a significant decreasing and insignificant decreasing trend in Farta and Yilmana Densa districts, respectively. Maximum and minimum temperatures in both sites showed increasing trends. Rainfall happenings become more unanticipated, owing to the precipitation concentration index and anomaly trends. The majority of the indicators for extreme rainfall at both sites showed a downward trend. With the exception of the duration of cold spells, the annual lowest value of the daily minimum temperature, cold days and cold nights, the majority of the extreme temperature indices at both sites showed an increasing trend. The principal crop yields were connected with rainfall and temperature fluctuation, according to Pearson's product–moment correlation results, although the effects varied by crop and region. In summary, rising temperatures and decreasing precipitation may have a negative influence on agricultural activities, especially crop productivity.

The emergence of projected scaled patterns of extreme temperatures over Europe

This work investigates the scalability of extreme temperatures over the European domain with global warming levels. We have used the EURO-CORDEX ensemble of regional model simulations at 0.11° resolution for daily minimum and maximum temperatures to analyze future changes in extreme weather daily events. Scaling with the annual mean global warming modeled by the driving GCM was applied to future extreme temperature indices changes. Regional changes in each index were scaled by corresponding global warming levels obtained from GCMs. This approach asserts that regional patterns of climate change and average global temperature change are linearly related. It can provide information regarding climate change for periods or emission scenarios when no simulations exist. According to the results, the annual minimum of the lowest temperature of the day (TNn) increases more than the annual maximum of the highest temperature of the day (TXx) for Europe. The multi-model mean of the changes in scaled patterns of extreme temperatures emerges early, around 2020, even before it becomes robust. Individual scaled patterns of TNn and TXx emerge from around 2040.

Anthropogenic emissions and land use/cover change contributions to extreme temperature changes over China

Extreme weather and climate events tend to increase and strengthen as global warming intensifies, which severely affect human life and sustainable economic development. Therefore, based on the Detection and Attribution Model Intercomparison Project (DAMIP) and the Land Use Model Intercomparison Project (LUMIP) provided by the Coupled Model Intercomparison Project phase 6 (CMIP6), the optimal fingerprinting method is applied to quantify the greenhouse gases (GHG), aerosols (AA), natural forcing (NAT), and land use/cover change (LUCC) contributions to the intensity, frequency, and duration of extreme temperatures in China during 1960—2020. The results show that GHG is the main driver of extreme temperature changes in China, except for ice days (ID0) and cold spells (CSDI). GHG causes an increase in warm spells (WSDI) by about 10 days and an extension of the growing season length (GSL) by 6 to 8 days. The Tibetan Plateau is the region with the strongest extreme temperature changes influenced by GHG. AA forcing has a cooling effect that partially offsets the warming effect of GHG, especially in southeastern China. It should be pointed out that AA forcing is also the main driver for the changes in diurnal temperature range (DTR) in southeastern China, which exceeds the GHG contribution. Additionally, LUCC has greater impact in nighttime extreme temperature indices changes than the regional AA, and becomes the second dominant factor beside GHG. LUCC leads to an attributable cooling contribution of 0.34 °C for the maximum of daily minimum temperatures (TNx). Spatially, the LUCC effects on extreme temperature changes are stronger in western China than in eastern China. The more robust estimation of the GHG, AA, NAT, and LUCC contributions over distinct regions provides an advanced understanding of anthropogenic impacts on regional extreme temperatures, which is expected to be an important reference for regional climate change adaptation and mitigation.

Impact of climate change on climate extreme indices in Kaduna River basin, Nigeria.

This study examined the impact of climate change on climate extreme indices in the Kaduna River basin, Nigeria. Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal-Wallis test, and the modified Mann-Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980-2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. The CMIP6 should be used to compare with the results of this study to provide a detailed assessment of the current implication of climate change on the catchment.

Evaluation of ERA5-Simulated Temperature and Its Extremes for Australia

Atmospheric reanalysis products offer high-resolution and long-term gridded datasets that can often be used as an alternative or a supplement to observational data. Although more accessible than typical observational data and deemed fit for climate change studies, reanalysis data can show biases resulting from data assimilation approaches. Thus, a thorough evaluation of the reanalysis product over the region and metric of study is critical. Here, we evaluate the performance of the latest generation of ECMWF reanalysis, ERA5, in simulating mean and extreme temperatures over Australia for 1979–2020 versus high-quality gridded observations. We find ERA5 generally simulates maximum and minimum temperatures reasonably well (mean bias ~1.5 °C), even though it underestimates/overestimates the daily maximum/minimum temperatures, leading to a cold bias for Tmax and a warm bias for Tmin. ERA5 also underestimates the decadal warming trend in both Tmax and Tmin compared to the observations. Furthermore, ERA5 struggles to simulate the temporal variability of Tmin, leading to a markedly worse skill in Tmin than Tmax. In terms of extreme indices, ERA5 is skilled at capturing the spatial and temporal patterns and trends of extremes, albeit with the presence of biases in each index. This can partially be attributed to the warm bias in the minimum temperature. Overall, ERA5 captures the mean and extreme temperature indices over the Australian continent reasonably well, warranting its potential to supplement observations in aiding climate change-related studies, downscaling for boundary conditions, and climate model evaluation.

Spatiotemporal variability of extreme temperature indices and their implications over the heterogeneous river basin, India.

The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space-time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951-2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen's slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.

Analysis of trends in extreme temperature indices over selected areas in Peninsular Malaysia from 1991-2020

This study investigates the temporal variations of extreme temperature trends across three stations in Peninsular Malaysia with different settings, Petaling Jaya represents urban station, while Kluang and Batu Embun represent rural areas from 1991-2020. Eleven extreme warm and cold indices trends were analysed using the Mann-Kendall test, with Sen’s slope as a magnitude estimator. The findings revealed that the annual daily mean temperature, daily mean maximum temperature, and daily mean minimum temperature increased significantly at all stations by 0.04°C yr–1, 0.02°C yr–1, and 0.05°C yr–1, respectively. The station in the rural area shows a significant increment of an average of 0.03°C yr–1 for mean and warm extreme indices. Despite that, there were no changes in the total number of warm days and nights and cold days and nights for all settings. For the cold temperature indices, there were significant decreasing trends for the annual minimum of maximum temperature in rural areas, while urban station did not exhibit changes. All stations also exhibit similar annual patterns when compared although significantly higher readings were observed during El Niño events.

Impacts of Climate Change on Extreme Climate Indices in Türkiye Driven by High-Resolution Downscaled CMIP6 Climate Models

In this study, the latest release of all available Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models with two future scenarios of Shared Socio-Economic Pathways, SSP2-4.5 and SSP5-8.5, over the period 2015–2100 are utilized in diagnosing climate extremes in Türkiye. Coarse-resolution climate models were downscaled to a 0.1° × 0.1° (~9 km) spatial resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) dataset based on three types of quantile mapping: quantile mapping, detrended quantile mapping, and quantile delta mapping. The temporal variations of the 12 extreme precipitation indices (EPIs) and 12 extreme temperature indices (ETIs) from 2015 to 2100 consistently suggest drier conditions, in addition to more frequent and severe precipitation extremes and warming temperature extremes in Türkiye, under the two future scenarios. The SSP5-8.5 scenario indicates more severe water stress than the SSP2-4.5 scenario; the total precipitation decreases up to 20% for Aegean and Mediterranean regions of Türkiye. Precipitation extremes indicate a decrease in the frequency of heavy rains but an increase in very heavy rains and also an increasing amount of the total precipitation from very heavy rain days. Temperature extremes such as the coldest, warmest, and mean daily maximum temperature are expected to increase across all regions of Türkiye, indicating warming conditions by up to 7.5 °C by the end of the century. Additionally, the coldest daily maximums also exhibit higher variability to climate change in the subregions Aegean, Southeastern Anatolia, Marmara, and Mediterranean regions of Türkiye while the mean daily maximum temperature showed greater sensitivity in the Black Sea, Central Anatolia, and Eastern Anatolia regions.

Potential changes in climate indices in Alberta under projected global warming of 1.5–5 °C

Study regionAlberta province, Canada Study focusThe global average surface temperature has continuously warmed at an unprecedentedly rapid rate since the mid-20th century. Employing CMIP6 (Coupled Model Intercomparison Project Phase 6) climate projections, this study suggested a comprehensive framework with state-of-the-art techniques and evaluated potential changes in climate indices under the GMT changes of + 1.5 °C, + 2 °C, + 3 °C, + 4 °C, and + 5 °C in Alberta, Canada. New hydrological insights for the regionMain finding of this study is that a significant warming trend in annual mean temperature was projected from all of the selected CMIP6 climate projections in Alberta while there was no distinct trend in annual precipitation. Under the GMT changes from + 1.5 °C to + 5 °C, extreme cold temperature indices were warming at a larger rate in response to the GMT warming. In particular, the warming rate of the annual coldest minimum temperature in Alberta was 2.5 times faster than GMT warming. In addition, a potential decrease in summer precipitation was projected under the GMT warming, leading to a drier and warmer summer in the central and southern parts of Alberta. Furthermore, more extreme drought conditions were projected in Alberta under the GMT warming, indicating that the extreme drought conditions are likely to become more common in Alberta along with the GMT warming.

Divergent vegetation variation and the response to extreme climate events in the National Nature Reserves in Southwest China, 1961–2019

Southwest China (SWC) is a critical hotspot of biodiversity and highly sensitive to climate change. To better protect biodiversity, many National Nature Reserves have been established to maintain viable populations of species and minimize their habitat loss. However, the response of the normalized difference vegetation index (NDVI) in National Nature Reserves to extreme climate events has not been described. We analyzed NDVI changes and made several key observations: (1) from 1998 to 2019, annual average NDVI trends of 0.00421 yr−1, 0.00514 yr−1, and 0.00529 yr−1 were significantly increasing and NDVI values more than 0.8 were mainly concentrated at 1049–2349 m, 947–2547 m, 617–2817 m, in the National Nature Reserves of Hengduan Mountain (NNRHDM), Sichuan basin (NNRSCB), and Yunnan-Guizhou Plateau (NNRYGP), respectively. The change trends in different altitude bins were more significant in the NNRYGP; (2) extreme cold (warm) indices significantly decreased (increased) in the NNRHDM, NNRSCB and NNRYGP, extreme precipitation indices increased in the NNRHDM, and decreased in the NNRSCB and NNRYGP; (3) the combination of extreme temperature indices can better explain NDVI change in the NNRSCB and NNRYGP, and the combination of extreme temperature and precipitation indices can better explain the NDVI change in the NNRHDM on the monthly scale. Overall, the NDVI green trend is more obvious in the NNRYGP than in the NNRHDM and NNRSCB, and is more sensitive to extreme climate change, especially extreme temperature. Our findings showed that although the National Nature Reserves have achieved remarkable results in conservation, future work should focus on the development of adaptation and alleviation strategies to prevent extreme climate events (especially extreme temperature events) that could promote vegetation degradation in these sensitive areas.