Recent advances in visual-language models (VLMs) have led to significant improvements in a plethora of complex multimodal tasks like image captioning, report generation, and visual perception. However, generating text from meteorological data is highly challenging because the atmosphere is a chaotic system that is rapidly changing at various spatial and temporal scales. Given the complexity of atmospheric phenomena, it is critical to verifiably quantify the effectiveness of existing VLMs on weather forecasting data. In this work, we present SynopticBench, a high-quality dataset consisting of 1,367,041 text samples of Area Forecast Discussions created by the National Weather Service over the continental United States paired to images of 500 mb geopotential height, 2 m temperature, and 850 mb wind velocity in weather forecasts. We also present Synoptic Phenomena Alignment and Coverage Evaluation (Space), a novel evaluation framework that can be used to effectively estimate the quality of text descriptions of synoptic weather phenomena. Extensive experiments on generating forecast discussions using state-of-the-art VLMs show the sensitivity of existing evaluation metrics in this domain and enable further exploration into synoptic weather and climate text generation.

Next-generation high-resolution (km-scale) climate models promise unprecedented accuracy in climate projections, but realizing their potential requires robust methods to quantify how well simulations align with real-world observations. Average-based metrics conventionally used for climate model evaluation ignore the physics encoded in the fine-scale structures of km-scale simulations. To overcome this limitation, we propose a novel, statistically principled evaluation methodology based on the likelihood function of a generative image model. Our method provides a continuous similarity metric derived from the likelihood distribution of observation and simulation snapshots, which can redefine the evaluation, intercomparison, and parameter tuning of high-resolution climate models. We demonstrate the applicability and interpretability of this method by evaluating convective clouds simulated by two state-of-the-art global km-scale models, using their outgoing infrared radiation fields. This work establishes a scalable pathway toward observation-based evaluation of next-generation climate simulations.

Language models (LMs) have attracted the attention of researchers from the natural language processing (NLP) and machine learning (ML) communities working in specialized domains, including climate change. NLP and ML practitioners have been making efforts to reap the benefits of LMs of various sizes, including large language models, in order to both simplify and accelerate the processing of large collections of text data, and in doing so, help climate change stakeholders to gain a better understanding of past and current climate-related developments, thereby staying on top of both ongoing changes and increasing amounts of data. This paper presents a brief history of language models and ties LMs’ beginnings to them becoming an emerging technology for analysing and interacting with texts in the specialized domain of climate change. The paper reviews existing domain-specific LMs and systems based on general-purpose large language models for analysing climate change data, with special attention being paid to the LMs’ and LM-based systems’ functionalities, intended use and audience, architecture, the data used in their development, the applied evaluation methods, and their accessibility. The paper concludes with a brief overview of potential avenues for future research vis-à-vis the advantages and disadvantages of deploying LMs and LM-based solutions in a high-stakes scenario such as climate change research. For the convenience of readers, explanations of specialized terms used in NLP and ML are provided.

In a world grappling with escalating agrochemical pollution, this article explores the potential for shifting from a security-centric approach to a human rights-based approach to safeguard health, the environment, and biodiversity. By engaging with European Court of Human Rights jurisprudence related to environmental protection and climate change, the article critically assesses how to address state (in)action regarding pollutants such as pesticides through human rights litigation. In its analysis, the article highlights climate change litigation as a catalyst for change to assert states’ threefold obligations to respect, protect, and realize human rights. It concludes that the legal approaches developed in climate litigation – with regard to both procedural and substantive aspects – provide a strong basis for addressing the human rights impacts of agrochemical harm.

In recent years, courts across the world have increasingly held governments accountable for addressing climate change. While such rulings have fueled optimism about constitutional law as a vehicle for climate ambition, this Article argues that the role of constitutional law in advancing climate goals is far more complex and contested. Constitutions encapsulate diverse and sometimes conflicting values, which can create tensions when courts adjudicate climate policies. As government climate measures become more concrete, conflicts arise between rights, institutional structures, and political realities. Drawing on examples from Germany, Canada, and Mexico, this Article highlights the challenges of adjudicative uncertainty, the underspecificity of constitutional norms, and the polyvocality of constitutional values in the context of climate change. This Article concludes with recommendations for judges to adopt a principled, context-sensitive approach to constitutional climate adjudication, balancing the urgency of climate action with the complexities of state capacity and constitutional structures.

While the Sustainable Development Goals (SDGs) were being negotiated, global policymakers assumed that advances in data technology and statistical capabilities, what was dubbed the “data revolution”, would accelerate development outcomes by improving policy efficiency and accountability. The 2014 report to the United Nations Secretary General, “A World That Counts” framed the data-for-development agenda, and proposed four pathways to impact: measuring for accountability, generating disaggregated and real-time data supplies, improving policymaking, and implementing efficiency. The subsequent experience suggests that while many recommendations were implemented globally to advance the production of data and statistics, the impact on SDG outcomes has been inconsistent. Progress towards SDG targets has stalled despite advances in statistical systems capability, data production, and data analytics. The coherence of the SDG policy agenda has undoubtedly improved aspects of data collection and supply, with SDG frameworks standardizing greater indicator reporting. However, other events, including the response to COVID-19, have played catalytic roles in statistical system innovation. Overall, increased financing for statistical systems has not materialized, though planning and monitoring of these national systems may have longer-term impacts. This article reviews how assumptions about the data revolution have evolved and where new assumptions are necessary to advance the impact across the data value chain. These include focusing on measuring what matters most for decision-making needs across polycentric institutions, leveraging the SDGs for global data standardization and strategic financial mobilization, closing data gaps while enhancing policymaker analytic capabilities, and fostering collective intelligence to drive data innovation, credible information, and sustainable development outcomes.

The debate over whether, when, and by whom reparations should be paid for climate-related loss and damage has been central to the struggle over the role of international law in responding to climate change. The push for climate reparations is an attempt to have international law treat the harm caused by climate change with the same gravity as issues such as the damage caused by war, gross violations of human rights, or injury to the economic interests of foreign investors. At stake in that struggle is a broader question that goes to the heart of the global political economy: who should bear the social costs of industrialization and technological development on a global scale?

Climate change-related environmental harms have been observed to negatively affect mental health. While policymakers and courts around the world widely recognise the impacts of climate change on physical heath as potentially endangering human rights, the implications of climate change for mental health have received significantly less attention. This paper analyzed five cases that challenged national response to climate change and the resulting impacts on mental health before four different international human rights protection bodies. Four out of these five cases were dismissed either because the petitioners did not seek prior action before the national authorities, or because their claims were deemed unsubstantiated. Despite these outcomes, the protection bodies’ treatment of these petitions as well as various other ongoing developments show that the human rights approach to climate change and mental health is gradually emerging at the international and domestic levels, but it is still in its early days and there are various challenges to it.

Catalyzed by the surge in climate litigation worldwide, this article examines the tension between the moral imperatives of intergenerational justice and the operational constraints of positivist legal frameworks. It hypothesizes that while positivist doctrine prima facie challenges judicial application of intergenerational justice principles, reconciliation is possible through contextually attuned adjudication and evolved conceptions of legal principles for the Anthropocene. The article explores three key litigation strategies: dynamic interpretation of existing rights, application of constitutional future generations clauses, and procedural mechanisms for representing future interests. Building on European climate judgments, it analyzes how these approaches strain positivist tenets and animate separation-of-powers objections. The article argues that addressing interpretive and foundational challenges posed by climate change requires both doctrinal innovation and theoretical reconstruction. It shows how contextual constitutionalism can help courts to acknowledge intergenerational duties while preserving legal determinacy, and explores how positivism might evolve to accommodate multigenerational climate governance. Situating leading cases within debates between positivism and non-positivist theories, the article offers a roadmap for developing a framework of legal validity suited to the era-defining challenge of climate change.

Predominant climate club research emphasizes state-centric clubs that alter the incentive structure and bargaining context for climate cooperation. This focus on national governments, however, leaves climate clubs vulnerable to political turbulence afflicting individual club members. Subnational governments are an important yet often overlooked type of actor in the club literature. This article contributes to understanding the role and nature of subnational government-led clubs in transnational climate governance and lawmaking through qualitative case studies of the Western Climate Initiative and the C40 Cities Climate Leadership Group. I identify the distinguishing characteristics that these clubs manifest in their membership and functions, as formalized through legal arrangements. I demonstrate that these clubs have the potential to increase structural stability, withstand political changes, and enhance the legitimacy and efficacy of climate action. They do so by functioning not only as organizations that create incentives for committing to legal norms and mechanisms for deterring free riding but also as communities of practice that generate shared understandings, resources, and norms to sustain club cooperation in pursuing a shared commitment to climate action. As such, each club applies a mix of rationalist approaches to benefit generation and constructivist approaches to community building.

This reflection considers the contribution of Prof Larry Gostin to global health law and the lessons for climate action.

This article analyzes the application of environmental impact assessment as a tool for climate change mitigation from a global comparative perspective. It firstly confirms that, despite persistent resistance in a few jurisdictions, climate effect assessment is now widely applied on a global scale. Yet the article also shows that this practice has faced recurrent practical and conceptual issues, in particular, concerning the determination of the significance of a project’s climate effect and the assessment of indirect effects. Lastly, this article assesses how states have addressed these issues and identifies good practices. In doing so, the article illustrates the potential of functionalist comparative analysis in advancing our understanding of climate law and suggesting policy-relevant conclusions.

Classical approaches for flood prediction apply numerical methods for the solution of partial differential equations that capture the physics of inundation processes (e.g., the 2D Shallow Water equations). However, traditional inundation models are still unable to satisfy the requirements of many relevant applications, including early-warning systems, high-resolution (or large spatial domain) simulations, and robust inference over distributions of inputs (e.g., rainfall events). Machine learning (ML) approaches are a promising alternative to physics-based models due to their ability to efficiently capture correlations between relevant inputs and outputs in a data-driven fashion. In particular, once trained, ML models can be tested/deployed much more efficiently than classical approaches. Yet, few ML-based solutions for spatio-temporal flood prediction have been developed, and their reliability/accuracy is poorly understood. In this paper, we propose FloodGNN-GRU, a spatio-temporal flood prediction model that combines a graph neural network (GNN) and a gated recurrent unit (GRU) architecture. Compared to existing approaches, FloodGNN-GRU (i) employs a graph-based model (GNN); (ii) operates on both spatial and temporal dimensions; and (iii) processes the water flow velocities as vector features, instead of scalar features. We evaluate FloodGNN-GRU using a LISFLOOD-FP simulation of Hurricane Harvey (2017) in Houston, Texas. Our results, based on several metrics, show that FloodGNN-GRU outperforms several data-driven alternatives in terms of accuracy. Moreover, our approach can be trained 100x faster and tested 1000x faster than the time required to run a comparable simulation. These findings illustrate the potential of ML-based methods to efficiently emulate physics-based inundation models, especially for short-term predictions.

Datafication—the increase in data generation and advancements in data analysis—offers new possibilities for governing and tackling worldwide challenges such as climate change. However, employing data in policymaking carries various risks, such as exacerbating inequalities, introducing biases, and creating gaps in access. This paper articulates 10 core tensions related to climate data and its implications for climate data governance, ranging from the diversity of data sources and stakeholders to issues of quality, access, and the balancing act between local needs and global imperatives. Through examining these tensions, the article advocates for a paradigm shift towards multi-stakeholder governance, data stewardship, and equitable data practices to harness the potential of climate data for the public good. It underscores the critical role of data stewards in navigating these challenges, fostering a responsible data ecology, and ultimately contributing to a more sustainable and just approach to climate action and broader social issues.

In March 2023 the EU-funded CHERISH project published its free user-guide and methodology for investigating heritage and climate change in the coastal and maritime environment (Barker and Corns 2023). This paper provides an overview of the publication, specifically the CHERISH toolkit – the 15 approaches employed by the multi-disciplinary project to investigate at-risk heritage sites in Wales and Ireland. Using the eroding coastal hillfort of Dinas Dinlle in Wales as a case study, the toolkit which combines air, land and sea-based investigation techniques is highlighted. This article will assist users going forward in identifying relevant approaches to the study of their own at-risk sites. It is relevant to a wide-ranging audience anywhere in the world, taking into consideration a variety of requirements such as the environment, budget, and outputs.

Carbon leakage – the increase of greenhouse gas emissions in foreign jurisdictions following the introduction of domestic or regional climate mitigation measures – raises key questions in the climate change debate. This includes whether carbon leakage constitutes a threat to the environmental integrity of climate policies and, if so, how this could be mitigated. Through the use of four hypothetical models of international climate change regime, this article argues that international climate change law is a key factor in answering this two-part question. Firstly, the article demonstrates that the architecture of international climate change law affects whether carbon leakage can be considered as undermining the mitigation objective of climate policies. Secondly, it draws attention to the interaction – and potential tension – between carbon leakage prevention measures and international climate change law.

This article presents a systematic literature review of publications from 2014 to 2021 using “archaeological site” and “climate change” as keywords, in addition to several terms representing forms of stakeholder engagement. Articles were thematically coded to explore trends at the intersection of climate change, archaeology, and local and Traditional stakeholders. Results show that nearly half of the selected publications did not include local and Traditional stakeholder engagement in studies related to climate adaptation planning for archaeological sites. Synthesis of the results with insights gained from other literature on decolonizing archaeology showed that potential reasons for this gap include (1) the academic publishing culture, (2) archaeology as a predominantly Western discipline, and (3) increasingly available tools for climate change adaptation planning for archaeological sites. This article calls on the academic community to consider holistic stewardship using a landscape approach and to use climate change adaptation planning to elevate local and Traditional stakeholder input and values.

This essay describes the author's quest for effective, large-scale political actions to stop the burning of fossil fuels. What are the nuts and bolts of collective organizing at scales large enough to effect substantial change? Frustrated both by widespread public pessimism and by the politics most often articulated in literary studies, Levine finds a working political model in the divestment movement; a methodological model in formalist analysis; and a theory of collective aesthetics and politics in post-Victorian Jane Addams.