Search Results (428)

The rising global temperature and changing climate patterns have increased the frequency and intensity of extreme heat events, droughts, and heavy precipitation, significantly affecting agriculture, water resources, and ecosystems. Solar radiation management (SRM) has been proposed as a geoengineering strategy to mitigate these effects by reducing incoming solar radiation. This study evaluated future trends and variability in rainfall and temperature extremes in the Philippines under GeoMIP (G6Solar and G6Sulfur) and ScenarioMIP (SSP2-4.5 and SSP5-8.5) projections. Using five General Circulation Models (GCMs) and a suite of 10 climate indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), changes in extreme precipitation and temperature across different climate zones in the Philippines were assessed. Climate projections for the future (2041–2070) scenario were analyzed using bias correction, downscaling, and spatial interpolation techniques. Trend analysis was evaluated using the Mann–Kendall test and Sen’s slope estimator, while variability was assessed through statistical methods. The results show widespread warming and increased extreme precipitation, but these trends vary significantly across regions. Non-uniform responses emerge across scenarios, with some northern regions experiencing decreases in specific precipitation indices despite the broader warming trend under SRM and non-SRM conditions. These findings provide critical insights into the potential impacts of SRM on future climate extremes in the Philippines and guidance on climate policy recommendations for decision-makers and stakeholders. Full article

Extreme climate events, particularly precipitation extremes, pose significant risks to ecosystems, infrastructure, and socio-economic systems globally. In Peru, the diversity of its climate, driven by its complex topography, makes it highly vulnerable to such events, especially in the Andes and Amazon regions. This study evaluates the performance of 25 CMIP6 GCMs in simulating extreme precipitation events during both the wet and dry seasons at the national level. Gridded precipitation data from the PISCO product and CMIP6 model simulations for the period 1981–2014 were used to estimate extreme precipitation indices, including Rx1day, Rx5day, SDII, CDD, CWD, R10mm, and PRCPTOT. Performance was assessed using statistical metrics such as PBIAS, NRMSE, and the Pattern Correlation Coefficient (PCC), integrated through a TOPSIS ranking. Results indicate that NorESM2-MM, MPI-ESM1-2-LR, and CESM2 exhibit the best performance, achieving TOPSIS scores above 0.8. These models show high spatial correlation (PCC frequently >0.8) and relatively low biases. In contrast, models like FGOALS-g3 and CanESM5 show significant limitations, with PBIAS exceeding 80% in Rx1day and Rx5day and TOPSIS scores below 0.5. The ensemble reveals a persistent ‘drizzle bias,’ with wet day frequency (R1mm) generally overestimated by 20–40% in the wet season and by 40–80% during the dry season across most CMIP6 models. Furthermore, indices of temporal persistence (CWD and CDD) remain the most challenging, with CWD overestimations often exceeding 100–200%. These findings highlight the critical need for statistical or dynamical downscaling, together with bias correction, before using CMIP6 projections for local adaptation strategies in the Andes and Amazon regions. Full article

Global climate change threatens regional biodiversity, yet multi-model assessments tracking future range shifts of endangered mammals remain limited. This study evaluated future habitat suitability for five legally protected terrestrial mammals in the Republic of Korea (Lutra lutra, Martes flavigula, Prionailurus bengalensis, Naemorhedus caudatus, and Pteromys volans). Using the MaxEnt framework, we projected distributions across four Shared Socioeconomic Pathways (SSPs) and future horizons (2021–2100) using five CMIP6 global climate models (GCMs). Projections revealed distinct, species-specific dynamics across consensus models. Four taxa (L. lutra, P. bengalensis, N. caudatus, and P. volans) exhibited progressive range contractions, with suitable habitats declining by −95% to −100% under high carbon emission scenarios. Conversely, M. flavigula demonstrated expanding trajectories, reflecting high-latitude range-edge responses. Prominent inter-model variations in the INM-CM5-0 and BCC-CSM2-MR models highlighted predictive uncertainties. We conclude that real-world habitat occupancy will be heavily constrained by non-climatic anthropogenic barriers, including fence networks, road systems, and climate-driven extensive wildfires. Consequently, proactive conservation should prioritize reinforcing verified contemporary core habitats along the Baekdudaegan Mountain Range and establishing structural corridors rather than relying solely on volatile bioclimatic projections. Full article

Climate change is continuously intensifying extreme climatic events and their compound effects. Assessments based on changes in single climate variables are increasingly insufficient for identifying future integrated climate pressure. Based on 23 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study projected future changes in precipitation and temperature under different emission scenarios in the Yellow River Diversion Receiving Area of Henan Province. An integrated climate pressure index was further developed to identify the spatial pattern of climate pressure and changes in its dominant drivers. The results show that: (1) The study area will experience continuous warming with wetting, with precipitation increases mainly reflected in enhanced intensity and extremes. In the far future, the median increases in Rx1day, Rx5day, and R95pTOT reach 43.22 mm, 87.33 mm, and 310.54 mm, respectively. (2) High-value zones of key climate indicators show clear spatial aggregation. High ΔR95pTOT values remain concentrated in the southeast, high ΔT_max values shift toward the south and southeast, and ΔT_min shows weaker spatial differentiation but broader high-value coverage. (3) Future integrated climate pressure is generally higher in the eastern and southeastern areas and lower in the western and northwestern areas, with a stable high-pressure core around Zhoukou–Shangqiu–Kaifeng. (4) The dominant drivers shift from the dominance of ΔT_max under low-emission scenarios to the combined dominance of ΔT_max and ΔRx1day under high-emission scenarios, indicating a gradual transition in the source of future integrated climate pressure from a single warming signal to the combined effects of warming and extreme precipitation. Full article

High-resolution projections of near-surface winds are crucial for ocean circulation and sea level studies in Southeast Asia, a region characterized by complex coastlines and monsoon variability. This study evaluates the added value of dynamical downscaling using the Weather Research and Forecasting (WRF) model at 9 km resolution, driven by two CMIP6 global climate models (EC-Earth3 and MPI-ESM1-2-HR), to simulate 10 m wind climatology over the Southeast Asian seas. Comparisons were made against ERA5 reanalysis and the parent CMIP6 GCMs, focusing on seasonal mean patterns, interannual variability, and the annual cycle. The WRF simulations demonstrate substantial improvement in capturing the spatial structures of monsoonal winds and regional circulation features. Future wind projections under SSP2-4.5 and SSP5-8.5 scenarios reveal seasonally and spatially heterogeneous trends. The downscaled models project strengthening of winter monsoon winds over the Southeast Asian seas and a weakening of summer monsoon flows, with implications for upper ocean dynamics and regional sea level patterns. The leading modes of variability from EOF analysis indicate basin-wide wind anomalies modulated by periodic signals at ~1 year and ~2–7 years, likely driven by ENSO and the Asian monsoon. The power spectra of principal components reveal that internal variability persists across scenarios, though with increased signal-to-noise ratios (SNRs) in the WRF projections toward the end of the 21st century. Full article

Six river basins of the Upper Indus Basin (UIB), Pakistan, representing glacier-dominated (Hunza and Shyok), snow-dominated (Gilgit, Chitral, and Astore), and rain–snow-mixed (Swat) hydroclimatic regimes are examined to evaluate the suitability of uncorrected CORDEX-SA climate data for hydrological modelling. Data scarcity and complex high-mountain topography make it difficult to obtain reliable inputs, and although bias correction is widely applied to regional climate model (RCM) outputs, it can distort the climate signal and introduce additional uncertainties. This study investigates the extent to which uncorrected outputs from the CORDEX South Asia ensemble can directly simulate daily streamflow in a data-scarce mountainous environment. Nine GCM–RCM combinations, comprising five global climate models and three regional climate models (COSMO-crCLIM-v1-1, REMO2015, and RegCM4-7), are used to drive the HBV-IANIGLA snow–glacier hydrological model over 1981–2005, with calibration from 1981 to 1999 and validation from 2000 to 2005. Several CORDEX-SA members reproduce daily streamflow with acceptable skill without bias correction, but performance is strongly regime-dependent. Snow-dominated basins perform best, followed by glacier-dominated basins, while the rain–snow-mixed basin remains most challenging. COSMO-crCLIM-v1-1 and RegCM4-7 outperform REMO2015 on average. The results indicate that uncorrected CORDEX-SA members can support hydrological assessments in snow-dominated catchments, whereas glacier-dominated and monsoon-influenced basins require additional treatment of climate forcing. Full article

Urban Heat Islands (UHIs) intensify extreme heat, raise energy demand, and risk citizen thermal comfort in densely built cities. However, spatially detailed, scenario-differentiated estimates of future UHI intensity are still limited for complex coastal mountainous cities. This study set out to forecast UHI intensity variations in Busan, South Korea, under SSP2-4.5 and SSP5-8.5 scenarios. Daily temperatures from 19 automatic weather stations (2010–2014) were spatially interpolated using Empirical Bayesian Kriging Regression (EBKR), which included elevation and coastline distance variables. Among the 16 CMIP6 Global Climate Models (GCMs) tested, CNRM-CM6-1 (r = 0.902, RMSE = 4.937 °C) was chosen and bias-corrected using Empirical Quantile Mapping (EQM). The results reveal that mean maximum UHI intensity rises gradually, with ΔUHI (compared with the 2010–2014 baseline of 0.90 °C) reaching +7.03 °C (SSP2-4.5) and +9.60 °C (SSP5-8.5) in the far future, roughly 1.43 times more under the high-emission scenario. Summer through autumn has a UHI intensity increase, whereas long-term warming concentrates in Busan’s urban core. These findings inform targeted urban heat adaptation strategies, prioritizing green infrastructure, cool urban surfaces, and energy-resilient city planning to protect human well-being. Full article

Climate change threatens nitrogen cycling in agricultural ecosystems. Optimizing sowing dates and nitrogen management for maize–soybean intercropping is critical for sustainable production in the North China Plain (NCP). Using a calibrated Agricultural Production Systems Simulator (APSIM) model driven by three representative global climate models (GCMs) selected from 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, we evaluated management strategies under two Shared Socioeconomic Pathway scenarios (SSP2-4.5 and SSP5-8.5) across three climatic zones for near-term (2030–2059) and long-term (2070–2099) periods. Under SSP5-8.5, warming was 1.8–2.2 times greater than under SSP2-4.5, nitrate nitrogen (NO₃⁻-N) leaching increased by 12.1%, and nitrate storage in the 100–150 cm soil layer rose by 53.4% in Zone III. Biological nitrogen fixation contributed 20.1–29.1% of soybean nitrogen uptake under low nitrogen and 14.9–23.4% under medium nitrogen. Optimal strategies were identified: sowing on 7 June (S3) with medium nitrogen (220.8 kg N ha⁻¹) under SSP2-4.5, and advancing sowing to 28 May (S2) with medium nitrogen under SSP5-8.5 to alleviate heat stress. This study reveals a climate-driven “earlier supply–shortened demand–concentrated leaching” mismatch, providing adaptive management guidance for maize–soybean intercropping systems in the NCP. Full article

This study investigates the relative contributions of climate change and human activities to future seasonal runoff changes in the Jinsha River (JSR) Basin (western China), with particular emphasis on quantifying the influence of multiple sources of uncertainty on attribution results. An integrated framework combining global climate models (GCMs), shared socioeconomic pathways (SSPs), hydrological models (HMs), seasonal-scale Budyko models, and variance analysis (VAAN) is developed. The conclusions were as follows: (1) The mutation year of runoff in JSR was 1984. (2) Both the ABCD and dynamic water balance model with variable time scales (DWBM) hydrological models performed excellently in simulating historical runoff, with Nash–Sutcliffe efficiency (NSE) values exceeding 0.85 and relative errors within 10%. (3) Under the SSP119 scenario, human activities dominate runoff changes in spring, summer, and autumn, contributing 13.5 mm, 48.1 mm, and 30.5 mm, respectively, while climate change dominates in winter with a contribution of −10.7 mm. (4) Under the SSP245 and SSP585 scenarios, human activities remain the dominant factor for summer (46.5 mm and −47.4 mm) and autumn (29.1 mm and −30.8 mm), whereas climate change dominates in spring (−14.3 mm and −14.5 mm) and winter (−13.3 mm and −14.2 mm). (5) The interactions among the HMs, GCMs, and SSPs are the primary source of uncertainty, contributing 44.45% to 82.03% of the total variance in attribution results across different seasons. Full article

Against the backdrop of urbanization and global warming, reducing carbon emissions and achieving carbon neutrality have emerged as focal points in current urban ecological research. Urban green infrastructure (UGI) serves as the primary natural carbon sink within cities; therefore, investigating and optimizing its carbon sequestration services is a crucial step toward realizing carbon neutrality and fostering sustainable urban development. As the core components of urban ecosystems, urban parks provide essential ecosystem services that play a pivotal role in expanding carbon sinks, facilitating energy conservation and emission reduction, and enhancing urban climate resilience. This paper takes 20 parks in Linyi City’s central urban area as examples, systematically quantifies the carbon sequestration effect of urban parks in the central urban area of Linyi City from 2019 to 2024 using methods such as the Carnegie–Ames–Stanford Approach (CASA) and the gravity model, and quantitatively evaluates the equity of urban residents’ access to these services. The study shows that the overall annual average carbon sequestration rate of urban parks in Linyi City’s central area over nearly six years ranges from 202.02 gC·m⁻²·a⁻¹ to 279.31 gC·m⁻²·a⁻¹, while individual park annual averages range from 171.29 to 332.76 gC·m⁻²·a⁻¹, falling within the normal range for cities at the same latitude; in terms of vegetation carbon sequestration capacity, woody plant communities dominate in this region, with annual average carbon sequestration rates approximately 10% higher than those dominated by herbaceous vegetation. In terms of intrinsic activity performance of carbon sequestration, overall, woody-dominated plant communities exhibit greater stability and resilience under extreme weather conditions, experiencing smaller impacts on ecological functions but longer recovery cycles to peak levels. Regarding equity in the supply and demand of ecosystem services, the Gini coefficient in the study area is 0.59, indicating an extremely imbalanced state; within the same park service range, up to 60% of residents do not benefit from carbon sequestration ecosystem services. The urban supply–demand mismatch reveals that approximately 20% of the population resides in high-demand–low-supply areas, experiencing extreme ecological deprivation; only about 13% of the population falls into the high-demand–high-supply category, this group being the high-benefit recipients who enjoy both spatial convenience and high-quality ecological welfare. The theoretical implications for urban green space planning: according to the results, merely expanding park green space area to increase per capita access is myopic and inadvisable in central urban park planning. Instead, greater emphasis should be placed on enhancing ecological service levels beyond basic area requirements, comprehensively improving vegetation quality and ecosystem service capacity of parks. In old urban areas constrained by land use, the hierarchical structure of vegetation should be strengthened, and micro green spaces should have enhanced ecological service capabilities to improve residents’ access rights through higher service quality. In newly developed urban areas, planning should balance quantity and quality to serve more people and alleviate urban ecological pressures. Overall, by quantitatively assessing the carbon sequestration capacity and the socio-spatial equity of ecosystem services provided by urban parks in Linyi City, this study offers robust empirical evidence and methodological tools for sustainable urban planning, ultimately fostering the sustainable development of urban ecosystems. Full article

The impact of climate change on rainfall extremes has become increasingly obvious in many climatic regions including arid regions where extreme precipitation events are thought to have augmented or at least intensified. Driven by global factors such as greenhouse gas emissions, deforestation, and industrialization, climate change has augmented hydrological variability, thus making traditional stationary models inadequate for the estimation of extreme rainfall at various return periods. Extreme value analyses, which were traditionally derived under the assumption of stationarity (i.e., constant statistical properties over time) and typically do not account for temporal variability or external climatic drivers (e.g., temperature or large-scale climate indices), may lead to inaccurate estimation of rainfall quantiles under changing climate conditions. This paper presents a structured review of applied methodologies for quantifying the influence of climate change on extreme rainfall events, with special attention to how non-stationarity is addressed in arid regions applications, which was not a major focus in previous review papers. Relevant statistical techniques, extreme value theory, machine learning models, and high-resolution climate simulations are reviewed. From an initial pool of over 340 studies, 91 were selected based on their relevance to quantify rainfall extremes induced by climate change in arid regions. Based on the reviewed studies, the analysis revealed a strong reliance on trend analysis of downscaled Global Climate Models (GCMs) and Regional Climate Models (RCMs) within a stationary framework, with limited integration of covariates, other than time, in non-stationary frequency analysis to estimate the climate change-related value. This review identifies the research gaps in the scientific literature related to climate change impact assessment on extreme rainfall in arid regions. It emphasizes the necessity for adopting more robust hybrid approaches, adopting statistical distributions more suitable to arid conditions, careful treatment of outliers, conducting regional analyses to better understand the overall climate behavior of the region, addressing the impact on short-duration rainfall, integrating key climatic drivers through the incorporation of additional climate covariates and the impact of climate change on sub-daily rainfall patterns. Full article

Plants must constantly adapt to biotic and abiotic stressors, which the global climate change crisis has intensified. To monitor plant health and predict their ability to face these challenges, various target molecules, such as hormones, glucose, and reactive oxygen species, are used as proxies for their physiological status. This review provides a systematic assessment of the current state of biosensor technology, an innovative analytical approach designed for in situ, minimally invasive, and real-time monitoring. Using the PICO (Problem, Intervention, Comparison, and Outcome) strategy, relevant research papers were identified. The review highlights how biosensors can detect physiological responses to stress before visual symptoms manifest, offering a significant advantage over traditional, often destructive, laboratory techniques, like gas chromatography–mass spectrometer (GC-MS) or high-performance liquid chromatography (HPLC). These advancements aim to improve precision agriculture and forestry management by providing sustainable methods to assess resilience in changing environments. Finally, the challenges of translating research from model organisms to complex woody species and choosing the correct target are discussed, and future perspectives, including the integration of biosensors with Artificial Intelligence-driven predictive models for large-scale environmental monitoring, are outlined. Full article

This study presents a comprehensive assessment of historical and projected variability in the wind climate over Ireland and its adjacent marine regions, including the North Atlantic Ocean, Irish Sea, and Celtic Sea. First, the long-term wind characteristics are examined using the ERA5 reanalysis dataset for the historical period (1979–2008), followed by an evaluation of five CMIP6 Global Climate Models (GCMs) to determine their performance in representing regional wind climatology. Based on spatial validation and relative bias analyses, the most suitable model is selected to investigate future wind changes under the SSP2-4.5 and 5-8.5 scenarios. The CMIP6 historical data is also compared locally at seven measurement stations. Two future projections are considered for the near-term (2031–2060) and mid-term (2071–2100), focusing on inter- and intra-annual variability and extreme wind behaviour. The results indicate an overall decrease in mean wind speed across the study area, with more pronounced reductions under SSP5-8.5 and during the mid-term period. In terms of seasonality, reductions are more pronounced during winter and summer than in the transitional seasons. According to the extreme value analysis based on the generalised extreme value distribution, general declines in extreme values are detected at selected critical locations, especially at wind speeds with large return periods. Full article

This study examines the historical and anticipated effects of climate change on essential hydroclimatic variables (temperature, precipitation, evapotranspiration, and soil moisture) in the Southeastern Anatolia Project (GAP) region of Türkiye, a semi-arid and agriculturally significant basin experiencing heightened water stress. The analysis employs a collection of CMIP6 Global Climate Models (GCM) and integrates three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5), utilizing statistical bias correction methods such as Delta Change, Quantile Mapping (QM), and Empirical Quantile Mapping (EQM) to improve the regional accuracy of the projections. The ACCESS-CM2 model, validated with data from Türkiye’s Meteorological General Directorate (MGM), was chosen for comprehensive spatial mapping, utilizing Inverse Distance Weighting (IDW) interpolation across seven temporal intervals encompassing past, present, and future periods. The findings indicate a steady increase in temperature and evapotranspiration, especially under high-emission scenarios, with temperature rises above +4 °C and considerable water losses anticipated by century’s end. Soil moisture exhibits a declining tendency, particularly in the southern and eastern regions, signifying increasing drought susceptibility. Precipitation patterns demonstrate significant spatial variability and rising uncertainty, with relative error (RE%) values increasing under SSP5-8.5. Historical data from 1963 to 2022 corroborate these conclusions, indicating a progressive shift towards a warmer and drier regional climate. These observations highlight the importance of climate adaptation strategies and water management in the GAP region. The research provides decision-makers a high-resolution, bias-corrected hydroclimatic dataset. Full article

The Upper Indus Basin (UIB) plays a crucial role in water security and socio-economic development in Pakistan. Under changing climatic conditions, the sustainable management of the water resources of the UIB needs accurate and reliable projections of hydroclimatic variables. Given the limited assessments of hydroclimatic simulations from CMIP6 models in the region, this study assessed the uncertainties associated with the historical simulations of 16 CMIP6 GCMs in the UIB. The observations of 34 in situ weather stations were used as reference, while the performances of GCMs were assessed based on widely used evaluation indices, including correlation coefficient (CC), bias, relative bias (rBIAS), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Taylor diagram, and the performance diagram. Results of the evaluation indices indicated that most of the considered GCMs failed to represent the observed precipitation in the UIB. Correlations between the simulations of GCMs and the reference observations were generally low; CCs ranged from −0.24 to 0.16. All GCMs exhibited negative NSE values (ranging between −2.79 and −0.51). The values of RMSE (59.36 to 98.43 mm/month) and rBIAS (9 to 96%) were also very high. Among the considered GCMs, INM-CM4-8 and EC-Earth3-Veg-LR showed comparatively lower RMSE values, moderate rBIAS, and higher CC values. Three GCMs (MRI-ESM2-0, GFDL-ESM4, and CNRM-CM6-1) performed very poorly, with high negative NSE and significant overestimations. Among the 16 GCMs, EC-Earth3-Veg-LR had the highest composite score and better performance across all considered indices. The overall findings of this study indicated that none of the CMIP6-based GCMs (in their raw form) performed satisfactorily in the UIB of Pakistan; therefore, the application of bias-correction techniques is strongly recommended before direct application of these projections for climate impact and adaptation studies in this mountainous region. The results will be useful for the hydroclimatic data users and algorithm developers of global climate models. Full article