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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 articleAlmalki YR, Karmpadakis I, 2026,
Uncertainty analysis of oscillating water column experiments under regular and random wave conditions
, Renewable Energy, Vol: 271, ISSN: 0960-1481This paper presents a rigorous uncertainty analysis of experimental testing of an oscillating water column device. Quantifying experimental uncertainty is essential for establishing the confidence level of laboratory data and enabling a reliable transition to full-scale applications. Previous studies have focused on deterministic performance, overlooking the statistical variability inherent in random wave conditions. To address this gap, the Monte Carlo method was applied to evaluate uncertainties in oscillating water column experiments conducted under both regular and random wave conditions. A camera-based edge-detection system was employed to capture the spatio-temporal evolution of the free surface within the chamber, enabling high-accuracy assessment of pneumatic power output. The analysis examined the effects of the number of wave cycles, test duration, and random realisations on power estimation. The analysis also assessed the repeatability error in the time series for several measured and calculated quantities. Results indicate excellent repeatability, with standard deviations below 1% for all measured quantities and expanded uncertainties of approximately 1% under regular waves and 2.5% under random waves, the latter reflecting inherent variability in realistic conditions. These findings validate the robustness of the proposed measurement and analysis framework, establishing a practical methodology for quantifying uncertainty in oscillating water column experiments and improving the reliability of early-stage testing.
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Journal articleKristoffersen JC, Kabel T, Georgakis CT, et al., 2026,
Spatio-temporal measurement of laboratory wave fields using LiDAR
, Coastal Engineering, Vol: 209, ISSN: 0378-3839Accurate spatio-temporal measurements of the free-surface elevation are essential for understanding wave evolution, wave breaking, and wave-structure interaction. In laboratory studies, conventional wave gauges provide reliable point measurements but become intrusive and impractical when extended to dense spatial arrays. This study evaluates the capability of a commercially available 3D LiDAR system to resolve the spatio-temporal evolution of regular and irregular waves in a wave flume, through direct comparison with high-resolution camera and wave-gauge measurements.The LiDAR is deployed non-intrusively to capture free-surface elevation over a spatial extent exceeding two wavelengths with high spatial and temporal resolution. Regular and irregular wave conditions are investigated over a sloping bathymetry, including breaking waves. Quantitative comparisons are conducted in the time, frequency, and spatial domains, as well as individual wave statistics. For irregular sea states, significant wave height, individual wave heights, periods, and crest heights derived from LiDAR measurements show close agreement with wave gauge estimates, with root-mean-square errors typically below 6% of the significant wave height and correlation coefficients exceeding 0.97 outside the immediate vicinity of the LiDAR.Systematic deviations are observed directly beneath the LiDAR. Under breaking conditions, the LiDAR preferentially captures the densest part of the overturning crest and aerated surface, revealing inherent differences between optical and probe-based definitions of the free surface. These effects are quantified, and practical guidance on sensor placement, data processing, and interpretation is provided. Overall, the results demonstrate that LiDAR offers a robust and efficient alternative to dense wave gauge arrays for laboratory studies requiring spatio-temporal resolution of wave fields.
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Journal articleWright W, Craske J, Karmpadakis I, 2026,
Real-time phase-resolved wave prediction over planar coastal bathymetries using U-Net convolutional neural networks
, Coastal Engineering, Vol: 209, ISSN: 0378-3839Real-time, phase-resolved forecasting of waves is essential for safe operations in the coastal zone, for example, by enabling early-warning systems to inform real-time decision-making. However, non-linear transformations, depth variations and wave breaking limit the accuracy of theoretical models. This study presents a data-driven alternative using convolutional neural networks to predict nearshore surface elevation time series. The proposed method is developed for long-crested waves over planar slopes, predicting surface elevations up to approximately 6 peak periods in advance. Specifically, a U-Net architecture with three encoding and three decoding stages and approximately 200,000 trainable parameters is used, with the prediction based on a short time window from a single offshore gauge. Laboratory experiments of long-crested waves propagating over sloping beds were used for training and testing, covering multiple bed slopes and a wide range of spectral shapes, peak periods, and steepnesses. Model performance was compared against predictions from linear and second-order wave theories with shoaling corrections. The neural network reproduced the measured wave evolution with consistently lower errors than the theoretical models, particularly in shallow water where nonlinearity and breaking become dominant. It also captured wave arrival times with higher accuracy than the theoretical models, and showed robustness when applied to unseen sea states or slightly noisy input signals. These results show that within this laboratory regime, neural networks can extend phase-resolved wave prediction into the coastal zone, complementing traditional theoretical approaches and offering a practical framework which, with further development, could provide real-time operational forecasting based on offshore wave data.
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Journal articleZhou Z, Chandresh R, Whittaker A, et al., 2026,
Sediment supply controls on channel morphological adjustments to tectonics and lithology
, Earth and Planetary Science Letters, Vol: 685, ISSN: 0012-821XIn nature, rivers not only incise bedrock but also transport sediment supplied to them; however, incision is often assumed to dominate over sediment transport in shaping channel geometry so as to exploit the simplicity of the stream power law. This likely flawed assumption raises fundamental concerns about inverting channel morphology to map external forcing, such as active deformation. Central to this issue is how relative sediment flux (i.e. sediment supply relative to transport capacity, Qs/Qt) modulates the efficacy of excess shear stress in incising bedrock, known as the relative sediment flux function. Two competing functions have been proposed, but resolving them in the field has proven challenging to date. Here, we address this issue by contrasting two rivers in the Gulf of Corinth, Greece that traverse comparable gradients in tectonics and lithology with distinct relative sediment fluxes. We show that the sediment-rich Phoenix river, with an estimated Qs/Qt value ∼0.8, has a much lower sensitivity of excess shear stress to tributary sediment input than the sediment-poor Sithas river. To our knowledge, this represents the first field evidence supporting the function with a markedly decreased sensitivity to Qs/Qt at high Qs/Qt values. This allows us to clarify the importance and mechanisms of channel slope versus width adjustment in reaching the excess shear stress required for equilibrium. As a result, we outline the conditions under which the widely used stream power law and its associated metrics, such as steepness and knickpoints, can (or cannot) be sensibly employed.
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Journal articleKhurana MP, Katsiferis A, Scheidwasser N, et al., 2026,
Large-scale genomic surveillance reveals immunosuppression drives mutation dynamics in persistent SARS-CoV-2 infections.
, Nat CommunPersistent SARS-CoV-2 infections have been hypothesized to play a key role in the emergence of variants of concern. However, the factors determining which individuals are at risk and their viral molecular signatures during infection remain poorly understood. Using Denmark's extensive COVID-19 surveillance, comprising over 700,000 genomes, we identify 303 persistent infections and, critically, link them to health and sociodemographic data. Our analysis confirms the hypothesis that immunocompromised individuals are at the highest risk of experiencing persistent infections. Other disease groups associated with mortality, such as diabetes, show no such associations. Among these persistent infections, the viral sequences exhibit signs of positive selection, with recurrent mutations linked to treatment resistance. Our findings suggest that immunosuppression plays a key role in the emergence of novelty in persistent infections.
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OtherRoi-Cohen O, Hadary G, Wall CJ, et al., 2026,
Supplementary material to "Systematic Observation-Based Estimate of Effective Radiative Forcing from Aerosol–Cloud Interactions"
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Journal articleFraser K, Cibrelus L, Horton J, et al., 2026,
Yellow fever outbreak potential in Djibouti, Somalia and Yemen: a mathematical modelling study
, BMC Global and Public Health, ISSN: 2731-913XBackground: The importation of arbovirus diseases into new countries is a global concern. This risk is exacerbated by human movement and climate changeeffects. In the World Health Organisation (WHO) Eastern Mediterranean regional office, three countries- Djibouti, Somalia, and Yemen- are currently classified aspotential or moderate risk for yellow fever (YF) outbreaks.Methods: Here we present a quantitative assessment of the risk of introduction and propagation of yellow fever virus (YFV) transmission in Djibouti, Somalia and Yemen. This modelling has two components: i) projecting the risk of importation of infectious individuals into the countries of interest using a radiation model of human movement and ii) estimating the risk of onward transmission given importation using a dynamic compartmental model of yellow fever virustransmission. Both components are multiplicatively combined to give an overall relative outbreak risk combining both risk of importation and risk of an outbreak given importation.Results: We show that areas such as the western coast of Yemen, regions of Somalia bordering Ethiopia and Kenya, and Djibouti City have potential for YFoutbreaks (where the estimated probability of an outbreak given an imported infectious case is over 50%). This is due to environmental suitability for transmission based on factors such as temperature and projected human mobility between endemic and at-risk regions.Conclusions: Countries bordering existing YF endemic regions are potentially vulnerable to both introduction of YF cases and subsequent outbreak spread. Thispromotes the awareness of YF importation potential when conducting clinical surveillance in at-risk countries.
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Journal articleDong Y, Lu K, Hwang Y-T, et al., 2026,
Tropical impacts of the Southern Ocean underestimated by mean-state biases.
, Sci Adv, Vol: 12Observed sea-surface temperature (SST) trends over recent decades feature cooling in the tropical eastern Pacific and the Southern Ocean (SO). Growing evidence suggests that tropical cooling may partly stem from remote impacts of the SO. Using a hierarchy of multimodel simulations, we demonstrate that these teleconnections are robustly modulated by the mean-state intertropical convergence zone (ITCZ): Models with a more realistic ITCZ simulate a stronger tropical SST response to SO forcing via stronger wind-evaporation-SST feedback. When realistic Antarctic meltwater forcing is included, correcting a model's tropical mean-state bias yields a stronger tropical cooling response to meltwater-driven SO cooling, improving the agreement between simulated and observed SST trends. Our results suggest that the SO's contribution to tropical warming patterns is systematically underestimated due to model mean-state biases. Improving representations of the mean-state climate is therefore critical for accurately assessing large-scale climate responses associated with historical and future warming patterns.
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OtherM Jaison A, Ceppi P, Wilson Kemsley S, 2026,
Supplementary material to "The role of the QBO for tropical high-cloud variability in CMIP6 models and observations"
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