Regional Climate Change Research Articles

Temperature increase may not necessarily penalize future yields of three major crops in Xinjiang, Northwest China

Future food production is at risk due to climate change, particularly in arid regions with limited water resources and extensive irrigated agriculture. This study utilized the DSSAT model in conjunction with downscaled data from 21 global climate models (GCMs) under two shared socioeconomic pathways (SSP2–4.5 and SSP5–8.5) to assess the impact of projected climate change on irrigated crop phenology, yield, and evapotranspiration (ETc) of cotton, maize, and winter wheat in the Shihezi region of Xinjiang, China. The results indicated that temperature and precipitation in the region were expected to increase gradually from 2021 to 2100. Climate change has resulted in earlier anthesis and physiological maturity of all three crops. Future climatic conditions could reduce maize yields to 16.02 %. Conversely, the yields of cotton and winter wheat increased, with cotton yields rising by 1.23–10.94 % and winter wheat yields by 3.19–14.07 %. Additionally, ETc for cotton, maize, and winter wheat could rise in the future. The irrigation water demands could increase by 41.1–96.4 mm for cotton and 27.3–37.9 mm for maize, while the demand for winter wheat could decrease by 0.5–36.2 mm. Warming was significantly correlated with the changes in the yield and water use efficiency (WUE) of cotton, maize, and winter wheat. The temperature increases of +0.5°C to +3.0°C (relative to baseline) at 0.5°C intervals were analyzed to evaluate their effects on yield and WUE. The yields varied from −0.93 % to 6.15 % for cotton, −43.42 % to −7.99 % for maize, and 4.28–9.92 % for winter wheat. The WUE changes ranged from −29.03 % to −1.08 % for cotton, −43.06 % to −7.66 % for maize, and 0.69–3.47 % for winter wheat. Contrary to the common belief that rising temperatures generally harm crop yields, our study suggests that temperature fluctuations may benefit certain crops in specific regions. These results could provide theoretical guidance for implementing adaptive measures to future climate change in regions with conditions similar to Shihezi, Xinjiang, China, to ensure crop security and sustainable water management.

Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes

Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed.

Seasonal Variations of Ice-Covered Lake Ecosystems in the Context of Climate Warming: A Review

The period of freezing is an important phenological characteristic of lakes in the Northern Hemisphere, exhibiting higher sensitivity to regional climate changes and aiding in the detection of Earth’s response to climate change. This review systematically examines 1141 articles on seasonal frozen lakes from 1991 to 2021, aiming to understand the seasonal variations and control conditions of ice-covered lakes. For the former, we discussed the physical structure and growth characteristics of seasonal ice cover, changes in water environmental conditions and primary production, accumulation and transformation of CO2 beneath the ice, and the role of winter lakes as carbon sources or sinks. We also proposed a concept of structural stratification based on the differences in physical properties of ice and solute content. The latter provided an overview of the ice-covered period (−1.2 d decade−1), lake evaporation (+16% by the end of the 21st century), the response of planktonic organisms (earlier spring blooming: 2.17 d year−1) to global climate change, the impact of greenhouse gas emissions on ice-free events, and the influence of individual characteristics such as depth, latitude, and elevation on the seasonal frozen lakes. Finally, future research directions for seasonally ice-covered lakes are discussed. Considering the limited and less systematic research conducted so far, this study aims to use bibliometric methods to synthesize and describe the trends and main research points of seasonal ice-covered lakes so as to lay an important foundation for scholars in this field to better understand the existing research progress and explore future research directions.

Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities.

Characteristics of air temperature on the southern and southeastern slopes of the Greater Caucasus in the modern period

In the paper, the characteristics of air temperature in the south part of the Greater Caucasus region and the effects of regional climate changes on this area in the modern period were studied. In the research, the air temperature observation data of 8 hydrometeorological stations operating in the region for the years 1961-2022 were used, and the modern classification of the average monthly, seasonal, annual and altitude time-space distributions of temperature was given.Based on the data of hydrometeorological stations in the south and southeast piedmonts of the Greater Caucasus Mountains, an electronic map was prepared for the distribution of average annual air temperature indicators on the territory, and for determining the indicators of areas without data. DEM (Digital Elevation Model) files obtained fromthe decoding of satellite images (satellite data) were used in the prepared map. The research shows that July and August are the hottest months when the temperature is 22.80C, while January is the coldest by temperature about 0.50C in this region. In this part of the Greater Caucasus province, in the cold months of the year, the average monthly temperature is indicating below 00C in the area located above the moderately mountainous part of the region. The dynamics of the average indicators of temperature of the multi-year period (1961-2022) are expressed by a positive trend. The average annual temperature distribution map of the territory shows that the air temperature is 15.00C in the low mountainous part of the region, while it decreases upwards and, according to the adiabatic law, equals 00C at altitudes above 3500 m. According to the climate studies conducted in the region, compared to the years 1981-2010, it was found that there was a temperature increase of 1.20C in the mountainous region in 2011-2022. Also, a comparative analysis of 10-year averages of air temperature trends, using the Holt- Winters model, shows that temperatures in 2100 are expected to increase by 3.00C compared to 2030. In the region, climate changes that worsen every year lead to water scarcity, desertification, drought, etc. and can expand the area of recurrence of environmental crises and destructive natural events. This research can be used in future climate change mitigation , climate atlases, infrastructure, agricultural and industrial projects in the region.

ENVIRONMENTAL CONSEQUENCES OF CLIMATE CHANGE: GLOBAL CHALLENGES AND REGIONAL RESPONSES

The issue of environmental problems is particularly important today. The relevance is determined by the fact that global challenges arise every day, and global warming causes serious climate change and threatens the disappearance of the ecosystem. The purpose of the article is to analyze current trends and consequences of global and regional climate change. Methodology. A set of general scientific methods (analytical and synthetic hermeneutic, pragmatic, and generalization techniques) was employed to achieve the research goal and ensure the scientific reliability of the results. Scientific novelty. The study reveals the main problems of nature management and changes in nature caused by anthropogenic activities. The importance of the population's counteraction to negative impacts on the planet’s ecosystem is emphasized. The study examines the main causes of climate change and substantiates the global risks associated with this phenomenon. The impact of climate change on the economic sector is analyzed. Special attention is paid to the terms “climate crisis” and “global climate change”. It is found that modern environmental problems include an increase in the number of wars, ocean pollution, global warming, resource shortages, and others. Each of these problems poses a serious threat to the development of any state. It is proved that in the context of political instability, Ukraine does not have the full potential to implement scientifically based balanced measures. The absence of an effective state policy on sustainable environmental management poses a threat to the economy and environment of Ukraine. It is necessary to find ways to solve the identified problems and consider promising areas for improving the factors that affect the environment and the environmental situation in general. Improving the environmental situation will allow Ukraine to rise to a new level in the global ranking. The conclusions state that climate change threatens not only the economic and environmental security of the state, but also national security in general.

The impact of climate change and environmental regulation on energy poverty: evidence from China

BackgroundEnvironmental pollution and energy poverty have always been serious challenges for the global energy system.ResultsBased on the panel data of 30 provinces in China from 2005 to 2020, this paper uses FE and sys-GMM models to explore the impact of environmental regulations and climate change on energy poverty. The results show that climate change increases energy poverty, with rising energy for cooling in hot summers, and unchanged income in the short term. Moreover, environmental regulation plays a moderating role between climate change and energy poverty. Specifically, economical environmental regulation has a negative moderating effect, while legal and supervised environmental regulations have positive moderating effects. Finally, the national basic energy poverty line used is lower than that in the eastern region, higher than that in the western region, and close to that in the central region, which reflects the heterogeneity of energy poverty in different regions of China.ConclusionsThe findings in this paper clarify the nexus between climate change, environmental regulation, and energy poverty, addressing in this way a gap in existing research, which has great significance for environmental and energy policy makers.

Maternal And preGnancy hEalth duriNg elevaTed heAt – MAGENTA: Novel data linkages and methods to understand the impacts of climate change on deprived communities in Wales and London.

Background and ObjectivesThe health impacts from changing climate, including increasing frequency, severity, and duration of heat are projected to worsen. The MAGENTA study will explore the impact of heat on pregnancy-related outcomes for deprived communities in Wales and London. ApproachMAGENTA will create linked data cohorts capturing some of the most deprived and diverse communities in Europe to analyse the impact deprivation, ethnicity, heatwaves, and urban heat islands have on pregnancy-related health. A consented data-linked cohort of pregnant women will provide biological samples and undergo direct measurement of themselves and their home environments to understand the heat stress response of pregnant women not acclimatised to heat during their pregnancy. Core to the study is our public involvement strategy which includes pregnant women and women who have recently delivered a baby, co-producing the research and how results are communicated. ResultsOur electronic cohorts cover &gt;10 years of climate and health data and capture all births in Wales &gt;300,000 and &gt;1.1 million in London. We will present how the novel data linkages can work in multiple trusted research environments, approaches for sharing methods in geospatial modelling and statistics across the project and preliminary findings from the study. Findings and ImplicationsThe MAGENTA study will help inform expectant parents, families and policy makers how to prepare for climate change in temperate regions. The comparison between different populations in Wales and London will give insight on where policies and interventions can be generally applied and where they need to be contextually adapted.

Assessing the separate contribution of the domain, RCM and GCM to the uncertainty in CORDEX simulations over the overlapped regions that include Jordan

AbstractThe Coordinated Regional Downscaling Experiment (CORDEX) offers a framework for regional climate downscaling experiments over continental‐based domains that overlap; this allows for conducting multimodel analysis. However, a question is raised about the uncertainty due to domain selection. This study aims to evaluate the sensitivity of CORDEX simulations to the choices of domain, downscaling regional climate model (RCM) and driving global model (GCM) over a Middle East study region, including Jordan. Understanding these sensitivities helps assess simulation uncertainties and enhance regional climate change projections. Taylor diagrams and variance decomposition analyses were used to analyse seasonal temperature and precipitation climatologies. The results indicate that the domain contribution to variance is negligible, whereas the choice of GCM and RCM strongly influences simulations. Variance in temperature is significantly impacted by the driving model (GCM), whereas RCM has a higher influence on precipitation, which reflects their large‐scale versus local nature. The results of this study support the development of multidomain ensembles since projections produced by the same GCM–RCM model combination are consistent across different overlapping domains. On the other hand, this approach facilitates the consideration as well as a comprehensive quantification of the uncertainties arising from the utilization of multiple GCMs and RCMs within such an ensemble. This improves the reliability of regional climate information, thus facilitating the development of effective adaptation strategies and mitigation plans.

Spatial Heterogeneity and Key Influencing Factors of Soil Organic Carbon, Soil Total Nitrogen, and Carbon to Nitrogen Ratio in main plantations of Torreya grandis cv. Merrillii

Soil organic carbon （SOC） and soil total nitrogen （STN） serve as important indicators of the elemental balance within forest ecosystems reflecting soil fertility and quality. Accurate knowledge regarding the spatial variability of regional SOC, STN, and C∶N ratio and their influencing factors is of great significance for precise fertilization and soil health. In this study, a total of 117 topsoil samples （0-20 cm in depth） based on a 1 km×1 km grid were collected in the Torreya grandis cv. Merrillii plantation in Zhejiang Province. A combination of multi-dimensional statistical approaches （random forest model, structural equation model, redundancy analysis, and variation partitioning analysis） and diverse spatial analytical techniques （geostatistics, Moran's I index, etc.） were applied to reveal the spatial distributions and influencing factors of SOC, STN, and C∶N ratio in the Torreya. grandis cv. Merrillii region. The results showed that the average ω（SOC）, ω（STN）, and C∶N ratio were 17.63 g·kg-1, 1.48 g·kg-1, and 12.65, respectively, and their coefficients of variation were 68.08%, 67.41%, and 46.03%, respectively, indicating a moderate degree of variability. In general, the SOC, STN, and C∶N ratio of the Torreya grandis cv. Merrillii plantations were at an intermediate level in the national plantation. The semi-variance results showed that the nugget/sill values of SOC, STN, and C∶N ratio were 49.98%, 45.88%, and 49.93%, respectively, demonstrating a moderate level of spatial autocorrelation. The spatial distribution results showed that SOC, STN, and C∶N ratio decreased from northeast to southwest, with the majority of the region exhibiting above-medium fertility levels of SOC. The results of correlation analysis and redundancy analysis indicated that AN, AP, and AK were significantly correlated with both SOC, STN, and C∶N ratio （P&lt;0.05）. The results of random forest, structural equation model, and variation partitioning analysis evidenced that the main influencing factors of SOC and STN were soil-available nutrients （AN, AP, and AK）. Therefore, our results could provide important insights for enhancing soil carbon and nitrogen pools in special plantations in Zhejiang Province, enhancing the capacity of plantations to adapt to regional climate change through ecological measures such as appropriate fertilization practices and strategic understory vegetation cultivation.

Comparison of the risks and drivers of compound hot-dry and hot-wet extremes in a warming world

Abstract Compound hot-dry (CHD) and compound hot-wet (CHW) extremes have both intensified under global warming, posing exacerbated socio-economic threats compared to univariate extremes. This study presents a comprehensive assessment and comparison of the historical changes and driving factors behind CHD and CHW using observational data and climate model simulations. Findings indicate a notable surge in CHD and CHW occurrences, with CHW experiencing a higher increasing rate. Our investigation further reveals that anthropogenic climate change predominantly drives the increase in both types of compound extremes, especially for CHW. In contrast, land-atmosphere feedbacks have a limited impact on CHW at a global scale, but substantially contributes to the rise in CHD by reinforcing the negative precipitation-temperature coupling. This influence even surpasses that of anthropogenic climate change in specific regions. Understanding these variations and underlying causes is crucial for improving prediction accuracy and mitigating the impacts of compound extremes.

Climate change and deer in boreal and temperate regions: From physiology to population dynamics and species distributions.

Climate change causes far-reaching disruption in nature, where tolerance thresholds already have been exceeded for some plants and animals. In the short term, deer may respond to climate through individual physiological and behavioral responses. Over time, individual responses can aggregate to the population level and ultimately lead to evolutionary adaptations. We systematically reviewed the literature (published 2000-2022) to summarize the effect of temperature, rainfall, snow, combined measures (e.g., the North Atlantic Oscillation), and extreme events, on deer species inhabiting boreal and temperate forests in terms of their physiology, spatial use, and population dynamics. We targeted deer species that inhabit relevant biomes in North America, Europe, and Asia: moose, roe deer, wapiti, red deer, sika deer, fallow deer, white-tailed deer, mule deer, caribou, and reindeer. Our review (218 papers) shows that many deer populations will likely benefit in part from warmer winters, but hotter and drier summers may exceed their physiological tolerances. We found support for deer expressing both morphological, physiological, and behavioral plasticity in response to climate variability. For example, some deer species can limit the effects of harsh weather conditions by modifying habitat use and daily activity patterns, while the physiological responses of female deer can lead to long-lasting effects on population dynamics. We identified 20 patterns, among which some illustrate antagonistic pathways, suggesting that detrimental effects will cancel out some of the benefits of climate change. Our findings highlight the influence of local variables (e.g., population density and predation) on how deer will respond to climatic conditions. We identified several knowledge gaps, such as studies regarding the potential impact on these animals of extreme weather events, snow type, and wetter autumns. The patterns we have identified in this literature review should help managers understand how populations of deer may be affected by regionally projected futures regarding temperature, rainfall, and snow.

Trends in extreme rainfall and their relationship to flooding episodes in Vhembe district, South Africa

Extreme weather events, such as heavy rainfall, are being increased by climate change in various regions, and such events often cause floods. This study examined the trends and variability of extreme rainfall indices using daily rainfall data (1981–2023) from three study sites at different socio-economic development spectra in Vhembe District, Limpopo Province, South Africa. The analyses focus on indices such as the annual total rainfall from wet days (PRCPTOT), the maximum number of consecutive dry days (CDD) and wet days (CWD), annual maximum 1-day and 5-day rainfall (RX1 day and RX5 day), the Simple Daily Intensity Index (SDII), the number of days exceeding varying amounts of precipitation (R10, R20, R40) and the annual number of wet days with rainfall greater than the 95th and 99th percentile (R95p and R99p) of the 1981–2023 daily rainfall. We discuss the observed trends in extreme rainfall indices in light of the actual flood occurrences to establish linkages. Several statistically significant and marginal changes in extreme rainfall trends were identified and provided key insights into reported flooding events in the district—flooding episodes were mainly attributed to the significant increases in total precipitation (PRCPTOT) and rainfall exceeding the 99th percentile of daily rainfall (R99p). Other significant contributors were declining CDD and increasing RX1day at Duthuni, increasing R40 at Musina as well as increasing R1 and declining CDD at Sane. However, the low altitude, urbanization, poor waste management and inadequate drainage systems were among the key non-climatic drivers of flood risk across the study sites, but these warrant further investigation. The complex interplay between climatic and non-climatic drivers of flood risk underscores the importance of localized climate studies and the need for adaptive strategies to minimize loss and damage. Overall, this research provides valuable insights into localized extreme rainfall trends, which are essential for developing site-specific flood mitigation and adaptation strategies. However, such initiatives require placing vulnerable communities at the centre in order to develop solutions that are locally led and relevant.

Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use

Abstract Rising affluence and a warming climate mean that the demand for air conditioning (AC) is rising rapidly, as society adapts to climate extremes. Here we present findings from a new methodological framework to flexibly couple and emulate these growing demands into a global integrated assessment model (IAM), subsequently representing the positive feedbacks between rising temperatures, growth in cooling demand, and carbon emissions. In assessing global and regional climate change impacts on cooling energy demand, the emulator incorporates climate model uncertainties and can explore behavioural and adaptation-related assumptions on setpoint temperature and access to cooling. It is also agnostic to the emissions and climate warming trajectory, enabling the IAM to run new policy-relevant scenarios (Current Policies, 2 °C and 1.5 °C) with climate impacts that do not follow Representative Concentration Pathways. We find that climate model uncertainty has a significant effect, more than doubling the increase in electricity demand, when comparing the 95th percentile cases to the median of the climate model ensemble. Residential AC cooling energy demands are expected to increase by 150% by 2050 whilst providing universal access to AC would result in the order of a 400% increase. Depending on the region, under current policies and limited mitigation, climate change could bring in the order of 10%–20% higher cooling-related electricity demands by 2050, and approximately 50% by 2100. Set point temperature has an important moderating role—increasing internal set-point from 23 °C to 26 °C, approximately halves the growth in electricity demand, for the majority of scenarios and regions. This effect is so strong that the change in set point temperature to both residential and commercial sectors outweighs the growth in demand that would occur by providing universal access to AC by 2050 to the 40% of the global population who would otherwise not afford it.

Hourly Downscaling of Daily Climate Projection Data for the Nakdong River Basin using the Nearest Neighbor Search Method

Hydrological and water quality models have been continuously improved for assessing the impacts of climate change. Accordingly, meteorological data with high spatial and temporal resolutions are required. Current climate change scenario-based projections provide meteorological data on monthly and daily scales, which are too coarse for regional climate change assessments. In this study, we propose a temporal downscaling method based on the nearest neighbor search methodology for temporal downscaling from daily time-step meteorological data to hourly time-step data. To verify the temporal downscaling method, historical meteorological data from weather stations located in the Nakdong River basin were used, and the normalized root mean square error (NRMSE) between observational and simulated data was calculated. Consequently, the simulation errors were approximately 2–4% for temperature and precipitation, and approximately 7–16% for wind speed, relative humidity, and solar radiation. Next, using the temporal downscaling method, hourly future climate projection data were derived from the daily SSP5-8.5 climate scenario projections retrieved from weather stations in the target area. According to the downscaled hourly climate projection results, under the SSP5-8.5 climate change scenario, the future annual maximum temperature is projected to increase by up to 7.0 ℃ and 5.6 ℃ at Daegu and Busan weather stations, compared to the reference period maximum temperatures of 36.2 ℃ and 33 ℃, respectively. The annual precipitation duration is projected to decrease by up to 103.3 h (21.25%) and 157.2 h (27.57%), compared to the reference period durations of 486.2 h and 570.2 h, respectively. Annual precipitation intensity is projected to increase by up to 0.71 mm/h (33.5%) and 0.83 mm/h (31.2%), compared to the reference period precipitation intensities of 2.12 mm/h and 2.66 mm/h, respectively. The temporal downscaling method presented in this study is expected to contribute to the preparation of meteorological input data to effectively simulate detailed hourly rainfall runoff and the behavior of water quality factors, thereby improving the assessment of climate change impacts.

A simple subtropical high‐pressure system index over the South Atlantic

AbstractThis research introduces a novel index for the South Atlantic High Pressure (SAHP) system to enhance understanding of regional climate variability and change. Subtropical highs significantly influence regional climates, yet comprehensive indices to measure their behaviours are lacking. Utilizing ERA5 reanalysis data from 1940 to 2023, the proposed index estimates a weighted centroid of the area surrounding the maximum sea level pressure within a 3 hPa range. This method ensures robustness and flexibility in contiguous area estimation specific to subtropical high events. Results showed the index effectively reflects the position and intensity of the SAHP. The study reveals that latitudinal variability of the SAHP has a strong unimodal structure, whereas longitudinal variability exhibits a bimodal structure. Seasonal patterns of the index show noticeable changes, with winter (JJA) and spring (SON) months having relatively high index values compared to summer (DJF) and autumn (MAM) months, underscoring the intra‐annual variability of the SAHP index. During ENSO events, the mean centroid position of the SAHP shifts significantly, moving westwards and polewards during El Niño and showing greater stability during La Niña. The index, with minimal computation requirements and flexibility, can be applied across diverse datasets, aiding in the assessment of future subtropical high changes.

The Evaluation of Climate Change Competitiveness via DEA Models and Shannon Entropy: EU Regions.

The purpose of this paper is to assess the efficiency of climate change competitiveness via a case study on EU regions by using the data envelopment analysis (DEA) model and Shannon entropy. First, on the same premise as similar composite indicators, we develop a DEA model to assess the relative performance of the regions in climate change competitiveness. Then, we extend our calculations with a DEA-like model and Shannon entropy to derive global estimates of a new competitiveness index by using common weights. Results show that the proposed DEA-Entropy model enables the construction of a regional climate change competitiveness index among all regions via a set of common weights. The proposed model's common weight structure demonstrates more discriminative power compared to the weights obtained through pure DEA or DEA-like methods. In order to validate the proposed DEA-Entropy model, it was applied to 120 EU regions. The results are meaningful for the regions to improve their competitiveness.

Long-term trend of dust event duration over Northwest China

Dust events in Northwest China have become more variable under regional climate change. Prior research has largely overlooked the spatial-temporal distribution of dust event duration (DED) and its long-term trend. This study systematically analyzed the spatial and temporal variations of DED in Northwest China and explored their associated factors using satellite-derived air quality datasets during 2000–2021. We find that dust event frequency (DEF) and DED generally showed a significant decreasing trend since 2000, but in 2013, DEF and DED started to rebound, with DED in particular, showing a more pronounced rebound in most parts of Northwest China. Correlation analysis with many factors suggests that the rise in near-surface wind speed since 2013 may primarily account for the increase in DEF and DED by enhancing dust generation and suppressing dust dry deposition processes. Further projections reveal that regions close to dust sources are likely to have more frequent and prolonged dust events, while areas far from dust sources will experience a decrease in DEF and DED in the future. These findings are crucial for understanding dust event variations and for guiding local dust management strategies.

Response of alpine vegetation function to climate change in the Tibetan Plateau: A perspective from solar-induced chlorophyll fluorescence

Vegetation change in the Tibetan Plateau (TP) is a crucial indicator of climate change in alpine regions. Previous studies have reported an overall greening trend in the vegetation structure across the TP, especially in its northeastern part, in response to a warming climate. However, variations in the vegetation function and the possible drivers remain poorly understood. Considering the optimal temperature for plants in TP is usually higher than the current temperature, our hypothesis is the function and structure of alpine vegetation have changed synchronously over past few decades. To test this hypothesis, we analyzed satellite-observed solar-induced chlorophyll fluorescence (SIF) and leaf area index (LAI) in the Yellow River source (YRS) region in the northeastern TP to quantify the long-term trends in vegetation functional and structural states, respectively. The results suggest that from 1982 to 2018, SIF increased significantly in 77.71 % of the YRS area, resulting in a significant upward trend of 0.52 × 10−3 mW m−2 nm−1 sr−1 yr−1 (p < 0.001) for the regional-mean SIF. This represents a 16.1 % increase in SIF, which is close in magnitude to the increase in LAI over the same period. The synchronous changes between vegetation function and structure suggest that improved greenness corresponds to a similar level of change in carbon uptake across YRS. Additionally, we used a multiple regression approach to quantify the contribution of climatic factors to SIF changes in YRS. Our analyses show that the increases in SIF were primarily driven by rising temperatures. Spatially, temperature dominated SIF changes in most parts of YRS, except for certain dry parts in the northern and western YRS, where precipitation had a greater impact. Our results are crucial for a comprehensive understanding of climate regulations on vegetation structure and function in high-elevation regions.

How do household crop and livestock production adapt to extreme climatic events? —Insights from a typical agro-pastoral ecotone on the Qinghai[sbnd]Tibet Plateau

The increased threat of more frequent extreme climatic events (ECEs) poses significant challenges to household crops and livestock production, exacerbated by their weak adaptability. To explore ways to improve households’ adaptation awareness and behaviors, a typical agro-pastoral ecotone on the northeastern QinghaiTibet Plateau (QTP), Gonghe Sandy Land (GSL), was selected as the study area. This study analyses the impact of ECEs, and the adaptation measures taken by households, and innovatively includes the perception of government services from households in the factor system to identify the key driving mechanism of coping strategies. The results revealed that (1) 74.07 % of households experienced an extreme drought, and livestock production was 29.63 % more affected than crop production was; (2) households adopted different adaptation strategies with weather forecasts being the prevailing measure among households for crop (25.93 %) and livestock (42.33 %) production adaptation; and (3) different strategies are key to improving adaptive capacity. We found that production experience sharing, demographic and economic coordination, and early warning system and support by the government can increase farmers' awareness of adaptation strategies. While enhancing cognitive levels regarding measures, optimizing a household’s health, rationally allocating assets, and increasing timely and stable government services can improve adaptive behaviors. On the basis of these findings, this study provides recommendations from the perspective of human well-being, with the aim to achieving the SDGs, enhancing regional climate change adaptation capacity and ensuring the safety and efficiency of crop and livestock production in the QTP.