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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 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 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 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 Y, Warder SC, Benmoufok EF, et al., 2026,
Geographic variability in reanalysis wind speed biases: A high-resolution bias correction approach for UK wind energy
, Energy Conversion and Management, Vol: 352, ISSN: 0196-8904Reanalysis datasets have become indispensable tools for wind resource assessment and wind power simulation, offering long-term and spatially continuous wind fields across large regions. However, they inherently contain systematic wind speed biases arising from various factors, including simplified physical parameterizations, observational uncertainties, and limited spatial resolution. Among these, low spatial resolution poses a particular challenge for capturing local variability accurately. Whereas prevailing industry practice generally relies on either no bias correction or coarse, nationally uniform adjustments, we extend and thoroughly analyse a recently proposed spatially resolved, cluster-based bias correction framework. This approach is designed to better account for local heterogeneity and is applied to 319 wind farms across the United Kingdom to evaluate its effectiveness. Results show that this method reduced monthly wind power simulation errors by more than 32% compared to the uncorrected ERA5 reanalysis dataset. The method is further applied to the MERRA-2 dataset for comparative evaluation, demonstrating its effectiveness and robustness for different reanalysis products. In contrast to prior studies, which rarely quantify the influence of topography on reanalysis biases, this research presents a detailed spatial mapping of bias correction factors across the UK. The analysis reveals that for wind energy applications, ERA5 wind speed errors exhibit strong spatial variability, with the most significant underestimations in the Scottish Highlands and mountainous areas of Wales. These findings highlight the importance of explicitly accounting for geographic variability when correcting reanalysis wind speeds, and provide new insights into region-specific bias patterns relevant for high-resolution wind energy modelling.
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Journal articleJia H, Quaas J, Kroese W, et al., 2026,
Optimal choice of proxy for cloud condensation nuclei reduces uncertainty in aerosol-cloud-climate forcing.
, Sci Adv, Vol: 12Aerosol-cloud interactions (ACI) remain the largest uncertainty in anthropogenic climate forcings. Observation-based estimates of instantaneous radiative forcing from ACI (RFaci; the Twomey effect) rely on the choice of aerosol quantities as proxies for cloud condensation nuclei (CCN) concentrations, which differ in their ability to represent cloud-base CCN and data accuracy. Using diverse observations and aerosol-climate models, we evaluate the utility of different proxies with two independent approaches. Both approaches reveal that surface CCN exhibits the smallest bias in predicting RFaci (+5%), followed by aerosol index, surface sulfate and column CCN with similar biases of +25%, while aerosol optical depth and column sulfate show the largest biases (-60% and +92%). Constraining RFaci with the optimal proxy reduces uncertainty from 66 to 43%, yielding a less negative RFaci (-1.0 W m-2) than the unconstrained case (-1.2 W m-2). Our findings highlight the crucial role of proxy constraint in reconciling and improving RFaci estimates.
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Journal articleWilson Kemsley S, Nowack P, Ceppi P, 2026,
Recent Cloud Controlling Factor Analyses Indicate Higher Climate Sensitivity
, Geophysical Research Letters, Vol: 53, ISSN: 0094-8276Cloud feedback is a dominant source of uncertainty in climate model estimates of equilibrium climate sensitivity (ECS). Cloud controlling factor analysis can observationally constrain cloud feedback. For the first time, we use separate rather than unified frameworks to assess high- and low-cloud feedbacks and constrain the net cloud feedback and subsequently, the ECS. We find a robustly positive cloud feedback (i.e., a negative feedback is (Formula presented.) % probable), indicating that clouds amplify global warming. We assess the individual and combined impacts of our cloud feedback constraints on ECS using three approaches. Two indicate an upward ECS shift with reduced uncertainty, preserving ECS–feedback correlations but using cloud feedback as a single line of evidence. The third, a Bayesian framework combining multiple lines of evidence, also suggests a higher ECS but with a smaller increase and broader confidence range.
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Journal articleGan W, Alizadeh N, Best M, et al., 2026,
An eco-evolutionary optimality model explains theacclimated temperature response of photosynthesis
, New Phytologist, ISSN: 0028-646XThe optimal temperature of net photosynthesis (Topt) generally increases with plant growth temperature. Changes in Topt are associated with changes in the maximum carboxylation capacity at 25 °C (Vcmax25) and the maximum electron transport rate at 25 °C (Jmax25). The ratio between Jmax25 and Vcmax25 declines with warming. Accurate representation of leaf-level photosynthetic responses to temperature is essential for realistic projections of the terrestrial carbon cycle and its response to ongoing climate changes. However, many land-surface models incorporate thermal acclimation through empirical approaches and through assigning distinct but static parameter values to plant functional types (PFTs). Eco-evolutionary optimality approaches provide a simpler way of modelling photosynthesis without recourse to PFTs. Here we use the sub-daily P model, an eco-evolutionary optimality-based model of photosynthesis that explicitly separates the instantaneous and acclimated responses of photosynthetic parameters to temperature to investigate how optimal temperature changes with growth temperature, as represented by leaf or air temperature. We show that the simulated responses are consistent with observations from both controlled experiments and eddy-covariance flux tower data. We show that changes in Topt, and in the assimilation rate at Topt, are caused by changes in carboxylation capacity and electron transport rate that follow directly from the hypotheses underlying the model.
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