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Journal articleEiden E, Pritchard ME, Galetto F, et al., 2025,
Quantifying the Eruptive Flux on Venus With VenSAR Informed by Observations From Earth and Io
, JOURNAL OF GEOPHYSICAL RESEARCH-PLANETS, Vol: 130, ISSN: 2169-9097 -
Journal articleJones G, Zhang Z, Clayton K, et al., 2025,
Utilizing Soil Centrifugation for Accurate Estimates of Carbon Dioxide Removal via Enhanced Rock Weathering
, ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol: 59, Pages: 27305-27315, ISSN: 0013-936X -
Journal articleGavasso-Rita YL, Zaerpour M, Abdelmoaty H, et al., 2025,
Rainfed spring canola yield response to changing heat and water stress in the Canadian Prairie region
, AGRICULTURAL WATER MANAGEMENT, Vol: 322, ISSN: 0378-3774 -
Journal articlePonsonby J, Teoh R, Karcher B, et al., 2025,
An updated microphysical model for particle activation in contrails: the role of volatile plume particles
, Atmospheric Chemistry and Physics (ACP), Vol: 25, Pages: 18617-18637, ISSN: 1680-7316Global simulations suggest the mean annual contrail cirrus net radiative forcing is comparable to that of aviation's accumulated CO2 emissions. Currently, these simulations assume non-volatile particulate matter (nvPM) and ambient particles are the only source of condensation nuclei, omitting activation of volatile particulate matter (vPM) formed in the nascent plume. Here, we extend a microphysical model to include vPM and benchmark this against a more advanced parcel model (pyrcel) modified to treat contrail formation. We explore how the apparent emission index (EI) of contrail ice crystals (AEIice) scales with EInvPM, vPM properties, ambient temperature, and aircraft/fuel characteristics. We find model agreement within 10 %–30 % in the previously defined “soot-poor” regime. However, discrepancies increase non-linearly (up to 60 %) in the “soot-rich” regime, due to differing treatment of droplet growth. Both models predict that, in the “soot-poor” regime, AEIice approaches 1016 kg−1 for low ambient temperatures (< 210 K) and sulfur-rich vPM, which is comparable to estimates in the “soot-rich” regime. Moreover, our sensitivity analyses suggest that the point of transition between the “soot-poor” and “soot-rich” regimes is a dynamic threshold that ranges from 1013–1016 kg−1 and depends sensitively on ambient temperature and vPM properties, underlining the need for vPM emission characterisation measurements. We suggest that existing contrail simulations omitting vPM activation may underestimate AEIice, especially for flights powered by lean-burn engines. Furthermore, our results imply that, under these conditions, AEIice might be reduced by (i) reducing fuel sulfur content, (ii) minimising organic emissions, and/or (iii) avoiding cooler regions of the atmosphere.
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Journal articleKeeping TR, Shepherd TG, Prentice IC, et al., 2025,
Influence of global climate modes on wildfire occurrence in the contiguous United States under recent and future climates
, Climate Dynamics, Vol: 64, ISSN: 0930-7575Predictable modes of climate variability, such as the El Niño Southern Oscillation (ENSO), have a major influence on regional weather patterns, an important control on wildfire occurrence. Although these global climate modes have been associated with historical variability in wildfire occurrence in the United States and are used to forecast seasonal wildfire risk, precise information about the spatial pattern and magnitude of their influence is lacking and the satellite record of wildfires is too short to address these issues. Here we use wildfire occurrence model with a large ensemble of 1600 simulated years from EC-Earth3 in a recent climate (2000–2009) and a future climate corresponding to + 2 °C global warming, to characterise the impact of specific climate modes on wildfire occurrence in the contiguous US. We show that ENSO, the Indian Ocean Dipole (IOD), and the 1-year lagged Tropical North Atlantic (TNA+1) have the greatest effect on annual fire occurrence—strongly contributed by the effect of these modes on hot, dry conditions in the Great Plains and precipitation in the southwestern US. El Niño is not significantly associated with wildfire occurrence in the northwestern US, contrary to expectation, but is associated with a later (earlier) wildfire season peak in the southwestern (southeastern) US. Under future warming, the AMO and PNA become a significant influence over most of the US, and the magnitude of impact of ENSO and TNA+1 increase strongly.
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Journal articleHaas O, Prentice IC, Harrison SP, 2025,
Wildfires on a changing planet
, Nature Communications, ISSN: 2041-1723The distribution of wildfires on Earth will change as climate, land-use, and vegetation change. We use global empirical models of burnt area, fire size and fire intensity to explore future wildfire trajectories under ~1.5 and 3-4 °C warming with middle of the road future socio-economic conditions. Even under ~1.5 °C warming we find a change in wildfire patterns by the end of the 21st century with reduced burning in tropical regions driven by changes in human activity but larger and more intense wildfires in extra-tropical regions driven by changes in climate and CO2. With low climate change mitigation, burnt areas increase greatly across all vegetation types, overwhelming the current global decline. These findings suggest that even with ambitious climate change mitigation, current fire-suppression policies will fail in much of the world and mitigation scenarios that rely on expanding forest areas will be unrealistic unless they are designed with wildfire risks in mind.
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Journal articleZhang Z, Jones G, Calabrese S, et al., 2025,
An Integrated Modelling Framework to Determine Terrestrial Carbon Dioxide Removal via Enhanced Rock Weathering
, GLOBAL CHANGE BIOLOGY, Vol: 31, ISSN: 1354-1013 -
Journal articleShahin K, DeWinter S, Berke O, et al., 2025,
The Ecological Factors Associated With the Survival, Establishment, and Movement of Aedes aegypti and Ae. albopictus: A Scoping Review
, JOURNAL OF APPLIED ENTOMOLOGY, ISSN: 0931-2048 -
Journal articleKatsiferis A, Joensen A, Petersen LV, et al., 2025,
"Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence".
, Npj Ment Health Res, Vol: 4Early detection and prevention of eating disorders (EDs) in adolescence are crucial yet challenging. We developed and validated diagnostic and prognostic models to predict EDs using data from 44,357 Danish National Birth Cohort participants. Models were trained to identify ED presence in early and late adolescence (11- and 18-year follow-up), utilizing approximately 100 predictors from self-reported and registry-based data. The machine learning model demonstrated strong discrimination for both tasks (diagnostic Area Under the receiver operating characteristic Curve = 81.3; prognostic AUC = 76.9), while a logistic regression model using the top 10 predictors achieved comparable performance. Sex, emotional symptoms, peer relationship and conduct problems, stress levels, parental BMI values, body dissatisfaction, and BMI at the 7-year follow-up emerged as key predictors. Our models showed potential utility in supporting clinical risk assessment, particularly for low-risk preventive interventions, though further validation studies are needed to evaluate their effectiveness in real-world clinical settings.
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Journal articleAl Khalili U, Karmpadakis I, 2025,
Breaking occurrence and dissipation in shortcrested waves in finite water
, Coastal Engineering, Vol: 202, ISSN: 0378-3839The understanding of wave breaking has long been a critical concern for engineers and scientists. However, accurately identifying the onset of breaking and quantifying the associated energy dissipation remain significant challenges. To address this, the present study develops a novel methodology to identify breaking wave events in shortcrested seas in finite water depths. This is achieved through a unique dataset which couples laboratory and numerically-generated waves. The data reflect realistic sea-states used in engineering design and cover a wide range of conditions from mild to extreme. Using the proposed algorithm, key physical properties of breaking waves are examined. In particular, the probability of wave breaking and the associated wave energy dissipation are quantified to provide a statistical description of their behaviour. Complementarily, waves exhibiting significant nonlinear amplifications are also identified and modelled in a similar manner. This enables traditional wave distributions to be decomposed into more detailed distributions of breaking and non-breaking waves. These insights are combined to define a new model that predicts crest height statistics in intermediate water depths. This new mixture model is shown to reproduce experimental measurements with high accuracy, while also providing critical additional information about wave breaking.
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