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Journal articleRoca Barcelo A, Schneider R, Pirani M, et al., 2026,
A satellite based machine learning approach for estimating high resolution daily average air temperature in a megacity in Brazil
, Scientific Reports, ISSN: 2045-2322Spatiotemporally resolved ambient temperature data are essential for environmental epidemiology, especially in urban areas where temperature can vary sharply over short distances, influencing population exposure. Additionally, heat distribution often reflects built environment patterns and may correlate with existing social and environmental disparities. Continuous temporal records at high spatial resolution are, however, often lacking, especially in low- and middle-income countries. We developed a generalizable tree-based machine learning approach to estimate daily mean temperatures at 500 x 500 metres resolution using São Paulo, a megacity in Brazil, as a case study, to demonstrate its utility in highly urbanized settingswith a heterogeneous urban fabric and unevenly distributed temperature monitoring stations. We trained a Random Forest model using open-access remote sensing data, along with derived products, and temperature measurements from 43 ground stations. To prevent overfitting and select relevant features, weemployed a forward feature selection algorithm with target-oriented (spatial) cross-validation. Hyperparameter tuning was performed using grid search approach. The model was validated through ten-fold station-based cross-validation and an external hold-out dataset. The model demonstrated strong performance (RMSERF = 0.80; R²RF = 0.95), with slightly reduced accuracy in rural areas (R²rural = 0.91; R²urban = 0.95). Compared to traditional multilinear approaches (RMSEMLR = 1.02; R²MLR = 0.92), the Random Forest model outperformed, likely due to its ability to better capture microclimates and complex relationships between data sources. This 500 x 500 metres daily temperature dataset is the first of its kind in South America, with the São Paulo pipeline and data freely accessible. The approach is adaptable to other regions with appropriate retraining and validation, enabling high-resolution exposure assessments.
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Journal articleLiu M, Prentice IC, Harrison SP, 2026,
A global analysis of pollen-based reconstructions of land climate changes during Dansgaard–Oeschger events
, Climate of the Past, ISSN: 1814-9324 -
Journal articleFargette N, Eastwood JP, Phan TD, et al., 2026,
Fluid and Kinetic Properties of the Near-Sun Heliospheric Current Sheet
, The Astrophysical Journal, Vol: 997, Pages: 174-174, ISSN: 0004-637X<jats:title>Abstract</jats:title> <jats:p> The heliospheric current sheet (HCS) is an important large-scale structure of the heliosphere, and, for the first time, the Parker Solar Probe (PSP) mission enables us to study its properties statistically, close to the Sun. We visually identify the 39 HCS crossings measured by PSP below 50 <jats:italic>R</jats:italic> <jats:sub>⊙</jats:sub> during encounters 6–21, and investigate the occurrence and properties of magnetic reconnection, the behavior of the spectral properties of the turbulent energy cascade, and the occurrence of kinetic instabilities at the HCS. We find that 82% of the HCS crossings present signatures of reconnection jets, showing that the HCS is continuously reconnecting close to the Sun. The proportion of inward and outward jets depends on heliocentric distance, and the main HCS reconnection X-line has a higher probability of being located close to the Alfvén surface. We also observe a radial asymmetry in jet acceleration, where inward jets do not reach the local Alfvén speed, contrary to outward jets. We find that turbulence levels are enhanced in the ion kinetic range, consistent with the triggering of an inverse cascade by magnetic reconnection. Finally, we highlight the ubiquity of magnetic hole trains in the high- <jats:italic>β</jats:italic> environment of the HCS, showing that the mirror mode instability plays a key role in regulating the ion temperature anisotropy in HCS reconnection. Our findings shed new light on the properties of magnetic reconnection in the high- <jats:italic>β</jats:italic> plasma environment of the HCS, its interplay with the turbulent cascade, and the role of the mirror mode instability. </jats
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Journal articleEbi KL, Haines A, Andrade RFS, et al., 2026,
Correction to: The attribution of human health outcomes to climate change: transdisciplinary practical guidance (Climatic Change, (2025), 178, 8, (143), 10.1007/s10584-025-03976-7)
, Climatic Change, Vol: 179, ISSN: 0165-0009The original article has been corrected. In this article Kathryn Bowen at affiliation ‘Melbourne Climate Futures; and Environment, Climate, and Global Health, University of Melbourne, Melbourne, Australia’ was missing from the author list. The section “Conflicts of Interest” was also missing and should have read: “Select authors declare potential interests arising from funding from Wellcome, NIH, NIHR, Oak Foundation, CDC, CSTE, WHO, Green Climate Fund, World Bank, Asia Development Bank, CIHR, SSHRC, NSF, NovoNordisc (sponsored travel), and honoraria for academic engagement from US universities. One author is a Wellcome employee. One author (KLE) is a Deputy Editor for Climatic Change.”
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Journal articleIm U, Samset BH, Nenes A, et al., 2026,
Aerosol‐cloud interactions: overcoming a barrier to projecting near‐term climate evolution and risk
, AGU Advances, Vol: 7, ISSN: 2576-604XAerosol-cloud interactions (ACI) are a major source of uncertainty in climate science, critically affecting our ability to project near-term climate evolution and assess societal risks. These interactions influence effective radiative forcing, cloud dynamics, and precipitation patterns, yet remain insufficiently constrained due to limitations in observations, modeling, and process understanding. This uncertainty hampers robust policy advice across multiple domains—from estimating remaining carbon budgets and climate sensitivity, to anticipating regional extreme events and evaluating climate interventions such as solar radiation modification. In many cases, the influence of ACI is either underappreciated or excluded from decision-making frameworks due to its complexity and lack of quantification. This perspective outlines a path forward to overcome these barriers by leveraging emerging opportunities in satellite remote sensing, ground-based and airborne observations, high-resolution climate modeling, and machine learning. We identify key areas where rapid progress is feasible, including improved retrievals of cloud microphysical properties, better representation of natural aerosols in a warming world, and enhanced integration of observational and modeling communities. Even as anthropogenic aerosol and its impacts on clouds is reducing owing to emissions controls, addressing ACI uncertainties remains essential for refining climate projections, supporting effective mitigation and adaptation strategies, and delivering actionable science to policymakers in a rapidly changing climate system.
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Journal articleSangkaew S, Daniels BC, Ming DK, et al., 2026,
Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand.
, PLOS Digit Health, Vol: 5Dengue severity prediction models are usually developed using hospitalized patient data, but triage and hospital admission are mainly evaluated in outpatient settings. This study developed models using clinical and laboratory data from patients in outpatient settings during the febrile phase. Data from two cohort studies in Vietnam and Thailand were used to develop and validate six models: logistic regression with warning signs, Lasso-selected logistic regression, random forest, extreme gradient boosted classification, support vector machine, and artificial neural network. Models predicted dengue shock syndrome (DSS) as the primary endpoint and moderate plasma leakage and/or DSS as the secondary endpoint. We assessed model performance, discrimination, and calibration, using sensitivity, specificity, accuracy, Brier score, AUROC, CITL, calibration slope, calibration plots, and decision curve analysis. The optimal model was the Lasso-selected logistic regression for predicting DSS and the combined endpoint of moderate plasma leakage and/or DSS (Brier score: 0.044 [95% CI 0.043, 0.044] and 0.104 [95% CI 0.104, 0.105]; AUROC: 0.789 [95% CI 0.787, 0.791] and 0.741 [95% CI 0.740, 0.742]). We identified hematocrit, platelet count, lymphocyte count, and aspartate aminotransferase as predictors for DSS, and abdominal pain or tenderness, vomiting, mucosal bleeding, white blood cell count, lymphocyte count, platelet count, aspartate aminotransferase, and serum albumin as predictors for the secondary endpoint. Logistic regression and machine learning models using clinical and laboratory data during the febrile phase can support early prediction of severe disease in outpatient settings. Integrating risk prediction models into a decision support system could improve triage and optimize healthcare and resource allocation in endemic and resource-limited areas.
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Journal articleDavies B, Gribbin T, King O, et al., 2026,
Palaeoglacier reconstruction and dynamics of Cordillera Vilcanota in the tropical high Peruvian Andes
, Earth Surface Processes and Landforms, Vol: 51, ISSN: 0197-9337<jats:title>Abstract</jats:title> <jats:p> Tropical glaciers are important indicators of climate change, provide freshwater resources for downstream communities, and form an important component of the hydrological cycle. Understanding the dynamics and patterns of behaviour of tropical palaeoglaciers is important for interpreting their sensitivities and vulnerabilities. Glacier advances in the high tropical Peruvian Andes occurred multiple times during the last glacial cycle and Holocene, leaving complex geomorphological evidence on the landscape. The substantial topographic, geological and climatic variability in this region leads to high geomorphic diversity. However, few detailed geomorphological studies have been conducted to date, leading to considerable uncertainty in the behaviours and drivers of tropical palaeoglaciers. Here, we provide a detailed geomorphological analysis of the Cordillera Vilcanota, Cusco region, southern Peru (71°W, 13.7°S), and use morphostratigraphic principles to reconstruct the former maximum icefield extent and palaeoglacier advances. Across this domain, we mapped ~23,000 features encompassing five key environments: glacier, subglacial, ice‐marginal, fluvial and lacustrine. The mapped features show evidence of both modern‐day polythermal and temperate ice margins, with low meltwater volumes leading to small‐scale glaciofluvial landform formation. However, larger moraines, beyond those well‐dated to the Younger Dryas and Antarctic Cold Reversal, assumed to represent Last Glacial Maximum and earlier advances, suggest that conditions were temperate and drained by more substantial rivers, with coupled flow of ice and till, and evidence of subglacial scouring, drumlin formation and the deposition of substantial moraines and large palaeosandar. Our reconstructed maximum icefield covers 2,660 km <jats:sup>2</jats:sup> and wa
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Journal articleBerden J, Hanley-Cook GT, Chimera B, et al., 2026,
Synergies between food biodiversity, processing levels, and the EAT-Lancet diet for nutrient adequacy and environmental sustainability: a multiobjective optimization using the European Prospective Investigation into Cancer and Nutrition cohort.
, Am J Clin Nutr, Vol: 123BACKGROUND: Diets have become increasingly monotonous and high in ultraprocessed foods (UPFs), contributing to poor health outcomes and environmental degradation. Although sustainable diets, food biodiversity, and food processing levels have each been linked to nutritional and environmental outcomes, their combined impact has not been assessed. OBJECTIVES: This study aims to examine whether food biodiversity, intakes of UPFs, and adherence to the EAT-Lancet diet can simultaneously optimize nutrient adequacy while reducing environmental impacts. METHODS: Using data from 368,733 adults in the European Prospective Investigation into Cancer and Nutrition, we assessed associations and interactions between dietary species richness (DSR) (disaggregated into DSRPlant and DSRAnimal), food processing levels (Nova categories; % g/d), and adherence to EAT-Lancet recommendations [healthy reference diet (HRD) score; 0-140 points] with the Probability of Adequate Nutrient Intake Diet (PANDiet) score, dietary greenhouse gas emissions (GHGe; kg CO2-eq/d), and land use (m2/d). Regression models subsequently informed multiobjective optimization to identify optimal dietary patterns balancing nutritional and environmental outcomes. RESULTS: Compared with observed diets, optimal diets showed a mean HRD score increase of 13.91 (95% confidence interval: 13.89, 13.93) points; DSRPlant increased by mean of 1.36 (1.35, 1.37) species, and a mean substitution of 12.44 (12.40, 12.49) percentage points of UPFs with unprocessed or minimally processed foods. Correspondingly, the mean PANDiet score increased by 4.12 (4.10, 4.14) percentage points, whereas GHGe and land use reduced by 1.07 (1.05, 1.09) kg CO2-eq/d and 1.43 (1.41, 1.45) m2/d, respectively. CONCLUSIONS: Diets that adhere to the EAT-Lancet diet, are more biodiverse, and prioritize unprocessed and minimally processed foods over UPFs, have the potential to synergistically enhance nutrient adequacy while minimizing environmental impacts. T
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Journal articleXu H, Wang H, Prentice IC, et al., 2026,
Global variation in the ratio of sapwood to leaf area explained by optimality principles
, New Phytologist, ISSN: 0028-646X• The sapwood area supporting a given leaf area (Huber value, vH) reflects the coupling between carbon uptake and water transport and loss at a whole-plant level. Geographic variation in vH presumably reflect plant strategic adaptations but the lack of a general explanation for such variation hinders its representation in vegetation models and assessment of how its impact on the global carbon and water cycles. • Here we develop a simple hydraulic trait model to predict optimal vH by matching stem water supply and leaf water loss, and test its performance against two extensive plant hydraulic datasets. • We show that our eco-evolutionary optimality-based model explains nearly 60% of global vH variation in response to light, vapour pressure deficit, temperature and sapwood conductivity. Enhanced hydraulic efficiency with warmer temperatures reduces the sapwood area required to support a given leaf area, whereas high irradiance (supporting increased photosynthetic capacity) and drier air increase it. • This study thus provides a route to modelling variation in functional traits through the coordination of carbon uptake and water transport processes.
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Journal articleSymons TL, Moran A, Balzarolo A, et al., 2026,
Projected impacts of climate change on malaria in Africa.
, NatureThe implications of climate change for malaria eradication this century remain poorly resolved1,2. Many studies focus on parasite and vector ecology in isolation, neglecting the interactions between climate, malaria control and the socioeconomic environment, including disruption from extreme weather3,4. Here we integrate 25 years of African data on climate, malaria burden and control, socioeconomic factors, and extreme weather. Using a geotemporal model linked to an ensemble of climate projections under the Shared Socioeconomic Pathway 2-4.5 (SSP 2-4.5) scenario5, we estimate the future impact of climate change on malaria burden in Africa, including both ecological and disruptive effects. Our findings indicate that climate change could lead to 123 million (projection range 49.5 million to 203 million) additional malaria cases and 532,000 (195,000-912,000) additional deaths in Africa between 2024 and 2050 under current control levels. Contrary to the prevailing focus on ecological mechanisms, extreme weather events emerge as the primary driver of increased risk, accounting for 79% (50-94%) of additional cases and 93% (70-100%) of additional deaths. Most increases stem from intensification in existing endemic areas rather than range expansion, with significant regional variation in impact. These results highlight the urgent need for climate-resilient malaria control strategies and robust emergency response systems to safeguard progress towards malaria eradication.
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