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ReportBiermann O, Lawrance E, Jennings N, et al., 2032,
Protecting mental health from extreme heat: recommendations for policy and action
, Publisher: Grantham Institute, Imperial College London -
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 articleAuestad H, Shibu A, Ceppi P, et al., 2026,
The latent heating feedback effect on storm tracks in current and future climates
, Npj Climate and Atmospheric Science, Vol: 9Extratropical storms release latent heat as they transport warm, moist air poleward and upward. That latent heating feeds back on storms by intensifying individual cyclones and by altering the environmental conditions for the growth of storms, constituting a latent heating-dynamics feedback. As the climate warms, storm-track latent heating increases, but the role of this feedback in a future climate remains unclear. Using atmospheric general-circulation model experiments that separate the coupled heating-dynamics feedback from climatological changes in latent heating, we show that this feedback plays a leading-order role in intensifying storm tracks under +4 K warming. The feedback increases lower-tropospheric storm intensity, compensating for reduced baroclinicity, while its upper-tropospheric effect is seasonal: amplifying summer eddies but damping winter ones. The feedback is critical for storms that grow in moist environments, typical for summer and warmer climates, underscoring the need for accurate representation of moist processes in climate models.
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Journal articleDe Lorm TA, Heon SP, Bernard H, et al., 2026,
Bird and mammal communities transform without collapsing in response to oil palm replanting
, Forest Ecology and Management, Vol: 619, ISSN: 0378-1127Replanting a tree plantation - that is, clearing old trees and replacing these with young plants - drastically alters its habitat structure and microclimate, leading to changes in its biodiversity. Nevertheless, we lack an understanding of how the replanting of oil palm plantations - the most prominent oil crop globally - affects mammals and birds, while the amount of area replanted will increase exponentially over the next decades. We therefore studied how the bird and mammal community of an oil palm plantation in Malaysian Borneo changed in response to replanting. Using camera traps for mammals, and acoustic recordings and BirdNET for birds, we show that both communities are transformed. Mammal species richness slightly dropped, because of the absence from replanted plantations of long- and pig-tailed macaques (Macaca fascicularis and M. nemestrina), both endangered primate species. Bird species richness did not significantly differ between replanted and mature plantations. However, the detection frequency of most species changed, as the bird community shifted from forest and woodland species towards open-habitat species. The detection frequency and species richness of different trophic niches stayed largely constant, indicating that birds will still be able to perform similar high-level ecological functions in replanted plantations. Overall, our results show that replanting reshaped mammal and bird communities, but that their diversity and abundance does not collapse. These findings stress the importance of staggered replanting, as opposed to replanting large stretches of plantation in one go, to bolster landscape level biodiversity, and underscore that tree plantations are temporally dynamic habitats.
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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.
, Proc Natl Acad Sci U S A, Vol: 123 -
Journal articleLi H-Y, Lawrence JA, Mason PJ, et al., 2026,
Observing the phenological characteristics of winter food crops with spectral indices
, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol: XI-3-2026, Pages: 741-748<jats:p>Abstract. This study is based on the best crop classification result generated by the proposed unsupervised Machine Learning (ML) method in Li et al., 2025a, using the spectral indices calculated by the formula with spectral bands from Sentinel-2 image products, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI) and Normalized Difference Moisture Index (NDMI). The patterns and characteristics of these spectral indices, across arable fields with different crop types following the winter growing seasons, have not yet been analyzed in detail. This research aims to provide a comprehensive study of each input spectral index and its impact on the crop classification model. Each spectral index is analyzed across a series of crop fields, using Sentinel-2 images, carefully selected to follow the patterns of winter crop phenology, and the results of unsupervised classification for each crop type in Norfolk, UK are successfully generated and analyzed. The different growing rates between winter barley and wheat have been classified found on a monthly basis using Sentinel-2 RGB images and thus the images during the harvest time, May and June, can support crop classifications. Wild grasses or other plants on the fields led to some crop misclassification from November to March in the Sentinel-2 RGB images. Similarity between winter barley and wheat and the different sowing time among the same type of crop also led to misclassification. In future these misclassifications could be avoided through better understanding of the relation between spectral indices and crop planting cycles.</jats:p>
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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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