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Journal articleChen A, Wang J, Toumi R, et al., 2025,
Impact of tropical cyclone precipitation on fluvial discharge in the Lancang‒Mekong river basin
, Geophysical Research Letters, Vol: 52, ISSN: 0094-8276Tropical cyclone precipitation (TCP) and associated floods have caused widespread damage globally. Despite growing evidence of significant changes in the activity of tropical cyclones (TCs) in recent decades, the influence of TCs on regional flooding remains poorly understood. Here, we distinguish the role of TCs in fluvial discharge by explicitly simulating discharge with and without observed TCP in the Lancang‒Mekong River Basin, a vulnerable TC hotspot. Our results show that TCs typically contributed approximately 30% of annual maximum discharge during 1967–2015. However, for rare and high-magnitude floods (long return periods), TCs are the dominant driver of extreme discharge events. Moreover, spatial changes in TC-induced discharge are closely related to changes in TCP and TC tracks, showing increasing trends upstream but decreasing trends downstream. This study reveals significant spatiotemporal differences in TC-induced discharges and provides a methodology for quantifying the role of TCs in fluvial discharge.
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Journal articleCoupland H, Scheidwasser N, Katsiferis A, et al., 2025,
Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis
, BMC Public Health, Vol: 25, ISSN: 1471-2458BackgroundUnderstanding the complex interplay between life course exposures, such as adverse childhood experiences and environmental factors, and disease risk is essential for developing effective public health interventions. Traditional epidemiological methods, such as regression models and risk scoring, are limited in their ability to capture the non-linear and temporally dynamic nature of these relationships. Deep learning (DL) and explainable artificial intelligence (XAI) are increasingly applied within healthcare settings to identify influential risk factors and enable personalised interventions. However, significant gaps remain in understanding their utility and limitations, especially for sparse longitudinal life course data and how the influential patterns identified using explainability are linked to underlying causal mechanisms.MethodsWe conducted a controlled simulation study to assess the performance of various state-of-the-art DL architectures including CNNs and (attention-based) RNNs against XGBoost and logistic regression. Input data was simulated to reflect a generic and generalisable scenario with different rules used to generate multiple realistic outcomes based upon epidemiological concepts. Multiple metrics were used to assess model performance in the presence of class imbalance and SHAP values were calculated.ResultsWe find that DL methods can accurately detect dynamic relationships that baseline linear models and tree-based methods cannot. However, there is no one model that consistently outperforms the others across all scenarios. We further identify the superior performance of DL models in handling sparse feature availability over time compared to traditional machine learning approaches. Additionally, we examine the interpretability provided by SHAP values, demonstrating that these explanations often misalign with causal relationships, despite excellent predictive and calibrative performance.ConclusionsThese insights provide a foundation for
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Journal articleSullivan NB, Meyers SR, Levy RH, et al., 2025,
Obliquity disruption and Antarctic ice sheet dynamics over a 2.4-Myr astronomical grand cycle
, Science Advances, Vol: 11, ISSN: 2375-2548Marine δ18O data reveal astronomical forcing of the climate and cryosphere during the Miocene, when atmospheric Pco2 was on par with emissions scenarios over the next century. This inspired hypotheses for how Milankovitch cycles, ice-ocean interactions, and greenhouse gases influence ice volume. Mass balance controls for marine and terrestrial ice sheets differ, and proxy data collected far from Antarctica provide valuable but limited insight into regional processes. We evaluate clast abundance data from Antarctic marine sedimentary records, observing a strong signal of eccentricity and precession coincident with a terrestrial ice sheet and a clear obliquity signal at the margins of a marine ice sheet. These analyses are integrated with a synthesis of proxy data, and we argue that high variance in obliquity forcing (mediated and enhanced by the ocean and atmosphere) can inhibit ice sheet growth, even when insolation forcing is conducive to glaciation. This “obliquity disruption” explains cryosphere variability before the existence of large northern hemisphere ice sheets.
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Journal articleAl-Kaisy R, Bhatt S, Duchene DA, 2025,
Distinct evolutionary regimes across domains of the <i>Plasmodium falciparum</i> CSP gene
, SCIENTIFIC REPORTS, Vol: 15, ISSN: 2045-2322 -
Journal articlePrieur M, Robin C, Braun J, et al., 2025,
Climate Control on Erosion: Evolution of Sediment Flux From Mountainous Catchments During a Global Warming Event, PETM, Southern Pyrenees, Spain
, GEOPHYSICAL RESEARCH LETTERS, Vol: 52, ISSN: 0094-8276- Cite
- Citations: 3
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Journal articleWilson Kemsley S, Nowack P, Ceppi P, 2025,
Climate models underestimate global decreases in high‐cloud amount with warming
, Geophysical Research Letters, Vol: 52, ISSN: 0094-8276Cloud feedback has prevailed as a leading source of uncertainty in climate model projections under increasing atmospheric carbon dioxide. Cloud-controlling factor (CCF) analysis is an approach used to observationally constrain cloud feedback, and subsequently the climate sensitivity. Although high clouds contribute significantly toward uncertainty, they have received comparatively little attention in CCF and other observational analyses. Here we use CCF analysis for the first time to constrain the high-cloud radiative feedback, focusing on the cloud amount component owing to its dominant contribution to uncertainty in high-cloud feedback. Globally, observations indicate larger decreases in high cloudiness than state-of-the-art climate models suggest. In fact, half of the 16 models considered here predict radiative feedbacks inconsistent with observations, likely due to misrepresenting the stability iris mechanism. Despite the suggested strong high-cloud amount decreases with warming, observations point toward a near-neutral net high-cloud amount radiative feedback, owing to almost canceling longwave and shortwave contributions.
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Journal articleKeeping TR, ZHOU B, Cai W, et al., 2025,
Present and future interannual variability in wildfire occurrence: a large ensemble application to the United States
, Frontiers in Forests and Global Change, Vol: 8, ISSN: 2624-893XRealistic projections of future wildfires need to account for both the stochastic nature of climate and the randomness of individual fire events. Here we adopt a probabilistic approach to predict current and future fire probabilities using a large ensemble of 1,600 modelled years representing different stochastic realisations of the climate during a modern reference period (2000–2009) and a future characterised by an additional 2°C global warming. This allows us to characterise the distribution of fire years for the contiguous United States, including extreme years when the number of fires or the length of the fire season exceeded those seen in the short observational record. We show that spread in the distribution of fire years in the reference period is higher in areas with a high mean number of fires, but that there is variation in this relationship with regions of proportionally higher variability in the Great Plains and southwestern United States. The principal drivers of variability in simulated fire years are related either to interannual variability in fuel production or atmospheric moisture controls on fuel drying, but there are distinct geographic patterns in which each of these is the dominant control. The ensemble also shows considerable spread in fire season length, with regions such as the southwestern United States being vulnerable to very long fire seasons in extreme fire years. The mean number of fires increases with an additional 2°C warming, but the spread of the distribution increases even more across three quarters of the contiguous United States. Warming has a strong effect on the likelihood of less fire-prone regions of the northern United States to experience extreme fire years. It also has a strong amplifying effect on annual fire occurrence and fire season length in already fire-prone regions of the western United States. The area in which fuel availability is the dominant control on fire occurrence increases substantially wit
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Journal articleMenegale F, Vezzosi L, Tirani M, et al., 2025,
Impact of routine prophylaxis with monoclonal antibodies and maternal immunisation to prevent respiratory syncytial virus hospitalisations, Lombardy region, Italy, 2024/25 season
, EUROSURVEILLANCE, Vol: 30, ISSN: 1025-496X- Cite
- Citations: 1
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Journal articleLim A, Shearer FM, Sewalk K, et al., 2025,
The overlapping global distribution of dengue, chikungunya, Zika and yellow fever
, Nature Communications, Vol: 16, ISSN: 2041-1723Arboviruses transmitted mainly by Aedes (Stegomyia) aegypti and Ae. albopictus, including dengue, chikungunya, and Zika viruses, and yellow fever virus in urban settings, pose an escalating global threat. Existing risk maps, often hampered by surveillance biases, may underestimate or misrepresent the true distribution of these diseases and do not incorporate epidemiological similarities despite shared vector species. We address this by generating new global environmental suitability maps for Aedes-borne arboviruses using a multi-disease ecological niche model with a nested surveillance model fit to a dataset of over 21,000 occurrence points. This reveals a convergence in suitability around a common global distribution with recent spread of chikungunya and Zika closely aligning with areas suitable for dengue. We estimate that 5.66 (95% confidence interval 5.64-5.68) billion people live in areas suitable for dengue, chikungunya and Zika and 1.54 (1.53-1.54) billion people for yellow fever. We find large national and subnational differences in surveillance capabilities with higher income more accessible areas more likely to detect, diagnose and report viral diseases, which may have led to overestimation of risk in the United States and Europe. When combined with estimates of uncertainty, these suitability maps can be used by ministries of health to target limited surveillance and intervention resources in new strategies against these emerging threats.
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Journal articleBrady OJ, Bastos LS, Caldwell JM, et al., 2025,
Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections
, PLoS Computational Biology, Vol: 21, Pages: e1012771-e1012771, ISSN: 1553-734XGlobal risk maps are an important tool for assessing the global threat of mosquito and tick-transmitted arboviral diseases. Public health officials increasingly rely on risk maps to understand the drivers of transmission, forecast spread, identify gaps in surveillance, estimate disease burden, and target and evaluate the impact of interventions. Here, we describe how current approaches to mapping arboviral diseases have become unnecessarily siloed, ignoring the strengths and weaknesses of different data types and methods. This places limits on data and model output comparability, uncertainty estimation and generalisation that limit the answers they can provide to some of the most pressing questions in arbovirus control. We argue for a new generation of risk mapping models that jointly infer risk from multiple data types. We outline how this can be achieved conceptually and show how this new framework creates opportunities to better integrate epidemiological understanding and uncertainty quantification. We advocate for more co-development of risk maps among modellers and end-users to better enable risk maps to inform public health decisions. Prospective validation of risk maps for specific applications can inform further targeted data collection and subsequent model refinement in an iterative manner. If the expanding use of arbovirus risk maps for control is to continue, methods must develop and adapt to changing questions, interventions and data availability.
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