Extreme event attribution aims to elucidate the link between global climate change, extreme weather events, and the harms experienced on the ground by people, property, and nature. It therefore allows the disentangling of different drivers of extreme weather from human-induced climate change and hence provides valuable information to adapt to climate change and to assess loss and damage. However, providing such assessments systematically is currently out of reach. This is due to limitations in attribution science, including the capacity for studying different types of events, as well as the geographical heterogeneity of both climate and impact data availability. Here, we review current knowledge of the influences of climate change on five different extreme weather hazards (extreme temperatures, heavy rainfall, drought, wildfire, tropical cyclones), the impacts of recent extreme weather events of each type, and thus the degree to which various impacts are attributable to climate change. For instance, heat extremes have increased in likelihood and intensity worldwide due to climate change, with tens of thousands of deaths directly attributable. This is likely a significant underestimate due to the limited availability of impact information in lower- and middle-income countries. Meanwhile, tropical cyclone rainfall and storm surge height have increased for individual events and across all basins. In the North Atlantic basin, climate change amplified the rainfall of events that, combined, caused half a trillion USD in damages. At the same time, severe droughts in many parts of the world are not attributable to climate change. To advance our understanding of present-day extreme weather impacts due to climate change developments on several levels are required. These include improving the recording of extreme weather impacts around the world, improving the coverage of attribution studies across different events and regions, and using attribution studies to explore the contributions of both climate and non-climate drivers of impacts.

As a direct consequence of extreme monsoon rainfall throughout the summer 2022 season Pakistan experienced the worst flooding in its history. We employ a probabilistic event attribution methodology as well as a detailed assessment of the dynamics to understand the role of climate change in this event. Many of the available state-of-the-art climate models struggle to simulate these rainfall characteristics. Those that pass our evaluation test generally show a much smaller change in likelihood and intensity of extreme rainfall than the trend we found in the observations. This discrepancy suggests that long-term variability, or processes that our evaluation may not capture, can play an important role, rendering it infeasible to quantify the overall role of human-induced climate change. However, the majority of models and observations we have analysed show that intense rainfall has become heavier as Pakistan has warmed. Some of these models suggest climate change could have increased the rainfall intensity up to 50%. The devastating impacts were also driven by the proximity of human settlements, infrastructure (homes, buildings, bridges), and agricultural land to flood plains, inadequate infrastructure, limited ex-ante risk reduction capacity, an outdated river management system, underlying vulnerabilities driven by high poverty rates and socioeconomic factors (e.g. gender, age, income, and education), and ongoing political and economic instability. Both current conditions and the potential further increase in extreme peaks in rainfall over Pakistan in light of anthropogenic climate change, highlight the urgent need to reduce vulnerability to extreme weather in Pakistan.

The hydrologic cycle is a fundamental component of the climate system with critical societal and ecological relevance. Yet gaps persist in our understanding of water fluxes and their response to increased greenhouse gas forcing. The stable isotope ratios of oxygen and hydrogen in water provide a unique opportunity to evaluate hydrological processes and investigate their role in the variability of the climate system and its sensitivity to change. Water isotopes also form the basis of many paleoclimate proxies in a variety of archives, including ice cores, lake and marine sediments, corals, and speleothems. These records hold most of the available information about past hydrologic variability prior to instrumental observations. Water isotopes thus provide a 'common currency' that links paleoclimate archives to modern observations, allowing us to evaluate hydrologic processes and their effects on climate variability on a wide range of time and length scales. Building on previous literature summarizing advancements in water isotopic measurements and modeling and describe water isotopic applications for understanding hydrological processes, this topical review reflects on new insights about climate variability from isotopic studies. We highlight new work and opportunities to enhance our understanding and predictive skill and offer a set of recommendations to advance observational and model-based tools for climate research. Finally, we highlight opportunities to better constrain climate sensitivity and identify anthropogenically-driven hydrologic changes within the inherently noisy background of natural climate variability.

Farmers in the US are adopting a range of strategies to deal with climate change impacts, from changing planting dates to using advanced technologies. Studies on farmers' adaptation in US agriculture focus on a variety of topics and provide an understanding of how farmers adapt to climate change impacts, which adaptation strategies offer better outcomes, and what challenges need to be addressed for effective adaptations. Nevertheless, we lack a comprehensive view of adaptation studies focusing on US farmers' adaptations. A review of adaptation studies in US agriculture context will help us to understand current adaptation research trends and realize future research potential. To fulfill this gap, this study systematically reviewed peer-reviewed studies on adaptation to climate change in US agriculture. A systematic search on the Web of Science and Google Scholar platforms generated 95 articles for final review. These studies were categorized under five themes based on their topical relevance: (i) reporting on-farm adaptations, (ii) exploring potential adaptations, (iii) evaluating specific adaptations, (iv) challenges of adaptations, and (v) perceptions toward adaptations. A skewed distribution of studies under these themes has been observed; a majority of the studies focused on evaluating specific adaptations (47%) followed by exploring potential adaptations (22%), while reporting on-farm adaptations (17%), challenges of adaptations (6%), and perception towards adaptations (8%) received less attention. In this article, key findings under each theme are presented and some areas for future research focus are discussed. These findings indicate the need for more attention to documenting on-farm adaptation strategies and the associated challenges while emphasizing other themes.

Each run of an integrated assessment models produces a single mitigation pathway consistent with stated objectives (e.g. maximum temperature) and optimizing some objective function (e.g. minimizing total discounted costs of mitigation). Even though models can be run thousands of times, it is unclear how built-in assumptions constrain the final set of pathways. Here we aim at broadly exploring the space of possible mitigation scenarios for a given mitigation target, and at characterizing the sets of pathways that are (near-)optimal, taking uncertainties into account. We produce an extensive set of CO₂ emission pathways that stay below 2 °C of warming using a reduced-form climate-carbon model with a 1000 different physical states. We then identify 18 sets of quasi 'least-cost' mitigation pathways, under six assumptions about cost functions and three different cost minimization functions embarking different visions of intergenerational cost distribution. A first key outcome is that the absence or presence of inertia in the cost function plays a pivotal role in the resulting set of least-cost pathways. Second, despite inherent structural differences, we find common pathways across the 18 combinations in 96% of the physical states studied. Interpreting these common pathways as robust economically and in terms of intergenerational distribution, we shed light on some of their characteristics, even though these robust pathways differ for each physical state.

The Indian Ganga basin (IGB) is one of the most valuable socioeconomic regions in the Indian subcontinent. The IGB supports more than half a billion people due to an abundant supply of freshwater for agro-industrial purposes, primarily through Indian Summer Monsoon (ISM) rainfall contributions (∼85%). Any alterations in ISM characteristics would significantly affect freshwater availability, and as a result, socioeconomic activities would be affected. Therefore, in this study, we have attempted to assess how the monsoon rain spell characteristics, i.e. peak, volume, and duration, altered historically between 1901 to 2019. We further analyzed the specific IGB regions where monsoon rain spell changes are more prominent and their hydrological implications. Our estimates reveal that short-duration high-magnitude rain spells have significantly increased across the major regions of the IGB after 1960, which implies the increased probabilities of flash flood hazards. At the same time, the rain spell volumes have been depleted across the IGB after 1960, especially in the eastern Indo-Gangetic plains and southern IGB regions, indicating increased drought frequencies. Further, Himalayan regions, i.e. upper Ganga, upper Yamuna, and upper Ghaghra, have demonstrated increasing magnitudes of rain spell peaks, volume, and duration post-1960. In addition, the continuous warming and anthropogenic alterations might further exaggerate the current situation. Thus, these inferences are helpful for river basin management strategies to deal with the extreme hydrological disasters in the IGB.

Over recent decades, the retreat of Kilimanjaro's glaciers has been portrayed as a beacon of climate change. The decline of glaciers over the 20^th century, however, is evident for all tropical glaciers in East Africa, including those found on Mount Kenya and in the Rwenzori Range. More recent studies have focused on Kilimanjaro and Mount Kenya but the Rwenzori Range has not been considered for nearly two decades, which introduces an uncertainty about the remaining glacierization in East Africa. Therefore, the present study provides insights into the most recent glacier extents of all three mountain regions using a manual, multitemporal analysis of high-resolution satellite images for the years 2021/2022. The glacierization in East Africa is estimated to be 1.36 km², with a glacier area of 0.98 km² on Kilimanjaro, 0.069 km² on Mount Kenya and 0.38 km² in the Rwenzori Range. The uncertainty is determined to be within 12.5%. Compared to previous estimations, the overall area has declined by more than a half of its early 21^st century extent. Being mainly controlled by high-altitude hygric seasonality, these glaciers are particularly valuable indicators of tropical climate variability and climate change.

Efforts to assess risks to the financial system associated with climate change are growing. These commonly combine the use of integrated assessment models to obtain possible changes in global mean temperature (GMT) and then use coupled climate models to map those changes onto finer spatial scales to estimate changes in other variables. Other methods use data mined from 'ensembles of opportunity' such as the Coupled Model Intercomparison Project (CMIP). Several challenges with current approaches have been identified. Here, we focus on demonstrating the issues inherent in applying global 'top-down' climate scenarios to explore financial risks at geographical scales of relevance to financial institutions (e.g. city-scale). We use data mined from the CMIP to determine the degree to which estimates of GMT can be used to estimate changes in the annual extremes of temperature and rainfall, two compound events (heatwaves and drought, and extreme rain and strong winds), and whether the emission scenario provides insights into the change in the 20, 50 and 100 year return values for temperature and rainfall. We show that GMT provides little insight on how acute risks likely material to the financial sector ('material extremes') will change at a city-scale. We conclude that 'top-down' approaches are likely to be flawed when applied at a granular scale, and that there are risks in employing the approaches used by, for example, the Network of Central Banks and Supervisors for Greening the Financial System. Most fundamental, uncertainty associated with projections of future climate extremes must be propagated through to estimating risk. We strongly encourage a review of existing top-down approaches before they develop into de facto standards and note that existing approaches that use a 'bottom-up' strategy (e.g. catastrophe modelling and storylines) are more likely to enable a robust assessment of material risk.

Nepal recognizes climate change as a significant threat to its economy, communities, and environment. Climate variability is one of the major causes of food insecurity, poverty, and inequality in the country. Marginalized and vulnerable communities, particularly women, suffer the most severe consequences of climate change. In this paper, we qualitatively analyze primary and secondary data to understand how gender considerations are integrated into agriculture and climate change policies. It aims to identify gaps in integrating gender considerations into policies and practices. Climate change's challenges on agriculture and food security have been identified in most agricultural policies; however, those policies remain quiet on the gender-specific impacts of climate change. Representation mandates are not sufficiently linked with officials' overall performance, resulting in limited representation of women in budget formulation, project and program design, planning, and resource and opportunity allocation. As a way forward, our analysis suggests addressing the gaps at the policy and institutional levels. For instance, to effectively address climate change, policies should be developed with a gender-inclusive approach, along with budgetary allocations that consider the gender-specific impact of climate change. Promoting gender equality in climate-resilient agriculture in Nepal requires measures such as empowering women's networks, establishing linkages with extension services that focus on women-led cooperatives, and investing in affordable and climate-smart tools and machinery that are women-friendly. The study offers important insights for policymakers to create gender-inclusive policies. It highlights the opportunity to coordinate inter-agency responses among stakeholders and sustain ongoing national policy dialogues to identify actions required to meet the nationally determined contributions' commitments.

Potatoes as a food crop contribute to zero hunger: Sustainable Development Goal 2. Over the years, the global potato supply has increased by more than double consumption. Changing climatic conditions are a significant determinant of crop growth and development due to the impacts of meteorological conditions, such as temperature, precipitation, and solar radiation, on yields, placing nations under the threat of food insecurity. Potatoes are prone to climatic variables such as heat, precipitation, atmospheric carbon dioxide (CO₂), droughts, and unexpected frosts. A crop simulation model (CSM) is useful for assessing the effects of climate and various cultivation environments on potato growth and yields. This article aims to review recent literature on known and potential effects of climate change on global potato yields and further highlights tools and methods for assessing those effects. In particular, this review will explore (1) global potato production, growth and varieties; (2) a review of the mechanisms by which changing climates impact potato yields; (3) a review of CSMs as tools for assessing the impacts of climate change on potato yields, and (4) most importantly, this review identifies critical gaps in data availability, modeling tools, and adaptation measures, that lays a foundation for future research toward sustainable potato production under the changing climate.

Do female policymakers encourage the production of renewable energy compared to their male counterparts? Using instrumental variables, we conduct a cross-country analysis of 39 high-income countries for the years 1997–2020 using quota laws and women's suffrage as instruments for women's participation in the parliament. We find that a 1 percentage point increase in the proportion of women in the legislature increases renewable energy production by 1.54 percentage points. This study suggests that fostering policies that boost women's participation in policy-making positions is beneficial, especially when considering the positive spillover to other countries.

Compound climate events are major threats to crop production under climate change. However, the heterogeneity in the impact of compound events on crop yield and its drivers remain poorly understood. Herein, we used empirical approach to evaluate the impact of compound hot–dry (HD) and cold–wet (CW) events on maize yield in China at the county level from 1990 to 2016, with a special focus on the spatial heterogeneity. Our findings indicate comparable impact of extremely compound CW events (−12.8 ± 3.6%) on maize yield loss to extremely compound HD events (−11.3 ± 2.1%). The spatial pattern of compound HD and CW events impacts on maize yield was dominantly associated with moisture regime, followed by management practices and soil properties. Specifically, drier counties and counties with less fraction of clay soil and organic carbon tend to experience greater yield loss due to compound HD events, and wet condition, excessive fertilizer, clay soil and rich organic carbon aggravate the maize yield loss due to compound CW events. Moreover, the land–atmosphere coupling exacerbated the heterogeneous yield impact through divergent heat transfer. In drier regions, the greater proportion of sensible heat creates a positive feedback between drier land and hotter atmosphere. In contrast, the greater proportion of latent heat in wetter regions results in a positive feedback between wetter land and colder atmosphere. Our results highlighted a critical element to explore in further studies focused on the land–atmosphere coupling in agricultural risk under climate change.

The 2016 Antarctic sea ice extent (SIE) drop was a rapid decrease that led to persistent low sea ice conditions. The event was triggered by atmospheric anomalies, but the potential preconditioning role of the ocean is unsettled. Here, we use sensitivity experiments with a fully-coupled regional climate model to elucidate the impact of the ocean conditions on the drop and on the persistence of the negative SIE anomalies during 2017. In particular, we re-initialize the model in January 2016 using different ocean and sea ice conditions, keeping lateral boundary forcings in the atmosphere and ocean unchanged. We find that the state of the Southern Ocean in early 2016 does not determine whether the drop occurs or not, but indeed has an impact on its amplitude and regional characteristics. Our results also indicate that the ocean initialization affects the sea ice recovery after the drop in the short term (one year), especially in the Weddell sector. The ocean's influence appears not to be linked to the ocean surface and sea-ice initialization, but rather to the sub-surface conditions (between 50 m and 150 m) and heat exchange fluctuations at the regional scale, while the atmospheric forcing triggering the drop is driven by the large-scale circulation.

The signal-to-noise ratio paradox is interpreted as the climate model's ability to predict observations better than the model itself. This view is counterintuitive, given that climate models are simplified numerical representations of complex earth system dynamics. A revised interpretation is provided here: the signal-to-noise ratio paradox represents excessive noise in climate predictions and projections. Noise is potentially reducible, providing a scientific basis for improving the signal in regional climate projections. The signal-to-noise ratio paradox was assessed in long-term climate projections using single-model and multi-model large ensemble climate data. A null hypothesis was constructed by performing bootstrap resampling of climate model ensembles to test its ability to predict the 20th-century temperature and precipitation trends locally and compare it with the observations. The rejection of the null hypothesis indicates the existence of a paradox. The multi-model large ensemble does not reject the null hypothesis in most places globally. The rejection rate in the single-model large ensemble is related to the model's fidelity to simulate internal climate variability rather than its ensemble size. For regions where the null hypothesis is rejected in the multi-model large ensemble, for example, India, the paradox is caused by a smaller signal strength in the climate model's ensemble. The signal strength was improved by 100% through ensemble selection and based on past performance, which reduced uncertainty in India's 30-year temperature projections by 25%. Consistent with previous studies, precipitation projections are noisier, leading to a paradox metric value 2–3 times higher than that of the temperature projections. The application of ensemble selection methodology significantly decreased uncertainty in precipitation projections for the United Kingdom, Western Australia, and Northeastern America by 47%, 36%, and 20%, respectively. Overall, this study makes a unique contribution by reducing uncertainty at the temporal scale, specifically in estimating trends using the signal-to-noise ratio paradox metric.

Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime stratosphere. Such extreme events are characterized by substantial disruption to the stratospheric polar vortex, which can be categorized into displacement and splitting types depending on the morphology of the disrupted vortex. Moreover, SSWs are usually followed by anomalous tropospheric circulation regimes that are important for subseasonal-to-seasonal prediction. Thus, monitoring the genesis and evolution of SSWs is crucial and deserves further advancement. Despite several analysis methods that have been used to study the evolution of SSWs, the ability of deep learning methods has not yet been explored, mainly due to the relative scarcity of observed events. To overcome the limited observational sample size, we use data from historical simulations of the Whole Atmosphere Community Climate Model version 6 to identify thousands of simulated SSWs, and use their spatial patterns to train the deep learning model. We utilize a convolutional neural network combined with a variational auto-encoder (VAE)—a generative deep learning model—to construct a phase diagram that characterizes the SSW evolution. This approach not only allows us to create a latent space that encapsulates the essential features of the vortex structure during SSWs, but also offers new insights into its spatiotemporal evolution mapping onto the phase diagram. The constructed phase diagram depicts a continuous transition of the vortex pattern during SSWs. Notably, it provides a new perspective for discussing the evolutionary paths of SSWs: the VAE gives a better-reconstructed vortex morphology and more clearly organized vortex regimes for both displacement-type and split-type events than those obtained from principal component analysis. Our results provide an innovative phase diagram to portray the evolution of SSWs, in which particularly the splitting SSWs are better characterized. Our findings support the future use of deep learning techniques to study the underlying dynamics of extreme stratospheric vortex phenomena, and to establish a benchmark to evaluate model performance in simulating SSWs.

Potatoes as a food crop contribute to zero hunger: Sustainable Development Goal 2. Over the years, the global potato supply has increased by more than double consumption. Changing climatic conditions are a significant determinant of crop growth and development due to the impacts of meteorological conditions, such as temperature, precipitation, and solar radiation, on yields, placing nations under the threat of food insecurity. Potatoes are prone to climatic variables such as heat, precipitation, atmospheric carbon dioxide (CO₂), droughts, and unexpected frosts. A crop simulation model (CSM) is useful for assessing the effects of climate and various cultivation environments on potato growth and yields. This article aims to review recent literature on known and potential effects of climate change on global potato yields and further highlights tools and methods for assessing those effects. In particular, this review will explore (1) global potato production, growth and varieties; (2) a review of the mechanisms by which changing climates impact potato yields; (3) a review of CSMs as tools for assessing the impacts of climate change on potato yields, and (4) most importantly, this review identifies critical gaps in data availability, modeling tools, and adaptation measures, that lays a foundation for future research toward sustainable potato production under the changing climate.

Aided by progress in the theoretical understanding, new knowledge on tropical rainfall annual cycle changes under global warming background has been advanced in the past decade. In this review, we focus on recent advances in understanding the changes of tropical rainfall annual cycle, including its four distinct features: amplitude, pattern shift, phase and wet/dry season length changes. In a warming climate, the amplitude of tropical rainfall annual cycle is enhanced, more evidently over ocean, while the phase of tropical rainfall annual cycle is delayed, mainly over land. The former is explained by the wet-get-wetter mechanism and the latter is explained by the enhanced effective atmospheric heat capacity and increased convective barrier. The phase delay over land has already emerged in the past four decades. The pattern shift under warming is marked by two features: equatorward shift of the inter-tropical convergence zone throughout the year and the land-to-ocean precipitation shift in the rainy season. The former is explained by the upped-ante mechanism and/or related to the enhanced equatorial warming in a warmer world. The latter is suggested to be caused by the opposite land and ocean surface temperature annual cycle changes in the tropics. Over tropical rainforest regions such as Amazon and Congo Basin, the dry season has lengthened in the recent decades, but the fundamental reason is still unclear. Despite the notable progress of the last decade, many gaps remain in understanding the mechanism, quantifying and attributing the emergence, narrowing the inter-model uncertainty, and evaluating the impact of tropical rainfall annual cycle changes, motivating future work guided by some directions proposed in this review.

Anthropogenic aerosol emissions are expected to change rapidly over the coming decades, driving strong, spatially complex trends in temperature, hydroclimate, and extreme events both near and far from emission sources. Under-resourced, highly populated regions often bear the brunt of aerosols' climate and air quality effects, amplifying risk through heightened exposure and vulnerability. However, many policy-facing evaluations of near-term climate risk, including those in the latest Intergovernmental Panel on Climate Change assessment report, underrepresent aerosols' complex and regionally diverse climate effects, reducing them to a globally averaged offset to greenhouse gas warming. We argue that this constitutes a major missing element in society's ability to prepare for future climate change. We outline a pathway towards progress and call for greater interaction between the aerosol research, impact modeling, scenario development, and risk assessment communities.

The hydrologic cycle is a fundamental component of the climate system with critical societal and ecological relevance. Yet gaps persist in our understanding of water fluxes and their response to increased greenhouse gas forcing. The stable isotope ratios of oxygen and hydrogen in water provide a unique opportunity to evaluate hydrological processes and investigate their role in the variability of the climate system and its sensitivity to change. Water isotopes also form the basis of many paleoclimate proxies in a variety of archives, including ice cores, lake and marine sediments, corals, and speleothems. These records hold most of the available information about past hydrologic variability prior to instrumental observations. Water isotopes thus provide a 'common currency' that links paleoclimate archives to modern observations, allowing us to evaluate hydrologic processes and their effects on climate variability on a wide range of time and length scales. Building on previous literature summarizing advancements in water isotopic measurements and modeling and describe water isotopic applications for understanding hydrological processes, this topical review reflects on new insights about climate variability from isotopic studies. We highlight new work and opportunities to enhance our understanding and predictive skill and offer a set of recommendations to advance observational and model-based tools for climate research. Finally, we highlight opportunities to better constrain climate sensitivity and identify anthropogenically-driven hydrologic changes within the inherently noisy background of natural climate variability.

While evidence of Indigenous Peoples' climate knowledge and adaptation practices is readily available in Canada, regional variations are poorly understood, and proper representation and recognition in academic and planning contexts is scarce. Much less still is known about the health and environmental impacts of climate change on these communities. This review sought to report and assess the evidence of such impacts on Indigenous Peoples in Atlantic Canada over the past two decades. Current published studies focused on Indigenous Peoples' knowledge and perceptions and highlight government policy for adaptation measurements. We systematically searched publications between January 2002 and March 2022 from the Web of Science, PubMed, Google Scholar, and Science Direct databases, screening for (1) environmental and (2) health impacts of climate change on Indigenous Peoples. Fifty-six articles were selected and thoroughly reviewed using the GRADE approach to assess the quality of the evidence. The quality of evidence ranged from low to moderate, and the evidentiary foundation for links between climate change and health effects was weak. We thus find an opportunity for future research to focus on climate-related effects on the health and lands of Indigenous Peoples within Atlantic Canada, especially concerning impacts on mental health.