The Eocene-Oligocene transition was the crucial turning point when Earth's climate shifted to its current icehouse state. Understanding how the marine biosphere responded during this transition is not well-constrained, appearing as a simple extinction pulse in low temporal resolution global compendia. Here we design an artificial-intelligence-inspired metaheuristics algorithm to construct a high-resolution global species richness history across the Eocene-Oligocene transition for the rich foraminifera fossil record with an imputed ~29,000-year resolution. The revealed diversity dynamics are complex and differ for each foraminiferal group with distinct ecology. Planktonic and shallow-water larger benthic foraminifera show steady diversity levels in the early phases of the transition in the latest Eocene after a long-term reduction, while the deeper-water small benthic foraminifera radiate notably and then decline over the same interval. In the earliest Oligocene, the planktonic and larger foraminifera suffer major species losses coincident with the first continental-scale ice sheet formed on Antarctica, while small benthic foraminifera diversity holds steady, followed by an accelerating lowering as the early Oligocene proceeds. These findings reveal complicated and ecologically differentiated environment-life processes, indicating the importance of high-resolution temporal data for dissecting out ecological responses to major environmental changes.

Extreme fire weather (hot, dry, and windy conditions) has intensified globally, yet formally attributing this trend to anthropogenic climate change remains challenging. Here, we analyze global trends in extreme fire weather days (FWI95d, annual count of days with Fire Weather Index above the 95th percentile) over 1980–2023, using climate model ensembles, observational data, and fingerprint detection techniques. We find that the observed increase in extreme fire weather bears a clear externally forced signal, detectable at 99% confidence above natural variability and attributable to human-induced climate change. This emerging human-induced fingerprint on extreme fire weather highlights a benchmark for climate science and underscores the urgency of integrating these insights into wildfire risk management and adaptation strategies.

This is the absence of accountability between massive carbon emissions by industrial activities and the reported climate damage, which is one of the most impactful governance failures of modern times. Although the scientific evidence of most anthropogenic greenhouse gas emissions can be traced to a few identifiable producers of fossil fuels, the so-called carbon majors have theoretically immature and practically inadequate legal mechanisms through which the afflicted communities could seek redress, and have been fragmented. The article fills a key theoretical gap in the current body of literature: the literature on attribution science, climate litigation, corporate accountability, and climate governance has proceeded to develop individually, but no single theoretical model has ever brought together these four strands into a consistent accountability structure of emitter accountability. This article creates such a framework by relying on a systematic conceptual review of peer-reviewed scholarship in environmental law, climate science, governance theory, and tort doctrine. It is theorized that, when incorporated with the changing legal standards in causation and corporate knowledge-liability theory and climate governance theory, climate attribution science facilitates the creation of a plausible and analytically sound attribution chain between large emitters and reported climate damage and actionable claims to remedy. The article connotes four conceptual findings: causal-legal accountability chain; the typology of legal barriers and the theoretical resolutions; the nexus of corporate knowledge-deception-liability; and the reparative architecture in loss and damage with legal redress. These results are pulled together into a coherent and multi-strand theoretical model of emitter accountability arranged into four analytically separate strands of scientific accountability based on attribution, legal accountability based on tort and human rights, moral accountability based on the knowledge-deception nexus, and governance accountability based on litigation as a regulatory tool. With the rising development of attribution science and increasing judicial faith in probabilistic causal evidence, attribution litigation targeting climate change has an opportunity to rapidly become not just a fringe enforcement tool, but a structural-level implementative instrument of climate accountability with implications to both legal science and climate justice movement, and global regulation design.

Estimating conditional extreme quantiles is essential for assessing tail risks in complex systems, with broad applications in finance, climate science, engineering, and beyond. While classical extreme value theory provides a foundational framework, recent advances, particularly semiparametric and nonparametric methods, including approaches based on quantile regression, machine learning, and deep learning, have greatly enriched the methodological landscape. This modern review synthesizes these developments, covering traditional likelihood-based methods, semiparametric approaches, and tree-based and deep learning techniques, including higher-order refinements.

Foundation models for geospatial artificial intelligence (GeoAI) exhibit a striking dichotomy: they excel at pixel-wise tasks such as classification and segmentation, yet struggle with instance-level tasks requiring identification of multiple discrete elements. This limitation, termed the ICL-Hard barrier, arises from fundamental computational constraints of constant-depth transformers, which cannot solve problems reducible to sparse parity for more than J* = O(log log n) ≈ 2–4 objects. The barrier manifests across geospatial domains: geodetic source localization, remote sensing object detection, climate extreme event identification, and multi-modal counting tasks all fail when the number of targets exceeds this threshold. This paper introduces a unified theoretical framework demonstrating that chain-of-thought (CoT) prompting provides a universal mechanism to overcome ICL-Hard barriers across all GeoAI domains. We prove that autoregressive token generation amplifies effective computational depth: generating T intermediate tokens increases effective depth from L to L + γT, enabling transformers to escape AC⁰/TC⁰ circuit limitations and solve previously intractable tasks. Our main contributions include: (1) the CoT Depth Amplification Theorem, proving that T tokens provide effective depth Θ(T); (2) tight bounds on token complexity, establishing T = Ω(J) tokens are necessary and T = O(J log J) sufficient for J-element detection; (3) a smooth success probability scaling law P(success) = (1 − e^{−αT/J})^J; (4) the CoT Threshold Shift Theorem, showing the effective threshold increases to J*_{CoT}(T) ≈ J* + T; and (5) equivalence conditions under which CoT matches fine-tuned model performance without parameter updates. We validate the framework across four GeoAI domains—geodesy, remote sensing, climate science, and multi-modal applications—deriving domain-specific token multipliers (κ ∈ [2.5, 4.5]) and practical deployment guidelines. The theory yields eight testable predictions for empirical validation. This work completes a theoretical arc: where previous work established what is hard for in-context learning in GeoAI, we now show how to overcome these barriers through principled application of chain-of-thought reasoning.

This study evaluates the quality of ionosphere-corrected bending angle products from 12 satellite radio occultation (RO) missions, with data provided by the ROM SAF and CDAAC data centers. The missions include MetOp-B/C, Sentinel-6, Spire, COSMIC-2, KOMPSAT-5, and TerraSAR-X. The assessment focuses on bending angle quality control (QC), bias and noise characteristics at 65–80 km altitude, and statistical errors, with ERA5 data used as the reference. For quality control, Spire products achieved the highest pass rate, exceeding 99%. Products from the two MetOp satellites and Sentinel-6 exhibited pass rates above approximately 90%. The COSMIC-2 series had a pass rate of ~81%, while KOMPSAT-5 and TerraSAR-X had pass rates of 62% and 68%, respectively. Concerning bending angle biases, slightly larger biases were observed in MetOp setting events. Biases from other missions were mostly within the range of 0–0.05 μrad. Regarding noises, Sentinel-6 recorded the smallest bending angle noise (0.87 μrad), whereas TerraSAR-X (2.3 μrad) and KOMPSAT-5 (1.9 μrad) showed the largest noise magnitudes. Systematic differences in bending angles from all 12 RO missions were generally consistent below 60 km, while their standard deviations show good consistency below 35 km. In the middle stratosphere (35–50 km), MetOp-B/C and Sentinel-6 displayed the smallest standard deviations. Spire values are 1–2% larger, COSMIC-2 values 5–10% larger, and TerraSAR-X values the largest. Since ERA5 data also contain inherent uncertainties, particularly above 60 km, the findings of this study can only serve as a preliminary reference for users applying these datasets in weather and climate research. Future work will investigate the detailed causes of discrepancies among different datasets at high altitudes.

ABSTRACT Extreme climate conditions can weaken governance and expand opportunities for violent actors to take control over dwindling resources, yet counterinsurgency (COIN) literature rarely treats climate change as an operational driver. This study introduces a Counterinsurgency Induced by Climate Change (COIN-CC) framework for mitigating organized violence by coupling security studies with climate science. It links resource degradation with increased dependence on non-state providers and illicit economies, and examines how climate data, adaptation training, education, and targeted operations help weaken this link. The framework specifies the limits under which climate-focused counterinsurgency is effective, yielding testable propositions for conflict management during environmental shocks.

ABSTRACT Ship logbooks represent a critical source of historical meteorological data, providing valuable observations of barometric pressure, air temperature, sea surface temperature, wind force and direction, and other variables. Substantial quantities of these records are unavailable to climate science as they have not yet been transcribed. We present ‘Weather Rescue at Sea’, a citizen‐science project which transcribed millions of weather observations contained in 19th Century UK Royal Navy ship logbooks. We describe the logbook structure and weather observation‐taking instructions and discuss significant challenges with the translation of handwritten text into accurate data due to errors arising from ambiguous handwriting, historical terminology, and inconsistent metadata. We present the dataset and explore its spatio‐temporal characteristics. The corrected and quality‐assured datasets will enhance climate reanalyses and other historical reconstructions of the pre‐ and early industrial climate by providing more input meteorological data. Furthermore, we highlight emerging tools, such as AI‐driven transcription correction, and outline remaining challenges in fully leveraging these historical records to advance climate science.

Purpose This article analyzes the gendered “invisible work” in Latin American and Caribbean climate collaborations. Using case studies from Jamaica, Panama, Colombia and Brazil, we examine the unacknowledged sociocultural and sociospatial labor of women, including mediation, network navigation and emotional management that underpins community projects and territorial defense. This work, often masked by tokenistic inclusion, is racialized and spatially embedded. We argue that effective, equitable transdisciplinary practice requires centering these contributions, moving beyond visual representation to genuinely value the relational and logistical labor that sustains socioecological collaboration in the context of climate challenges. Design/methodology/approach This is a qualitative study involving interviews, storytelling, documents, project reports, secondary data and field visits to sites in Colombia and Panama in 2022 and 2024. For these cases, reflective conversations were held both formally and informally with community members involved in local environmental initiatives. These discussions enabled self-reflection, identifying barriers and enablers of conservation. A key element was citizen action deliberative spaces (conversatorios de acción ciudadana), which integrated diverse knowledge into decision-making, with contributions through Canva presentations, stories, photos, testimonies, poems and autobiographies that highlighted social and environmental dynamics. In-depth interviews with women involved in transdisciplinary projects lasted about an hour, following an open-ended protocol to explore their experiences, roles, challenges, recognition, and perceptions of value. Each interview ended with a prompt to identify the “elephant in the room,” encouraging reflection on unnoticed tasks, invisible labor, and emotional or logistical contributions. Findings The findings highlight that women play a crucial role in climate and sustainability efforts, with activists, researchers and funding directors doing unseen work beyond official activities. Community leaders fighting violence organize resistance under fragile conditions, using media to protect and raise awareness. Researchers and funders build trust, provide emotional support and help marginalized groups access resources. These practices, advocacy, care, tactical engagement and networking are unpaid and unrecognized, but essential for maintaining collaborations and ensuring long-term project success. Research limitations/implications Our feminist methodological framework insists on the critical importance of “on-the-ground” work and everyday practice. This is not merely an intellectual interest at the micro-level, but a political commitment to understanding sociality and collaboration not as abstract infrastructure, but as a concrete series of interactions in which power, difference and labor are negotiated. By making these routine exchanges the primary unit of analysis, we provide a helpful lens for making visible and, therefore, transforming the mechanisms of inequality that are reproduced or challenged in daily practice. Practical implications Given the growth of such projects in climate and socioecological research, our study offers a critical, actionable framework. The mechanisms we identify can be used to audit and improve ongoing collaborations. Those involved in collaborative research work can apply our analytical lens to document, expose and name this invisible work, conceptualized for decades yet persistently ignored. This moves beyond academic critique to a practice of social justice and care, translating longstanding theory into a concrete commitment to recognize, validate and reward the essential work that holds collaboration together. Social implications Our study’s contribution lies in its robust case scope and regional breadth. We present a transferable approach for revealing invisible work within transdisciplinary collaborations. By engaging data from four countries with diverse participants, including academic, funders and Indigenous women, we identify consistent mechanisms of work erasure that cut across different epistemological affiliations and project types. This broad, comparative scope enables us to argue that the invisibility of work is not an isolated issue but a systemic feature of transdisciplinary collaborations. Originality/value This study contributes to the broader understanding of invisible work by connecting empirical evidence to actionable suggestions for funding agencies, academic institutions and policy actors. It exposes the “gendered elephant in the room,” the essential, often unacknowledged work performed predominantly by women that sustains transdisciplinary collaborations and underscores the need to move beyond symbolic recognition toward structural and procedural changes that value all forms of work, visible and invisible, in climate change research. Our intersectional framework of connecting the embodied, everyday work of women to the structural institutional logic is highly applicable. It offers a helpful tool for other fields where similar dynamics of invisible labor and epistemic exclusion hinder collaboration and reinforce inequality.

ABSTRACT This article examines how sensory and digital ethnography can enrich climate education through relational, arts-led methodologies. Conducted amid the COVID-19 pandemic, our ethnographic project, Global Youth (Digital) Citizen-Artists and their Publics, engaged over 500 secondary and university drama students across six global sites. As pandemic restrictions necessitated a shift to virtual spaces, we embraced sensory ethnographic approaches, focusing on feeling with participants through creative methodologies such as site-specific theatre-making and soundscape workshops. Through these methods, we uncovered how youth collaboratively construct new socio-ecological imaginaries, moving beyond individualistic climate education models towards intergenerational and intercultural engagement. We argue that attunement to sensory and more-than-human dimensions of climate research fosters alternative ways of knowing and engaging with the environmental crisis. Our findings suggest that theatre and performance can serve as transformative spaces for climate education, offering relational, embodied and participatory approaches that challenge conventional pedagogies.

The development of passive microwave sensors traces back to Robert Dicke's pioneering experiments in the 1940s. Since then, microwave radiometry has evolved into a key tool for Earth observation, strengthened by data from multiple satellite missions operating across different wavelengths. This paper reviews the state of the art in microwave radiometry for monitoring land surfaces. After introducing the theoretical foundations underpinning current missions, we present an overview of major satellite instruments. We then examine early theoretical advances in retrieving soil moisture and snow properties, two applications that contributed to the future development of satellite microwave radiometry missions for the observation of surface variables. Particular attention is given to radiative transfer theory and its solutions, which model the effects of roughness, vegetation, and snow cover. These approaches form the basis of today's retrieval algorithms and remain central to future missions. Subsequent sections highlight the use of passive microwave data for estimating a variety of surface variables, the role of passive microwave in data assimilation systems and forthcoming missions dedicated to land monitoring. The review concludes with key achievements, ongoing challenges, and open issues-such as soil moisture retrieval under dense vegetation or snow property retrieval in melting conditions. Addressing these limitations is critical to fully exploiting microwave radiometry in the context of climate research and mitigation strategies.

ABSTRACT The Free Press likes to publish provocative takes that punch holes in established ideologies. When that ethos is applied to the subject of climate change, the result is a largely one-sided perspective that depicts climate science and climate experts as suspect and not to be trusted. Will this outlook carry over to CBS News, now that the founder of the publication, Bari Weiss, is the editor-in-chief?

Abstract Ice cloud fraction (CF ice ) plays a critical role in the Earth's radiation budget and climate system. However, conventional cloud parameterizations in general circulation models are often inadequate to simulate CF ice and its vertical structure. This study evaluates the Neural Network‐Based Scale‐Adaptive cloud fraction scheme (NSA) in the Taiwan Earth System Model version 1 (TaiESM1), with a focus on its impact on ice cloud simulation and radiative processes. The NSA scheme, trained on CloudSat and ECMWF data, incorporates multiple environmental variables and cloud condensates. Two 10‐year AMIP simulations—one with the default scheme and the other with NSA—were conducted to assess differences in CF ice , cloud radiative forcing, and top‐of‐atmosphere (TOA) radiation. Versus the default scheme, the NSA scheme reduces the global annual mean CF ice , from 29.9% to 25.2%, which is closer to 22.4% based on MODIS‐COSP. The decrease in high‐cloud cover is significant, by 25% globally, and 50% in Antarctica. The NSA scheme also corrects the unrealistic glaciation of low‐level stratus clouds. As a result, the total cloud fraction (CF total ) is reduced from 56.3% to 48.6%, in much better agreement with the CloudSat‐COSP value of 49.8%. The associated changes in cloud radiative forcing and TOA radiation are also calculated to illustrate the effects of these cloud cover changes. Discussion on further improvement of cloud microphysics in TaiESM1 in conjunction with the NSA cloud cover scheme is presented.

Seasonal aerosol variations at the Land-Ocean boundary: Insights from a global AERONET network analysis.

Expanding the dynamic climate change impact model for dynamic LCA based on the climate science

Participatory research and community engagement in climate and health research.

Contributions of decision science to ethical priority-setting in climate and health research.

Research has become essential in addressing the effects of climate change on human health and that of the biosphere. However, the ethical regulation of such research remains insufficiently developed, particularly with the current consolidation of research ethics committees as the reference standard to review and approve of health research. Unlike human-centred health research, climate and health research extends beyond humans to include biotic and abiotic components. This aspect challenges the human-centred approach to ethics that has traditionally defined the role of research ethics committees. In this analysis, we seek to re-examine the role of these committees in guaranteeing the ethical review of climate change and health research and the possibility of extending beyond its limited, human-centred scope. We also discuss the ethical concerns and considerations from the global and African-centred perspective that research ethics committees should address. We recommend that institutions hosting these committees implement two initiatives. First, restructure research ethics committees to include experts in climate change and health, as well as activists and representatives of Indigenous communities who are knowledgeable about the links between health and climate. Second, support initiatives to build the capacity of committee members, for example by developing training curricula on climate change and health research. These curricula should aim to strengthen the ability of committees to identify and address key issues including justice, intergenerational ethics and community-specific norms and values.

Faced with climate change challenges, artificial intelligence (AI) is redefining the way of sustainable building design. In this work, how AI technologies, including foundation models and generative systems, are reshaping architectural practice in responding to climate change is discussed. We explored how large language models, multimodal systems, and automated design generation have evolved from traditional computational methods, and the transformative potential of these novel approaches, especially when dealing with climate change challenges. While AI holds powerful tools for sustainable architecture, we argue that the successful implementation of those tools requires careful integration of technical capabilities, practice frameworks, and regulatory considerations. To advance AI-driven sustainable building design while providing effective future climate response, research priorities and policy recommendations are put forward in this study.

Perspective: A utilitarian approach to prioritization in climate and health research