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Journal articleZhao J, Paschalis A, Gentine P, et al., 2026,
Limited capability of current satellite solar-induced chlorophyll fluorescence reconstructions to capture stomatal responses to environmental stresses
, Communications Earth and Environment, Vol: 7Quantification of the impact of environmental stress on terrestrial vegetation photosynthesis is crucial for our understanding of the global carbon cycle, particularly under a changing climate. Vegetation responses to environmental stress manifest first as plant physiological changes, and at later stages through changes in canopy structure. Here we leverage CO<inf>2</inf> and water flux data from 103 eddy covariance towers and satellite thermal images to assess whether current satellite reconstructions of solar-induced chlorophyll fluorescence capture these plant mechanisms. After removing seasonality using standardized anomalies (z-scores), we found that the relationship between tower-observed gross primary productivity and fluorescence reconstructions considerably weakened across a wide range of biomes. This loss of correlation results from a decoupling between stomatal responses and the physiological emission yield (Φ<inf>F</inf>) of fluorescence reconstructions during soil and atmospheric dry periods. The consequence is that productivity derived from fluorescence reconstructions will be progressively overestimated as dry conditions persist.
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Journal articleWarder SC, Piggott MD, 2026,
Wind farm wake losses under future build-out scenarios
, Wind Energy and Engineering Research, Vol: 5, Pages: 100025-100025, ISSN: 2950-3604 -
Journal articleAlmalki YR, Karmpadakis I, 2026,
Effects of OWC geometry on total energy efficiency and turbulent dissipation
, Applied Ocean Research, Vol: 170, ISSN: 0141-1187This study investigates the impact of front and back wall geometries on the performance of oscillating water column (OWC) devices embedded in a fixed caisson breakwater. Combining experimental and numerical approaches, we analyse vortex formation and energy efficiency in relation to draft designs. Experiments, conducted at the Hydrodynamics Laboratory, Imperial College London, explores widest variety of draft shapes in the literature, including sharp and rounded profiles. Large eddy simulations (LES) were conducted using OpenFOAM® at laboratory and field scales to assess scale effects. The simulations accurately reproduce experimental data and reveal how draft geometry influences vortex dynamics, turbulence, and energy efficiency. It is shown that the design of the front and back drafts of an OWC can have a profound impact on its energy efficiency. In quantifying the generation of turbulence across different geometries, guidance is provided towards the most efficient geometries as well as the effects of physical model scale. Physical insight in this study provide clear recommendations for practical considerations in the design of OWC.
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Journal articleShu S, Yang X, Ming Z, et al., 2026,
A carbon reduction incentive model for crowdsourced urban freight: Facilitating freight pooling and electric truck adoption
, Transportation Research Part E Logistics and Transportation Review, Vol: 209, ISSN: 1366-5545Urban freight transport faces significant decarbonization pressure, yet existing strategies such as freight pooling and electric truck adoption often struggle with limited uptake due to operational complexities, costs, and infrastructure challenges. Critically, current research lacks an integrated, operational incentive framework specifically designed for multi-stakeholder participation in urban crowdsourced logistics, where task-level operational decisions across multiple stakeholders play a central role in system-level carbon reduction. This study introduces a Carbon Reduction Incentive Model (CRIM) that addresses this gap. The CRIM incentivizes individual shippers and independent carriers within a crowdsourced logistics system by assigning task-level rewards for freight pooling and electric truck usage. Rewards are quantified by tonne-kilometer savings relative to conventional individual diesel deliveries, further adjusted by a time-based factor to encourage off-peak operations. The CRIM is embedded within an enhanced pick-up and delivery model that explicitly accounts for stakeholder cost components, vehicle heterogeneity, charging requirements, and time-sensitive feasibility (PDPTW-HEC). To optimize the system’s complex trade-off between costs and carbon emissions, a customized heuristic algorithm is developed. Scenario-based case studies using real-world data and international carbon accounting standards validate the proposed incentive model’s performance. Results demonstrate that CRIM can achieve 9.5–38.1% higher electric truck adoption and an 8.4–28.7% reduction in total carbon emissions. This framework offers a practical and scalable approach for designing and evaluating task-level carbon reduction incentives in urban freight operations.
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Journal articleDu T, Taylor S, Salah P, et al., 2026,
Accelerating tropical cyclone wave height estimation via machine learning and deep latent surrogates
, Ocean Engineering, Vol: 352, ISSN: 0029-8018Tropical cyclones (TCs) are a major driver of coastal damage and require reliable risk assessment–particularly for extreme coastal waves. Classical partial differential equation (PDE)- based wave models such as SWAN, WAVEWATCH III and MIKE21 have long been used for such estimations, but remain computationally expensive, with practitioners increasingly requiring faster, lightweight tools. This study presents machine learning (ML) and deep learning (DL) surrogates that emulate commercial-grade wind-to-wave models. Our modelling framework aims to estimate Significant Wave Height (H<inf>s</inf>) during TCs, and we target its common underestimation in ML models. The data pre-processing pipeline explicitly targets the under-estimation of the maximum values of H<inf>s</inf>. It combines oversampling of rare extremes, loss functions weighted toward high-impact cases, and dimensionality reduction via principal component analysis (PCA) to rebalance inputs in a latent space. We evaluate both point-trained tree ensembles for nearshore estimation (Random Forest, XGBoost), and architectures that model space-time structure–convolutional neural networks (CNNs), temporal convolutional networks (TCNs), and long short-term memory (LSTM) networks–in order to capture the complex space-time dependencies in wave dynamics that simpler models fail to represent. We find that a PCA-TCN-LSTM surrogate results in the best peak H<inf>s</inf> estimation. Across models, runtime drops from around 40 hours on CPU clusters to seconds on a personal computer while maintaining high accuracy for H<inf>s</inf> (MSE (Formula presented), R(Formula presented) ). These surrogates provide practical tools for scientists, engineers, and first responders to conduct low-cost, real-time coastal-hazard assessment and strengthen climate resilience.
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Journal articleMillar O, Ma L, Karmpadakis I, 2026,
Experimental assessment and prediction of wave loading around abrupt depth transitions
, Coastal Engineering, Vol: 206, ISSN: 0378-3839Abrupt depth transitions cause significant changes in the characteristics of the wave field, increasing the non-linearity of the wave train and the likelihood of extreme events. The free surface elevation and wave kinematics exhibit different spatial behaviour depending on the local bathymetry. As a result, the critical location for wave loading cannot be identified from the free field properties alone. This study presents the results of a comprehensive experimental analysis of wave loading on a vertical cylinder around a shoal bathymetry. Extreme crest heights are most prevalent immediately downstream of the crest of the shoal, while extreme loads are found to be most frequent above the crest. However, this is influenced by the presence of wave breaking, which generates enhanced loading events of increased magnitude. The prediction of wave loading using Morison’s equation is investigated, with wave kinematics estimated using linear random wave theory and a numerical model (SWASH). The findings demonstrate the importance of the empirical inertia coefficient, which must reflect both the loading regime and the choice of kinematics model.
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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, Vol: 250, Pages: 181-193, 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 articleStewart JA, Robinson LF, Rae JWB, et al., 2026,
Accumulation of remineralised carbon and nutrients in the mid-depth Atlantic during Heinrich Stadial 1 and the Younger Dryas
, Earth and Planetary Science Letters, Vol: 679, ISSN: 0012-821XAtmospheric CO<inf>2</inf> and the temperature of the interior Atlantic Ocean both increased in 2-steps during the last deglaciation, particularly during Heinrich Stadial 1 (HS1; ∼16 ka) and the Younger Dryas (YD; ∼12 ka). However, what drove these punctuated rises remains a long-standing question. The role of deep-ocean carbon storage, release, and redistribution continues to be debated. To establish the role of ocean circulation in deglacial carbon and nutrient cycling, we present new multi-proxy data in sub-fossil corals from mid-depths in the Equatorial Atlantic, including boron isotopes (δ<sup>11</sup>B; seawater pH), Ba/Ca (seawater [Ba] and refractory nutrients), and neodymium isotopes (ε<inf>Nd</inf>; provenance of seawater signal). Corals are dated to a precise radiometric age scale and combined with previously published radiocarbon and temperature proxy measurements on the same samples. Our data reveal abrupt intervals (∼500 years) of notably low pH, Ba-rich, and radiocarbon-depleted (old) waters at 15.4 and 12.0 ka during HS1 and the YD at depths of ∼1700 m. However, very low ε<inf>Nd</inf> (unradiogenic) values suggest that these corals were bathed in northern-sourced Atlantic waters throughout the deglaciation. These results imply that these (old) carbon- and nutrient-rich intermediate waters were not sourced from the carbon- and nutrient-rich Southern Ocean via Antarctic Intermediate Water (AAIW). Instead, carbon and nutrient accumulation at mid-depths in the tropical Atlantic was likely the result of remineralisation of organic matter at times of Atlantic Meridional Overturning Circulation (AMOC) slowdown. The Atlantic Ocean interior was therefore accumulating heat and carbon during these times when deepwater flushing was minimal, thus acting to partially dampen atmospheric CO<inf>2</inf> rise and warming caused by ventilation of the Southern and Pacific
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Journal articleKhurana MP, Brünnich Sloth MM, Scheidwasser N, et al., 2026,
SARS-CoV-2 reinfections and subsequent risk of hospital-diagnosed post-acute sequelae in Denmark (2020-2022): a nationwide cohort study.
, Lancet Reg Health Eur, Vol: 63BACKGROUND: Post-acute sequelae of COVID-19 (PASC), or long COVID, are a public health concern. While most recover from SARS-CoV-2 infections within weeks, some experience persistent symptoms. Here, we quantified the association between repeated SARS-CoV-2 infections and the risk of hospital-diagnosed PASC. METHODS: We conducted a nationwide register-based cohort study of all adults in Denmark (≥18 years) with at least one SARS-CoV-2 PCR or antigen test between April 1, 2020, and December 31, 2022. Participants were followed from first test until long COVID diagnosis (ICD-10: B948A), death, emigration, three SARS-CoV-2 infections, or end of study. Risk of long COVID diagnosis was estimated at three timepoints after study entry (180 days, 1 year, 2 years) and the outcomes were assessed during the 180 days after each timepoint. Cause-specific Cox models treated death as a competing risk, with number of infections and vaccination status as time-varying covariates. Absolute risks and differences were estimated using G-computation. Analyses were stratified by sex, income, and vaccination status. Secondary analyses assessed fatigue and headache (ICD-10), excluding individuals with prior diagnoses. FINDINGS: Of 4,418,544 individuals, 6942 (0.16%) were diagnosed with long COVID. The absolute risk of a diagnosis increased following reinfection (0.73% [95% CI 0.69-0.77] after one infection vs. 1.16% [1.05-1.30] after two infections at 180 days), but differences were small and decreased over time. Risks following reinfection were similar across sex and income strata. Absolute risk decreased with prior vaccinations. Secondary analyses showed no increased risk of fatigue or headache after primary infection. A small increase in fatigue risk was observed after reinfection at 1 year (RD 0.03% [0.01-0.05]), but not for headache. INTERPRETATION: Reinfection increases long COVID risk; however, the absolute increase after reinfection is smaller than that observed after a primary inf
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Journal articleWang S, Yang P, Brindley HE, et al., 2026,
Enhanced Full Spectral Temperature-Dependent Refractive Index of Liquid Water From Supercooled to Ambient Conditions
, Geophysical Research Letters, Vol: 53, ISSN: 0094-8276A new compilation of the complex refractive index of liquid water is presented, spanning temperatures from (Formula presented.) (near homogeneous freezing) to (Formula presented.) K and wavelengths from (Formula presented.) μm to 10 m. The real part of the refractive index is derived using the Kramers–Kronig relation, where the imaginary part is constrained by measurements reported in literature and validated through the f-sum rule. The result reveals a significant temperature dependence, especially at wavelengths beyond the near-infrared. Sensitivity analyses in the infrared split-window and microwave spectral regime demonstrate substantial differences in bulk optical properties between supercooled and ambient conditions. These findings manifest the importance of accounting for temperature-dependent refractive indices in optical radiative transfer and simulations.
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