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Between Individual Belief and National Context: Relative Positioning and Expectations of Collective Climate Action in Europe -
An Early Documented Late-Spring Heatwave in Iberia Under Pre-Industrial Climatic Conditions -
Field Validation of ENVI-Met for Outdoor Thermal Comfort Assessment in a Dense Tropical Neighbourhood: A Case Study on Reunion Island -
Impact-Based Analysis of Weather-Related Hazards in Greece (2000–2025): Insights from the High-Impact Weather Events Database (HIWE-DB)
Journal Description
Climate
Climate
is a scientific, peer-reviewed, open access journal of climate science published online monthly by MDPI. The American Society of Adaptation Professionals (ASAP) is affiliated with Climate and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), GEOBASE, GeoRef, AGRIS, and other databases.
- Journal Rank: JCR - Q2 (Meteorology and Atmospheric Sciences) / CiteScore - Q1 (Atmospheric Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.5 days after submission; acceptance to publication is undertaken in 3.7 days (median values for papers published in this journal in the first half of 2026).
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- Journal Cluster of Geospatial and Earth Sciences: Remote Sensing, Atmosphere, Geosciences, Climate, Quaternary, Earth, Geographies, Geomatics, Meteorology and Fossil Studies.
Impact Factor:
4.0 (2025);
5-Year Impact Factor:
4.1 (2025)
Latest Articles
Developing an Agricultural Drought Prediction Framework for Timor-Leste
Climate 2026, 14(9), 194; https://doi.org/10.3390/cli14090194 - 13 Sep 2026
Abstract
Agricultural drought is a natural hazard which has disastrous impacts on populations, economies and the environment in drought-vulnerable countries. This study develops an agricultural drought prediction framework for Timor-Leste—a least developed country in Southeast Asia. Currently, long-range forecasting capabilities and proactive agricultural drought
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Agricultural drought is a natural hazard which has disastrous impacts on populations, economies and the environment in drought-vulnerable countries. This study develops an agricultural drought prediction framework for Timor-Leste—a least developed country in Southeast Asia. Currently, long-range forecasting capabilities and proactive agricultural drought management practices in Timor-Leste remain limited, constraining the country’s ability to prepare for and respond to periodic drought events. This research evaluated the effectiveness of drought prediction in Timor-Leste using seasonal rainfall outlooks from a dynamical Global Climate Model (GCM): the European Centre for Medium-Range Weather Forecasts’ (ECMWF) Seasonal Forecast System 5 (SEAS5), across different drought and non-drought events. SEAS5 effectively predicted an increased probability of below-average median rainfall over Timor-Leste for the case study of the 2015–2016 El Niño-induced drought and was assessed as having higher probabilistic skill across the wider study area compared with the Australian Bureau of Meteorology’s (BoM) Australian Community Climate and Earth-System Simulator (ACCESS-S2). While some limitations exist in raw forecast skill at times of year when predictability is lower, outlooks from both GCMs could, with sound communication, be applied to predict drought’s onset, peak and end. This research serves as a foundational step toward the development of an agricultural drought early warning system in Timor-Leste.
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(This article belongs to the Special Issue Climate and Weather Extremes (Third Edition))
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Spatiotemporal Trends in Temperature and Rainfall Across the Aberdare Forest Ecosystem, Kenya (1994–2024)
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Julius W. Kamau, Thuita Thenya and Jacinta Maweu
Climate 2026, 14(9), 193; https://doi.org/10.3390/cli14090193 - 13 Sep 2026
Abstract
Montane forests are among the most critical terrestrial biodiversity reservoirs, acting as climate-regulating systems and water towers, and are vital for human well-being across the world. However, they are facing diverse, repeated, and contradictory challenges due to climate variability and change that may
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Montane forests are among the most critical terrestrial biodiversity reservoirs, acting as climate-regulating systems and water towers, and are vital for human well-being across the world. However, they are facing diverse, repeated, and contradictory challenges due to climate variability and change that may undermine their resilience. This study analyzed spatiotemporal trends in temperature and rainfall in the Aberdare Forest ecosystem, Kenya, over the past three decades (1994–2024). ERA5-Land (temperature, 9 km resolution) and CHIRPS-v3 (precipitation, 5 km resolution) provided the gridded climate datasets and were validated against observed station data from the Kenya Meteorological Department (KMD). Trend detection utilized the non-parametric Mann–Kendall (MK) test, while Sen’s slope estimator quantified the magnitude and direction of change. Results show a statistically significant positive trend in temperature, while rainfall demonstrated a statistically non-significant positive trend over the study period. Spatial analysis revealed statistically significant warming across the forest, bamboo, and moorland ecological zones, while rainfall showed positive but non-significant trends, with the magnitude of both temperature and rainfall trends varying across zones. These findings provide robust empirical evidence of significant temporal and spatial warming with no clear corresponding directional change in rainfall, underscoring increasing climate pressures on the Aberdare Forest ecosystem. The results establish one of the first empirically validated long-term climate trend baselines for the Aberdare Forest ecosystem. This benchmark provides critical evidence to inform adaptive governance and responsive management for enhanced resilience of East Africa’s montane forests.
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Open AccessArticle
Changes in Mean Annual Precipitation over the Brazilian and Nearby Regions from 1940 to 2099 Based on Weather Stations, Reanalyses, and the CMIP6 Model Ensemble
by
Ilya Serykh, Svetlana Krasheninnikova, Mariia Safonova, Tatiana Gorbunova, Roman Gorbunov, Fabio L. P. Krykhtine and Osvaldo M. Rezende
Climate 2026, 14(9), 192; https://doi.org/10.3390/cli14090192 - 11 Sep 2026
Abstract
This study analyzes changes in precipitation over the extended Brazilian region (10° N–40° S; 75° W–25° W) based on data from meteorological stations, reanalyses (ECMWF ERA5, NASA MERRA-2), the NOAA PREC reconstruction, and an ensemble of 32 CMIP6 models. According to weather station
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This study analyzes changes in precipitation over the extended Brazilian region (10° N–40° S; 75° W–25° W) based on data from meteorological stations, reanalyses (ECMWF ERA5, NASA MERRA-2), the NOAA PREC reconstruction, and an ensemble of 32 CMIP6 models. According to weather station data and the ERA5 reanalysis for the period 1996–2025, a slight decrease in mean precipitation (P) is observed in the whole region (ΔPWeather station = −44 mm/year, ΔPERA5 = −174 mm/year). A comparison of the periods 1996–2025 and 1940–1969 using CMIP6 ensemble also indicates a reduction in regional average precipitation (ΔPAve = −18 mm/year). The reanalyses show a decline in precipitation beginning approximately in the 1980s, accompanied by significant interannual and interdecadal variability associated with natural climate variability. Future projections under high radiative forcing scenarios (SSP3-7.0 and SSP5-8.5) indicate a continued decline in average precipitation in the study region throughout the 21st century. Low forcing scenarios (SSP1-2.6 and SSP2-4.5) suggest only weak changes in average precipitation over the period 2026–2099. However, the precipitation changes are spatially heterogeneous in the study region. According to the ERA5 reanalysis (1940–2025), precipitation has increased over the northern and southern parts of the Brazilian territory, while a decrease was observed in its central portion. All CMIP6 SSP ensemble projections considered (2026–2099) indicate a decrease in precipitation in the north and an increase in the south of the Brazilian region. Moreover, with increasing radiative forcing, the reduction in precipitation in the Amazon River basin and adjacent territories will intensify.
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(This article belongs to the Section Climate Dynamics and Modelling)
Open AccessArticle
From North Atlantic Cooling to Omega Blocking: Dynamical Pathways to the June 2026 European Heatwave
by
Sotirios T. Arsenis, Ioannis Kapsomenakis and Panagiotis T. Nastos
Climate 2026, 14(9), 191; https://doi.org/10.3390/cli14090191 - 11 Sep 2026
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Atmospheric blocking is a major feature of mid-latitude circulation and is closely associated with the occurrence of extreme weather events over Europe. Among the different blocking regimes, Omega blocks are characterized by their persistent tripolar structure, which favors the development of prolonged heatwaves.
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Atmospheric blocking is a major feature of mid-latitude circulation and is closely associated with the occurrence of extreme weather events over Europe. Among the different blocking regimes, Omega blocks are characterized by their persistent tripolar structure, which favors the development of prolonged heatwaves. In late June 2026, a major early—summer heatwave affected Western and Central Europe. This study investigates the synoptic, dynamical, and climatic mechanisms associated with this event using ERA5 reanalysis data. The evolution of the 500 hPa geopotential height, 850 hPa temperature, polar jet stream, Rossby wave breaking, and dynamical tropopause (2 PVU) was analyzed together with anomalies in sea surface temperature (SST), meridional temperature gradient, soil moisture, and sensible heat flux. The results show that the heatwave developed under a persistent Omega blocking pattern, which promoted strong subsidence, enhanced solar heating, and sustained warm air advection over Western Europe. Anomalously dry soils and enhanced sensible heat flux were also observed, suggesting a potential amplifying role of land–atmosphere interactions during the event. In addition, a pronounced cold SST anomaly over the subpolar North Atlantic preceded the onset of the blocking and coincided with a weakened meridional temperature gradient and reduced lower-tropospheric baroclinicity along the climatological position of the polar jet stream. Findings align with studies that North Atlantic cooling, including weakened Atlantic Meridional Overturning Circulation (AMOC), may favor summer Omega blocking and European heatwaves. However, the June 2026 SST anomaly’s origin is undetermined from this single analysis and is not attributed to AMOC variability.
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Open AccessArticle
Physics-Guided Neural Networks for Physically Consistent SPEI: A Bias Correction Framework for Drought Monitoring over Southern Africa
by
Gizaw Mengistu Tsidu
Climate 2026, 14(9), 190; https://doi.org/10.3390/cli14090190 - 10 Sep 2026
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The Standardised Precipitation-Evapotranspiration Index (SPEI) is widely used for drought monitoring, but reanalysis datasets like ERA5-Land contain biases that affect its reliability. Conventional bias correction, which adjusts variables independently, can violate the physical relationship between precipitation and PET. We introduce a Physics-Guided Neural
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The Standardised Precipitation-Evapotranspiration Index (SPEI) is widely used for drought monitoring, but reanalysis datasets like ERA5-Land contain biases that affect its reliability. Conventional bias correction, which adjusts variables independently, can violate the physical relationship between precipitation and PET. We introduce a Physics-Guided Neural Network (PGNN) that corrects both variables simultaneously while enforcing a water-balance constraint within the loss function. Using a rigorous temporal split (training 1950–2010, validation 2011–2018, testing 2019–2025), the PGNN framework shows improved performance compared with quantile distribution mapping (QDM). On the validation period (2011–2018), the correction reduces precipitation RMSE by 32% (from 40.4 to 27.6 mm month−1) and increases PET R2 from −2.820 to 0.890, compared with QDM’s 0.326. For the 3-month agricultural drought index, PGNN reduces RMSE by 27% (from 0.440 to 0.320), increases R2 from 0.737 to 0.857, and improves correlation from 0.890 to 0.943, whereas QDM shows only marginal improvement (R2 = 0.715). Consistent gains are observed across all timescales, with PGNN-corrected SPEI achieving R2 exceeding 0.78 for the 1-, 3-, and 6-month agricultural indices. At longer timescales, PGNN shows improved skill, achieving R2 = 0.101 and 0.113 for 12- and 24-month SPEI, respectively—outperforming QDM (R2 = 0.043 and 0.032). The period-specific analysis reveals two distinct degradation mechanisms: error accumulation in the water balance explains the decline from short to long timescales, while climate non-stationarity explains the degradation from the validation period to the test period. In the independent test period (2019–2025), both methods show substantial degradation: PGNN precipitation RMSE increases from 27.6 to 32.0 mm month−1 (+16%), PET R2 drops from 0.890 to 0.458 (−49%), and longer timescales (9–24 months) yield negative R2 values. Categorical metrics for extreme drought detection show mixed results on the validation period: the Critical Success Index (CSI) for Extreme Dry conditions (3-month SPEI) decreases from 0.269 (raw) to 0.164 for PGNN, while QDM achieves 0.277. This unexpected behaviour is attributed to the differing event distribution in the validation period. However, PGNN improves detection for severe and moderate dry classes, and Cohen’s Kappa increases from 0.552 (raw) to 0.646 (PGNN) compared with QDM’s 0.493. The water-balance constraint ensures climatic consistency and preserves the annual cycle (PET peaks in October at 148.1 mm month−1, close to the CRU reference of 152.2 mm month−1), while QDM introduces a two-month phase shift. The framework offers a potential scalable pathway for operational drought monitoring, but the test-period degradation suggests a need for adaptive correction methods that can track evolving climate baselines.
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Open AccessArticle
Climate Services for Heat-Risk Management at the Dakar 2026 Youth Olympic Games: A Competition-Zone Assessment Using WBGT and UTCI
by
Mory Toure, Papa Ngor Ndiaye, Ibrahima Diouf, Aissatou Faye, Abdoulaye Diouf, Diabel Ndiaye, Oumar Konte, Sadibou Ba and Papa Babacar Diagne
Climate 2026, 14(9), 189; https://doi.org/10.3390/cli14090189 - 9 Sep 2026
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Heat stress has become a major concern for the organization of outdoor sporting events under a warming climate, yet climatological decision-support frameworks remain scarce for African Olympic venues. This study aims to characterize climatological heat risk during the Dakar 2026 Youth Olympic Games
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Heat stress has become a major concern for the organization of outdoor sporting events under a warming climate, yet climatological decision-support frameworks remain scarce for African Olympic venues. This study aims to characterize climatological heat risk during the Dakar 2026 Youth Olympic Games (YOG) and to develop an operational framework to support competition scheduling. Hourly ERA5 reanalysis data (1991–2025), restricted to the official Games period (31 October–13 November), were used to compute the Wet-Bulb Globe Temperature (WBGT) and the Universal Thermal Climate Index (UTCI). Long-term trends, climatological diurnal cycles, and hourly probabilities of exceeding operational heat-stress thresholds were analyzed across the three competition zones (Dakar, Diamniadio, and Saly). Heat stress increased significantly over the study period, with WBGT P90 trends ranging from +0.33 to +0.47 °C decade−1, while UTCI P90 also increased significantly in Dakar and Diamniadio. A persistent spatial gradient (Saly > Diamniadio > Dakar) was identified, and the highest heat-stress levels consistently occurred between 11:00 and 17:00, peaking around 14:00–15:00. Integrating WBGT and UTCI within a hierarchical decision-support framework showed that early morning (before 09:00) and evening (after 19:00) provide the most favorable periods for outdoor competitions, whereas midday requires precautionary measures or schedule adjustments, particularly in Saly and Diamniadio. These findings demonstrate that climatological information can be translated into actionable climate services supporting heat-risk management for major sporting events. Although intended for long-term planning, the proposed framework should be complemented by real-time weather forecasts during the Games to support operational decision making.
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Open AccessArticle
Satellite-Based Daily Precipitation Bias Correction in a Tropical Mountainous Region Using Functional Generalized Additive Mixed Models: A Case Study in Valle del Cauca, Colombia
by
David Arango-Londoño, Delia Ortega-Lenis, Mauricio A. Mazo-Lopera, Johan Steven Aparicio, Diego Soto and Paula Moraga
Climate 2026, 14(9), 188; https://doi.org/10.3390/cli14090188 - 9 Sep 2026
Abstract
Accurate correction of daily satellite-derived precipitation estimates in data-scarce tropical regions remains a critical challenge for climate monitoring, agriculture, and public health. Satellite products such as CHIRPS offer broad spatial coverage but exhibit systematic biases relative to ground-based observations particularly in complex terrain
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Accurate correction of daily satellite-derived precipitation estimates in data-scarce tropical regions remains a critical challenge for climate monitoring, agriculture, and public health. Satellite products such as CHIRPS offer broad spatial coverage but exhibit systematic biases relative to ground-based observations particularly in complex terrain under bimodal tropical regimes influenced by ENSO. We propose a Functional Generalised Additive Mixed Model (FGAMM) that corrects CHIRPS-derived precipitation estimates by treating the annual accumulated precipitation curve as a functional response and the satellite accumulation curve as a functional covariate, while incorporating station-level random effects and the Southern Oscillation Index. This functional formulation targets the systematic, slowly varying bias between satellite and ground-station accumulation, the quantity most relevant for water-balance applications such as reservoir management and agricultural planning rather than day-to-day storm nowcasting. Applied to 62 IDEAM stations in the Valle del Cauca department of Colombia (2012–2020), the FGAMM achieves a mean cross-validation RMSE of 0.68 mm/day (95% bootstrap CI: 0.61–0.75), a substantially lower error than linear regression, SVM, and Random Forest within this dataset, where the gap is statistically significant across all competing methods. This magnitude of advantage is not reproduced when applying the same fitting-and-differencing pipeline, via a simplified concurrent approximation, to an independent national-network dataset; we discuss the methodological factors that likely contribute to this discrepancy—including an inherent smoothness asymmetry between the penalised-spline FGAMM fit and the unconstrained benchmark models, and differences in validation design between the two checks—in the Discussion, and treat the true size of the FGAMM’s advantage as an open question pending a fully controlled comparison. Corrected estimates are currently restricted to the calibrated station locations; because CHIRPS provides near-global daily coverage from 1981 to the present, we discuss how the same modelling approach could in principle be applied to other tropical or subtropical regions with a sparse reference station network, including areas of Latin America, sub-Saharan Africa, and South Asia where station density is similarly limited.
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(This article belongs to the Special Issue Advances in Data Assimilation for Weather and Climate Prediction)
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Climate Change Awareness Among Palestinians in the West Bank: A Cross-Sectional Study of Socioeconomic and Contextual Factors
by
Maryam W. Fasfous, Mohamed N. Abdel-Fattah and Sarah A. Ibrahim
Climate 2026, 14(9), 187; https://doi.org/10.3390/cli14090187 - 9 Sep 2026
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Climate change awareness is increasingly recognized as a multidimensional issue related to understanding climate risks, adoption, and climate-friendly behaviors. This paper aims to explore and assess the socioeconomic contextual factors of climate change awareness in the West Bank, Palestine. In order to achieve
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Climate change awareness is increasingly recognized as a multidimensional issue related to understanding climate risks, adoption, and climate-friendly behaviors. This paper aims to explore and assess the socioeconomic contextual factors of climate change awareness in the West Bank, Palestine. In order to achieve this, a cross-sectional online survey, conducted from September to November 2024 and including 1059 participants, was carried out to measure awareness based on the Climate Change Awareness Scale (CCAS) to investigate knowledge, attitudes, personal concern, multiplicative action, and climate-friendly behavior. Descriptive statistics have demonstrated a moderate overall awareness level (M = 3.97 and SD = 0.71) with comparatively high climate-friendly behavior (M = 4.43 and SD = 0.89). On the other hand, the lowest scores were reported for multiplicative action (M = 3.30 and SD = 0.97). Additionally, multiple linear regression with robust variance estimators showed that awareness is statistically correlated with age and some contextual factors related to place of residence. In other words, younger age groups have shown lower awareness than that of older adults. Moreover, individuals living in refugee camps have demonstrated lower awareness when compared to village residents. Similarly, people in contact areas with the occupation manifested higher awareness than that of those in non-contact areas. On the contrary, gender, education level, and monthly income did not show statistical significance. These results indicate that climate change awareness in the West Bank is formed via social and contextual differences. Furthermore, recognizing these differences requires taking into account the local context when designing climate awareness programs and urging collaborative efforts.
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Open AccessArticle
The Role of Stratosphere–Troposphere Vertical Shear of the Zonal Wind in the QBO-MJO Relationship
by
Paul E. Roundy
Climate 2026, 14(9), 186; https://doi.org/10.3390/cli14090186 - 8 Sep 2026
Abstract
Vertical shear of the zonal wind across the equatorial tropopause over the Indian Ocean to the Maritime Continent is caused by a combination of the quasi-biennial oscillation (QBO) of the stratosphere and the seasonal cycle and interannual variability of the upper troposphere. The
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Vertical shear of the zonal wind across the equatorial tropopause over the Indian Ocean to the Maritime Continent is caused by a combination of the quasi-biennial oscillation (QBO) of the stratosphere and the seasonal cycle and interannual variability of the upper troposphere. The Madden–Julian Oscillation (MJO) has been previously observed to be more active during the easterly than the westerly phase of the QBO. Kelvin waves interacting with the background flow explain most of the propagation characteristics of the MJO in the equatorial upper troposphere. This work assesses the hypothesis that Kelvin wave propagation under conditions of easterly wind in both the upper troposphere and stratosphere maintains the upper tropospheric MJO circulation, but that this signal is disrupted with westerly wind shear that likely includes critical layers that would prevent Kelvin wave energy from passing. Linear regression of reanalysis data against an MJO index shows more coherent downward propagating Kelvin waves during easterly shear and no Kelvin-wave-like signal near the tropopause during conditions expected to include critical layers there. Historical analysis of this vertical shear shows that it is the primary focus of enhanced MJO variance with the easterly QBO, yielding the seasonally enhanced signal December through February and the erratic variability from year to year due to tropospheric contributions to shear. A wavenumber frequency spectrum analysis of lower stratospheric zonal wind shows that power shifts from high to low frequency between QBO westerly to easterly phases, consistent with Kelvin waves propagating at the phase speed range of the MJO during easterly QBO.
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(This article belongs to the Section Climate Dynamics and Modelling)
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“Delivering on Expectations”: The Potential of the Climate Budget to Facilitate Transformative Change
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Julie Høie Nygård, Elin Anita Nilsen and Mette Talseth Solnørdal
Climate 2026, 14(9), 185; https://doi.org/10.3390/cli14090185 - 8 Sep 2026
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Climate budgets are increasingly used by municipalities as a framework for monitoring emissions and aligning local actions with broader climate goals. Despite their growing use, their potential to support transformative climate change remains underexplored. This study examines the formal configuration of management controls
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Climate budgets are increasingly used by municipalities as a framework for monitoring emissions and aligning local actions with broader climate goals. Despite their growing use, their potential to support transformative climate change remains underexplored. This study examines the formal configuration of management controls in climate budgets and discusses their potential to facilitate transformative change. Drawing on the levers of control framework and the concept of dynamic tensions, we conducted a qualitative document study of climate budgets from eight municipalities in Norway. Our findings identify a predominance of controls centred on targets, indicators, and monitoring. By contrast, controls intended to promote dialogue and learning, establish behavioural boundaries, or articulate shared values and purpose were considerably less visible. The climate budgets are therefore well equipped to monitor predefined emission-reduction measures and support accountability, but provide a more limited formal basis for questioning assumptions, reconsidering priorities, and developing new responses. The study shows that performance information may contribute to transformative change when used as an input to dialogue and learning processes. Municipalities should therefore complement emissions monitoring with clear climate-related boundaries, shared purpose, and processes that use performance information to explore alternative responses.
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Soil Salinization Under Climatic and Management Drivers: A Multi-Scale Comparative Synthesis of Nine Long-Term Case Studies
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Traianos Minos, Alkiviadis Stamatakis, Dimitrios Kalaronis and Evangelia E. Golia
Climate 2026, 14(9), 184; https://doi.org/10.3390/cli14090184 - 4 Sep 2026
Abstract
Soil salinization affects more than 424 million hectares of topsoil worldwide and is associated with yield losses of 18–43% in drylands, yet its long-term dynamics have rarely been compared across regions on a common basis. This work synthesizes nine case studies from a
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Soil salinization affects more than 424 million hectares of topsoil worldwide and is associated with yield losses of 18–43% in drylands, yet its long-term dynamics have rarely been compared across regions on a common basis. This work synthesizes nine case studies from a systematic, PRISMA-guided search, selected purposively to maximize variation in climate, scale and method. ECe (dS/m) and total salt content (g/kg) have no general conversion, so the eight ECe-based studies are compared class by class using their own figures; the single g/kg study contributes direction only, and no values are pooled. The largest transfers occur between the intermediate and higher classes, while the non-saline class changes little in most settings; intensification of already salt-affected land therefore predominates over conversion of new land, though the latter also occurs (Bachu County). The drivers reported by the source studies are irrigation expansion, inadequate drainage, and shallow saline groundwater, with rising evaporative demand and reduced leaching as amplifiers; since no study partitions the change between climatic and management causes, this ordering is descriptive, not a causal attribution. The synthesis is hypothesis-generating; its main product is a direction-of-change summary table separating observed trends from projections.
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(This article belongs to the Section Climate and Environment)
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Open AccessArticle
Comparative Evaluation of Machine Learning Models for Global Horizontal Irradiance Estimation in an Arid Coastal Climate
by
Jimmy Rosales-Huamaní, Odón Sánchez-Ccoyllo, María Álvarez-Paucar and Oscar Toapanta-Cunalata
Climate 2026, 14(9), 183; https://doi.org/10.3390/cli14090183 - 3 Sep 2026
Abstract
Accurate estimation of global horizontal irradiance (GHI) is relevant for characterizing solar resources in regions with limited measurement infrastructure. This study compared six machine learning models Polynomial Ridge Regression, Decision Tree, Random Forest, XGBoost, Artificial Neural Network, and K–Nearest Neighbors for the contemporaneous
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Accurate estimation of global horizontal irradiance (GHI) is relevant for characterizing solar resources in regions with limited measurement infrastructure. This study compared six machine learning models Polynomial Ridge Regression, Decision Tree, Random Forest, XGBoost, Artificial Neural Network, and K–Nearest Neighbors for the contemporaneous estimation of GHI at a five-minute resolution in an arid coastal climate. After quality control and restriction to daytime periods, 45,024 observations were analyzed using five external chronological blocks with an expanding-window scheme, generating 31,517 out-of-sample estimates. XGBoost achieved the highest and the lowest RMSE (108.01 ± 13.67 W m−2), whereas Random Forest yielded lower MAE, WMAPE, and MASE values. DM–HAC sensitivity analysis favored XGBoost under squared-error loss for six of the seven evaluated bandwidths, whereas no significant difference between XGBoost and Random Forest was found under absolute-error loss. None of the models achieved the nominal conformal coverage level of 90%; XGBoost showed the highest empirical coverage and the narrowest prediction intervals (PICP = 0.791; PINAW = 0.312). Predictor-set reduction improved the performance of four of the six algorithms. Overall, XGBoost and Random Forest exhibited complementary performance profiles, indicating that model selection should jointly consider predictive accuracy, temporal stability, predictor sensitivity, and uncertainty.
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(This article belongs to the Special Issue Meteorological Forecasting and Modeling in Climatology)
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Open AccessArticle
Integrating Hydro-Geomorphological Analysis into Regional Sediment Management: Insights from the Rio Geremeas Basin (Sardinia, Italy)
by
Demurtas Valentino, Sulis Andrea, Azzena Costantino, Alamanni Federico, Carboni Andrea, Luise Giovanni, Manconi Veronica, Mancosu Gianluigi, Sabatini Andrea, Santona Giulio, Orrù Paolo Emanuele and Deiana Giacomo
Climate 2026, 14(9), 182; https://doi.org/10.3390/cli14090182 - 2 Sep 2026
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Mitigating flood risk and planning river corridors in Torrent-type Basins (TBs) requires integrated frameworks that link hydro-geomorphological processes with ecological quality, a combination currently lacking in regional planning. This study presents an integrated hydro-geomorphological and ecological analysis of the Rio Geremeas catchment (Sardinia,
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Mitigating flood risk and planning river corridors in Torrent-type Basins (TBs) requires integrated frameworks that link hydro-geomorphological processes with ecological quality, a combination currently lacking in regional planning. This study presents an integrated hydro-geomorphological and ecological analysis of the Rio Geremeas catchment (Sardinia, Italy), developed within the Regional Sediment Management Plan (PGS). The approach combines multi-scale geomorphological mapping, the IDRAIM eco-morphological framework, and 2D hydro-morphodynamic modelling (MIKE 21C), supported by field surveys and remote sensing of sediment source areas and biological assemblages (riparian vegetation, macroinvertebrates, and fish). Results reveal a direct link between altered sediment dynamics and ecological degradation, with confined reaches showing lower biotic diversity compared to mobile, morphologically functional reaches. 50-year flood simulations identified critical erosion/deposition zones, guiding targeted proposals: restoring sediment continuity by removing hydraulic constraints, controlling invasive species, and managing the river mouth to favour the migration of the European eel. This work provides a transferable, interdisciplinary approach for sediment- and ecosystem-informed river basin planning, directly supporting the EU Water Framework Directive and climate resilience strategies.
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Open AccessArticle
Error Variations in the Sub-Seasonal Precipitation Prediction of the BCC-CPS-S2Sv2 in Summer over China
by
Yifan Wang, Ya Tuo, Qingquan Li and Guolin Feng
Climate 2026, 14(9), 181; https://doi.org/10.3390/cli14090181 - 1 Sep 2026
Abstract
The application of sub-seasonal prediction error analysis and interpretation remains a key research frontier of climate prediction. Related studies are essential for deepening the understanding of model performance, investigating error sources, and improving model prediction accuracy. Based on four perspectives—basic characteristics, spatial and
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The application of sub-seasonal prediction error analysis and interpretation remains a key research frontier of climate prediction. Related studies are essential for deepening the understanding of model performance, investigating error sources, and improving model prediction accuracy. Based on four perspectives—basic characteristics, spatial and temporal consistency, and potential causes of prediction errors—this study systematically investigates the sub-seasonal hindcast errors of summer precipitation over China from the state-of-the-art model system BCC–CPS–S2Sv2 (S2Sv2). The findings reveal that (1) S2Sv2 shows significant prediction skill in the first three pentads and shows an obvious increase in RMSE and a decrease in SCC of the precipitation prediction. This feature is quite similar to other operational models. (2) The prediction error primarily presents typical modes such as the meridional dipole, triple or consistent patterns. Prediction errors maintain spatial consistency in South China, the middle and lower reaches of the Yangtze River, and North China. Temporal consistency of the prediction errors shows a significant correlation between neighboring pentads, which diminishes as the prediction period extends, with a better correlation lasting for 2–4 pentads in some areas. (3) The prediction errors of S2Sv2 are significantly correlated with major circulation patterns in East Asia and key area sea surface temperatures (SSTs). For instance, errors are larger when the West Pacific Subtropical High is stronger, but smaller when the East Asia Trough is stronger. Errors also decrease with higher SSTs in the central equatorial Pacific and increase with higher SSTs in the tropical North Atlantic. This study provides valuable insights into the limitations of the S2Sv2 model and offers important references for error correction and model application.
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(This article belongs to the Section Climate Dynamics and Modelling)
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Open AccessArticle
Factors Contributing to the Effectiveness Ratings of the Climate Change Adaptation Projects in Agriculture: Implications from the Developing Countries
by
Yuki Shiga and Rajib Shaw
Climate 2026, 14(9), 180; https://doi.org/10.3390/cli14090180 - 1 Sep 2026
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As the impact of climate change becomes increasingly disruptive worldwide, the gap in adaptation finance is widening. Despite advancements in adaptation planning and implementation in every sector and region, the growing resource gap necessitates more ‘effective’ climate adaptation projects. Against this backdrop, the
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As the impact of climate change becomes increasingly disruptive worldwide, the gap in adaptation finance is widening. Despite advancements in adaptation planning and implementation in every sector and region, the growing resource gap necessitates more ‘effective’ climate adaptation projects. Against this backdrop, the purpose of this paper is to provide an exploratory analysis to identify and examine potential factors associated with the effectiveness ratings of multilateral-funded agricultural climate adaptation projects that have been implemented on the ground, completed and documented. Forty-four projects from multilateral funds were collected and analyzed for this purpose. The study employed a two-pronged approach to cross-complement the implications—key contributing factors to the effectiveness, rated per the actual outcome of the projects, were identified from the terminal evaluation documents of the highly satisfactory and unsatisfactory projects (conventional content analysis); and, effectiveness ratings were assessed against various socio-economic indicators of the countries where the projects were executed through Spearman’s correlation analysis to identify the possible association. The results implied that the contributing factors associated with the effectiveness ratings converge around several elements: (i) capacity building and education; (ii) local engagement and social inclusion; (iii) healthy and resilient livelihood; and, (iv) governance and commitment. Additionally, social inequality indicated its relevance to the project effectiveness ratings. While effectiveness ratings were found to have a positive and moderate correlation (r: 0.274; p < 0.1) with Inequality-adjusted Human Development Index (IHDI), such a correlation was not explicit with HDI. In addition, the study found strong correlations with multiple ‘inequality’ indicators—the gender inequality index, inequality-adjusted life expectancy index and inequality-adjusted income index. These results from the various ‘inequality-adjusted’ indexes further suggest the importance of considering all levels of the community, particularly those groups in the most disadvantageous positions, often farmers, to close the inequality gap. Overall, the findings and implications from this study are expected to provide a basis for future climate adaptation investments in agriculture.
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Open AccessArticle
Basin-Scale Vegetation Sensitivity to Relative Humidity Variability Across Türkiye Under CMIP6 SSP Pathways
by
Mehmet Ali Çelik, Adile Bilik, Yasin Paşa and Ishak Pacal
Climate 2026, 14(9), 179; https://doi.org/10.3390/cli14090179 - 1 Sep 2026
Abstract
Relative humidity (RH) provides complementary information on atmospheric moisture conditions that is not captured by precipitation alone. This study characterizes basin-scale RH-associated vegetation sensitivity across Türkiye’s 25 hydrographic basins using MODIS MOD13A1 NDVI observations for 2000–2024 and processed CMIP6-derived RH products for SSP1-2.6,
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Relative humidity (RH) provides complementary information on atmospheric moisture conditions that is not captured by precipitation alone. This study characterizes basin-scale RH-associated vegetation sensitivity across Türkiye’s 25 hydrographic basins using MODIS MOD13A1 NDVI observations for 2000–2024 and processed CMIP6-derived RH products for SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 at the 2040, 2060, 2080 and 2100 single-year horizons. MODIS and climate-model products are summarized independently at basin scale, avoiding pixel-level fusion across different native resolutions. The archived 25-by-4 pathway sensitivity matrix is evaluated using cross-pathway dispersion, a robust median-magnitude check, hierarchical clustering with silhouette diagnostics, and explicit percentile-based hotspot criteria. Mean absolute sensitivity is 0.0244, 0.0236, 0.0368, and 0.0386 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. Antalya, Küçük Menderes, Konya Closed, Ceyhan, and Aras satisfy the magnitude and persistence criteria, and all five also exceed the upper-quartile threshold for median absolute sensitivity. Silhouette diagnostics do not support a unique three-cluster solution, favoring explicit threshold-based response classes. The results are interpreted as comparative basin screening rather than causal attribution or validated vegetation forecasting. Because the retained processed products do not preserve model/member provenance or the horizon-level intermediate values used to generate the pathway index, model-specific and predictive interpretations are deliberately avoided.
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(This article belongs to the Special Issue Evolving Plant Phenology Responses and Resilience in a Changing Climate)
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Response of Mesospheric Temperature and Water Vapor to Volcanic Activity
by
Xia Sheng, Zheng Sheng and Shengtao Feng
Climate 2026, 14(9), 178; https://doi.org/10.3390/cli14090178 - 30 Aug 2026
Abstract
Stratospheric sulfate aerosol loading from volcanic eruptions conventionally perturbs planetary radiation budgets, inducing either global-scale cooling or regional thermal anomalies. The January 2022 eruption of Hunga Tonga–Hunga Ha’apai constituted a pronounced departure from this archetype. Here, ERA5 reanalysis products are leveraged to characterise
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Stratospheric sulfate aerosol loading from volcanic eruptions conventionally perturbs planetary radiation budgets, inducing either global-scale cooling or regional thermal anomalies. The January 2022 eruption of Hunga Tonga–Hunga Ha’apai constituted a pronounced departure from this archetype. Here, ERA5 reanalysis products are leveraged to characterise thermodynamic and moisture perturbations at 0.01 hPa (~80 km) throughout the post-eruptive interval. The submarine event delivered approximately 146 Tg of water vapour into the stratosphere, with the plume ascending to 57 km and penetrating the tropical tropopause; a substantial fraction of this anomalous moisture subsequently persisted at mesopause altitudes, forming a reservoir that dissipates only gradually and perturbs upper-atmospheric chemistry across multi-year horizons. Nonlinear interactions between radiative forcing and dynamical feedbacks, both attributable to this exceptional water vapour burden, are elucidated. These observational constraints should advance understanding of upper-atmospheric responses to major volcanic events characterised by substantial water vapour emission.
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(This article belongs to the Section Weather, Events and Impacts)
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Open AccessEssay
The Climate Bathtub at 25: Expanding a Popular Physical Metaphor to Illuminate Social Challenges of Climate Change
by
Jonah Busch
Climate 2026, 14(9), 177; https://doi.org/10.3390/cli14090177 - 28 Aug 2026
Abstract
The “climate bathtub,” originated by Linda Booth Sweeney and John Sterman in 2001, has provided an elegant metaphor for the physical problem of climate change for 25 years. In this metaphor, as long as inflows of water into a bathtub from a faucet
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The “climate bathtub,” originated by Linda Booth Sweeney and John Sterman in 2001, has provided an elegant metaphor for the physical problem of climate change for 25 years. In this metaphor, as long as inflows of water into a bathtub from a faucet (greenhouse gas emissions) exceed outflows through a drain (removals), then the stock of water in the bathtub (atmospheric concentrations) will rise, with eventual consequences when the bathtub overflows. This bathtub metaphor is simple, relatable, urgent, and apt. But climate change is a social problem, not just a physical problem. By adding four new elements to the climate bathtub—multiple faucets; multiple drains; multiple damage thresholds; and multiple people—the climate bathtub metaphor can illuminate a wide range of social challenges related to climate change. This expanded climate bathtub metaphor offers insights on how individuals prioritize climate actions; why collective disagreements emerge; and hierarchical levels of climate cooperation. It suggests heuristics that policymakers, practitioners, and future climate professionals can use to evaluate potential climate actions. This more complex, but more versatile, version of the climate bathtub can supplement the original metaphor as a public communications tool and motivator for climate action.
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(This article belongs to the Section Climate and Economics)
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Open AccessPerspective
Oxygen Footprint Regulates Dryland Carbon Cycling
by
Dongliang Han, Jianping Huang, Lei Ding and Guolong Zhang
Climate 2026, 14(9), 176; https://doi.org/10.3390/cli14090176 - 26 Aug 2026
Abstract
In the Anthropocene, dryland ecosystems—natural and semi-natural ecosystems—are highly sensitive to anthropogenic warming, and their carbon cycling dynamics are easily disrupted by this trend. In this perspective, we propose a novel yet long-overlooked conceptual framework. It reveals that the oxygen footprint, defined as
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In the Anthropocene, dryland ecosystems—natural and semi-natural ecosystems—are highly sensitive to anthropogenic warming, and their carbon cycling dynamics are easily disrupted by this trend. In this perspective, we propose a novel yet long-overlooked conceptual framework. It reveals that the oxygen footprint, defined as the atmospheric oxygen consumption-to-production ratio, could serve as an important diagnostic indicator for assessing the impacts of anthropogenic warming on dryland carbon cycling. This framework can be divided into three parts, including the increasing oxygen footprint, warming effects on dryland carbon cycling, and direct or indirect links between oxygen footprint and carbon-cycle responses. In short, it centers on the core logical chain: oxygen footprint-anthropogenic warming-dryland carbon cycling. This work strives to enhance dryland sustainability by filling essential knowledge gaps, combining separate research findings, and providing actionable field guidance for ecosystem management.
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(This article belongs to the Special Issue Climate-Ecosystem Feedbacks in Cold and Arid Regions)
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Towards Integrated Climate Services: Platforms Supporting Environmental and Agricultural Resilience in Portugal
by
Carlos A. Pereira, João Ferreira, Vanda C. Pires, Paula Drumond, Eduardo Castanho, Ricardo Deus, Tânia Moura and Rita M. Durão
Climate 2026, 14(9), 175; https://doi.org/10.3390/cli14090175 - 26 Aug 2026
Abstract
The Portuguese agricultural sector has suffered a profound transformation over recent decades, evolving from traditional to increasingly technology-driven systems. Throughout this transition, climate and meteorological conditions have remained key drivers of agricultural productivity. Today, Portuguese agriculture faces growing challenges associated with climate change,
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The Portuguese agricultural sector has suffered a profound transformation over recent decades, evolving from traditional to increasingly technology-driven systems. Throughout this transition, climate and meteorological conditions have remained key drivers of agricultural productivity. Today, Portuguese agriculture faces growing challenges associated with climate change, including more frequent and intense heatwaves, droughts, and floods. Consequently, reliable climate information and decision-support tools are essential for strengthening resilience and promoting sustainable management. To address these needs, the Portuguese Institute for the Sea and Atmosphere (IPMA) developed two complementary climate service platforms for mainland Portugal: AgroClima and DataClima. The first provides observations from IPMA’s meteorological network, ECMWF forecasts, and agroclimatic indicators such as temperature, precipitation, soil water, and so-called agroclimatic warnings. The second offers historical climate information including WRFv4.2 simulations dynamically downscaled from ERA5 (1981–present), in situ observations (1941–present), and climate normals. Evaluation of the WRFv4.2 regionalization against IPMA observations shows a systematic underestimation of precipitation and air temperature, while mean wind speed is generally overestimated. Despite these biases, the downscaled WRFv4.2 dataset demonstrates sufficient accuracy to support operational climate services, providing valuable help for environmental monitoring, climate adaptation, and decision-making in agriculture and water resource management across Portugal.
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(This article belongs to the Section Climate Adaptation and Mitigation)
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