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• Direct and indirect climate impacts of solid waste landfills in California were quantified. • Direct impacts were positive and high whereas indirect impacts were negative and low. • Cover characteristics including material type and areal extent control climate impacts. • Global warming potential time horizon significantly influences climate impact results. • Integration of direct and indirect impacts advances landfill climate mitigation strategies. Climate impacts of landfill gas emissions were investigated for 20- and 100-year time horizons to identify the effects of atmospheric lifetimes of short- and long-lived drivers. Direct and indirect climate impacts were determined for methane and 79 trace species. The impacts were quantified using global warming potential, GWP (direct and indirect); atmospheric degradation (direct); tropospheric ozone forming potential (indirect); secondary aerosol forming potential (indirect) and stratospheric ozone depleting potential (indirect). Effects of cover characteristics, landfill operational conditions, and season on emissions were assessed. Analysis was conducted at five operating municipal solid waste landfills in California, which collectively contained 13% of the waste in place in the state. Climate impacts were determined to be primarily due to direct emissions (99.5 to 115%) with indirect emissions contributing −15 to 0.5%. Methane emissions were 35 to 99% of the total emissions and the remainder mainly greenhouse gases (hydro)chlorofluorocarbons (up to 42% of total emissions) and nitrous oxide. Cover types affected emissions, where the highest emissions were generally from intermediate covers with the largest relative landfill surface areas. Landfill-specific direct emissions varied between 683 and 103,411 and between 381 and 37,925 Mg CO 2 -eq./yr for 20- and 100-yr time horizons, respectively. Total emissions (direct + indirect) were 680 to 103,600 (20-yr) and were 374 to 38,108 (100-yr) Mg CO 2 -eq./yr. Analysis time horizon significantly affected emissions. The 20-yr direct and total emissions were consistently higher than the 100-yr emissions by up to 2.5 times. Detailed analysis of time-dependent climate effects can inform strategies to mitigate climate change impacts of landfill gas emissions. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Quantifying the effect of thermal stress on milk yields is essential to effectively manage present and future risks in dairy systems. Despite the existence of numerous heat indices designed to communicate stress thresholds, little information is available regarding the accuracy of different indices in estimating milk yield losses from both cold and heat stress at large spatiotemporal scales. To address this gap, we comparatively analyzed the performance of existing thermal indices in capturing US milk yield response to both cold and heat stress at the national scale. We selected 4 commonly used thermal indices: the temperature-humidity index (THI), black globe humidity index (BGHI), adjusted temperature-humidity index (THIadj), and comprehensive climate index (CCI). Using a statistical panel regression model with observational and reanalysis weather data from 1981 to 2020, we systematically compared the patterns of yield sensitivities and statistical performance of the 4 indices. We found that the US state-level milk yield variability was better explained by the THIadj and CCI, which combine the effects of temperature, humidity, wind, and solar radiation. Our analysis also reveals continuous and nonlinear responses of milk yields to a range of cold to heat stresses across all 4 indices. This implies that solely relying on fixed thresholds of these indices to model milk yield changes may be insufficient to capture cumulative thermal stress. Cold extremes reduced milk yields comparably to those affected by heat extremes on the national scale. Additionally, we found large spatial variability in milk yield sensitivities, implying further limitations to the use of fixed thresholds across locations. Moreover, we found decreased yield sensitivity to thermal stress in the most recent 2 decades, suggesting adaptive changes in management to reduce weather-related risks. [ABSTRACT FROM AUTHOR]

The Montenegrin climate is characterised as very heterogeneous due to its complex topography. The viticultural heritage, dating back to before the Roman empire, is settled in a Mediterranean climate region, located south of the capital Podgorica, where climate conditions favour red wine production. However, an overall increase in warmer and drier periods affects traditional viticulture. The present study aims to discuss climate change impacts on Montenegrin viticulture. Bioclimatic indices, ensembled from five climate models, were analysed for both historical (1981–2010) and future (2041–2070) periods upon three socio-economic pathways: SSP1-2.6, SSP3-7.0 and SSP5-8.5. CHELSA (≈1 km) was the selected dataset for this analysis. Obtained results for all scenarios have shown the suppression of baseline conditions for viticulture. The average summer temperature might reach around 29.5 °C, and the growing season average temperature could become higher than 23.5 °C, advancing phenological events. The Winkler index is estimated to range from 2900 °C up to 3100 °C, which is too hot for viticulture. Montenegrin viticulture requires the application of adaptation measures focused on reducing temperature-increase impacts. The implementation of adaptation measures shall start in the coming years, to assure the lasting productivity and sustainability of viticulture. [ABSTRACT FROM AUTHOR]

Globally, impacts of climate change display an increasingly negative development of marine biomass, but there is large regional variability. In this analysis of future climate change on stock productivity proxies for the North Sea, the Norwegian Sea, and the Barents Sea, we have provided calculations of accumulated directional effects as a function of climate exposure and sensitivity attributes. Based on modelled changes in physical and biogeochemical variables from three scenarios and knowledge of 13 different stocks' habitats and response to climate variations, climate exposures have been weighted, and corresponding directions these have on the stocks have been decided. SSP1‐2.6 gives mostly a weak cooling in all regions with almost negligible impacts on all stocks. SSP2‐4.5 and SSP5‐8.5 both provide warmer conditions in the long term but are significantly different in the last 30 years of the century when the SSP5‐8.5 warming is much stronger. The results show that it is the current stocks of cod and Calanus finmarchicusin the North Sea, and polar cod and capelin in the Barents Sea that will be most negatively affected by strong warming. Stocks that can migrate north into the northern seas such as hake in the Norwegian Sea, or stocks that are near the middle of the preferred temperature range such as mackerel and herring in the Norwegian Sea and cod and Calanus finmarchicus in the Barents Sea, are the winners in a warmer climate. The highly different impacts between the three scenarios show that multiple scenario studies of this kind matter. [ABSTRACT FROM AUTHOR]

Mangroves play a crucial role in improving the water quality of mangrove wetlands. However, current research faces challenges, such as the difficulty in quantifying the impact of mangroves on water quality and the unclear pathways of influence. This study utilized remote sensing imagery to investigate the long-term changes in mangrove forests in the Futian Mangrove National Nature Reserve and constructed a water quality index based on water quality data. Finally, structural equation modeling was employed to explore the pathways of influence and quantify the impact effects of mangroves, climate, and water quality. The study findings revealed several key points: (1) The mangrove forests in the Futian Mangrove National Nature Reserve exhibited a trend of expansion towards the ocean during this period. (2) The seasonal and annual characteristics of water quality in Shenzhen Bay indicated a significant improvement in water quality from 2000 to 2020. (3) Mangroves have significant direct and indirect impacts on water quality, which are more pronounced than the effects of climate factors. These findings not only offer insights for the environmental management and conservation of Shenzhen Bay but also provide support for future comprehensive studies on the response relationships between the morphology, species, and physiological characteristics of mangroves and water quality. [ABSTRACT FROM AUTHOR]

Climate change and rainfall variability are driving many farmers to adopt irrigation, who were historically within rain-dependent regions, to sustain crop productivity. In the Mid-Atlantic region, irrigation in agriculture has grown since the 1980s due to rising temperatures and changes in precipitation patterns. Dry summers and uneven seasonal rainfall have necessitated a shift toward irrigation, particularly in Maryland's Coastal Plain. However, high dependence on confined groundwater for irrigation around this area has strained aquifers. To mitigate this strain, exploring alternative water sources is now important. This study investigates the viability of utilizing treated wastewater from plants as an irrigation substitute in Maryland's Coastal Plain. Using the Soil and Water Assessment Tool, the research evaluates crop productivity and irrigation needs under various climate scenarios. Results indicate that recycled water from nearby treatment plants can meet crop water requirements during wet years and partially during moderate and dry years, reducing aquifer reliance by 56 and 30%, respectively. This framework aims to boost yields while conserving freshwater resources. By serving as a decision support tool, stakeholders can assess the feasibility of recycled water for irrigation, thereby potentially reducing strain on confined aquifers. HIGHLIGHTS This study presents a method for utilizing reclaimed water for irrigation.; We conducted crop yield simulations for corn and soybeans, the most dominant crops in the region.; Using the SWAT model, we have calculated the water requirements for corn and soybean under various climatic conditions.; We have determined the proportion of the total water requirement that can be sourced by water reuse.;

This study investigated the variability of agricultural drought severity, as depicted by vegetation indices, and the bias in identifying drought events when considering a stationary vs nonstationary climate reference. The work leveraged gridded climate data (NCEP CFSv2, CHIRPS 1981–2022), soil properties (OpenLandMap), satellite imagery (Sentinel2/Landsat, 2000–2022), and future climate projections (NEX-GDDP, 2050) together with local knowledge of selected farms, to augment drought monitoring techniques and identify potential issues for agriculture. For the study domain, significant differences were observed when comparing drought characteristics using stationary and nonstationary drought indexes, with biases being not ubiquitous in either space or time of year. When developing sustainable drought mitigation and adaptation strategies, decision-makers should carefully address this uncertainty to avoid a possible underestimation of drought magnitude. Results showed a drought increase (∼50%) by the mid and late twenty-first century. Projection of future climate highlighted an even more significant impact (∼80%) with a wide variability of risk across the domain. As drought impact was also related to soil organic carbon (SOC), our results suggest that improving SOC content could be a sustainable strategy for enhancing soil drought resilience, especially in areas commonly characterized by low concentrations of organic carbon and nutrients. The analysis highlighted that drought impacts were also modulated by investment in irrigation infrastructure and irrigation efficiency. Researchers and land managers could apply the proposed analysis design to address historical, current and future indicators of vegetation conditions within irrigated regions. By providing spatio-temporal information on the patterns of drought impacts and their bias, this study supports identifying priority regions for targeted drought risk reduction and adaptation options, including water resources and soil management sustainability criteria, to move towards more resilient agricultural systems.

Abstract We present a thorough analysis of the scientific production in climate change and adaptation in Brazil. We conducted a bibliometric and bibliographic review to reveal and discuss how climate change research has been carried out. We compared a broader climate change research dataset with a more specific climate adaptation research dataset, to understand the main differences and convergence points, and how science conducted from a mitigation and adaptation perspective demonstrates potential to confront the climate change challenges and drivers in Brazil. Four main clusters of investigation within climate change were detected: (1) impacts on forest and plant development, (2) land use and ecology, (3) adaptation/governance, and (4) climate/atmospheric studies. Only about 5% of the total studies on climate change address adaptation, for which three main clusters of research were identified: (1) adaptation actions and policies; (2) urban environment, vulnerability, and health and (3) food and coastal impacts. Although there are strong research trends in climate change for the Amazon Forest, there was less evidence of studies concerning climate adaptation for this and other Brazilian biomes, smaller cities, rural and traditional communities, and poorer regions. Our results shed a light on the more commonly chosen research topics, their strongest points and potential gaps and trends. This can contribute to the scientific communication and implementation of climate actions in Brazil, and a better understanding of the climate science knowledge from the perspective of a middle-income country.

The accurate partitioning of precipitation is important for snowpack and streamflow simulations in mountainous regions. However, most hydrologic models employ a partitioning method based on near‐surface air temperature (Ta) and the sensitivity of hydrologic simulations to the selection of the precipitation partitioning method (PPM) under climate change has been underexplored. To address this, we compared simulations using two PPMs, a conventional Ta‐based scheme and a wet‐bulb temperature (Tw)‐based approach (TA and TW) with the Variable Infiltration Capacity model. Our study focused on the Colorado River Basin (CRB) which is vulnerable to future snowfall and streamflow reductions due to climate warming. Historical simulations demonstrated the improved performance of the TW scheme in simulating snowfall fraction (SF) and snow water equivalent (SWE) in relation to in situ observations and a gridded SWE product. Subsequently, we evaluated the influence of the PPM selection on snowpack and streamflow projections from eight climate models under two emissions scenarios. The ensemble median results revealed more substantial decline in annual SF with the TW scheme as compared to the TA method (−12% vs. −9%) from the historical (1976–2005) to the far future (2066–2095) period, especially at lower elevations. Hydrologic simulations showed that the TA scheme underestimated reductions in SWE and streamflow (Q) under warming as compared to the TW scheme. This finding has important implications on future projections for the CRB and in other mountainous basins where climate warming can shift conditions from snowfall to rainfall. Key Points: The precipitation partitioning method (PPM) impacts the simulation of snowfall and snowpack dynamics in the Colorado River Basin (CRB)The selection of PPM also determines the sensitivity of streamflow projections in the CRB under climate change scenariosSpatial variations in the impact of PPM reveal distinct climate change sensitivities in high and low elevation regions in the CRB [ABSTRACT FROM AUTHOR]

Local climate change impacts hit hard at smallholder farmers in developing countries due to their overreliance on rain-fed farming. Whilst ecosystem-based adaptation (EbA) to climate change holds an answer to smallholder farmers' adaptation needs, there are inadequate empirical results from the field to support formulation of effective adaptation strategies. Hence, this paper aimed at investigating EbA practices adopted by farmers, in North-Western Ghana to ameliorate local climate change impacts, with emphasis on benefits and associated challenges. The study methods were cross-sectional survey, mixed methods, and case study approach, with all data collected in the Wa West District of Ghana. Data were gathered using questionnaire, focus group discussion (FGD) and interviews. Data from the questionnaire were entered into SPSS version 20 to analyze associations between the variables using cross-tabulation. Descriptive statistics, in the form of percentages and frequencies, were then generated from the survey data and integrated with the qualitative information in the presentation of results. Also, statistical significance of the relationships was analyzed using chi-square test. Chi-squared tests were done to determine associations between socio-economic and environmental factors, and challenges and benefits associated with the adoption of EbA practices. This was necessary to give further insights to inform policy-related decisions aimed at addressing EbA adoption challenges. The results revealed that farmers use a combination of practices rather than single practice. Farmers integrated crops and trees and keep local breed of farm animals. Benefits of EbA included improvements in soil fertility leading to high crop yield and availability of food and income. Challenges of adopting EbA were the long timespan for benefits of most EbA practices to begin to accrue, lack of extension information and increasing cost of hired labour. It is recommended that tree integration should emphasis trees with multiple benefits and compatible with food crops. For the achievement of food security policy objectives, agroforestry promotion should focus on the integration of fruit tree crops and food crops to address hunger and increase farmers' incomes. Mobile extension technology should be exploited by government for the provision of EbA-specific information to farmers to bridge the extension services gap. Climate change policy of government should include the planting of non-fruit trees as windbreaks to provide multi-purpose benefits including provision of stakes for yam farmers, live-fencing and firewood for women. There is the need for government policy objectives to include EbA by farmers under the flagship 'planting for food and jobs' programme. [ABSTRACT FROM AUTHOR]

Projected changes to spring phenological indicators (such as first leaf and first bloom) are of importance to assessing the impacts of climate change on ecosystems and species. The risk of false springs (when a killing freeze occurs after plants of interest bloom), which can cause ecological and economic damage, is also projected to change across much of the United States. Given the coarse nature of global climate models, downscaled climate projections have commonly been used to assess local changes in spring phenological indices. Few studies that examine the influence of the sources of uncertainty sources in the downscaling approach on projections of phenological changes. This study examines the influence of sources of uncertainty on projections of spring phenological indicators and false spring risk using the South Central United States. The downscaled climate projections were created using three statistical downscaling techniques applied with three gridded observation datasets as training data and three global climate models. This study finds that projections of spring phenological indicators and false spring risk are primarily sensitive to the choice of global climate models. However, this study also finds that the formulation of the downscaling approach can cause errors representing the daily low‐temperature distribution, which can cause errors in false spring risk by failing to capture the timing between the last spring freeze and the first bloom. One should carefully consider the downscaling approach used when using downscaled climate projections to assess changes to spring phenology and false spring risk. [ABSTRACT FROM AUTHOR]

This collaborative paper explores the interconnections between climate change and the mental health and well‐being of Indigenous women in Western Canada. As the impacts of climate change intensify globally, vulnerable populations, particularly Indigenous communities, face disproportionate and multifaceted challenges. Centering on Indigenous women in Western Canada, this study explores how the climate crisis magnifies Indigenous communities' mental health disparities. Drawing from the Indigenist feminist research approach, the investigation focuses on Indigenous women's lived experiences, perceptions, and land‐based coping strategies amidst climate challenges, while simultaneously addressing the unique social, cultural, and historical factors influencing their mental health vulnerabilities within the context of climate change. The findings shed light on the complex relationships between environmental degradation, ongoing colonial impacts on traditional practices, and the mental well‐being of Indigenous women. Concluding with implications for policy and community‐led interventions, this research contributes to the discourse on the intersectionality of climate change impacts and mental health, particularly focusing on Indigenous women in Western Canada. [ABSTRACT FROM AUTHOR]

Science, engineering, and society increasingly require integrative thinking about emerging problems in complex systems, a notion referred to as convergence science. Due to the concurrent pressures of two main stressors—rapid climate change and industrialization, Arctic research demands such a paradigm of scientific inquiry. This perspective represents a synthesis of a vision for its application in Arctic system studies, developed by a group of disciplinary experts consisting of social and earth system scientists, ecologists, and engineers. Our objective is to demonstrate how convergence research questions can be developed via a holistic view of system interactions that are then parsed into material links and concrete inquiries of disciplinary and interdisciplinary nature. We illustrate the application of the convergence science paradigm to several forms of Arctic stressors using the Yamal Peninsula of the Russian Arctic as a representative natural laboratory with a biogeographic gradient from the forest‐tundra ecotone to the high Arctic. Plain Language Summary: This paper represents a synthesis of conceptual analyses, case study analyses, and practical thoughts on the application of convergence science in Arctic change studies. During a virtual workshop in 2020, a diverse, multi‐national team of authors consisting of social scientists, engineers, earth system scientists, and ecologists came together to formulate broad, scientifically, and societally important questions on how the Arctic system in the Yamal Peninsula of Western Siberia responds to pressures of rapidly changing climate and increasing industrialization. The team "engineered" a novel approach for expert (representing a disciplinary domain) and non‐expert (representatives of other disciplines) communication and at the workshop conclusion developed several convergence science questions of high appeal. Three of such questions are presented in this manuscript to illustrate how the search and identification of appropriate mechanistic linkages are critical to the development of system‐level understanding of stressor impact propagation. The need to understand underlying disciplinary and cross‐disciplinary mechanisms connecting Arctic system elements is viewed to be an inherent part of the convergence science approach. Through pursuit of such understanding, the approach naturally leads to other novel emerging questions, thereby stimulating further application of the process of integrative thinking. Key Points: Arctic research demands convergence science as essential method to understand impacts from novel stressorsAn integrative approach is developed by a diverse team to formulate questions that cannot be fully addressed within disciplinary studiesA convergence science analysis is illustrated for three questions applicable to Yamal, Russian Arctic, a microcosm of the changing Arctic [ABSTRACT FROM AUTHOR]

Explaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning‐informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments. Plain Language Summary: We developed a machine learning‐based approach to investigate why the effects of changes in climate and land cover (LC) on floods vary spatially. To inform the model, we used climate and LC data for 1,268 catchments in Great Britain over four decades. We found that increasing rainfall and urban development tend to lead to larger floods, especially in rivers fed largely by groundwater. In contrast, rising air temperature and afforestation, in the absence of any changes in rainfall, lead to smaller floods, particularly in areas where rivers are less fed by groundwater. We believe that our findings can be used to develop more targeted flood management strategies. Key Points: We employ partial dependence analysis and sensitivity testing to assess where changes in climate or land cover might affect flooding the mostRising precipitation and urbanization tend to lead to larger floods in catchments with high baseflow contribution and low runoff ratioRising air temperature with unchanged precipitation lowers flood magnitudes, especially in dry catchments with low baseflow index [ABSTRACT FROM AUTHOR]

The influence of climatic factors on landslides triggers and displacement rates is a crucial research topic, especially due to the growing need to understand the evolution of climate change in historical periods of intense precipitation and anomalous temperature increases. Italy, highly prone to hydrogeological instability, extremely its mountainous regions such as the Alps, stands as a pertinent subject area for instability scenarios. However, the interpretation of climate effects on landslides is still an open issue. This work proposed a simplified methodology for investigating the displacements of three slow- moving landslides located in the Western Alps of Piemonte region, in response to significant meteorological events evaluated from reference normal of precipitation and temperature trends over the reference period 1991-2020. Another purpose is to emphasize the advantages of using different monitoring techniques by comparing displacement time series measured with in situ and remote sensing instruments, to detect ground deformation processes of these gravitational phenomena. The existence of a robust monitoring network, coupled with InSAR dataset support, has allowed detecting climatic factors' impact on displacement rates for the outlined case studies. The results have demonstrated the relationships between the identified climatic events and variations in displacement time series, as well as the potential of integrating field observations and InSAR techniques to improve the interpretation of landslide dynamics. Although this study has laid the basis for understanding the influence of climatic factors on landslide displacements, there is still much to investigate and refine. The proposed preliminary analysis will further improve the ability to predict, monitor and mitigate landslide risk under changing climate conditions. [ABSTRACT FROM AUTHOR]

We present a thorough analysis of the scientific production in climate change and adaptation in Brazil. We conducted a bibliometric and bibliographic review to reveal and discuss how climate change research has been carried out. We compared a broader climate change research dataset with a more specific climate adaptation research dataset, to understand the main differences and convergence points, and how science conducted from a mitigation and adaptation perspective demonstrates potential to confront the climate change challenges and drivers in Brazil. Four main clusters of investigation within climate change were detected: (1) impacts on forest and plant development, (2) land use and ecology, (3) adaptation/governance, and (4) climate/atmospheric studies. Only about 5% of the total studies on climate change address adaptation, for which three main clusters of research were identified: (1) adaptation actions and policies; (2) urban environment, vulnerability, and health and (3) food and coastal impacts. Although there are strong research trends in climate change for the Amazon Forest, there was less evidence of studies concerning climate adaptation for this and other Brazilian biomes, smaller cities, rural and traditional communities, and poorer regions. Our results shed a light on the more commonly chosen research topics, their strongest points and potential gaps and trends. This can contribute to the scientific communication and implementation of climate actions in Brazil, and a better understanding of the climate science knowledge from the perspective of a middle-income country. [ABSTRACT FROM AUTHOR]

We utilize a global warming level (GWL) lens to evaluate global and regional patterns of agricultural impacts as global surface temperature increases, providing a unique perspective on the experience of stakeholders with continued warming in the 21st century. We analyze crop productivity outputs from 11 crop models simulating 5 climate models under 3 emissions scenarios across 4 crops within the AgMIP/ISIMIP Phase 3 ensemble. We categorize regional productivity changes (without adaptation) into 9 characteristic climate change response patterns, identifying consistent increases and decreases as well as non‐linear (peak or dip) responses indicative of inflection points reversing trends as GWLs increase. Many maize regions and pockets of wheat, rice and soybean show peak decrease patterns where initial increases may lull stakeholders into complacency or maladaptation before productivity shifts to losses at higher GWLs. Although the GWL perspective has proven useful in connecting diverse climate models and emissions scenarios, we identify multiple pitfalls that recommend proceeding with caution when applying this approach to climate impacts. Chief among these is that carbon dioxide (CO2) concentrations at any GWL depend on a climate model's transient climate response (TCR). Higher CO2 concentrations generally benefit crop productivity, so this leads to more pessimistic agricultural projections for so‐called "hot" models and can skew multi‐model ensemble results as models with high TCR are disproportionately likely to reach higher GWLs. While there are strong connections between many climatic impact‐drivers and GWLs, vulnerability and exposure components of food system risk are strongly dependent on development pathways. Plain Language Summary: This study uses the latest ensemble of crop and climate models with a framework to identify cropping systems and regions where non‐linear aspects of agricultural system response to climate change could lead to complacency or maladaptation. Results will help readers understand the different experiences and contextual adaptation needs for food systems in the coming decades. This framework could be widely applied across all impacts sectors. Analysis also highlights that climate models with particularly strong climate sensitivity lead more pessimistic projections by crop impacts, underscoring the need for special care in the use of climate model ensembles for impact assessments. Key Points: Climate change response patterns show regional farming systems where initial positive changes give way to long‐term detrimental impactsRegional and species‐dependent patterns of agricultural impacts on benchmark global warming levels prioritize risk management strategiesClimate models with high climate sensitivity have less CO2 benefit on a given global warming level (GWL) and are over‐represented in ensembles of higher GWLs [ABSTRACT FROM AUTHOR]

In the coastal regions of the western United States, atmospheric rivers (ARs) are associated with the largest precipitation generating storms and contribute up to half of annual precipitation, but the impact of ARs on the integrated hydrologic cycle, specifically on groundwater storage and hydrodynamics, is largely unknown. To better explore the hydrologic behavior of AR versus non-AR event precipitation, we present a novel combination of two water tracking methods (one in the atmosphere and one in the subsurface) to explicitly track the full lifecycle of water parcels generated by ARs. Simulations of northern California's Cosumnes River watershed during the record wet 2017 water year are performed via the coupling of a high-resolution regional climate model and a land surface-groundwater model accounting for lateral groundwater flow. Despite ARs contributing more precipitation than non-AR storms, we find less AR water is preferentially stored in aquifers by year end. Fractionally, ARs result in 300% less snow derived groundwater-recharged compared to non-AR precipitation. Rain-on-snow (RoS) plays an important role in AR-driven discharge, where over 50% of total discharge from ARs snow is from RoS events. Finally, despite record-breaking annual precipitation, simulated groundwater depletion occurs by year end due to estimates of groundwater pumping activities. The results from these simulations serve as a partial analogue of future hydrologic conditions where ARs are expected to intensify and provide a greater fraction of annual precipitation due to climate change.

Researchers have long hypothesized linkages between climate change, food security, and migration in low- and middle-income countries (LMICs). One such hypothesis is the “agricultural pathway,” which postulates that negative climate change impacts on food production harm livelihoods, which triggers rural out-migration, internally or abroad. Migration is thus an adaptation to cope with the impacts of climate change and bolster livelihoods. Recent evidence suggests that the agriculture pathway is a plausible mechanism to explain climate-related migration. But direct causal connections from climate impacts on food production to livelihood loss to rural out-migration have yet to be fully established. To guide future research on the climate-food-migration nexus, we present a conceptual framework that outlines the components and linkages underpinning the agricultural pathway in LMICs. We build on established environmental-migration conceptual frameworks that have informed empirical research and deepened our understanding of complex human-environmental systems. First, we provide an overview of the conceptual framework and its connection to the agricultural pathway hypothesis in the climate mobility literature. We then outline the primary components and linkages of the conceptual framework as they pertain to LMIC contexts, highlighting current research gaps and challenges relating to the agricultural pathway. Last, we discuss possible future research directions for the climate-food-migration nexus. By highlighting the complex, multiscale, interconnected linkages that underpin the agricultural pathway, our framework unpacks the multiple causal connections that currently lie hidden in the agricultural pathway hypothesis. [ABSTRACT FROM AUTHOR]

Stratospheric Aerosol Injection (SAI) is a proposed method of climate intervention aiming to reduce the impacts of human‐induced global warming by reflecting a portion of incoming solar radiation. Many studies have demonstrated that SAI would successfully reduce global‐mean surface air temperatures; however the vast array of model scenarios and strategies result in a diverse range of climate impacts. Here we compare two SAI strategies—a quasi‐ equatorial injection and a multi‐latitude off‐equatorial injection—simulated with the UK Earth System Model (UKESM1), both aiming to reduce the global‐mean surface temperature from that of a high‐end emissions scenario to that of a moderate emissions scenario. We compare changes in the surface and stratospheric climate under each strategy to determine how the climate response depends on the injection location. In agreement with previous studies, an equatorial injection results in a tropospheric overcooling in the tropics and a residual warming in the polar regions, with substantial changes to stratospheric temperatures, water vapor and circulation. Previous comparisons of equatorial versus off‐equatorial injection strategies are limited to two studies using different versions of the Community Earth System Model. Our study evaluates how the climate responds in UKESM1 under these injection strategies. Our results are broadly consistent with previous findings, concluding that an off‐equatorial injection strategy can minimize regional surface temperature and precipitation changes relative to the target. We also present more in‐depth analysis of the associated changes in Hadley Circulation and regional temperature changes, and call for a new series of inter‐model SAI comparisons using an off‐equatorial strategy. Plain Language Summary: Stratospheric Aerosol Injection (SAI) is a method to tackle the impacts of global warming and involves reflecting some of the sun's rays away from Earth. Different strategies for implementing SAI can have various effects on the climate. This study compares two strategies—one injecting at the equator and the other at different latitudes. Both strategies successfully lower global temperatures, but they also lead to different regional climate changes. The equatorial strategy cools the tropics too much and doesn't cool the poles enough. Whereas the off‐equatorial strategy minimizes some of the negative impacts seen in the equatorial strategy. In summary, injecting aerosols away from the equator avoids unfavorable climate impacts. Key Points: We compare the climate impacts of equatorial and multi‐latitude Stratospheric Aerosol Injection (SAI) strategies under the Geoengineering Model Intercomparison Project G6 frameworkWe demonstrate that an off‐equatorial multi‐latitude injection strategy minimizes unfavorable climate impactsThis research highlights the importance of injection location in determining the impacts of SAI on the climate [ABSTRACT FROM AUTHOR]

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This paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society. Plain Language Summary: To prepare society for the effects of future climate change, we need to know what the future climate will be like. In this paper we explain the method that is used to construct six different scenarios that describe possible future climates of the Netherlands. The scenarios make assumptions about future greenhouse gas emissions, and are based on the outcomes of climate models that simulate the response of the climate to these emissions. The KNMI'23 climate scenarios show that strongly reducing global emissions strongly reduces the expected changes in the climate of the Netherlands. In the scenario in which global emissions continue to rise until 2080, Dutch society will have to adapt to a much stronger increases in heat and precipitation extremes, increased risks of droughts with low river discharge in summer, and increased risk of flooding due to high river discharges in winter. In the coming years the climate scenario data will be used to evaluate what needs to be done to keep the country a safe place for people to live in and to thrive in, under changing climate conditions. Key Points: We present a methodology for the construction of regional climate scenarios using a storyline approach to partition uncertaintyResults from CMIP6 are reconstructed with a GCM‐RCM initial condition ensemble to produce high‐resolution scenario data for end‐usersSix scenario variants cover emission uncertainty (high, moderate, low) and uncertainty in the regional response (dry‐trending, wet‐trending) [ABSTRACT FROM AUTHOR]

Winter cover crops represent a land cover change that may sequester carbon in the soil and improve agricultural sustainability. Their adoption may also change the Earth's radiative balance and result in biophysical feedbacks to climate through alterations in albedo and latent heat fluxes. Understanding the mechanisms underlying these alterations to the radiative balance is important for making reliable future climate projections. However, data on cover crop biophysics are limited, requiring models to rely on data from summer plants for parameterization, likely biasing predictions. To address this gap, we measured the albedo and stomatal conductance of two summer crops and three winter crops on farms in the High Plains region of Texas. We also established a winter cover crop field experiment with two cover crops and fallow fields to estimate the change in albedo and latent heat flux that results from a switch to winter cover cropping. We found that albedo was significantly higher in winter‐like conditions than in summer‐like conditions due to an increase in plant albedo and a reduction in leaf area index. The albedo of winter cover crops was higher than the soil albedo, resulting in an increase in top‐of‐atmosphere reflected radiation of 7%–14% when converting from fallow fields to winter cover cropped fields. There was an additional cooling effect through doubling of the estimated latent heat flux caused by the presence of cover crops. The combined changes in albedo and latent heat resulted in a change in the surface energy balance that is associated with an overall cooling effect of winter cover crops on surface atmospheric temperatures. While this effect is likely to be region‐specific, these results strongly indicate that winter cover crops alter the surface albedo and latent heat flux of agricultural fields and provide a direct cooling effect in the High Plains region of Texas. [ABSTRACT FROM AUTHOR]

Ports are vital in the global trading system but are also vulnerable to climate-related threats. This problem has not been widely studied, especially in Egypt. However, there is an urgent need to address climate-related threats to Egyptian ports, which could have significant economic and trade-related consequences. Therefore, exploring the Egyptian port administrations' and stakeholders' perceptions regarding climatic hazards is an urgent and essential matter for sustainable and resilient ports, considering their strategic economic importance. Consequently, this article is the first to examine how the port authorities perceive and respond to climate hazards in one of the most important and largest commercial Egyptian ports; it also explores their adaptation strategies and plans, considering the national agenda to develop the coastal ports respecting the sustainable development pillars, and fills the gaps in regional and national studies on seaports and climatic hazards. The paper focuses on the Great Alexandria Port, which includes the Alexandria and El-Dekheila ports. A questionnaire is designed and distributed in different port sectors for gathering relevant data and conducting interviews, discussions, and workshops. The findings declare obvious concern about developing the Egyptian ports under the national projects umbrella. Also, the port authorities are aware of the risks of climate change and believe some adaptation measures are necessary and could be essential soon. They are taking steps to adapt their ports, considering a specific strategy that includes rehabilitating infrastructure (breakwater and berths), functional facilities, and services, improving drainage systems to enhance their port's resilience, and achieving the 2030 Agenda for Sustainable Development. Our research findings suggest that there are opportunities for scientists to engage with the port stakeholders to proactively develop plans to alleviate climate change's effects on their ports. [ABSTRACT FROM AUTHOR]

Copyright of Acta Colombiana de Psicologia is the property of Universidad Catolica de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Explaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning‐informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments.

Abstract Science, engineering, and society increasingly require integrative thinking about emerging problems in complex systems, a notion referred to as convergence science. Due to the concurrent pressures of two main stressors—rapid climate change and industrialization, Arctic research demands such a paradigm of scientific inquiry. This perspective represents a synthesis of a vision for its application in Arctic system studies, developed by a group of disciplinary experts consisting of social and earth system scientists, ecologists, and engineers. Our objective is to demonstrate how convergence research questions can be developed via a holistic view of system interactions that are then parsed into material links and concrete inquiries of disciplinary and interdisciplinary nature. We illustrate the application of the convergence science paradigm to several forms of Arctic stressors using the Yamal Peninsula of the Russian Arctic as a representative natural laboratory with a biogeographic gradient from the forest‐tundra ecotone to the high Arctic.

Abstract We utilize a global warming level (GWL) lens to evaluate global and regional patterns of agricultural impacts as global surface temperature increases, providing a unique perspective on the experience of stakeholders with continued warming in the 21st century. We analyze crop productivity outputs from 11 crop models simulating 5 climate models under 3 emissions scenarios across 4 crops within the AgMIP/ISIMIP Phase 3 ensemble. We categorize regional productivity changes (without adaptation) into 9 characteristic climate change response patterns, identifying consistent increases and decreases as well as non‐linear (peak or dip) responses indicative of inflection points reversing trends as GWLs increase. Many maize regions and pockets of wheat, rice and soybean show peak decrease patterns where initial increases may lull stakeholders into complacency or maladaptation before productivity shifts to losses at higher GWLs. Although the GWL perspective has proven useful in connecting diverse climate models and emissions scenarios, we identify multiple pitfalls that recommend proceeding with caution when applying this approach to climate impacts. Chief among these is that carbon dioxide (CO2) concentrations at any GWL depend on a climate model's transient climate response (TCR). Higher CO2 concentrations generally benefit crop productivity, so this leads to more pessimistic agricultural projections for so‐called “hot” models and can skew multi‐model ensemble results as models with high TCR are disproportionately likely to reach higher GWLs. While there are strong connections between many climatic impact‐drivers and GWLs, vulnerability and exposure components of food system risk are strongly dependent on development pathways.

This study addresses the imperative need for adaptation strategies to climate impacts in coastal areas, particularly vulnerable to climate change effects. Under the Interreg project “AdriaClim,” our research aims to provide a comprehensive account of predicted climate impacts and corresponding adaptation measures for three key sectors for the coastal economy of the Puglia region (Southern Italy): coastal erosion, aquaculture, and tourism. Analyzing regional climate projections, predicted climatic anomalies for Apulia have been delineated. Together with a comprehensive description of the investigated sectors, the expected sectoral climate impacts have been identified. The respective adaptation measures for each sector have been evaluated in terms of coherence with the studied context and effectiveness, among those proposed in the literature. In aquaculture, climate change may influence ecosystem structure, functionality, production cycles, infrastructure, and organism physiology. Integrated multitrophic aquaculture (IMTA) emerges as a solution to enhance the resilience of Apulian aquatic production systems to climate change. For coastal tourism, the vulnerability to various climate impacts is emphasized. These impacts can directly influence tourist flows or indirectly alter the attractiveness of the Apulian Adriatic coast. Given the sector's strong ties to anthropic, natural, and cultural elements, a comprehensive adaptive approach proves most effective. Concerning coastal erosion, results underscore the prevalence of numerous stretches of eroding low coastline, with future projections indicating scenarios of widespread retreat along the southern Adriatic coast. Consequently, protective, and coastal management measures become imperative. The analyses and the obtained results serves as a valuable knowledge framework essential for formulating a climate change adaptation plan for the Apulian Adriatic coast, aligning with the regional authorities' orientations.

The influence of climatic factors on landslides triggers and displacement rates is a crucial research topic, especially due to the growing need to understand the evolution of climate change in historical periods of intense precipitation and anomalous temperature increases. Italy, highly prone to hydrogeological instability, extremely its mountainous regions such as the Alps, stands as a pertinent subject area for instability scenarios. However, the interpretation of climate effects on landslides is still an open issue. This work proposed a simplified methodology for investigating the displacements of three slow-moving landslides located in the Western Alps of Piemonte region, in response to significant meteorological events evaluated from reference normal of precipitation and temperature trends over the reference period 1991–2020. Another purpose is to emphasize the advantages of using different monitoring techniques by comparing displacement time series measured with in situ and remote sensing instruments, to detect ground deformation processes of these gravitational phenomena. The existence of a robust monitoring network, coupled with InSAR dataset support, has allowed detecting climatic factors’ impact on displacement rates for the outlined case studies. The results have demonstrated the relationships between the identified climatic events and variations in displacement time series, as well as the potential of integrating field observations and InSAR techniques to improve the interpretation of landslide dynamics. Although this study has laid the basis for understanding the influence of climatic factors on landslide displacements, there is still much to investigate and refine. The proposed preliminary analysis will further improve the ability to predict, monitor and mitigate landslide risk under changing climate conditions.

Abstract To effectively navigate out of the climate crisis, a new interdisciplinary approach is needed to guide and facilitate research that integrates diverse understandings of how transitions evolve in intertwined social–environmental systems. The concept of tipping points, frequently used in the natural sciences and increasingly in the social sciences, can help elucidate processes underlying major social–environmental transitions. We develop the notion of interlinked ‘social–climatic tipping points’ in which desirability and intentionality are key constitutive features alongside stable states, feedbacks, reversibility and abruptness. We demonstrate the new insights that our interdisciplinary framework can provide by analysing the slowdown of the Atlantic Meridional Overturning Circulation and associated flooding of the Ahr Valley in Germany as a social–climatic tipping point. This framework can enable more sustainable and equitable futures by prioritising social–climatic tipping points for interdisciplinary research, identifying opportunities for action, and evaluating the nuanced desirability and acceptability of proposed solutions. Read the free Plain Language Summary for this article on the Journal blog.

The Montenegrin climate is characterised as very heterogeneous due to its complex topography. The viticultural heritage, dating back to before the Roman empire, is settled in a Mediterranean climate region, located south of the capital Podgorica, where climate conditions favour red wine production. However, an overall increase in warmer and drier periods affects traditional viticulture. The present study aims to discuss climate change impacts on Montenegrin viticulture. Bioclimatic indices, ensembled from five climate models, were analysed for both historical (1981–2010) and future (2041–2070) periods upon three socio-economic pathways: SSP1-2.6, SSP3-7.0 and SSP5-8.5. CHELSA (≈1 km) was the selected dataset for this analysis. Obtained results for all scenarios have shown the suppression of baseline conditions for viticulture. The average summer temperature might reach around 29.5 °C, and the growing season average temperature could become higher than 23.5 °C, advancing phenological events. The Winkler index is estimated to range from 2900 °C up to 3100 °C, which is too hot for viticulture. Montenegrin viticulture requires the application of adaptation measures focused on reducing temperature-increase impacts. The implementation of adaptation measures shall start in the coming years, to assure the lasting productivity and sustainability of viticulture.

Mangroves play a crucial role in improving the water quality of mangrove wetlands. However, current research faces challenges, such as the difficulty in quantifying the impact of mangroves on water quality and the unclear pathways of influence. This study utilized remote sensing imagery to investigate the long-term changes in mangrove forests in the Futian Mangrove National Nature Reserve and constructed a water quality index based on water quality data. Finally, structural equation modeling was employed to explore the pathways of influence and quantify the impact effects of mangroves, climate, and water quality. The study findings revealed several key points: (1) The mangrove forests in the Futian Mangrove National Nature Reserve exhibited a trend of expansion towards the ocean during this period. (2) The seasonal and annual characteristics of water quality in Shenzhen Bay indicated a significant improvement in water quality from 2000 to 2020. (3) Mangroves have significant direct and indirect impacts on water quality, which are more pronounced than the effects of climate factors. These findings not only offer insights for the environmental management and conservation of Shenzhen Bay but also provide support for future comprehensive studies on the response relationships between the morphology, species, and physiological characteristics of mangroves and water quality.

Local decisionmakers managing forests for sequestering carbon and mitigating disturbance face increasing pressures from climate change, while needing to derive revenue from forests resources and balance diverse stakeholder concerns. Decision support systems (DSS) that use freely available satellite data are effective tools for forest management decision-making, due to the broad spatial coverage and temporal consistency of this data. However, the literature demonstrates that many DSS developed by researchers incorporating satellite data are often not utilized by decision makers. This is in part due to a lack of stakeholder engagement in DSS development. Here, we employ an intentional stakeholder engagement process to develop an accessible DSS incorporating satellite data. This process is applied with the Yurok Tribe – an Indigenous community in California – towards the goal of managing forests for carbon sequestration. We use system architecture framework (SAF) to translate stakeholder objectives into targeted analyses and the environment-vulnerability-decision-technology (EVDT) integrated modeling framework to incorporate contextual local infrastructure and land ownership information alongside satellite data analyses. Satellite data analyses classify tree cover and track Normalized Difference Vegetation Index (NDVI) over time. We compare and show that trends in our analysis agree with independent global tree cover and biomass datasets. These analyses meet the Tribe's objective for tracking forest trends in Yurok carbon sequestration projects, while also detecting undesirable conifer encroachment on culturally-important prairies traditionally managed as open landscapes. Additionally, integration of community infrastructure data alongside satellite-derived drought, forest fire, and smoke density information via EVDT meets another Yurok Tribe objective of identifying negatively impacted Tribal areas for aid allocation. User feedback sessions with participating Yurok Tribe employees assess the prototype DSS and show 80% of users providing high ratings (≥75%) for information accuracy, relevancy, and sufficiency, demonstrating that our DSS development process overcomes previous gaps to DSS use and satellite data accessibility to augment Yurok Tribe forest management decision making. • System architecture translates stakeholder objectives into targeted datasets and analyses. • Satellite data elucidates contrasting canopy and biomass trends based on forest management. • Integrated satellite and local data augments carbon project management and aid allocation. • Feedback sessions show information relevancy, sufficiency, and accessibility for local users. • Design process produces a software system that augments the Yurok Tribe's forest management. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The frequency of hot days in much of the world is increasing. What is the impact of high temperatures on productivity? Can technology-based adaptation mitigate such effects of climate change? We provide some answers to these questions by examining how high outdoor temperatures affect a high-technology, precision manufacturing setting. Exploiting individual-level data on the quantity and quality of work done across 35,190 worker-shifts in a leading NYSE-listed silicon wafer maker in China, we evidence a negative effect of outdoor heat on productivity. The effects are large: in our preferred linear specification, an increase in wet bulb temperature of 10 ∘ C causes a reduction in output of 8.3%. Temperature effects exist even though the manufacturer's work-spaces are indoors and protected by high-quality climate control systems. Results are not driven by extreme weather events and are robust to alternative modelling approaches. They illustrate the potential future adverse economic effects of climate change in most of the industrialised world. [ABSTRACT FROM AUTHOR]

The climate model hierarchy encompasses models of varying complexity along different axes, ranging from idealized models that elegantly describe isolated mechanisms to fully coupled Earth system models that aspire to provide useable climate projections. Based on the second Model Hierarchies Workshop, which took place in 2022, we present perspectives on how this field has evolved since the first Model Hierarchies Workshop in 2016. In this period, we have witnessed a dramatic increase in the use of (a) machine learning in climate modeling and (b) climate models to estimate risks and influence decision making under climate change. Here, we discuss the implications of these growing areas of research and how we expect them to become integrated into the model hierarchies framework. Plain Language Summary: The climate modeling community often describes the variety of models used to understand and simulate climate processes as a hierarchy in complexity. Simple idealized models exist at the bottom of the hierarchy and are useful for explaining underlying physics, while fully coupled Earth system models exist at the top of the hierarchy and aim to provide useable climate projections. We present perspectives on how the model hierarchy field is evolving, focusing on two noticeable changes in recent years. Firstly, models are increasingly using machine learning, and secondly, there has been a growing interest in the usability of climate models, for instance, for estimating risks associated with climate change. Here, we discuss the implications of these growing areas of interest and how we expect them to become integrated into the model hierarchies framework. Key Points: Inspired by the 2022 Model Hierarchies workshop, we present perspectives on how the field is evolvingFirstly, we note the growing interest in the use of machine learning which presents another way to ascend or descend the model hierarchySecondly, we discuss the role of the model hierarchy for actionable climate science and decision‐making [ABSTRACT FROM AUTHOR]

Copyright of People & Nature is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Stratospheric Aerosol Injection (SAI) is a proposed method of climate intervention aiming to reduce the impacts of human‐induced global warming by reflecting a portion of incoming solar radiation. Many studies have demonstrated that SAI would successfully reduce global‐mean surface air temperatures; however the vast array of model scenarios and strategies result in a diverse range of climate impacts. Here we compare two SAI strategies—a quasi‐ equatorial injection and a multi‐latitude off‐equatorial injection—simulated with the UK Earth System Model (UKESM1), both aiming to reduce the global‐mean surface temperature from that of a high‐end emissions scenario to that of a moderate emissions scenario. We compare changes in the surface and stratospheric climate under each strategy to determine how the climate response depends on the injection location. In agreement with previous studies, an equatorial injection results in a tropospheric overcooling in the tropics and a residual warming in the polar regions, with substantial changes to stratospheric temperatures, water vapor and circulation. Previous comparisons of equatorial versus off‐equatorial injection strategies are limited to two studies using different versions of the Community Earth System Model. Our study evaluates how the climate responds in UKESM1 under these injection strategies. Our results are broadly consistent with previous findings, concluding that an off‐equatorial injection strategy can minimize regional surface temperature and precipitation changes relative to the target. We also present more in‐depth analysis of the associated changes in Hadley Circulation and regional temperature changes, and call for a new series of inter‐model SAI comparisons using an off‐equatorial strategy.

Abstract Winter cover crops represent a land cover change that may sequester carbon in the soil and improve agricultural sustainability. Their adoption may also change the Earth's radiative balance and result in biophysical feedbacks to climate through alterations in albedo and latent heat fluxes. Understanding the mechanisms underlying these alterations to the radiative balance is important for making reliable future climate projections. However, data on cover crop biophysics are limited, requiring models to rely on data from summer plants for parameterization, likely biasing predictions. To address this gap, we measured the albedo and stomatal conductance of two summer crops and three winter crops on farms in the High Plains region of Texas. We also established a winter cover crop field experiment with two cover crops and fallow fields to estimate the change in albedo and latent heat flux that results from a switch to winter cover cropping. We found that albedo was significantly higher in winter‐like conditions than in summer‐like conditions due to an increase in plant albedo and a reduction in leaf area index. The albedo of winter cover crops was higher than the soil albedo, resulting in an increase in top‐of‐atmosphere reflected radiation of 7%–14% when converting from fallow fields to winter cover cropped fields. There was an additional cooling effect through doubling of the estimated latent heat flux caused by the presence of cover crops. The combined changes in albedo and latent heat resulted in a change in the surface energy balance that is associated with an overall cooling effect of winter cover crops on surface atmospheric temperatures. While this effect is likely to be region‐specific, these results strongly indicate that winter cover crops alter the surface albedo and latent heat flux of agricultural fields and provide a direct cooling effect in the High Plains region of Texas.

Abstract This paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society.

Summary: Concerns have been raised against using carbon pricing for fighting climate change, as these might disproportionally affect lower-income households and thus increase inequality. However, the distributional implications of climate policies will depend on policy design and their capacity to reduce climate change impacts. To quantify the interaction between climate policy and climate benefits on economic inequality, we develop a numerical climate-economic framework featuring both within- and between-country income heterogeneity. We implement a well-below 2°C target and examine a variety of redistributive schemes. We find that climate change impacts increase economic inequalities within and across countries. Climate policy reduces this increase by half, and an appropriate transfer scheme targeted at low-income households can eliminate it altogether. An equal per-capita redistribution scheme leads to most of the population being economically better off already today. The analysis shows the importance of including climate-economic benefits in the evaluation of climate policies. Science for society statement: Inequality and climate change are two key, and interlinked, challenges for the 21st century. We find that climate policies on their own are likely to increase inequality. A “climate dividend” or redistributing carbon revenues to households, however, can more than offset this inequality increase, allowing for a reduction in global inequality. Because climate impacts are likely to increase in the future, leading to further economic disparities, the inequality-improving effect of attaining Paris Agreement goal of keeping global warming below 2°C increases even further. In essence, the research underscores the importance of considering the impact of climate policies on inequality and demonstrates that well-designed strategies can simultaneously combat climate change and reduce economic disparities. Moreover, we find that the well-below 2°C target can make a large majority of about 58% of the world’s population better off than without strong climate action, already by 2030.

As it is a commentary we do not have a usual abstract Today’s energy systems are critically dependent on water for hydropower and thermoelectric power generation, which relies on a large amount of cooling water. However, even with increasing climatic pressures and growing concerns surrounding energy security, the availability of high-quality integrated water data sets remains poor (1, 2). This is surprising considering broad climate impacts on energy systems worldwide. Numerous regions experienced water-related power generation issues throughout the 2010 s, including coal generation during the 2015 Polish drought, repeated curtailment of French nuclear plants, and hydropower curtailment in the US, Romania, China, Ghana, and Brazil, among others (3). The 2022 heat waves saw broad water-energy issues across the EU, with France altering regulatory requirements to keep nuclear plants running (resulting in higher water temperatures) (4) and hydroelectric curtailment across several countries. There is clear evidence of impacts on energy security today (5) and how this issue is poised to become more acute into the 21st century (6, 7).

The increasing intensity and frequency of climate impacts exacerbate pressures on front-line local communities. This calls for location-specific adaptation strategies. Alignment of strategies with respective National Climate Change Strategy is key for the overall sustainability of initiatives and local communities. The work presented in this paper examines the adoption and prioritization of climate adaptation policies at the local level based on a case study of the Adansi North District (AND) in Ghana. An assessment of the extent to which climate adaptation policies are captured and budgeted for was done via a review of the district’s medium-term development and key political actors were interviewed to assess the level of priority they place on climate adaptation. Findings from the study reveal that 41% of the locally adopted policies directly align with stipulated national level policies. We attribute the adoption of climate policies in AND to local political actors having higher education which has afforded them good understanding of the climate change phenomenon, being experienced professionals and having to work within institutional rubrics that make climate policy formulation a requirement. However, little priority is given to these policies for implementation, mainly through the non-allocation of funds. We account for this with the weak environmental advocacy in the district and exchange between actors on adaptation. Furthermore, partisan actors who already wield veto powers and can promote policies that may not necessarily support adaptation measures, often do so, since their interest is to become popular among electorates who also prefer infrastructure over environmental policies. We conclude that although climate adaptation policies are fairly adopted and budgeted for in AND, they have not received commensurate priority for implementation. Recommendations are proposed for addressing this.

Flooding disasters are occurring more frequently across the globe. Resilience capacity also differs with developing countries less able to withstand the devastating impacts of flooding. The level and type of action to prevent or mitigate flooding is dependent on the prevalent views on its principal drivers. This research engages with experts in the field of flood risk management in Nigeria, Africa's most populous country where the threats and frequency of flooding has increased over the years. An in-depth qualitative case study was conducted with semi-structured interviews used as the primary data collection tool. Understanding and distinguishing the core drivers of flooding according to context is pertinent because in a situation where the drivers are deemed to be factors beyond human control, there is risk of little action. The experts in this research acknowledged the influence of climate change on flooding which has led to increase in rainfall intensity, frequency and duration. However, they attributed the flooding being experienced in the research location to be due to more controllable human factors including lack of infrastructure, poor urban planning and governance, and weak implementation and enforcement of laws and policies. A mix of structural and non-structural solutions were recommended to better manage flooding and achieve the goal of sustainable cities and settlements.