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Anthropogenic forcing of the climate is estimated to have increased the likelihood of the 2015-2017 Western Cape drought, also called 'Day Zero' drought, by a factor of three, with a projected additional threefold increase of risk in a world with 2 °C warming. Here, we assess the potential for geoengineering using stratospheric aerosols injection (SAI) to offset the risk of 'Day Zero' level droughts in a high emission future climate using climate model simulations from the Stratospheric Aerosol Geoengineering Large Ensemble Project. Our findings suggest that keeping the global mean temperature at 2020 levels through SAI would offset the projected end century risk of 'Day Zero' level droughts by approximately 90%, keeping the risk of such droughts similar to today's level. Precipitation is maintained at present-day levels in the simulations analysed here, because SAI (i) keeps westerlies near the South Western Cape in the future, as in the present-day, and (ii) induces the reduction or reversal of the upward trend in southern annular mode. These results are, however, specific to the SAI design considered here because using different model, different SAI deployment experiments, or analysing a different location might lead to different conclusions.

On 3 May 2025, a severe hailstorm affected Paris and parts of western Europe. We assess whether anthropogenic climate change contributed to its intensity using ERA5 reanalyses and an analog‐based attribution framework. The synoptic pattern featured a cut‐off low and a surface cold front. We identify circulation analogs to 3 May 2025 in two periods, that is, a cooler “past” (1974–1999) and a warmer “present” (1999–2024), and compare thermodynamic conditions under otherwise similar large‐scale flow. Hail probability and size are estimated with two models: (i) a logistic formulation using Convective Available Potential Energy (CAPE), deep‐layer wind shear, and convective precipitation, and (ii) an extended model including freezing‐level height and 850 hPa temperature. Models are calibrated with Île‐de‐France observations and validated independently. Present‐day analogs exhibit significantly higher CAPE (+200 J/kg), a higher freezing level (+100–200 m), and similar deep‐layer shear, yielding larger hail probability (+30%) and size (+2 cm). These results indicate that human‐induced warming likely enhanced the hailstorm severity in this synoptic setting. We analyzed the 3 May 2025 Paris hailstorm to assess climate change's influence using ERA5 data and analogue‐based attribution. Comparing present and past atmospheric patterns, we found that today's warmer climate shows stronger instability and energy, favoring large hail formation. Model simulations and observations confirm that such storms are now more intense and more likely. While natural variability remains important, human‐induced warming has likely amplified hailstorm intensity, offering insights for improving future severe weather risk assessments.

The 2017 North Atlantic hurricane season caused the highest disaster-related losses ever seen in the United States, with many people asking questions about the causes and liabilities for the impacts of these kinds of events. As climate-related loss and damage mount, there is growing interest in the role of law in dealing with the complex and multi-scalar problem of climate change. This article builds on a shorter piece entitled 'Acts of God, human influence and litigation' published by the authors in Nature Geoscience in August 2017. It is an interdisciplinary and cross-jurisdictional analysis of the emerging science of extreme weather event attribution (which analyses the human impact on extreme weather events), and the implications this new science may have for the law, litigation and the scope of the duty of care of a range of actors. We suggest that the science of event attribution may become a driver of litigation, as it shifts understanding of what weather is expected and, relevantly for law, foreseeable. This may have an impact on the duties of government actors as well as private parties. We explain the discipline of event attribution science to lawyers, discuss some technical issues related to the use of this evidence in court and make some suggestions regarding the types of 'climate change' cases it may influence. We conclude that the first kind of litigation to emerge is most likely to arise from failures to adapt to, or to prepare for, our changing climate.

The question whether a single extreme climate event, such as a hurricane or heatwave, can be attributed to human induced climate change has become a vibrant field of research and discussion in recent years. Proponents of the most common approach (probabilistic event attribution) argue for using single event attribution for advancing climate policy, not least in the context of loss and damages, while critics are raising concerns about inductive risks which may result in misguided policies. Here, we present six ethical predicaments, rooted in epistemic choices of single event attribution for policy making, with a focus on problems related to loss and damage. Our results show that probabilistic event attribution is particularly sensitive to these predicaments, rendering the choice of method value laden and hence political. Our review shows how the putatively apolitical approach becomes political and deeply problematic from a climate justice perspective. We also suggest that extreme event attribution (EEA) is becoming more and more irrelevant for projecting loss and damages as socio‐ecological systems are increasingly destabilized by climate change. We conclude by suggesting a more causality driven approach for understanding loss and damage, that is, less prone to the ethical predicaments of EEA. Plain Language Summary When climate change induced extremes hit society, there is often a call for responsibility—who is to blame, or even liability—who should pay for the loss and damage. Loss and damage is the most controversial part of the global climate change policy regime, and particularly so questions about liability. Extreme event attribution (EEA) using frequentist statistics, that is, determining what (and ultimately perhaps even who) was the cause of the damage from an extreme event, has received much attention lately as a putatively objective scientific approach. We argue however, that methodological choices can render EEA highly value laden and political. We describe six such situation where frequentist statistics may cause ethical predicaments. Instead we propose a more causality driven approach (Comparative Risk Assessment) for advancing our understanding of causality between climate change and loss and damage. Key Points Methodological choices for extreme event attribution (EEA), particularly frequentist statistics, render climate science political and value laden Increasing instability of socio‐ecological systems is rendering EEA less relevant for understanding loss and damage We propose a causality driven approach for assessing loss and damage, less prone to ethical predicaments than statistics driven approaches

[Purpose/Significance] In the open science environment, the mode of collaborative knowledge production is widely used, and the types of scientific outputs are becoming increasingly diverse. Therefore, effective identification and recognition of various contribution contents made by different contributors in open research is crucial to improve transparent disclosure and effective use of research output's contribution data. [Method/Process] Based on the systematic study of domestic and foreign scholarly contribution theories and practices, this study proposes a scholarly contribution analysis system for open science. From the perspective of the creation process of scientific research outputs, research on the attribution of scientific contributions is carried out, focusing on three aspects: the identification of contributors and contribution elements, the calculation of the degree of contribution, and the representation of contribution data. In addition, a paper from the open access platform PLoS One is selected as an empirical case to demonstrate the validity of the process and method of attributing scholarly contributions. It was found that the contribution score is inversely proportional to the number of contributors, i.e., the more the number of contributors, the less likely it is to have a high contribution score. The more contribution elements contributors participate in, the higher the contribution score they receive. If contributors participate in the same contribution elements, and their contribution scores may also be the same. For the same contribution factor, the higher the number of contributors, the lower the contribution degree of each contributor. This paper summarizes the application scenarios of academic contributions from three aspects: clearly distinguishing author and non-author contributors, establishing correlation between contribution data and other LOD ontologies, establishing attribution and responsibility mechanism for academic contributions, and improving the index system of scientific research evaluation. [Results/Conclusions] Academic contribution is an important part of the open science system. It is an inevitable trend to improve the openness and transparency of academic contributions to various scientific research results. However, existing methods for attributing contributions are scattered, lack systematicity, and mainly apply to a certain type of achievement, which has certain limitations in terms of disciplines and achievements. In the future, the applicability of the contribution attribution method can be improved by expanding the application objects of contributions to include research papers, scientific data, scientific software, preprints, scientific notes, and more, establishing a common contribution element system for all types of research outputs, and optimizing the method of calculating the contribution rate. Finally, it is necessary to continuously enrich and expand the application scope and application scenarios of scientific research output's contribution data.

The last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science.

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ∘C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region.

Airborne pollen has major respiratory health impacts and anthropogenic climate change may increase pollen concentrations and extend pollen seasons. While greenhouse and field studies indicate that pollen concentrations are correlated with temperature, a formal detection and attribution of the role of anthropogenic climate change in continental pollen seasons is urgently needed. Here, we use long-term pollen data from 60 North American stations from 1990 to 2018, spanning 821 site-years of data, and Earth system model simulations to quantify the role of human-caused climate change in continental patterns in pollen concentrations. We find widespread advances and lengthening of pollen seasons (+20 d) and increases in pollen concentrations (+21%) across North America, which are strongly coupled to observed warming. Human forcing of the climate system contributed ∼50% (interquartile range: 19–84%) of the trend in pollen seasons and ∼8% (4–14%) of the trend in pollen concentrations. Our results reveal that anthropogenic climate change has already exacerbated pollen seasons in the past three decades with attendant deleterious effects on respiratory health.

Greenwashing is a phenomenon that is linked to scandals that often occur at the supply-chain level. Nevertheless, research on this subject remains in its infancy; much more is needed to advance our understanding of stakeholders' reactions to greenwashing. We propose here a new typology of greenwashing, based on the locus of discrepancy, i.e. the point along the supply-chain where the discrepancy between 'responsible words' and 'irresponsible walks' occurs. With three experiments, we tested how the different forms of greenwashing affect stakeholders' reactions, from both ethical (blame attributions) and business (intention to invest) perspectives. We developed our hypotheses by building on attribution theory, which seeks to account for how observers construct perceptions about events. We had anticipated that the more internal, controllable and intentional the discrepancy is, the greater the blame attributed to a company is, and the lower the intention to invest will be. When greenwashing occurs at a company level (direct greenwashing), this results in a higher level of blame attribution, while the intention to invest falls. Indirect greenwashing refers to a misbehaviour perpetrated by a supplier who claims to be sustainable, and which results in a less negative impact on a supplied company. We also propose the vicarious greenwashing, which occurs when the behaviour of a supplier is in breach of a company's claims of sustainability. This type of greenwashing is nevertheless detrimental to investment. The findings here advance our understanding of how greenwashing shapes stakeholders' reactions, and highlight the need for the careful management of the supply-chain.