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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 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 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 articlePugsley G, Gryspeerdt E, Nair V, 2026,
Reply to Yu et al.: Meteorological covariations do not reproduce diurnal cloud fraction response to aerosol
, Proceedings of the National Academy of Sciences, Vol: 123, ISSN: 0027-8424 -
OtherDavidova N, Gallardo i Peres G, Gao Y, et al., 2026,
Multi-scale surface characterisation of recently erupted lava flows on the Reykjanes Peninsula: a 2026 field campaign in support of EnVision mission science development
<jats:p>In preparation for the Envision mission to Venus, multi-frequency airborne Synthetic Aperture Radar (SAR) data collected in August 2023 over Iceland, by the DLR for NASA JPL and the VERITAS mission team (using the F-SAR system) [2-5], has been analysed across a series of basaltic lava flows of differing ages. Fieldwork in July-August 2026 in the Reykjanes Peninsula has provided some vital ground-truth (surface roughness and very high-resolution topography) for our analysis. Our investigations are aimed at better understanding the scales and scattering characteristics of young basaltic volcanic landscapes, and SAR data-processing algorithms applied to volcanic terrain data.Analysis of the 2023 F-SAR data at Askja enabled characterisation of seven lava flow units from 1961 (Vikrahraun) to >6100 yr BP using the full-polarimetric X-, S-, and L-band SAR with paired pin-profilometer and drone Digital Elevation Model (DEM) data (Figure 1). Across the Askja flow sequence, mean backscatter decreases systematically with flow age, and decomposition techniques partitions the flows sequence by facies. We now extend this framework to the Reykjanes Peninsula. Figure 1. Representative surface units. Left: field photos; centre: drone orthomosaics; right: drone-derived DEMs. (A) Inflated pāhoehoe with pressure ridges; (B) a'ā-pāhoehoe contact; (C) tephra plain with aeolian bedforms. Horizontal scale bars and DEM elevation ranges (m a.s.l.) shown per row.Complementary analysis of field data across the fresher flows at the Reykjanes Peninsula enables bare-versus-mantled backscatter comparison of several flow units because of the occurrence of moss/lichen colonisation in a slightly warmer climate (Figure 2). Five age groups have been sampled: the Sundhnúkur-Svartsengi system, with nine flows emplaced between December 2023 to August 2025 along a c. 10 km fissure north of Grindavík (fieldwork ages 1-2.5 yr [6]); the Fagradalsfjall system comprising
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OtherCrouch E, Ghail R, Mason P, 2026,
Terrestrial Analogues for Polygonal Terrain on Venus
<jats:p>Polygons of various sizes cover more than 5% of the surface of Venus, almost as much of the surface as that covered by tesserae, and yet they have been largely ignored. The most recent study [1] identified 204 polygonal terrain locations covering approximately 8 Mm², using an automated algorithm that resulted in a northern hemisphere bias. Nonetheless, they found that 65% of the identified polygonal terrain is associated with small volcanoes, 25% with coronae, 18% with tesserae, and 20% with wrinkle ridges. Polygonal terrain is currently attributed to thermal contraction by cooling, whether of lava flows, or following heating by an intrusion [2], or in response to climate change [3].Our mapping of an additional 16 Mm² (Figure 1) reveals 6 types of polygonal terrain (plus unclassified), broadly divided into irregular (55% by area) and rectilinear patterns (37% by area). There appear to be two distinct size ranges of smaller cells close to the resolution limit (~100 m) and larger cells several km across, sometimes superposed. A thermal contraction origin by cooling is difficult to reconcile with the variety, shapes and sizes of polygons observed.A range of processes in addition to thermal contraction can generate polygonal patterns at varying scales on Earth and Mars including lava lakes, columnar joints, karst, diagenesis, ice wedge polygons (periglaciation), desiccation (mud cracks), evaporation (salt pans), and polygonal fault systems (PFS). The first four generate polygons on the metre scale, smaller than can be resolved in Magellan imagery. The next three can generate polygons from metres up to a few hundred metres across and may therefore have generated the smaller polygons observed on Venus. PFS have so far only been identified in some terrestrial sedimentary basins [4], but they do generate polygons up to several kilometres across, similar to the larger polygons observed on Venus.PFS form networks of small‐displacement no
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OtherGhail R, Crouch E, Mason P, 2026,
The Messinian Salinity Crisis and the Lost Oceans of Venus
<jats:p>Venus is the only other Earth-sized planet in our Solar System, volcanically active and rich in volatiles, but extremely hot, dry, and hostile to life. Models suggest either that Venus was always thus [1], or that in principle it could support oceans even today [2]. While the long history of the planet may be recorded in its ancient highlands, the lowland plains host a range of features suggestive of past water. Some canali are clearly lava channels [3], but others appear similar to fluvial [4] or submarine [5] channels, and our mapping of polygonal terrain implies a submarine sedimentary origin of them. The evidence is compelling that Venus once supported oceans and lost them.Possibly the closest terrestrial analogue for these conditions is the Messinian Salinity Crisis (MSC), 5·97 to 5·33 Ma ago, during which the Mediterranean Sea became repeatedly restricted, and evaporated in part or whole [6,7]. Nearly 10⁶ km³ of gypsum and halite, in places several kilometres thick, were precipitated [8], with exposed salt flats covering most of the Mediterranean. With sea level lowered by 2 km or more, the major rivers—notably the Nile, Rhone, Ebron and Po—carved deep canyons into the continental margins, helping to maintain brine pools within the deepest basins. Remarkably, faunal exchanges took place across this inhospitable landscape [9].Conditions on Venus were even more extreme. In the earliest stages of the runaway greenhouse, photochemical sulphur cycling in a hot steam‑rich atmosphere would have resulted in transient, intense episodes of sulphuric acid rainfall. Under these conditions, the subaerial uplands would have experienced extreme chemical weathering and flash flood erosion [10], rapidly depositing smectite-rich clays into saturated brine ocean basins, generating thick piles of salt-rich sediments. Extensive erosion of the Venus uplands and infilling of the plains basins with sediment and salt may in p
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OtherGallardo i Peres G, Mason P, Ghail R, et al., 2026,
Radar Scattering of Venus Terrains: Characterising Surface Roughness in Preparation for the Decade of Venus
<jats:p>The Magellan radar space mission [1] produced the largest, highest-resolution, most accurate survey of the surface of Venus to date. During the first cycle of the mission, its unique latitude-varying radar observation geometry allowed for the revisit of morphologically-equivalent terrains with a wide array of incidence angles, both in synthetic aperture radar (SAR) mode and in altimetry mode [2,3,4,5]. With the appropriate constraints and processing steps, this enables the reconstruction of the full scattering behaviour (a scattering curve) of distinct rock units [6] across approx. 0-50 degrees of local incidence, offering a unique opportunity to measure the mean radiometric signature of different terrain formations on Venus and, for the first time, map and characterise wavelength-scale surface roughness across the entire planet. Additionally, the derived scattering curves represent the reference scenarios that will enable radiometric comparison of Magellan SAR images with SAR data from upcoming Venus orbiters, even if the acquisition geometry between sensors for a specific target differs significantly [7,8].To perform this study, we have built a comprehensive method that blends SAR and altimetry backscatter, postprocessed Magellan topography, Magellan radiometry, and geological data from Venus; the method incorporates a physically-constrained stochastic model of uncertainty for each individual backscatter measurement, and provides the framework for scattering models and constraints to appropriately fit the data. Our scattering curve results have been extensively tested against different cycle 1 and cycle 2 SAR and altimetry data to investigate the heterogeneity and/or isotropy of the measured surfaces, and to internally validate the radiometric consistency (absolute and relative) of the observed backscatter estimates.In this work, we present insights into our findings regarding surface roughness properties for each terrain formation. We discuss the ge
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OtherMason PJ, 2026,
Defining Envision’s Regions of Interest (ROI): from a community-led feature list to a targeted imaging strategy
<jats:p>ESA’s Envision mission is a holistic investigation of Venus’ interior, surface and atmosphere, aimed at improving our understanding of the geodynamic, geological and climatic history and activity of our nearest planetary neighbour. To achieve that, we need a series of complimentary observations at global, regional and local scales, from different instruments and in different modes, and repeated observations for change detection. Envision will carry an integrated instrument suite of spectrometers (VenSpec), a Subsurface Radar Sounder (SRS) and a Synthetic Aperture Radar (VenSAR) that will collect these data. This will be further complemented by gravity and radio occultation observations using the satellite communications system to Earth.Whilst we would like to image the entire planet at high resolution using the SAR instrument, that is not feasible in the 6-sidereal-day nominal mission life, hence Envision’s imaging strategy is targeted: collecting nested, multi-modal images which will be used to answer its complex science questions. At least 20% of the surface of Venus will be imaged using the SAR at 30 m spatial resolution (twice and at different angles, for generation of stereo topography for at least 18% of the surface) with a smaller fraction imaged a third time (for change detection) or at dual-polarisation, and an even smaller fraction imaged at 10 m resolution for detailed geomorphological analysis. The stereo-derived topography and Envision’s near-global altimetry data will be vital to answer science questions involving Envision’s other instruments and for its holistic science investigations. Considering EnVision’s overall scientific objectives, as well as individual instrument goals, synergies, and limitations, the key question then becomes how we decide which areas should be imaged and in which modes?The Envision Science Working Team (SWT) formed an ROI Working Group (ROIWG), which was tasked with compiling
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