Table of contents

Forest-based measures to mitigate climate change are increasingly being acknowledged as options for meeting the global targets of the Paris Agreement. In this context, carbon pricing systems may foster carbon sequestration in forests and harvested wood products. Forest sector models (FSMs) are established simulation instruments for assessing the possible impacts of carbon pricing systems on forest-based mitigation potentials, forestry, and forest product markets. However, the characteristics of the implemented carbon pricing systems differ among these assessment tools. To map and evaluate this variability, we conducted a scoping review of carbon pricing systems in FSMs, following the RepOrting standards for Systematic Evidence Syntheses (ROSES). Drawing on 49 modeling studies, including 351 scenarios, we provide an overview of the state-of-the-art methods for implementing carbon pricing systems in FSMs, discuss technical aspects and uncertainties, and identify possible future research trends. Our results reveal similarities in the types of carbon pricing systems and differences regarding the system boundaries and carbon price-related characteristics of the implemented systems. Geographically, since most studies target either the Northern Hemisphere or the world, we found a lack of in-depth assessments in tropical and boreal countries. Further, additionality, permanence, and leakage of forest-related mitigation measures are addressed using different approaches with varying practicability. Mostly, the observed heterogeneity in the implemented carbon pricing systems can be related to the attributes of pre-existing modeling frameworks. We systematically collect and highlight tools to analyze the role of forest-based mitigation measures in the context of climate commitments and outline carbon pricing policies that could support their implementation. For future studies, the assessment of policy mixes involving carbon pricing and the inclusion of climate change effects on forest growth appear to be crucial for delivering more robust projections of forest-based mitigation potentials and, hence, for increasing the reliability of the forest-based contribution to climate mitigation actions.

Urban climate-related disaster risks are set to rise, driven by the interaction of two global megatrends: urbanization and climate change. A detailed understanding of whether, where and how cities are growing within or into hazard-prone areas is an urgent prerequisite for assessing future risk trajectories, risk-informed planning, and adaptation decisions. However, this analysis has been mostly neglected to date, as most climate change and disaster risk research has focused on the assessment of future hazard trends but less on the assessment of how socio-economic changes affect future hazard exposure. Urban growth and expansion modeling provide a powerful tool, given that urban growth is a major driver of future disaster risk in cities. The paper reviews the achievements lately made in urban growth and exposure modeling and assesses how they can be applied in the context of future-oriented urban risk assessment and the planning of adaptation measures. It also analyses which methodological challenges persist in urban growth and exposure modeling and how they might be overcome. These points pertain particularly to the need to consider and integrate (1) urban morphology patterns and potential linkages to exposure as well as vulnerability, (2) long-term time horizons to consider long-term developments, (3) feedbacks between urbanization trajectories and hazard trends, (4) the integration of future urban growth drivers and adaptation responses, (5) feedbacks between adaptation and urbanization, and (6) scenarios, which are developed within a commonly defined scenario framework.

Brown algae blooms and invasions have affected 29% of the Earth's coast, yet there is sparse evidence of the impacts and adaptations of these events. Through a systematic review of empirical literature on these blooms and invasions, we explore the prevalence of conventional analyses of environmental, economic, and social impacts, as well as opportunities for adaptation and valorisation. The study reveals crucial inconsistencies in the current evidence base on algae impacts: fragmented metrics for quantifying blooms and their effects; inconsistent application and testing of prevention measures (e.g. forecasting, early warning systems); reliance on removal as a management approach with limited evidence of associated costs; and scant evidence of the effectiveness of impact mitigation or adaptation strategies. With a focus on economic and societal dimensions of algae events, we introduce emerging opportunities within the blue economy for bloom utilization. The findings highlight the crucial need for harmonized monitoring protocols, robust cost-benefit analysis of management and adaptation options, and evidence of pathways to valorisation of algae biomass.

The digital sharing economy is commonly seen as a promising circular consumption model that could potentially deliver environmental benefits through more efficient use of existing product stocks. Yet whether sharing is indeed more environmentally benign than prevalent consumption models and what features shape platforms' sustainability remains unclear. To address this knowledge gap, we conduct a systematic literature review of empirical peer reviewed and conference proceeding publications. We screen over 2200 papers and compile a dataset of 155 empirical papers, and consolidate reported results on the environmental impacts of the sharing economy. We find that sharing is not inherently better from an environmental perspective. The type of resource shared, logistic operations, and the ways in which sharing influences users' consumption more broadly affect environmental outcomes. Sharing goods is generally associated with better environmental outcomes compared to shared accommodations or mobility. Within mobility, shared scooters and ride-hailing emerge as particularly prone to negative environmental outcomes. Contrary to previous suggestions, peer-to-peer sharing (vs. centralized ownership) does not seem to be a good proxy for environmental performance. As sharing becomes intertwined with urbanization, efforts to steer digital sharing towards environmental sustainability should consider system levels effects and take into account platform operations as well as potential changes in consumer behavior.

Agricultural practices that both support climate change mitigation and facilitate adaptation to a changing climate are critical for reducing greenhouse gas emissions while ensuring food security. This need has led to many claims regarding the potential for a variety of agricultural practices to achieve synergies between mitigation and adaptation in agriculture. However, the evidence for climate change mitigation and adaptation synergies in agriculture remains mixed. To evaluate such claims, we examined the evidence for these synergies by conducting a systematic review of peer-reviewed literature that make claims about outcomes for both climate change adaptation and mitigation in agriculture. Based on 87 articles identified, we show that synergistic outcomes are claimed more frequently than tradeoffs for all practices, yet the evidence was stronger for mixed and conflicting outcomes than for synergies. Indeed, claims of synergistic outcomes may be overstated, because these publications more often relied on secondary data rather than empirically evaluating adaptation and mitigation outcomes. We also show important gaps in the consideration and assessment of climate change adaptation and mitigation objectives and outcomes. This review highlights the critical need for more robust research, evidence, and evaluation of the adaptation and mitigation outcomes of agricultural practices, and the need to clarify the contexts of such results, in order to effectively support policies and practices that aim to promote synergistic outcomes and avoid conflicting outcomes.

Given the importance of El Niño–Southern oscillation (ENSO) teleconnection on the Southeast Asia (SEA) climate, the ENSO-induced precipitation and near-surface air temperature anomalies over SEA and its twenty sub-regions are compared between historical (1985–2014) and future (2070–2099) simulations using 30 models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Future projections suggest that the Philippines, Malay Peninsula, most of the Maritime Continent, and southern Indochina experience reduced (increased) precipitation in the future El Niño (La Niña) summer. Then, during autumn, amplification of ENSO-precipitation teleconnection is projected in the Borneo, Malay Peninsula, and northern Vietnam, raising flood concerns in these sub-regions in future La Niña autumn. During winter, projected ENSO-driven negative anomalies continue intensifying and shifting northeastward, resulting in drier (wetter) conditions for the Philippines and surrounding areas in future El Niño (La Niña). Conversely, a southeastward shift of ENSO-driven precipitation anomalies is projected in the following spring, leading to dampening (an amplification) of teleconnection over the western (eastern) part of SEA. Regarding near-surface air temperature, a 'land-sea contrast' pattern is seen, in which intensified ENSO-driven positive (negative) anomalies are projected over land (ocean). At the sub-region scale, robust amplifications in the ENSO teleconnection are mainly observed when only considering the land temperature. The most noticeable future changes are robust amplification of the ENSO-driven positive temperature anomalies in northern Indochina and Myanmar during winter. These sub-regions typically experience a cooler winter, suggesting that wintertime mean temperature there may be much higher under future El Niño conditions. The projected changes in ENSO-driven precipitation and near-surface air temperature anomalies both appear to scale with the radiative forcing, i.e. a higher radiative forcing corresponds to higher teleconnection changes and more sub-regions of SEA experience robust changes. These results suggest that significant ENSO teleconnection changes can be mitigated by minimizing future warming.

Previous studies have demonstrated an association between short-term exposure to ambient temperature and mortality. However, the long-term effects of elevated temperature and temperature variability on mortality have remained somewhat elusive in epidemiological studies. We conducted a comprehensive epidemiological study utilizing Chinese population census data from 2000 and 2010. Census-derived demographic and socioeconomic factors were paired with temperature data from the European Re-Analysis Land Dataset across 2823 counties. We employed a difference-in-difference approach to quantitatively examine the relationship between all-cause mortality and annual exposure to mean temperature and diurnal temperature range (DTR). Additionally, we evaluated the potential effects of socioeconomic and environmental covariate modifications on this relationship and calculated the attributable mortality. Lastly, we projected excess deaths attributable to annual temperature exposure under various shared socioeconomic pathways (SSPs, e.g. SSP126, SSP370, and SSP585). For each 1 °C rise in annual mean temperature and DTR, the mortality risk could increase by 6.12% (95% CI: 0.84%, 11.69%) and 7.72% (95% CI: 3.75%, 11.84%), respectively. Counties with high labor-force ratios and high NO₂ and O₃ concentrations appeared to be sensitive to the annual mean temperature and DTR. Climate warming from 2000 to 2010 may have resulted in 5.85 and 14.46 additional deaths per 10 000 people attributable to changes in annual mean temperature and DTR, respectively. The excess mortality related to changes in annual mean temperature and DTR is expected to increase in the future, with special attention warranted for long-term temperature changes in Southwest China. Our findings indicate that long-term mean temperature and DTR could significantly impact mortality rates. Given the spatial heterogeneity of increased mortality risk, the formulation of region-specific strategies to tackle climate change is crucial.

Over the past decade, California has experienced two multiyear droughts, resulting in water insecurity for communities and significant economic losses for the agricultural sector. Despite the recognition of water as a human right in the state since 2012, droughts consistently lead to the failure of thousands of domestic wells due to intensified groundwater pumping for irrigation purposes. In the Central Valley alone, groundwater sustains the livelihoods of thousands of individuals (and millions across the state) serving as their sole water source, rendering them vulnerable due to inadequate groundwater management. In this study, we present a spatial statistical model to identify critical localized factors within the food-water-human system that contribute to the vulnerability of domestic wells during droughts. Our results indicate that the depth of domestic wells, density of domestic and agricultural wells, socioeconomic conditions, and the extent of perennial crops play significant roles in predicting well failures during droughts. We show the implications of addressing these factors within the context of ongoing groundwater sustainability initiatives, and we propose strategies to safeguard the water source for thousands of individuals necessary to protect domestic wells.

Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the community atmosphere model, we explore how Irma's precipitation would change under various levels of climate warming. Analysis is focused on a 48 h period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3–7% K⁻¹ compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10–13% K⁻¹ and 17–21% K⁻¹, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16–26% K⁻¹, with local maxima increasing by 18–43% K⁻¹. All percent changes increase monotonically with warming level.

Hydroclimatic stresses can negatively impact crop production via water deficits (low soil water supply and high atmospheric demand) or surpluses (high soil water supply and low atmospheric demand). However, the impact of both stresses on crop yields at regional scales is not well understood. Here we quantified yield sensitivities and corresponding spatio-temporal yield losses of US rainfed maize, soybeans, sorghum, and spring wheat to hydroclimatic stresses by considering the joint impacts of root-zone soil moisture and atmospheric evaporative demand from 1981 to 2020. We show that crop yields can be reduced similarly by two major hydroclimatic hazards, which are defined as the most yield damaging conditions over time: 'Low Supply + High Demand' and 'High Supply + Low Demand'. However, more exposure to 'Low Supply + High Demand' hazard led to the largest annual yield losses (7%–17%) across all four crops over time. Modeled yield losses due to these hazards were significantly associated with crop insurance lost costs. The extent of yield losses varies considerably by crop and location, highlighting the need for crop-specific and regionally tailored adaptation strategies.

In 2019, the Government of India launched the National Clean Air Program to address the pervasive problem of poor air quality and the adverse effect on public health. Coordinated efforts to prevent agricultural burning of crop residues in Northwestern IGP (Indo-Gangetic Plain) have been implemented, but the practice is rapidly expanding into the populous Eastern IGP states, including Bihar, with uncertain consequences for regional air quality. This research has three objectives: (1) characterize historical rice residue burning trends since 2002 over space and time in Bihar State, (2) project future burning trajectories to 2050 under 'business as usual' and alternative scenarios of change, and (3) simulate air quality outcomes under each scenario to describe implications for public health. Six future burning scenarios were defined as maintenance of the 'status quo' fire extent, area expansion of burning at 'business as usual' rates, and a Northwest IGP analogue, of which both current rice yields and plausible yield intensification were considered for each case. The Community Earth System Model (CESM v2.1.0) was used to characterize the mid-century air quality impacts under each scenario. These analyses suggest that contemporary Bihar State burning levels contribute a small daily average proportion (8.1%) of the fine particle pollution load (i.e. PM_2.5, particles ⩽2.5 μm) during the burning months, but up to as much as 62% on the worst of winter days in Bihar's capital region. With a projected 142% 'business as usual' increase in burned area extent anticipated for 2050, Bihar's capital region may experience the equivalent of 30 PM_2.5 additional exceedance days, according to the WHO standard (24 h; exceedance level: 15 µg m⁻³), due to rice residue burning alone in the October to December period. If historical burning trends intensify and Bihar resembles the Northwest States of Punjab and Haryana by 2050, 46 d would exceed the WHO standard for PM_2.5 in Bihar's capital region.

To reduce the overaccumulation of carbon dioxide (CO₂) in the atmosphere, direct air CO₂ capture (DACC) technologies must (a) satisfy the process requirements for heat and electricity with energy that has few if any CO₂ emissions, and (b) physically isolate the CO₂ from the atmosphere after its extraction from the air. To isolate the CO₂ from the atmosphere at meaningful scale, the CO₂ will likely need to be geologically stored in deep saline aquifers. Here we propose to leverage geologic CO₂ storage (GCS) in sedimentary basin geothermal resources to produce geothermal heat and electricity for the process energy requirements of solid sorbent DACC. This sedimentary basin CO₂-driven geothermal utilization (SB-CO₂DGU, also known as CO₂ Plume Geothermal) circulates some of the emplaced CO₂ to extract geothermal heat in a closed loop between the subsurface reservoir and surface geothermal facility. The proposed integration of DACC and CO₂-driven geothermal Utilization and Storage (DACCUS) adds CO₂ from the air to this closed loop system that produces renewable energy for use in the DACC process. The strategy first primes the GCS reservoir with CO₂ from large point sources, and then integrates CO₂ from DACC facility to form the DACCUS system. We focus on the process integration of DACCUS and present a case study of its potential deployment and scaling in the Gulf Coast of the United States. We combine data from prior analyses for a novel investigation of two DACCUS configurations: (1) a DACCUS heat system uses the geothermal heat to regenerate the solid sorbent in the DACC process, and (2) a DACCUS heat and power system uses the electricity generated from the produced geothermal heat for the DACC process. In general, deeper CO₂ storage reservoirs (>3.5 km) with higher geothermal temperature gradients (>35 °C km⁻¹), may provide sufficient production wellhead temperatures (>100 °C), and satisfy the electric load in 93% of the combinations of reservoir characteristics we examined.

Robust carbon monitoring systems are needed for land managers to assess and mitigate the changing effects of ecosystem stress on western United States forests, where most aboveground carbon is stored in mountainous areas. Atmospheric carbon uptake via gross primary productivity (GPP) is an important indicator of ecosystem function and is particularly relevant to carbon monitoring systems. However, limited ground-based observations in remote areas with complex topography represent a significant challenge for tracking regional-scale GPP. Satellite observations can help bridge these monitoring gaps, but the accuracy of remote sensing methods for inferring GPP is still limited in montane evergreen needleleaf biomes, where (a) photosynthetic activity is largely decoupled from canopy structure and chlorophyll content, and (b) strong heterogeneity in phenology and atmospheric conditions is difficult to resolve in space and time. Using monthly solar-induced chlorophyll fluorescence (SIF) sampled at ∼4 km from the TROPOspheric Monitoring Instrument (TROPOMI), we show that high-resolution satellite-observed SIF followed ecological expectations of seasonal and elevational patterns of GPP across a 3000 m elevation gradient in the Sierra Nevada mountains of California. After accounting for the effects of high reflected radiance in TROPOMI SIF due to snow cover, the seasonal and elevational patterns of SIF were well correlated with GPP estimates from a machine-learning model (FLUXCOM) and a land surface model (CLM5.0-SP), outperforming other spectral vegetation indices. Differences in the seasonality of TROPOMI SIF and GPP estimates were likely attributed to misrepresentation of moisture limitation and winter photosynthetic activity in FLUXCOM and CLM5.0 respectively, as indicated by discrepancies with GPP derived from eddy covariance observations in the southern Sierra Nevada. These results suggest that satellite-observed SIF can serve as a useful diagnostic and constraint to improve upon estimates of GPP toward multiscale carbon monitoring systems in montane, evergreen conifer biomes at regional scales.

Peatland permafrost landforms, such as palsas and peat plateaus, often represent the most southern lowland permafrost occurrences in the Northern Hemisphere. While peatland permafrost is often found in continental conditions, over a thousand permafrost peatlands were recently identified along the previously understudied coastline of the Labrador Sea in northeastern Canada. The vulnerability of these landscapes to thaw is unknown but is expected to have hydrological and ecological impacts on important caribou habitat, the abundance of culturally relevant berries, and permafrost carbon storage. Using a combination of aerial photography (from 1948, 1985, 1992, 1994, and 2021) and high-resolution satellite imagery (from 2017, 2020, and 2021), we assess multi-decadal areal changes to peatland permafrost landforms at seven peatlands along the Labrador Sea coastline spanning from Red Bay (51.7° N) to north of Hopedale (55.7° N). Analyses reveal declines in permafrost extent of 33%–93% at individual sites, occurring at mean rates of 0.8%–1.5%/a. Permafrost loss was found to occur most rapidly at mixed palsa and peat plateau sites (mean rate of 1.4%/a), followed by palsa sites (mean rate of 1.2%/a) and peat plateau sites (mean rate of 0.9%/a). Patterns of permafrost loss also differed between landform types, with more complete loss of individual landforms at palsa sites and more lateral and internal loss of existing landforms at peat plateau and mixed sites. This widespread degradation of peatland permafrost over the past 28–73 years is attributed to regional warming and peatland greening. Understanding recent change to permafrost peatlands in coastal Labrador is an important step towards predicting future habitat change in northeastern Canada and will inform regional land management in areas dominated by these culturally important landforms.

Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model's performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study's results will help in further optimising WRF parameters to improve model simulation.

Global warming and sea level rise (SLR) not only increase the intensity and frequency of coastal hazards but also complicate associated dynamics. The exacerbated saltwater intrusion in this context will further be adversely affected by storms with deepening distances and growing duration, aside from the simultaneous coastal flooding they cause. Here, we investigate storm-induced saltwater intrusion and its responses to SLR in the Pearl River Estuary by numerical simulation. Predominant in competition with river runoffs, typhoons passing by cause fast stratification and dramatic increase of saltwater intrusion lengths via wind mixing. Stronger destratification and longer recovery time are linked to a narrow long channel, where the tidal excursion is weak owing to bay/channel-shape modulation. The rising sea levels enhance the tidal prism and shift the saline water universally to the upper reaches, and this impact tends to be amplified in the upper part of the bays owing to the narrowing bay shape and shoaling bathymetry. The saltwater intrusion length could be expressed as a linear relationship with the water level, but with divergent responses to storms, depending on bay/channel shapes. Amplification of saline intrusion is indicated in the channel-shaped estuary, but the farthest distance during a storm is less sensitive to SLR than in a bell-shaped estuary. The present study reveals the potential importance of storm-induced compound hazards to coastal communities, and highlights the notably specific salinity responses whereby tributary morphology.

Reduction of total phosphorus (TP) loads has long been a management focus of Chesapeake Bay restoration, but riverine monitoring stations have shown mixed temporal trends. To better understand the regional patterns and drivers of TP trends across the Bay watershed, we compiled and analyzed TP load data from 90 non-tidal network stations using clustering and random forest (RF) approaches. These stations were categorized into two distinct clusters of short-term (2013–2020) TP load trends, i.e. monotonic increase (n = 35) and monotonic decline (n = 55). RF models were developed to identify likely regional drivers of TP trend clusters. Reductions in point sources and agricultural nonpoint sources (i.e. fertilizer) both contributed to water-quality improvement in our period of analysis, thereby demonstrating the effectiveness of nutrient management and the importance of continuing such efforts. In addition, declining TP trends have a larger chance to occur in carbonate areas but a smaller chance in Coastal Plain areas, with the latter likely reflecting the effect of legacy P. To provide spatially explicit information, TP trend clusters were predicted for the entire watershed at the scale of river segments, which are more directly relevant to watershed planning. Among the 975 river segments, 544 (56%) and 431 (44%) were classified as 'monotonic increase' and 'monotonic decrease', respectively. Furthermore, these predicted TP trend clusters were paired with our previously published total nitrogen (TN) trend clusters, showing that TP and TN both declined in 185 segments (19%) and neither declined in 337 segments (35%). Broadly speaking, large-scale nutrient reduction efforts are underway in many regions to curb eutrophication. Water-quality responses and drivers may differ among systems, but our work provides important new evidence on the effectiveness of management efforts toward controlling point and nonpoint sources.

The Inflation Reduction Act (IRA) is regarded as the most prominent piece of federal climate legislation in the U.S. thus far. This paper investigates potential impacts of IRA on the power sector, which is the focus of many core IRA provisions. We summarize a multi-model comparison of IRA to identify robust findings and variation in power sector investments, emissions, and costs across 11 models of the U.S. energy system and electricity sector. Our results project that IRA incentives accelerate the deployment of low-emitting capacity, increasing average annual additions by up to 3.2 times current levels through 2035. CO₂ emissions reductions from electricity generation across models range from 47%–83% below 2005 in 2030 (68% average) and 66%–87% in 2035 (78% average). Our higher clean electricity deployment and lower emissions under IRA, compared with earlier U.S. modeling, change the baseline for future policymaking and analysis. IRA helps to bring projected U.S. power sector and economy-wide emissions closer to near-term climate targets; however, no models indicate that these targets will be met with IRA alone, which suggests that additional policies, incentives, and private sector actions are needed.

We apply climate attribution techniques to sea surface temperature time series from five regional North Pacific ecosystems to track the growth in human influence on ocean temperatures over the past seven decades (1950–2022). Using Bayesian estimates of the Fraction of Attributable Risk (FAR) and Risk Ratio (RR) derived from 23 global climate models, we show that human influence on regional ocean temperatures could first be detected in the 1970s and grew until 2014–2020 temperatures showed overwhelming evidence of human contribution. For the entire North Pacific, FAR and RR values show that temperatures have reached levels that were likely impossible in the preindustrial climate, indicating that the question of attribution is already obsolete at the basin scale. Regional results indicate the strongest evidence for human influence in the northernmost ecosystems (Eastern Bering Sea and Gulf of Alaska), though all regions showed FAR values > 0.98 for at least one year. Extreme regional SST values that were expected every 1000–10 000 years in the preindustrial climate are expected every 5–40 years in the current climate. We use the Gulf of Alaska sockeye salmon fishery to show how attribution time series may be used to contextualize the impacts of human-induced ocean warming on ecosystem services. We link negative warming effects on sockeye fishery catches to increasing human influence on regional temperatures (increasing FAR values), and we find that sockeye salmon migrating to sea in years with the strongest evidence for human effects on temperature (FAR ⩾ 0.98) produce catches 1.4 standard deviations below the long-term log mean. Attribution time series may be helpful indicators for better defining the human role in observed climate change impacts, and may thus help researchers, managers, and stakeholders to better understand and plan for the effects of climate change.

To forecast wind power generation in the scale of years to decades, outputs from climate models are often used. However, one major limitation of the data projected by these models is their coarse temporal resolution—usually not finer than three hours and sometimes as coarse as one month. Due to the non-linear relationship between wind speed and wind power, and the long forecast horizon considered, small changes in wind speed can result in big changes in projected wind power generation. Our study indicates that the distribution of observed 10 min wind speed data is relatively well preserved using three- or six-hourly instantaneous values. In contrast, daily or monthly values, as well as any averages, including three-hourly averages, are almost never capable of preserving the distribution of the underlying higher resolution data. Assuming that climate models behave in a similar manner to observations, our results indicate that output at three-hourly or six-hourly temporal resolution is high enough for multi-decadal wind power generation forecasting. In contrast, wind speed projections of lower temporal resolution, or averages over any time range, should be handled with care.

Global Integrated Assessment Models (IAMs) used to characterise mitigation pathways have very limited or no formal representation of lifestyles and lifestyle change. We demonstrate a novel approach to endogenously simulating low-carbon lifestyle heterogeneity and lifestyle change through soft-coupling with our new empirically-based LIFE model. Coupling LIFE to global IAMs enables dynamic simulation of distinctive lifestyle change contributions to targeted mitigation strategies. We set out the empirical basis of the LIFE model, the methodological steps for soft-coupling to a global IAM, and show results from a test application to the residential sector using the MESSAGEix-Buildings model. A first key insight is that coupling with the LIFE model introduces heterogeneous behaviour between 'engaged' types, who experience faster and higher reductions in final energy demand compared to 'disengaged' types. When we further simulate a widespread shift in normative values, this gap is closed. A second key insight is that drivers of lifestyle change, act differently across 'Improve' and 'Avoid' dimensions. The 'disengaged' types, characterised by lower incomes, are more highly responsive to energy saving 'Avoid' behaviours. Our approach demonstrates how improved understanding of lifestyle change dynamics and more realistic, empirically-based quantitative simulations in climate mitigation pathways enriches scientific and policy analysis of how to achieve Paris Climate Agreement goals.

Despite the urgent need, very few methods are able to efficiently remove methane from waste air with low cost and energy per unit volume, especially at the low concentrations found in emissions from e.g. wastewater treatment, livestock production, biogas production and mine ventilation. We present the first results of a novel method based on using chlorine atoms in the gas phase, thereby achieving high efficiency. A laboratory prototype of the methane eradication photochemical system (MEPS) technology achieves 58% removal efficiency with a flow capacity of 30 l min⁻¹; a reactor volume of 90 l; UV power input at 368 nm of 110 W; chlorine concentration of 99 ppm; and a methane concentration of 55 ppm; under these conditions the apparent quantum yield (AQY) ranged from 0.48% to 0.56% and the volumetric energy consumption ranged from 36 to 244 kJ m⁻³. The maximum achieved AQY with this system was 0.83%. A series of steps that can be taken to further improve performance are described. These metrics show that MEPS has the potential to be a viable method for eliminating low-concentration methane from waste air.

The intensities and occurrences of heat extremes are projected to increase in a warmer climate, and relevant policies have been established to address different warming levels. However, how climate extremes change at regional warming levels is not well-known because changes in temperature vary over different regions. This study investigated climate extremes and population exposure to these extremes at regional and global 1.5 °C or 2.0 °C warming over 58 reference regions with 16 Coupled Model Intercomparison Project, 6th phase models. The years of reaching local 1.5 °C or 2.0 °C warming occurred earlier than the timing of global warming over certain land areas, with more than 30 years advance in northern high latitude land areas. Heat extremes are projected to increase in all reference regions under regional and global 1.5 °C or 2.0 °C warming. Moving from regional to global 1.5 °C or 2.0 °C warming, heat extremes were found to increase over most land areas, especially over mid- and high-latitude areas. Population exposure to climate extremes increased over more than half the land regions under regional to global 1.5 °C or 2.0 °C warming. Changes in population exposure to absolute heat extremes were mainly generated by changes in population over about 34 land regions, whereas changes in population exposure to percentile-based heat extremes over more than 40 land regions were mostly due to changes in climate extremes. These results provided references to establish relevant strategies at regional scale to address possible risks related to climate extremes.

This study investigates the sensitivity of the boreal winter prediction skill of Community Atmosphere Model 5 to the choice of the dynamical core. Both finite volume (FV) and spectral element (SE) dynamical cores are tested. An additional FV with the SE topography (FV_SE) is also conducted to isolate the possible influence of the topography. The three dynamical core experiments, which ran from 2001/2002–2017/2018, are validated using Japanese 55 year reanalysis data. It turns out that the SE (−4.27 °C) has a smaller cold bias in boreal-winter surface air temperature (SAT) than the FV (−5.17 °C) and FV_SE (−5.29 °C), particularly in North America, East Asia, and Southern Europe/Northern Africa. Significant North Atlantic Oscillation-like biases are also identified in the mid-troposphere. These biases affect seasonal prediction skills. Although the overall prediction skills of boreal-winter SAT, quantified by the anomaly correlation coefficient (ACC), and root-mean-square error (RMSE), are reasonably good (ACC = 0.40 and RMSE = 0.47 in the mean values of SE, FV, and FV_SE), they significantly differ from one region to another, depending on the choice of dynamical cores. For North America and Southern Europe/Northern Africa, SE shows better skills than FV_SE and FV. Conversely, in East Asia, FV and FV_SE outperform SE. These results suggest that the appropriate choice of the dynamical cores and the bottom boundary conditions could improve the boreal-winter seasonal prediction on a regional scale.

The changing thermal state of permafrost is an important indicator of climate change in northern high latitude ecosystems. The seasonally thawed soil active layer thickness (ALT) overlying permafrost may be deepening as a consequence of enhanced polar warming and widespread permafrost thaw in northern permafrost regions (NPRs). The associated increase in ALT may have cascading effects on ecological and hydrological processes that impact climate feedback. However, past NPR studies have only provided a limited understanding of the spatially continuous patterns and trends of ALT due to a lack of long-term high spatial resolution ALT data across the NPR. Using a suite of observational biophysical variables and machine learning (ML) techniques trained with available in situ ALT network measurements (n = 2966 site-years), we produced annual estimates of ALT at 1 km resolution over the NPR from 2003 to 2020. Our ML-derived ALT dataset showed high accuracy (R² = 0.97) and low bias when compared with in situ ALT observations. We found the ALT distribution to be most strongly affected by local soil properties, followed by topographic elevation and land surface temperatures. Pair-wise site-level evaluation between our data-driven ALT with Circumpolar Active Layer Monitoring data indicated that about 80% of sites had a deepening ALT trend from 2003 to 2020. Based on our long-term gridded ALT data, about 65% of the NPR showed a deepening ALT trend, while the entire NPR showed a mean deepening trend of 0.11 ± 0.35 cm yr⁻¹ [25%–75% quantile: (−0.035, 0.204) cm yr⁻¹]. The estimated ALT trends were also sensitive to fire disturbance. Our new gridded ALT product provides an observationally constrained, updated understanding of the progression of thawing and the thermal state of permafrost in the NPR, as well as the underlying environmental drivers of these trends.

Decreasing soil moisture and increasing frequency and intensity of soil drought episodes are among the frequently discussed consequences of ongoing global climate change. To address this topic, a water balance model SoilClim forced by climate reanalysis ERA5-Land was applied on a global scale to analyze the spatiotemporal variability of changes in soil moisture anomalies. The results revealed that the soil relative available water (AWR) significantly decreased on 31.1% of global non-glaciated land and significantly increased on 5.3% of such global non-glaciated land in 1981–2021. Decreasing AWR trends were detected over all continents and were particularly pronounced in South America, which experienced significant drying on more than half of the continent. The main drought 'hotspots' were identified in equatorial Africa, a large part of South America, the Midwest United States, and in a belt extending from eastern Europe to eastern Asia. A seasonal analysis of region-specific patterns further suggested drying in Europe in summer but an absence of a drying trend in winter. These results were supported by an analysis of the area affected by percentile-based drought on individual continents, revealing statistically significant increasing trends of 5th- and 10th-percentile droughts on all continents except Australia at an annual scale. Nevertheless, summer and autumn drought frequency increases were also detected in Australia. The seasonal trends were the most rapid in South America and Europe (except in winter). The distributions of AWR values, evaluated by Z scores, shifted remarkably toward drier conditions during the 2001–2021 period, particularly in South America and Asia. These results underscore the alarming increase in soil drought on a global scale, highlighting the need for effective drought management strategies.

In recent years, the global trade in liquefied natural gas (LNG) has experienced significant growth, leading to a rise in the effect of embodied methane (CH₄) emissions between economies. This study investigates the spatiotemporal evolution of these CH₄ emissions embodied within the global LNG trade and examines the associated network characteristics between the years 2011 and 2021. The findings reveal a substantial increase of 43.3% CH₄ emissions embodied in global LNG trade, reaching a peak of 2.75 Tg in 2021, which equates to a monetary value exceeding 5 billion USD in terms of natural gas. Over the study period, these emissions aggregated to a total of 1987.92 Mt CO₂-eq and 718.06 Mt CO₂-eq, based on the respective global warming potential values over 20 year and 100 year timeframes. Our investigation of this complex network reveals the emergence of multiple robust hub economies, which have exerted significant influence over the dynamics of supply-demand in embodied CH₄ trade, as well as the structure of trade communities. Notably, the Asia-Pacific trading community has exhibited exceptional growth and prominence in this market. Such growth is directly related to an increase in embodied CH₄ emissions and their overall standing within this community's network. The US has steadily attained dominance within an export-oriented community, which encompasses economies in South and North America, as well as certain regions of Europe. Moreover, the redistribution of LNG-related CH₄ emissions among economies is significantly impacted by the intensity of production emissions and the volume of LNG trade. This reveals the potential of these hub economies to drive substantial reductions in CH₄ emissions by implementing targeted energy and climate policies, which they have launched. Reinforcing coalitions and fostering closer collaboration within these communities can provide a robust foundation for technological advancements and transformative changes in trade structures.

Urban vegetation experiences multiple natural and human impacts during urbanization, including land conversion, local environmental factors, and human management, which may bring positive or negative impacts on vegetation gross primary productivity (GPP) at multiple scales. In this study, we analyzed the spatial-temporal changes of GPP and three urbanization factors: land urbanization (impervious surface coverage), population urbanization (Population), and economic urbanization Gross domestic product (GDP) at city-district-grid scales in Beijing during 2000–2018. Overall, both GPP and three urbanization factors showed an increased trend. The relationships between GPP and urbanization factors exhibit diverse characteristics at multiple scales: unlike the linear relationship observed at city scale, the relationships at district and grid scales all demonstrated nonlinear relationship, even a U shape between GPP and population/GDP. Furthermore, the positive impact of urbanization on GPP increased and offset the negative impact of land conversion from 9.9% in 2000 to 35% in 2018, indicating that urban management and climate during urbanization effectively promote vegetation photosynthesis and neutralize the negative impact of urban area expansion. Our findings highlight the increased growth offset by urbanization on vegetation and the importance of analysis at a finer scale. Understanding these urbanization types' impact on vegetation is pivotal in formulating comprehensive strategies that foster sustainable urban development and preserve ecological balance.

The transition to solar-powered irrigation in South Asia offers an opportunity to cut greenhouse gas emissions and reduce dependency on expensive diesel. However, appropriate institutional and financial models are required to scale up this technology. Three different solar irrigation pump (SIP) implementation modalities coexist in Bangladesh, providing a good opportunity to evaluate and gain insightful knowledge on the solarization process. These conclusions are also applicable to neighboring countries dealing with comparable problems. The three models are (i) community-managed SIP model, (ii) individual ownership model, and (iii) fee-for-service model. In this article, we argue that the fee-for-service model involving a market-based approach and public-private partnership is the most promising in terms of addressing two main challenges in solarization, i.e. high capex financing requirement and generation of sufficient demand. In terms of achieving equity in SIP access and groundwater sustainability, all three models have their respective pros and cons. However, the financial sustainability of SIPs is under threat due to the significant project costs. It is imperative to expedite the integration of SIPs with the national power grid while implementing supportive government policies. This includes enhancing buy-back tariffs and introducing net-metering options to ensure long-term sustainability.

The Northeast Pacific Ocean (NEP) is one of the hotspots of marine heatwaves (MHWs) occurring in the global ocean. The causes of MHWs in this region have been widely investigated, but the physical processes underlying heatwaves and regional climate variability remain under debate. By analyzing interannual large-scale high-latitude atmospheric dynamics and oceanic physical conditions over the NEP, we show that winter-spring sea surface temperature (SST) anomalies are strongly correlated with winter-spring atmospheric blocking events over Alaska. The occurrence of weaker westerly wind over the subarctic region over the NEP during the period of the blocking, accompanies a shallower vertical mixed layer, less southward horizontal Ekman transport, and higher SST in the upper NEP. These findings establish a linkage between high-latitude atmospheric dynamics and subarctic oceanic conditions and reveal the physical mechanisms of this connection, providing new insight into the possible causes of MHW in the NEP.

In this study, we investigate and forecast the impact of crop production shocks on the global prices of three major international agricultural commodities: maize, soybean, and cocoa. We perform a thorough assessment of the forecasting performances of five econometric and machine learning models using 60 years of data. First, we train the models on production and price data to forecast the monthly price variations for each crop separately considering different time horizons. Next, we implement a cross-validation procedure to identify the models with the most accurate forecasting ability for each crop. After choosing the best forecaster, we identify the most influential producing areas using several local and global model-agnostic interpretation tools. Our findings indicate significant differences among commodities in terms of prediction accuracy, with cocoa exhibiting a higher level of prediction error compared to less volatile markets like maize and soybean. Our results reveal a significant influence of Northern America's maize and soybean production on the global prices of these commodities. The effects of production on prices are asymmetrical: small decreases in US production lead to substantial price increases, while small increases in production do not systematically decrease prices. In contrast, cocoa price variations are influenced by production coming from several regions, not from a single one.

This paper analyses how climate change might impact EU agricultural markets by mid-century, considering a large ensemble of climate change projections from different models, market adjustments and trade feedbacks. Applying consistent climate change driven productivity shocks to a global multi-commodity agricultural market model we show that the negative direct effects from climate change on crop production in the EU could be offset by market and trade adjustments. The simulations reveal that climate change has heterogeneous impacts across regions. EU farming sector, in particular, might actually benefit from climate change as the impacts on agricultural productivity are expected to be more severe in important non-EU production regions such as US, Russia and Ukraine, depending on the crop. Higher producer prices for important crops such as wheat, barley, grain maize, rice and soybeans, lead to an increase in EU production and exports. For instance EU wheat production could increase by 13% and exports by 28%, with 19% higher farm incomes on average than in a business-as-usual situation. Our study has several limitations. In particular, we do not consider CO2 fertilization effects and direct effect from climate change on livestock sector, climate variability and extreme weather effects. Notwithstanding, our findings highlight the heterogeneity of climate change impacts across regions, specifically Northern versus Southern Europe, and the importance of market and trade adjustments as economic adaptation mechanisms to climate change.

A record-breaking heatwave event occurred in North China from 22 to 24 June 2023, with temperatures >40 °C at many meteorological stations. This marked the first time that Beijing had reached or exceeded 40 °C for three consecutive days. However, the extent to which such exceptional heatwave events are related to anthropogenic climate change remains unclear. It is also unclear how frequent and intense such strong heatwave events will be in the future. We carried out a rapid attribution analysis to address these questions. Our findings show that the return period of this three-day heatwave event in North China is about 111 years (24.3, +∞) at the 2023 climate state. Both the empirical and coupled model approaches consistently showed that the intensity of 2023-like three-day heatwave events has significantly increased by at least 1.0 °C (range 0.8 °C–1.3 °C) due to anthropogenic climate change. Future projections indicate that 2023-like events in North China are likely to occur at least 1.6 (range 1.3–2.1) times throughout the remainder of this century and be 0.5 °C (range 0.2 °C–0.8 °C) more intense than those under the 2023 climate even if carbon neutrality is achieved based on the very low CO₂ emissions scenario simulations. For the intermediate emissions scenario, the occurrence probability of 2023-like events in the North China region by the end of this century will be 5.5 (range 4.9–6.3) times those under the 2023 climate, with an intensity 2.9 °C (range 2.4 °C–3.1 °C) higher than those under the 2023 climate. These findings highlight the need for adaptation measures to address the occurrence of 2023-like three-day heatwaves in North China in June even if carbon neutrality is achieved.

Governments in industrialized as well as emerging economies are racing to implement policies to accelerate clean energy innovation and capture the economic benefits of decarbonization. This paper explores which combination of technology-push and demand-pull policies best situates a country to lead in clean energy innovation, as new or dominant designs emerge and replace older technologies. A new analytical framework for green industrial policy is introduced regarding the alignment, misalignment, and deliberate misalignment of policies. This framework is applied to battery electric vehicle drivetrain technology to examine the use of policy alignment and misalignment by countries with big automakers as they pursue strategic green industrial policy. We find that countries that achieved early and sustained (not inconsistent) policy alignment gained a first-mover advantage compared with countries that deliberately or accidentally misaligned their policies. We also find that first-mover advantage can be lost due to deliberate misalignment of policies caused by an inability of governments to effectively incentivize their firms to develop and deploy cleaner and more efficient technologies. In situations where governments adopt misaligned or conflicting policies, incumbent industries tend to pursue their prior comparative advantage and maximize return from investments in prior technologies. We also find that deliberate misalignment of policies can be an effective catching-up strategy.

We investigate the drivers of global and regional changes in the potential for photovoltaic (PV) power production from the pre-industrial (1850) to present-day (1985–2014) and until the end of the century (2071–2100), based on output from the Coupled Model Intercomparison Project phase six (CMIP6). Our assessment separates regional contributions from changes in clouds, humidity, temperature, aerosols, and wind speed to the changes in PV power potentials for the first time. Present-day PV power potentials are adversely affected by anthropogenic aerosols compared to the pre-industrial, with a global decrease of the PV power potential by −1.3%. Our results highlight a globally averaged decrease in future PV power potentials primarily driven by temperature and humidity increases by −1.2% to more than −3.5%, depending on the scenario. Regionally different contributions of changes in clouds and aerosols cause heterogeneous spatial patterns in changes of PV potentials, with typically stronger (weaker) influences from clouds (aerosols) in SSP5-8.5 compared to SSP1-2.6. Our results imply that the uncertain response of clouds to warming and aerosol effects are hurdles in quantifying changes in the regional potentials for PV power production.

Top-down approaches, such as atmospheric inversions, are a promising tool for evaluating emission estimates based on activity-data. In particular, there is a need to examine carbon budgets at subnational scales (e.g. state/province), since this is where the climate mitigation policies occur. In this study, the subnational scale anthropogenic CO₂ emissions are estimated using a high-resolution global CO₂ inverse model. The approach is distinctive with the use of continuous atmospheric measurements from regional/urban networks along with background monitoring data for the period 2015–2019 in global inversion. The measurements from several urban areas of the U.S., Europe and Japan, together with recent high-resolution emission inventories and data-driven flux datasets were utilized to estimate the fossil emissions across the urban areas of the world. By jointly optimizing fossil fuel and natural fluxes, the model is able to contribute additional information to the evaluation of province–scale emissions, provided that sufficient regional network observations are available. The fossil CO₂ emission estimates over the U.S. states such as Indiana, Massachusetts, Connecticut, New York, Virginia and Maryland were found to have a reasonable agreement with the Environmental Protection Agency (EPA) inventory, and the model corrects the emissions substantially towards the EPA estimates for California and Indiana. The emission estimates over the United Kingdom, France and Germany are comparable with the regional inventory TNO–CAMS. We evaluated model estimates using independent aircraft observations, while comparison with the CarbonTracker model fluxes confirms ability to represent the biospheric fluxes. This study highlights the potential of the newly developed inverse modeling system to utilize the atmospheric data collected from the regional networks and other observation platforms for further enhancing the ability to perform top-down carbon budget assessment at subnational scales and support the monitoring and mitigation of greenhouse gas emissions.

The rapid and relentless development of urban areas highlights the importance of landscape multifunctionality. However, there is limited research on the temporal dynamics and climatic effects of urban landscape multifunctionality. This study aimed to address this gap by analyzing the features of multiple landscape functions triggered by seasonal climate change in different urban park types. In this study, we investigated five typical urban landscape functions (alleviating urban heat islands, vegetation growth, biodiversity promotion, alleviation of waterlogging, and provision of recreational activities) by establishing a set of indices: ecological supply capability (S_P), proportion of ecological supply (SP_P), capability of human benefits (B_P), and human benefits efficiency (BE_P) of urban parks. The average S_P of the landscape functions was 58% in summer and 46% in winter. During the transition from summer to winter, urban parks witnessed a significant decrease in SP_P for alleviating the urban heat island, dropping from 34% to 5%. The primary landscape functions shifted from alleviating the urban heat island (34%) and providing recreation (29%) to providing recreation (38%) and biodiversity promotion (29%). Concerning park types, nature parks provided the highest S_P, whereas community parks provided the highest BE_P. This study has useful implications for landscape management in urban parks, particularly regarding timely adjustments across seasonal climates. It is possible to promote sustainable and effective human well-being by maximizing landscape functions.

Global permafrost regions are undergoing significant changes due to global warming, whose assessments often rely on permafrost extent estimates derived from climate model simulations. These assessments employ a range of definitions for the presence of permafrost, leading to inconsistencies in the calculation of permafrost area. Here, we present permafrost area calculations using 10 different definitions for detecting permafrost presence based on either ground thermodynamics, soil hydrology, or air–ground coupling from an ensemble of 32 Earth system models. We find that variations between permafrost-presence definitions result in substantial differences of up to 18 million km², where any given model could both over- or underestimate the present-day permafrost area. Ground-thermodynamic-based definitions are, on average, comparable with observations but are subject to a large inter-model spread. The associated uncertainty of permafrost area estimates is reduced in definitions based on ground–air coupling. However, their representation of permafrost area strongly depends on how each model represents the ground–air coupling processes. The definition-based spread in permafrost area can affect estimates of permafrost-related impacts and feedbacks, such as quantifying permafrost carbon changes. For instance, the definition spread in permafrost area estimates can lead to differences in simulated permafrost-area soil carbon changes of up to 28%. We therefore emphasize the importance of consistent and well-justified permafrost-presence definitions for robust projections and accurate assessments of permafrost from climate model outputs.

The northward migration of the intertropical convergence zone (ITCZ) is a significant feature of the West African (WA) monsoon. An accurate simulation of ITCZ migration is essential for the realistic representation of WA precipitation in global coupled models. In this study, we employ the energetics and dynamics framework with a subset of CMIP6 models to investigate the bias in the simulated WA precipitation. Models were found to simulate more local positive (negative) shortwave cloud radiative forcing (SWCRF) in the Southeastern Atlantic Ocean (over the African continent). The effect of the excess local SWCRF is linked to the stagnation of the ITCZ latitudinal migration and the associated biases in the asymmetry index of precipitation. In the models, there is more (less) moist static energy in the lower (mid and upper) troposphere than in the reanalysis. The worst models have a stronger bias, especially over land. The vertical transport of moisture is confined to the boundary layer in the worst model ensemble. In most cases, the high-resolution coupled models show substantial northward migration of the ITCZ compared to the low-resolution models. Furthermore, the best-performing models capture local circulation and energetic processes more accurately than the worst-performing models.

Traditionally, large-scale thermoelectric power generation has been operated to reduce system operational costs. To expedite the mitigation of the harmful effects of climate change, many have proposed additional incentives for system operation (i.e. policies) that incorporate greenhouse gas emissions. However, such policies rarely consider unforeseen impacts on the volumes of water required for cooling thermoelectric plants as well as the potential effects on electricity production from water/climate-related stressors. We first create a case study representative of the thermoelectric-dominated water/energy systems in the Midwestern United States. Through this case study, our analysis investigates the tradeoffs of cost, water, emissions, and reliability in thermoelectric-dominated water/energy systems via policy analysis. Furthermore, we show how such policies respond differently to historic operational, climatological, and hydrological stressors. Specifically, we find that policies that focus on a single criterion can leave power systems vulnerable to reliability issues, operational cost increases, ecological impacts on riverine systems, and increased emissions. Therefore, consideration of many criteria (cost, water, emissions, and reliability) is necessary for creating an effective water-energy-emissions policy.

Climate change is driving urgent investments in decarbonization. One core decarbonization strategy is to electrify energy services that currently directly use fossil fuels, because electricity can be generated from zero greenhouse gas energy resources. Shifting fossil-based services to electricity, however, requires a major expansion of electricity supply and increases dependence on electricity for critical services. Home heating is a particular challenge, especially in very cold climates. Unserved heating loads can be fatal. Electrified heating is expected to drive peak loads (and thus overall grid size) due to high coincident and nondeferrable loads. This study shows that highly efficient housing presents an opportunity to simultaneously protect people and structurally reduce peak load, reducing the need for electricity supply infrastructure while increasing people's resilience to weather extremes. This study uses seven building efficiency scenarios from the National Renewable Energy Laboratory's End Use Saving Shapes to investigate the impact of residential building efficiency on grid size in 2050, using the example of Pierre, South Dakota as a very cold weather location that might also experience substantial new housing demand due to climate-induced human mobility. We find that the deepest efficiency electrification scenario we investigate reduces peak demand by about half relative to low-efficiency electrification. Costs of about $3900/kilowatt (kW) peak load reduction are competitive with the cost of new decarbonized supplies capable of meeting peak load, though building efficiency costs are usually privatized while supply expansion costs are distributed across ratepayers. Decarbonization scenarios suggest the US grid might need to expand by a factor of 5–8 in the next 25 years: extremely rapid growth will be needed regardless, but targets might not be reachable with inefficient end users. Residential building efficiency presents an urgent opportunity to reduce peak demand and provide safer and more resilient housing.

Despite the scientific progress in drought detection and forecasting, it remains challenging to accurately predict the corresponding impact of a drought event. This is due to the complex relationships between (multiple) drought indicators and adverse impacts across different places/hydroclimatic conditions, sectors, and spatiotemporal scales. In this study, we explored these relationships by analyzing the impacts of the severe 2018–2019 central European drought event in Germany. We first computed the standardized precipitation index (SPI), the standardized precipitation evaporation index (SPEI), the standardized soil moisture index (SSMI) and the standardized streamflow index (SSFI) over various accumulation periods, and then related these indicators to sectorial losses from the European drought impact report inventory (EDII) and media sources. To cope with the uncertainty associated with both drought indicators and impact data, we developed a fuzzy method to categorize them. Lastly, we applied the method at the region level (EU NUTS1) by correlating monthly time series. Our findings revealed strong and significant relationships between drought indicators and impacts over different accumulation periods, albeit in some cases region-specific and time-variant. Furthermore, our analysis established the interconnectedness between various sectors, which displayed systematically co-occurring impacts. As such, our work provides a new framework to explore drought indicators-impacts dependencies across space, time, sectors, and scales. In addition, it emphasizes the need to leverage available impact data to better forecast drought impacts.

The concept of ecological infrastructure (EI) as a lens for landscape management has the potential to address environmental challenges, such as biodiversity loss and ecosystem degradation, by instrumentalizing Nature's Contributions to People (NCP). NCPs stems from the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) and refers to the various ways in which the natural world provides benefits, values, and services that directly and indirectly contribute to the well-being, livelihoods, and cultural aspects of human societies. This research explores this potential by proposing an archetype analysis of social-ecological-technological systems (SETS) to manage EI. We derived archetypes using machine learning and clustering on a data-driven SETS framework co-produced with experts in EI management. The archetype analysis was conducted by combining K-means with hierarchical clustering on spatial patterns to generate clusters with similar configurations of social, ecological, and technological subsystems. The approach is illustrated for the canton of Geneva, Switzerland, which experiences high urbanization and ecological pressures. The resulting spatially explicit archetypes of SETS facilitate policy recommendations tailored to multifunctional landscapes, which can be used to derive coherent management strategies for EI. In addition, the approach demonstrates that by taking an integrated landscape approach and engaging with diverse stakeholders, it is possible to develop effective landscape-based management recommendations for promoting the sustainable provision of NCPs and biodiversity within the concept of EI.

Independently, both droughts and heatwaves can induce severe impacts on human and natural systems. However, when these two climate extremes occur concurrently in a given region, their compound impacts are often more pronounced. With the improvement in both the spatiotemporal resolution and representation of complex climate processes in the global climate models (GCMs), they are increasingly used to study future changes in these extremes and associated regional impacts. However, GCM selection for such impact assessments is generally based on historical performance and/or future mean changes, without considering individual or compound extremes. In contrast, this study evaluates historical performance and projected changes in heatwaves, droughts, and compound heatwave-droughts using an ensemble of GCMs from the latest Phase 6 of Coupled Models Intercomparison Project at a regional scale across the conterminous United States. Additionally, we explore the inter-model differences in the projected changes that are associated with various characteristics of extremes and the choice of drought indices. Our analysis reveals considerable variation among the GCMs, as well as substantial differences in the projected changes based on the choice of drought indices and region of interest. For example, the projected increases in both the frequency and intensity of drought and associated compound extreme days, based on the standardized precipitation evapotranspiration index far exceed those derived from the standard precipitation index. Further, the largest changes in the frequency of compound extremes are projected over the Southwest, South Central, and parts of the Southeast while the smallest changes are projected over the Northeast. Overall, this study provides important insights for the interpretation and selection of GCMs for future assessment studies that are crucial for the development of regional adaptation strategies in the face of climate change.

Satellite observations have shown evident vegetation greening in China during the last two decades. The biophysical effects of vegetation changes on near-surface air temperature (SAT) remain elusive because prior studies focused on the effects on land surface temperature (LST). SAT is more relevant to climate mitigation and adaptation, as this temperature is experienced by humans. Here, we provide the first observational evidence of the greening effects on SAT and SAT extremes in China during 2001–2018 using the 'space-for-time' method. The results show a negative SAT sensitivity to greening (–0.35 °C m² m^–2) over China and a cooling effect of −0.08 °C on SAT driven by vegetation greening during the study period. Such a cooling effect is stronger on high SAT extremes, particularly over arid/semiarid areas, where greening could bring an additional cooling of −0.04 °C on the hottest days. An attribution analysis suggests that the main driving factor for the cooling effect of greening is the evapotranspiration change for arid/semiarid regions and the aerodynamic resistance change for humid regions. This study reveals a considerable climate benefit of greening on SAT, which is more concerned with natural and human system health than the greening effects on LST.

The Indian Ocean dipole (IOD) is a remarkable interannual variability in the tropical Indian Ocean. The improved prediction of IOD is of a great value because of its large socioeconomic impacts. Previous studies reported that both El Niño-Southern Oscillation (ENSO) and South China Sea summer monsoon (SM) play a dominant role in the western and eastern pole of the IOD, respectively. They can be used as predictors of the IOD at 3 month lead beyond self-persistence. Here, we develop an empirical model of multi-factors in which the western pole is predicted by ENSO and persistence and the eastern pole is predicted by SM and persistence. This new empirical model outperforms largely the average level of the dynamical models from the North American multi-model ensemble (NMME) project in predicting the peak IOD in boreal autumn, with a correlation coefficient of ∼0.86 and a root mean square error of ∼0.24 °C. Furthermore, the hit rate of positive culminated IOD in this new empirical model is equivalent to that in current NMME models (above 65%), much higher than that for negative culminated IOD. This improvement of skill using the empirical model suggests a perspective for better understanding and predicting the IOD.

A significant haze event occurred in northern China from 16 to 21 November 2022. This study analyzed the haze spatial evolution, and meteorological influences by integrating ground and satellite measurements. Most data were obtained using aerosol lidar and wind lidar observations in suburban (Nanjiao Observation Station, NJOS) and urban Beijing (Haidian Observation Station, HDOS). The observations at NJOS and HDOS indicate the presence of a distinct layer of haze restricted to a height of up to 1500 m above the surface. However, the aerosol intensity at HDOS was comparatively lower (aerosol extinction coefficient: 1.39 ± 0.27 km⁻¹) than at NJOS (1.77 ± 0.38 km⁻¹), with approximately one day of time lag in response to the southerly winds. Though NJOS and HDOS presented a similar wind stratification structure, the downdraft under 1000 m influenced the surface air quality were significantly different. The intense downdraft at the lower height at HDOS prevented the vertical upward diffusion of accumulated ground pollutants, whose effect was similar to that of the inversion layer. That led to a more stable increasing trend of PM_2.5 at HDOS, with the shallowest planet boundary layer height of 242 m on 20 November. By contrast, NJOS in the transportation path was more regularly influenced by the southerly flow and presented cyclical PM_2.5 concentration. This study shows downdraft in urban environments acting as an accelerator for urban episodic PM_2.5 pollution, suggesting the complicated contribution from meteorological factors.

To better understand the processes of digitalisation, dematerialisation and decarbonisation, this paper examines the relationship between energy and information for the global economy since 1850. It presents the long run trends in energy intensity and communication intensity, as a proxy for total information intensity. The evidence suggests that, relative to GDP, global economic production has been reducing energy and increasing information use since 1913. The analysis indicates that it initially required little information to replace energy in production and that the ability to substitute away from energy and towards information has been declining. The result implies that the global economy is now reducing energy and increasing information at a substitution rate of 0.2 kB per kWh of conserved energy or 0.8 GB per tonne of carbon dioxide mitigated. As the price ratio of energy to information is currently higher than this marginal rate of substitution, there are incentives to further substitute information for energy. However, one conclusion is that (without the long run escalation of carbon prices) substitution away from energy and towards information is likely to cease within the next few decades and, beyond that, digitalisation will play a declining role in the decarbonisation process.

In the Western US, area burned and fire size have increased due to the influences of climate change, long-term fire suppression leading to higher fuel loads, and increased ignitions. However, evidence is less conclusive about increases in fire severity within these growing wildfire extents. Fires burn unevenly across landscapes, leaving islands of unburned or less impacted areas, known as fire refugia. Fire refugia may enhance post-fire ecosystem function and biodiversity by providing refuge to species and functioning as seed sources after fires. In this study, we evaluated whether the proportion and pattern of fire refugia within fire events have changed over time and across ecoregions. To do so, we processed all available Landsat 4–9 satellite imagery to identify fire refugia within the boundaries of large wildfires (405 ha+) in 16 forested ecoregions of the Western US. We found a significant change in % refugia from 1986–2021 only in one ecoregion—% refugia increased within fires in the Arizona/New Mexico Mountain ecoregion (AZ/NM). Excluding AZ/NM, we found no significant change in % refugia across the study area. Furthermore, we found no significant change in mean refugia patch size, patch density, or mean distance to refugia. As fire size increased, the amount of refugia increased proportionally. Evidence suggests that fires in AZ/NM had a higher proportion of reburns and, unlike the 15 other ecoregions, fires did not occur at higher elevation or within greener areas. We suggest several possibilities for why, with the exception of AZ/NM, ecoregions did not experience a significant change in the proportion and pattern of refugia. In summary, while area burned has increased over the past four decades, there are substantial and consistent patterns of refugia that could support post-fire recovery dependent on their spatial patterns and ability to function as seeds sources for neighboring burned patches.

Climate change trends in the upper Lancang river basin (LRB), a high-mountain area, are prominent on a global scale, and climate-induced land use change with increasing cropland and migration has been observed in the past decades and is expected to expand in the future. We assessed land use and sediment yield from the basin in the past and future under the synergistic impact of projected climate change and associated land use change. We found that the transition from grassland and forest to cropland under climatic change favorable to agriculture can be the topmost contributor to the sediment yield increase from the upper LRB, with an increaisng rate of 40.6% from the entire area and as high as 118% in some sub-areas. As the baisn serves as the source area of the Lancang-Mekong River Basin (LMRB), we call for coordinated management throughout the entire LMRB, given the complex sediment dynamics crossing scales, affected by both climate change and socioeconomic development in trans-boundary basin.

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5–9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11–12 cm and bias of 2.7–6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

Siberia is covered by 6 million km² of forest, which moderates climate as a carbon sink and a source of aerosol particles causing negative radiative effect. Aerosol particles in boreal forests frequently form via gas-to-particle conversion, known as new particle formation (NPF). Compared to boreal sites at similar latitudes, NPF was reported to occur less often in the Siberian forest. However, factors controlling NPF in Siberia remain unknown. Our results suggest that the combination of biogenic and anthropogenic contributions caused unexpectedly high monthly NPF frequency (50%) at the observatory Fonovaya in the West Siberian taiga during the Siberian 2020 heatwave. High frequency was due to early spring photosynthetic recovery, which boosted biogenic emissions into polluted air masses carrying SO₂. After mid-April, high temperatures and cleaner air masses led to less frequent (15%) and less intense NPF despite the increased emissions of natural organic vapors and ammonia. Furthermore, the contrast between the two spring periods was seen in cluster composition, particle-forming vapors (two times difference in sulfuric acid concentration), particle formation (J₃, 2.2 and 0.4 cm⁻³ s⁻¹) and growth rates (GR₂₋₃, 1.7 and 0.6 nm h⁻¹). Given the strong warming trend, our results suggest that within 25‒30 years, the monthly NPF frequency during early spring in the West Siberian taiga can reach 40%–60%, as in the European boreal sites.

The area of Arctic winter sea ice growth (WSIG) has expanded dramatically since winter 2008. Yet the thermodynamic and dynamic contributions to the abrupt increase in WSIG remain unclear. Here using an ice concentration budget, we characterized quantitatively the increasing WSIG and revealed the relative contributions of dynamics during 1985–2021. Ice dynamics related to ice convergence/divergence are compared in two representative regions. The northern Laptev Sea is a freezing-dominated ice growth region and is competitively driven by the ice convergence. While in northwest Beaufort Gyre (BG), the combined effects of freezing and ice divergence have both enhanced since 2008, and the dynamics contribute 84% to the significant WSIG intensification since 2008. Comparison of thermodynamic and dynamic contributions emphasized that the winter sea-ice expansion is influenced not only by winter freeze, but also by convergence/divergence relative to newly formed thinner and mobile ice. Furthermore, the amplified summer Beaufort High in the mid-2000s and its long-lasting memory of the wind-driven strengthened BG are partially attributed to the abrupt increased WSIG since 2008.

Temperature trends over the high-altitude mountains depict an increase with elevation during recent years. These stratified warming trends observed over the Himalayan-Tibetan (HT) regions are higher than the mean warming trends observed over low-elevation regions of South and East Asia, which is attributed to several factors including snow albedo feedback, clouds and water vapor feedback. In this study, we demonstrate the effects of deposition of absorbing aerosols like black carbon and dust on snow albedo and its implications for elevation-dependent warming (EDW). Though the aerosol concentration decreases with elevation, warming due to aerosol-induced snow darkening increases with elevation. Further, surface cooling due to the direct radiative effects (DRE) of aerosols is found to decrease with elevation, which also favors higher warming at high altitudes. The effects of both the deposition of absorbing aerosols on snow albedo and the surface cooling due to the DRE of atmospheric aerosols could strengthen EDW. This study clearly shows the potential of albedo feedback due to aerosol-cryosphere interaction as one of the physical mechanisms contributing to the observed EDW over the HT region.

Vertical land movements (VLM) play a crucial role in affecting the sea level rise along the coasts. They need to be estimated and included in the analysis for more accurate Sea Level (SL) projections. Here we focus on the Mediterranean basin characterized by spatially variable rates of VLM that affect the future SL along the coasts. To estimate the VLM rates we used geodetic data from continuous global navigation satellite system stations with time series longer than 4.5 years in the 1996–2023 interval, belonging to Euro-Mediterranean networks and located within 5 km from the coast. Revised SL projections up to the year 2150 are provided at 265 points on a geographical grid and at the locations of 51 tide gauges of the Permanent Service for Mean Sea Level, by including the estimated VLM in the SL projections released by the Intergovernmental Panel on Climate Change (IPCC) in the AR6 Report. Results show that the IPCC projections underestimate future SL along the coasts of the Mediterranean Sea since the effects of tectonics and other local factors were not properly considered. Here we show that revised SL projections at 2100, when compared to the IPCC, show a maximum and minimum differences of 1094 ± 103 mm and −773 ± 106 mm, respectively, with an average value that exceeds by about 80 mm that of the IPCC in the reference Shared Socio-economic Pathways and different global warming levels. Finally, the projections indicate that about 19.000 km² of the considered Mediterranean coasts will be more exposed to risk of inundation for the next decades, leading to enhanced impacts on the environment, human activities and infrastructures, thus suggesting the need for concrete actions to support vulnerable populations to adapt to the expected SL rise and coastal hazards by the end of this century.

Signatories to the Paris Agreement have pledged to keep global warming to well below 2 °C above pre-industrial levels and preferably below 1.5 °C above pre-industrial levels. Beyond over-shooting Paris Agreement warming levels followed by net negative emissions, achieving a state of net zero carbon dioxide emissions is required to satisfy Paris Agreement warming goals. Research on climate changes under net zero CO₂ emissions is very limited to date with no comprehensive analysis of changes in extremes. In this study, we use results from Earth System Models in the zero emissions commitment model intercomparison project to understand regional mean-state climate change patterns during a 100 year period following carbon dioxide emissions cessation. We also perform an initial study of the evolution of hot and cold monthly temperature extremes after net zero CO₂ emissions, including an assessment of how the change in frequency of temperature extremes affects areas of different levels of socioeconomic development based on regional Human Development Index (HDI). The results show that most land regions experience a fast and continuous cooling response following emissions cessation, with large areas of significant model agreement. In contrast, the Southern Ocean continues warming over the century after emissions cessation. The frequency of land-based local monthly high temperature extremes generally stays constant or decreases during the century after emissions cessation, however, decreases in heat extreme frequencies are generally less for locations with lower modern HDI than areas with higher HDI which suggests that inequality of climate change will remain an issue even after net zero CO₂ emissions. There is an evident emergence of local monthly cold extremes following emissions cessation with most significant impact over high HDI mid- and high-latitude land regions.

Gross primary productivity (GPP) is jointly controlled by the structural and physiological properties of the vegetation canopy and the changing environment. Recent studies showed notable changes in global GPP during recent decades and attributed it to dramatic environmental changes. Environmental changes can affect GPP by altering not only the biogeochemical characteristics of the photosynthesis system (direct effects) but also the structure of the vegetation canopy (indirect effects). However, comprehensively quantifying the multi-pathway effects of environmental change on GPP is currently challenging. We proposed a framework to analyse the changes in global GPP by combining a nested machine-learning model and a theoretical photosynthesis model. We quantified the direct and indirect effects of changes in key environmental factors (atmospheric CO₂ concentration, temperature, solar radiation, vapour pressure deficit (VPD), and soil moisture (SM)) on global GPP from 1982 to 2020. The results showed that direct and indirect absolute contributions of environmental changes on global GPP were 0.2819 Pg C yr⁻² and 0.1078 Pg C yr^−2. Direct and indirect effects for single environmental factors accounted for 1.36%–51.96% and 0.56%–18.37% of the total environmental effect. Among the direct effects, the positive contribution of elevated CO₂ concentration on GPP was the highest; and warming-induced GPP increase counteracted the negative effects. There was also a notable indirect effect, mainly through the influence of the leaf area index. In particular, the rising VPD and declining SM negatively impacted GPP more through the indirect pathway rather than the direct pathway, but not sufficient to offset the boost of warming over the past four decades. We provide new insights for understanding the effects of environmental changes on vegetation photosynthesis, which could help modelling and projection of the global carbon cycle in the context of dramatic global environmental change.

Global declines in insect populations have important implications for biodiversity and food security. To offset these declines, habitat restoration and enhancement in agricultural landscapes could mutually safeguard insect populations and their pollination services for crop production. The expansion of utility-scale solar energy development in agricultural landscapes presents an opportunity for the dual use of the land for energy production and biodiversity conservation through the establishment of grasses and forbs planted among and between the photovoltaic solar arrays ('solar-pollinator habitat'). We conducted a longitudinal field study across 5 years (2018–2022) to understand how insect communities responded to newly established habitat on solar energy facilities in agricultural landscapes by evaluating (1) temporal changes in flowering plant abundance and diversity; (2) temporal changes in insect abundance and diversity; and (3) the pollination services of solar-pollinator habitat by comparing pollinator visitation to agricultural fields near solar-pollinator habitat with other agricultural field locations. We found increases over time for all habitat and biodiversity metrics: floral rank, flowering plant species richness, insect group diversity, native bee abundance, and total insect abundance, with the most noticeable temporal increases in native bee abundance. We also found positive effects of proximity to solar-pollinator habitat on bee visitation to nearby soybean (Glycine max) fields. Bee visitation to soybean flowers adjacent to solar-pollinator habitat were comparable to bee visitation to soybeans adjacent to grassland areas enrolled in the Conservation Reserve Program, and greater than bee visitation to soybean field interior and roadside soybean flowers. Our observations highlight the relatively rapid (<4 year) insect community responses to grassland restoration activities and provide support for solar-pollinator habitat as a feasible conservation practice to safeguard biodiversity and increase food security in agricultural landscapes.

There is a consensus that a weakened Atlantic Meridional Overturning Circulation (AMOC) decreases mean surface temperature in the Northern Hemisphere, both over the ocean and the continents. However, the impacts of a reduced AMOC on cold extreme events have not yet been examined. We analyse the impacts of a reduced AMOC strength on extreme cold events over Europe using targeted sensitivity experiments with the EC-Earth3 climate model. Starting from a fully coupled ocean-atmosphere simulation in which the AMOC was artificially reduced, a set of atmosphere-only integrations with prescribed sea surface temperature and sea-ice cover was conducted to evaluate the effects of weakly and strongly reduced AMOC strength. Despite overall cooling, reduced AMOC leads to fewer winter cold spells in Europe. We find that the weakened AMOC intensifies near-surface meridional gradient temperature in the North Atlantic and Europe, thus providing the energy to boost the jet stream. A stronger jet stream leads to less atmospheric blocking, reducing the frequency of cold spells over Europe. Although limited to the output of one model, our results indicate that a reduced AMOC strength may play a role in shaping future climate change cold spells by modulating the strength of the jet stream and the frequency of atmospheric blocking.

Previous modelling and case studies highlight the impacts of antecedent soil moisture on precipitation, showing the connection between the anomalous land surface and atmospheric conditions. However, observational evidence is lacking, especially on daily timescales, primarily due to the difficulty in assessing the interaction between soil moisture and atmospheric variability and dataset quality. Using satellite retrievals, this study investigates the relationship between soil moisture and next-day precipitation in Australia. Analysing the 5 year soil moisture data from the Cyclone Global Navigation Satellite System, we find that soil moisture anomalies influence next-day precipitation probability where higher soil moisture is associated with a higher probability of precipitation, even allowing for precipitation persistence. We also find that this feedback is generally positive in northern Australia but slightly negative in the southern regions, suggesting regional dependence. Linkages between the persistence of dry/wet soil moisture days and the possibility of wildfires and floods are also discussed. These findings have direct implications for the management and predictions of extreme conditions.

Climate change has already impacted the health and wellbeing of ∼5 billion people globally. However, the potential influence of climate change mitigation and adaptation strategies on mental health and wellbeing outcomes in low-and-middle-income countries (LMICs) remains insufficiently understood. We aimed to determine the effect of these strategies on mental health and wellbeing outcomes among LMIC beneficiaries. We carried out a systematic review to identify intervention and case studies published from 2013 to 2022, searching OVID Medline, Embase, PsycINFO, Global Health, Cochrane Library, GreenFile, Web of Science, and a subset of studies from the 'Global Adaptation Mapping Initiative' database. We included controlled, quasi-experimental, pilot, and focussed case studies reporting mental health or wellbeing outcomes assessments of climate change mitigation and adaptation strategies. We categorised studies by design, geographic region, target population, setting, environmental hazard, strategy type and primary outcomes. PROSPERO registry: CRD42021262711. A total of 9532 studies were initially retrieved, and 15 studies involving 12 255 participants met the inclusion criteria. Among these, twelve studies described evidence from single-adaptation strategies in nine LMICs, while three reported mitigation programmes. Only two randomised evaluations assessed common mental disorders such as depression, trauma or anxiety using validated scales. Most studies evaluated broader wellbeing at the community and individual levels. Nine studies (53.3%) reported significant beneficial changes in mental health or wellbeing outcomes among beneficiaries, while six (46.7%) obtained mixed results linked to local and sociocultural factors. The interventions 'practical significance and overall impact remained unclear due to the heterogeneous reporting in program effectiveness, gaps in effect size assessments or qualitative insights. Our review highlights the scarcity and limited nature of the current evidence, underscoring the need for further equitable research. The ongoing global climate and mental health crises press us to fully understand and address these strategies' psychosocial impacts and translate these findings into effective policy and transdisciplinary action as an opportunity to prevent and ameliorate significant, long-term problems in the population's mental health and wellbeing.

Concurrent compound heatwaves (CCHWs) occurring simultaneously in multiple regions in the Northern Hemisphere (NH) pose high-end risks to human health and global supply chains. Over the past decade, CCHWs related to human health have substantially increased in occurrence. However, the mechanisms of the CCHWs remain uncertain. This work has revealed a significant relationship between the variability of summer CCHWs in the NH and changes in quasi-stationary waves during 1979–2021, which can be attributed to the variation of summer snow cover over the western Tibetan Plateau (SC_WTP). Excessive SC_WTP causes diabatic cooling by modulating the surface energy budget and stimulating a tripolar Rossby wave source. The atmospheric response to the SC_WTP-driven disturbance manifests as a circumglobal circulation pattern, weakening the meridional temperature gradients and causing a 'double jet stream' in the NH. These changes modulate the phase, amplitude and proportion of quasi-stationary waves with wavenumbers 4–6, leading to an increase in CCHWs in the NH. In addition, population exposure to CCHWs reaches 4.91 billion person-day when the SC_WTP increases by one standard deviation. Our study highlights the significance of early warning and forecasting implications related to SC_WTP for CCHWs that impact human health within the context of climate change.

Global mean sea level rise, driven by ice mass loss in Antarctic and Greenland Ice Sheets (AIS and GrIS), is a significant consequence of global warming. Although various ice sheet models have attempted to predict the ice mass balance and subsequent sea level changes, non-trivial disagreements between models exist. In this study, we employ an empirical approach to estimate the future (2050) ice mass changes for both ice sheets, assuming their historical patterns of ice dynamics would persist in the coming decades. To achieve this, we estimate decadal-scale ice discharge variations by subtracting the surface mass balance (SMB) from the observed ice mass changes and extrapolate linear trend and acceleration components of ice discharges up to 2050. We also consider future SMB data from Coupled Model Intercomparison Project phase 6 models to estimate net ice mass balance. Our estimates suggest that from 2021 to 2050, the global sea level rise due to AIS and GrIS ranges between 6–19 mm and 15–31 mm, respectively. Additionally, we investigate regional sea level variability resulting from geoid changes induced by ice mass changes in both regions, highlighting that heterogeneous sea level changes may cause more pronounced sea level rises in lower latitude regions, where major cities are located.

Compound hot-dry events (CHDEs) are among the deadliest climate hazards and are occurring with increasing frequency under global warming. The Yangtze River Basin in China experienced a record-breaking CHDE in the summer of 2022, causing severe damage to human societies and ecosystems. Recent studies have emphasized the role of atmospheric circulation anomalies in driving this event. However, the contribution of land–atmosphere feedback to the development of this event remains unclear. Here, we investigated the impacts of soil moisture-temperature coupling on the development of this concurrent heatwave and drought. We showed that large amounts of surface net radiation were partitioned to sensible heat instead of latent heat as the soil moisture-temperature coupling pattern shifted from energy-limited to water-limited under low soil moisture conditions, forming positive land–atmosphere feedback and leading to unprecedented hot extremes in August. The spatial heterogeneity of hot extremes was also largely modulated by the land–atmosphere coupling strength. Furthermore, enhanced land–atmosphere feedback has played an important role in intensifying CHDEs in this traditional humid region. This study improves the understanding of the development of CHDEs from three aspects, including timing, intensity, and spatial distribution, and enables more effective early warning of CHDEs.

Over the last two decades, there has been increasing interest in investigating the connection between the Asian-Pacific Oscillation (APO) and weather and climate on regional and global scales, but the impacts of the APO on sea surface temperature (SST) remains unclear. Using the multisource reanalysis dataset and observed SST data, we evaluated the interannual relationship between the APO and SST in the North Atlantic (NASST) during the period 1979–2016. The results show that there exists a statistically significant positive interannual relationship between APO and NASST and this connection can be attributed to the Rossby wave train that originates in Asia and propagates to Europe, which is triggered by the APO forcing. Further examination revealed that the cloud radiation, air–sea heat exchange and oceanic dynamic process induced by APO are crucial in modulating the interannual variability of the NASST. Additionally, the numerical simulation results from the linear baroclinic model also provide additional evidence for this linkage.

Multiple lines of observational evidence have indicated a significant wetting over the arid and semi-arid Northwest China (NWC) during recent decades, coinciding with a simultaneous sharp decline of dust events. Although recent studies have attributed NWC wetting to different anthropogenic and natural forcings, the mechanisms are not definitive and the regional wetting has been greatly underestimated in the Coupled Model Intercomparison Project historical simulations. Based on sensitivity experiments with different dust emission amounts using the NCAR Community Atmospheric Model version 5 (CAM5), here we find that decreasing dusts exert significant impacts on mixed-phase clouds through reducing the concentration of ice nucleating particles, increase the NWC precipitation and thus induce regional wetting through enhancing convection precipitation. A possible convection invigoration mechanism whereby the atmospheric vertical temperature gradient and convective instability are strengthened by reduced dusts, leading to convection invigoration and increased precipitation. These results are reinforced by simulations over the dust region in North Africa where mixed-phase and ice clouds are rare and reduced dusts do not increase precipitation. This study highlights the possible mechanism of dust-ice cloud interactions in recent NWC wetting and future regional climate change.

Compound hot extremes (CHEs) are receiving increasing attention due to their significant impacts on human health, ecosystems, and society compared to individual hot days or nights. While previous studies have focused on the characteristics of CHEs at individual points or stations, assessments of features for regional CHEs (RCHEs), which have a specific impact area and duration, are still lacking. This study aimed to investigate the climatic characteristics of RCHEs in mainland China by applying an objective identification technique for regional extreme events based on a compound index. The results show that 379 RCHEs were identified during 1961–2020, most of the events had a duration of 5–11 d and a maximum impacted area of approximately 460 10⁴ km². Long-duration RCHEs were found to have vigorous extreme intensity and large maximum impacted area. The middle and lower reaches of the Yangtze River were most susceptible to RCHEs, while the Yellow River Valley had the most robust positive trend of frequency for RCHEs, suggesting a significant risk of compound temperature disasters in this region. Furthermore, RCHEs in mainland China showed significant increasing trends in several aspects, such as annual frequency, integrated index, and single indices (e.g. duration, accumulated intensity, accumulated impacted area, and extreme intensity). These upward trends were accompanied by evident interdecadal variations, with low values before 1992 and high values after 1992. This study provides valuable insights into understanding and monitoring CHEs in China from the perspective of regional extremes.

During July–September 2022, heatwaves, droughts, forest fires and floods hit the Yangtze River Valley successively, constituting a spatio-temporally compounding event. Understanding its risks matters to disaster preparedness. Through searching for event analogues in single-model initial-condition large-ensemble climate simulations, we report that the 2022 unprecedentedly widespread and intense hot drought might have occurred as early as in the 1970s, and would become increasingly possible and spatially extensive with warming. This tendency is also supported by the conventional multi-model (CMIP6) projection, especially evident in larger ensembles. Lower reaches of the valley and parts of Southwest China have greater chances of repeated exposure to the 2022-like heat—drought—fire—flood quadruple compound events. In the presence of favorable internal variability in line with future warming, it is plausible to see more than half of the valley at simultaneous risk of the 2022-like quadruple compound event. Our possibility projection highlights the urgency of accelerating the existing univariate extremes—oriented adaptation measures to better address emerging threats from unfamiliar compound hazards.

Changes in winter snow cover in the Northern Hemisphere (NH) could have a profound impact on mid-latitude weather. Previous studies have focused on the role of regional, e.g. Eurasian or Tibetan, snow cover in summer precipitation anomaly, without considering the synergistic impacts of hemispheric wintertime snow. In this study, we find that the dominant pattern of the NH winter overall snow cover anomaly with a synergistic impact, has a stronger cross-seasonal association with the China's summer rainfall pattern than regional snow cover anomaly. We summarize three synergistic impact paths of regional snow cover. One is extratropical path, that is the westerlies are affected by less snow in Europe through the snow-soil moisture-atmospheric feedback, and the influence is strengthened by less snow in Mongolia through enhanced temperature anomalies. The second is subtropical path, that is the meridional thermal difference anomaly caused by more snow anomaly on the Tibetan Plateau is strengthened by less Mongolian snow and then impacts the behavior of the upper-tropospheric westerly jet. Third, concurrently, more North American snow enhances the above two synergistic influence paths via the Circumglobal Teleconnection pattern. These three paths can be simultaneously reflected in the associated circulations of the first mode of NH snow cover. Their synergistic impacts eventually influence the meridional East Asia-Pacific pattern circulation anomalies in summer, leading to increased precipitation in the Yangtze River Basin. The cross-seasonal influences of synergistic effects of multiple regional snow anomalies can be identified by CMIP6 multi-model ensembles, particularly the impact of European snow cover.

Irrigation expansion is often posed as a promising option to enhance food security. Here, we assess the influence of expansion of irrigation, primarily in rural areas of the contiguous United States (CONUS), on the intensification and spatial proliferation of freshwater scarcity. Results show rain-fed to irrigation-fed (RFtoIF) transition will result in an additional 169.6 million hectares or 22% of the total CONUS land area facing moderate or severe water scarcity. Analysis of just the 53 large urban clusters with 146 million residents shows that the transition will result in 97 million urban population facing water scarcity for at least one month per year on average versus 82 million before the irrigation expansion. Notably, none of the six large urban regions facing an increase in scarcity with RFtoIF transition are located in arid regions in part because the magnitude of impact is dependent on multiple factors including local water demand, abstractions in the river upstream, and the buffering capacity of ancillary water sources to cities. For these reasons, areas with higher population and industrialization also generally experience a relatively smaller change in scarcity than regions with lower water demand. While the exact magnitude of impacts are subject to simulation uncertainties despite efforts to exercise due diligence, the study unambiguously underscores the need for strategies aimed at boosting crop productivity to incorporate the effects on water availability throughout the entire extent of the flow networks, instead of solely focusing on the local level. The results further highlight that if irrigation expansion is poorly managed, it may increase urban water scarcity, thus also possibly increasing the likelihood of water conflict between urban and rural areas.

Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a 'potential' sunlit leaf area index (LAI_su) can separate a portion of LAI_su even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an 'effective' LAI_su to improve GPP estimation, which considers the comprehensive contribution of LAI_su when the canopy does and does not receive direct radiation. In particular, the new model decreases LAI_su to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m⁻² d⁻¹ and 1.53 g C m⁻² d⁻¹ to 1.36 g C m⁻² d⁻¹ and increasing the coefficient of determination (R²) from 0.74 and 0.79–0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m⁻² month⁻¹ and28.11 g C m⁻² month⁻¹ to 25.81 g C m⁻² month⁻¹ at a monthly scale and from 231.82 g C m⁻² yr⁻¹ and 221.60 g C m⁻² yr⁻¹–200.00 g C m⁻² yr⁻¹ at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m⁻² d⁻¹ and 0.46 g C m⁻² d⁻¹, respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07–0.16 than those from the TL-LUE model across the eight ChinaFLUX sites. The proposed parsimonious and effective DL-LUE model not only has great potential for improving global GPP estimations but also provides a more mechanism-based approach for partitioning the total GPP into its shaded and sunlit components.

Sea ice thickness (SIT), which is a crucial and sensitive indicator of climate change in the Antarctic, has a substantial impact on atmosphere-sea-ice-ocean interactions. Despite the slight thinning in SIT and reduction in sea ice volume (SIV) in the Antarctic in the recent decade, challenges remain in quantifying their changes, primarily because of the limited availability of high-quality long-term observational data. Therefore, it is crucial to accurately simulate Antarctic SIT and to assess the SIT simulation capability of state-of-the-art climate models. In this study, we evaluated historical simulations of SIT by 51 climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Envisat (ES) and CryoSat-2 (CS2) observations. Results revealed that most models can capture the seasonal cycles in SIV and that the CMIP6 multimodel mean (MMM) can reproduce the increasing and decreasing trends in the SIV anomaly based on ES and CS2 data, although the magnitudes of the trends in the SIV anomaly are underestimated. Additionally, the intermodel spread in simulations of SIT and SIV was found to be reduced (by 43%) from CMIP5 to CMIP6. Nevertheless, based on the CMIP6 MMM, substantial underestimations in SIV of 57.52% and 59.66% were found compared to those derived from ES and CS2 observations, respectively. The most notable underestimation in SIT was located in the sea ice deformation zone surrounding the northwestern Weddell Sea, coastal areas of the Bellingshausen and Amundsen seas, and the eastern Ross Sea. The substantial bias in the simulated SIT might result from deficiencies in simulating critical physical processes such as ocean heat transport, dynamic sea ice processes, and sea ice-ocean interactions. Therefore, increasing the model resolution and improving the representation of sea ice dynamics and the physical processes controlling sea ice-ocean interactions are essential for improving the accuracy of Antarctic sea ice simulation.

Weather extremes become more frequent and intense with climate change, but how weather extremes impact household wealth in the Global South remains elusive in many regions. We combined nationally representative quarterly household panel data with climate data to evaluate the impact of weather extremes on household poverty in Kyrgyzstan between 2013 and 2020. We evaluated multiple dimensions of poverty by quantifying changes in nutrition, education, health, and living standards. We used a linear quantile mixed model to relate the poverty dimensions with four salient weather extremes: cold winters, hot summers, excessive rains, and dry spells. Our findings show that all weather extremes harmed household wealth but with substantial spatial variation. Cold winters were the most detrimental, with negative consequences that continued into the subsequent year. Poor households suffered disproportionally more from extremes than rich ones. Our results underscore the need to initiate place-based adaptation options to cushion the adverse effects of extreme weather events on household wealth.

Predicting residential water use is critical to efficiently manage urban water resource systems. Simultaneously, understanding the factors driving residential water use is required to plan for future urban change and achieve effective water resource management. Current approaches examining residential water use identify the drivers of household water use through parametric or non-parametric statistical approaches. Parametric approaches have high predictive errors and lack the ability to accurately capture interactions between features but allow for easy interpretation. Non-parametric approaches have lower predictive errors and can capture non-linear feature interactions but do not allow for easy interpretation. We use non-parametric statistical models of household water use and recent advances in interpretable machine learning to understand the drivers of residential water use. Specifically, we use post-hoc interpretability methods to examine how drivers of water use interact, focusing on environmental, demographic, physical housing, and utility policy factors. We find all four categories of factors are important for estimating water use with environmental and utility policy factors playing the largest role. Additionally, we identify non-linear interactions between many variables within and across these classes. We show this approach provides both high predictive accuracy and identification of complex water use factors, offering important insight for urban water management.

The Indian Ocean Dipole (IOD) is a prominent mode of climate variability in the tropical Indian Ocean (IO). It exerts a significant influence on biological activities in this region. To elucidate the biological response to the IOD, previous research has introduced the biological dipole mode index (BDMI). However, the delineation of the region by the BDMI has limitations in capturing IOD-induced chlorophyll variations in the IO. By analyzing observational data and historical simulations from a Coupled Model Intercomparison Project model, this study shows that chlorophyll anomalies in the IO exhibit a dipole pattern in response to IOD. During the developing and mature phases of the positive IOD, we observe a substantial decrease in chlorophyll in the south-southwest of India, contrasting with a pronounced increase in the southeast of the IO. This response is attributed to anomalous southeasterly winds induced by IOD, which enhance nutrient upwelling in the southeastern IO and suppress it in the south-southwest of India, resulting in corresponding changes in surface chlorophyll blooms. Based on this finding, we propose a new biological dipole index that more robustly explains the surface chlorophyll response to IOD in the tropical IO. This study highlights the profound influence of IOD on oceanic chlorophyll and underscores the importance of a more comprehensive understanding of the associated biophysical interactions.

The Pacific Decadal Oscillation (PDO) and Pacific Meridional Mode (PMM) are prominent climate modes in the North Pacific with well-established impacts on tropical cyclone (TC) genesis in the western North Pacific (WNP) basin. While previous research has primarily focused on the roles of the PDO and PMM in regulating TC genesis through the modification of large-scale environmental factors, this study investigates the evolving influence of the PDO on WNP TC genesis since the 1950s. Remarkably, our analysis reveals a shift in the PDO-TC genesis relationship, transitioning from a significant negative correlation to a significant positive correlation since the 1990s. This shift is attributed to variations in the specific large-scale factors through which the PDO affects TC genesis. Furthermore, this study suggests that these changes appear to be linked to the PMM strengthening on the interdecadal timescale in recent decades. The linkage of the PMM strengthening to the PDO-related atmospheric circulation is further confirmed by the results of a 500 year pre-industrial numerical experiment, suggesting that the PMM strengthening may result from natural internal variability. The results underscore the non-stationary relationship between PDO and WNP TC genesis, with the PMM intensity probably influencing their relationship.

Climate change poses a serious threat to permafrost integrity, with expected warmer winters and increased precipitation, both raising permafrost temperatures and active layer thickness. Under ice-rich conditions, this can lead to increased thermokarst activity and a consequential transfer of soil organic matter to tundra ponds. Although these ponds are known as hotspots for CO₂ and CH₄ emissions, the dominant carbon sources for the production of greenhouse gases (GHGs) are still poorly studied, leading to uncertainty about their positive feedback to climate warming. This study investigates the potential for lateral thermo-erosion to cause increased GHG emissions from small and shallow tundra ponds found in Arctic ice-wedge polygonal landscapes. Detailed mapping of fine-scale erosive features revealed their strong impact on pond limnological characteristics. In addition to increasing organic matter inputs, providing carbon to heterotrophic microorganisms responsible for GHG production, thermokarst soil erosion also increases shore instability and water turbidity, limiting the establishment of aquatic vegetation—conditions that greatly increase GHG emissions from these aquatic systems. Ponds with more than 40% of the shoreline affected by lateral erosion experienced significantly higher rates of GHG emissions (∼1200 mmol CO₂ m⁻² yr⁻¹ and ∼250 mmol CH₄ m⁻² yr⁻¹) compared to ponds with no active shore erosion (∼30 mmol m⁻² yr⁻¹ for both GHG). Although most GHGs emitted as CO₂ and CH₄ had a modern radiocarbon signature, source apportionment models implied an increased importance of terrestrial carbon being emitted from ponds with erosive shorelines. If primary producers are unable to overcome the limitations associated with permafrost disturbances, this contribution of older carbon stocks may become more significant with rising permafrost temperatures.

Over the Amazon region, rainfall-induced changes to CO₂ pathways significantly impact humans and multiple ecosystems. Its resilience is of vital importance, and idealized CO₂ removal experiments indicate that declining trends in rainfall amounts are irreversible and exhibit a deficiency when the CO₂ concentration returns to the pre-industrial level. The irreversible decline in Amazon rainfall is mainly due to the weakened ascent, further led by two main causes. (1) Enhanced tropospheric warming and a wetter atmospheric boundary layer over the tropics during CO₂ removal generate a strong meridional gradient of temperature and specific humidity; driven by prevailing northeasterly winds, negative moist enthalpy advection occurs, which in turn weakens the ascent over the Amazon and results in anomalous drought. (2) The enhanced radiative cooling of atmospheric column. Driven by the negative lapse-rate feedback, the outgoing longwave radiative flux increases in the clear-sky atmosphere. As a result, the anomalous diabatic descent generates to maintain the energy balance of the atmospheric column. This result implies that the symmetric removal of CO₂ does not guarantee full recovery of regional precipitation.

Understanding how temperature affects coral reef fish recruitment success is crucial for assessing impacts of ocean warming on coral reef resilience. We utilized a long-term fish survey dataset along the west coast of Hawai'i Island to investigate the role of sea surface temperature (SST) in influencing recruitment timing and density. The dataset consisted of 17 years of surveys, with 25 sites annually surveyed in the months of May, July, September and November. We found that peak recruitment, i.e. the maximum number of recruits recorded across all surveys per year, usually occurred during July surveys. For sites where peak recruitment for that year occurred outside July, there were significantly fewer fish recruits than for sites whose peak recruitment occurred in July. In addition, the timing of peak recruitment is influenced by anomalously warm or cool years prior to spawning. The decrease in recruit density outside these times is likely influenced by recruits being exposed to temperatures warmer and cooler than their optimum. Our results show that climate variability is having an impact on the timing of peak recruitment, creating a mismatch between the thermal optimum of developing recruits and the thermal environment they develop in, negatively affecting recruit density in critical coral reef habitats. Altered and reduced recruitment has the potential to disrupt reef community structure and long-term fisheries sustainability in Hawai'i, with important management implications for coral reefs in the future.

A large fraction of the population in rural India continues to use biomass fuel for cooking and heating. In-utero exposure to the resulting household air pollution (HAP), is known to increase the risk of low birth weight (LBW). Mitigating HAP, by shifting to clean cooking fuel (CCF), is expected to minimize the risk associated with LBW. However, India also has high levels of ambient air pollution (AAP). Whether exposure to AAP modifies the effect of reducing HAP by switching to CCF on LBW is not known. The present study addressed this knowledge gap by analyzing the National Family Health Survey (2019–21) data of the most recent full-term, singleton, live births from rural households born after 2017 (n = 56 000). In-utero exposure to AAP was calculated from satellite-derived ambient fine particulate matter (PM_2.5) concentration at the level of the primary sampling unit for the pregnancy duration of the mothers. The moderation by ambient PM_2.5 level on the odds of LBW among CCF users was examined by logistic regression analysis with interaction. The adjusted odds ratio (aOR) of LBW was 7% lower among users of CCF. At the lowest Decile (20–37 μg m⁻³) of ambient PM_2.5 exposure, the aOR of LBW among CCF users was 0.83 (95% CI:0.81–0.85). At every 10th percentile increase in ambient PM_2.5 exposure (in the range 21–144 μg m⁻³), aOR increased gradually, reaching the value of 1 at PM_2.5 level of 93 μg m⁻³. Our results, therefore, suggest that the benefit of using CCF during pregnancy may be downgraded by moderate to high ambient PM_2.5 exposure.

The nucleation of iodic acid (HIO₃) and iodous acid (HIO₂) play a significant role in marine new particle formation (NPF) events. However, the inability to explain intensive NPF bursts in polluted coasts indicates the participation of potential precursors. Herein, we identified a novel nucleation mechanism of HIO₃–HIO₂ system enhanced by the urban pollutant sulfuric acid (H₂SO₄). We found that H₂SO₄ could largely enhance the cluster formation rates (J, cm⁻³ s⁻¹) of HIO₃–HIO₂ system, especially in high [H₂SO₄] regions near H₂SO₄ emission sources. The enhanced J of HIO₃–HIO₂–H₂SO₄ system performs better match than that of HIO₃–HIO₂ system with the observational rates of polluted coasts and polar regions, such as Zhejiang and Marambio. Moreover, the H₂SO₄-involved cluster formation is realized without Gibbs free energy barrier and dominate broadly in marine regions with rich H₂SO₄ and scarce iodine concentrations. These findings may help to explain some missing fluxes of marine new particles and emphasize the impact of urban components on marine nucleation processes.

Farmland abandonment is often associated with biophysical, political, or socio-economic changes, like droughts, economic reforms, rural-urban migration, or armed conflicts. Syria has seen several such changes in the period between 2000–2011, however, few assessments of how these factors have interacted with land abandonment have been carried out. In this study we investigate land abandonment patterns in northeast Syria, using a land use classification based on satellite data to indicate agricultural drought impacts and land abandonment. We combine these data with information on land use and migration patterns collected through a unique fieldwork, including surveys and interviews with Syrian farmers who had migrated to Turkey. Our analysis shows that drought coincided with a strong drop in cultivated croplands in 2008 and 2009. We also found a comparatively high cropland abandonment between 2001 and 2013, however no strong increases during or after drought years. Local insights indicate that migration took place during both normal years and drought years, and that most migrants had abandoned their lands after leaving Syria. We suggest that long-term mismanagement of water resources along with changes in the political economy, drove land abandonment in northeast Syria between 2001 and 2010. After 2011, armed conflict likely drove abandonment, but rates remained similar to the pre-conflict period. We discuss the potential of land abandonment as an indicator of rural migration in areas where migration data is sparse and conclude that more research is needed to understand the migration-land abandonment nexus, particularly in the Middle East.

Mesoscale eddies are prevalent throughout the global ocean and have significant implications on the exchange of heat, salt, volume, and biogeochemical properties. These small-scale features can potentially influence regional and global climate systems. However, the effects of climate change on ocean eddies remain uncertain due to limited long-term observational data. To address this knowledge gap, our study focuses on examining the impact of greenhouse warming on surface mesoscale eddy characteristics, utilizing a high-resolution climate simulation project. Our model experiments provided valuable insights into the potential effects of greenhouse warming on mesoscale eddies, suggesting that mesoscale eddies will likely become more frequent under greenhouse warming conditions and exhibit larger amplitudes and radii, especially in regions characterized by strong ocean currents such as the Antarctic Circumpolar Current and western boundary currents. However, a distinctive pattern emerged in the Gulf Stream, with increases in eddy occurrence and radius and significant decreases in eddy amplitude. This phenomenon can be attributed to the relationship between eddy lifespans and their properties. Specifically, in the Kuroshio Current, the amplitude of eddies increased due to the increased occurrence of long-lived eddies. In contrast, in the Gulf Stream, the amplitude of eddies decreased significantly due to the decreased occurrence of long-lived eddies. This distinction arises from the fact that long-lived eddies can accumulate more energy than shorter-lived eddies throughout their lifetime. These findings provide valuable insights into the complex dynamics of mesoscale eddies in a warming world.

The lack of affordable, reliable, and resilient energy services remains a challenge for many U.S. households. Few studies have investigated how temperature makes already vulnerable Black, low-income, and less-educated households more likely to experience energy poverty. We construct a unique 8-year historical panel dataset to unpack the relationship between temperatures and energy burdens, paying specific attention to additional burdens among the most vulnerable groups. We find that hot and cold temperatures have further exacerbated the disproportionate impact on energy burdens across regions and multiple vulnerable groups. Extremely low-income groups are ∼6 times more adversely affected by temperatures than high-income groups. Temperatures also put other already marginalized groups, such as those less-educated/unemployed/living in energy-inefficient old houses, at higher risk of falling into an energy poverty trap. Considering temperatures are the dominant feature differentiating households in their ability to meet basic electricity needs, we recommend more equitable and inclusive electrification strategies and compensation mechanisms for affected communities to improve energy equity.

While the global food system substantially contributes to environmental degradation and climate change, significant amounts of lost or wasted foods along the food supply chain actively contribute to global air pollution and related health risks. In this study, we use an environmentally-extended input–output model to quantify air pollution embedded in global food loss and waste (FLW) and investigate how FLW reduction policies can mitigate air pollution linked to food consumption, decreasing associated premature mortality risks across global regions. While estimating a positive impact of FLW reduction policies on decreasing air pollution levels (from −1.5% of SO₂ emissions to −10.2% of NH₃ emissions) and mortality reductions (over 67 000 lives worldwide) our findings highlight that rebound effects, wherein a reallocation of consumption from food to non-food commodities, decrease health and environmental benefits by over three quarters (compared to the case with no rebound). Such rebound effects can be substantially mitigated when final consumption shifts towards less pollution-intensive products, such as service activities, rather than conforming to the current composition of non-food consumption. Our results suggest that FLW-related policies would benefit from complementary measures that incentivize sustainable non-food consumption to effectively foster the transition towards a healthier and more sustainable planet.

Dairy manure is one of the largest sources of methane (CH₄) emissions and air pollution from agriculture. In a previous study, we showed that composting dairy manure with biochar substantially reduces CH₄ emissions and could help the dairy industry meet climate goals. However, it remained unclear whether biochar could also mitigate the emission of air pollutants and odor during composting. Here, we conducted a full-scale composting study at a dairy farm and monitored the emission of greenhouse gases (CO₂, CH₄, N₂O) and air pollutants (H₂S, VOCs, NO_x, NH₃) from compost piles amended with or without biochar. We found that amending compost with biochar significantly reduced total CH₄ emissions by 58% (±22%) and cut H₂S, VOCs, and NO_x emissions by 67% (±24%), 61% (±19%) and 70% (±22%), respectively. We attribute this reduction in emissions to improved oxygen diffusion from the porous biochar and the adsorption of gas precursors to the biochar surface. Interestingly, NO_x fluxes from the composting dairy manure were much higher than the few values reported in the literature, suggesting that dairy manure could also be a significant source of NO_x emissions. We estimate that biochar-composting of dairy manure would reduce the social cost of manure emissions from this farm by over $66 000 annually. Results from this study suggest that composting dairy manure with biochar, in addition to reducing CH₄ emissions, may help to improve air quality and the health and wellbeing of rural communities, but further studies are needed to test the quantitative impacts.

Over the past decades, ecological restoration initiatives in China have made great progress in restoring degraded forests and increasing vegetation coverage, yet the carbon sequestration effects of these initiatives in the context of climate change are not clear. In this study, we assessed the effects of vegetation restoration on gross primary production (GPP) in China's forestry engineering areas, where large-scale vegetation restoration programmes were launched, during 2001–2020 by disentangling the respective roles of land cover change (LCC), CO₂ fertilization, and climate changes using a two-leaf light use efficiency model. We found that LCC attributed by the vegetation restoration dominantly accelerated the increase of GPP in seven out of the eight areas, and CO₂ fertilization played a near-equivalent role in all areas. By contrast, the changes in different climate factors contributed to GPP variations diversely. The solar radiation variation greatly inhibited the vegetation GPP over time in seven out of these areas, and the changes in air temperature and vapor pressure deficit regulated GPP inter-annual variations without clear trends in all areas. This study advances our understanding of the contribution of China's afforestation on its forest GPP in a changing climate, which may help to better manage forests to tackle the challenge of the climate crisis in the future.

Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km² to 675 239 km². These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82%–95%), and the smallest was within the subtropical desert (range <10%–80%) and subtropical steppe ecological zones (range <10%–60%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. We found a forest area of 288 749 km² with high forest extent agreement, and 340 661 km² with medium forest extent agreement. The high-to-medium forest extent agreement of 629 410 km² is comparable to the official national estimate of 656 920 km². We found a high forest extent agreement across the Yucatan Peninsula and mountain areas in the Sierra Madre Oriental and Sierra Madre Occidental. The tropical dry forest and subtropical mountain system represent the two ecological zones with the highest areas of disagreement among RSB products. These findings show discrepancies in forest extent estimates across ecological zones in Mexico, where additional ground data and research are needed. Dataset available at https://doi.org/10.3334/ORNLDAAC/2320.

Racial-ethnic minority populations in the US are disproportionately exposed to airborne fine particulate matter (PM_2.5), but few national studies have focused individually on the sources that contribute to these disparities. We address this gap by conducting a comprehensive analysis of PM_2.5 exposure disparities by race-ethnicity in the US, focusing on three source-categories: mobile-sources, cooking, and all other sources combined. Our approach is based on high-resolution, national land-use regression estimates of source-resolved PM_2.5 components, derived from high-resolution aerosol mass spectrometer measurements. We find that each of these sources contributes approximately one-third of the overall PM_2.5 exposure disparities by race-ethnicity. While the importance of mobile-source tailpipe emissions is well recognized, our study underscores the significance of cooking emissions in creating PM_2.5 exposure disparities. This finding represents a potentially significant opportunity to reduce these disparities, as cooking emissions are currently largely unregulated. It has important implications for policymakers and public health advocates aiming to address the persistent issue of racial-ethnic disparities in air pollution.

As wildfires continue to worsen across western United States, forest managers are increasingly employing prescribed burns as a way to reduce excess fuels and future wildfire risk. While the ecological benefits of these fuel treatments are clear, little is known about the smoke exposure tradeoffs of using prescribed burns to mitigate wildfires, particularly among at-risk populations. Outdoor agricultural workers are a population at increased risk of smoke exposure because of their time spent outside and the physical demands of their work. Here, we assess the smoke exposure impacts among outdoor agricultural workers resulting from the implementation of six forest management scenarios proposed for a landscape in the Central Sierra, California. We leverage emissions estimates from LANDIS-II to model daily PM_2.5 concentrations with the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and link those to agricultural employment data from the Bureau of Labor Statistics. We find a u-shaped result, in that moderate amounts of prescribed burning result in the greatest reduction in total smoke exposure among outdoor agricultural workers, particularly during months of peak agricultural activity due to wildfire-specific smoke reductions. The reduction in total smoke exposure, relative to scenarios with the least amount of management, decreases as more prescribed burning is applied to the landscape due to the contributions of the fuel treatments themselves to overall smoke burden. The results of this analysis may contribute to preparedness efforts aimed at reducing smoke exposures among outdoor agricultural workers, while also informing forest management planning for this specific landscape.

About 25% of the emitted anthropogenic CO₂ is absorbed by the ocean and transported to the interior through key gateways, such as the Southern Ocean or the North Atlantic. Over the next few centuries, anthropogenic CO₂ is then redistributed by ocean circulation and stored mostly in the upper layers of the subtropical gyres. Because of the combined effects of (i) weakening buffering capacity, (ii) warming-induced lower solubility, (iii) changes in wind stress and (iv) changes in ocean circulation, there is a high confidence that the ocean sink will weaken in the future. Here, we use IPCC-class Earth System Model (ESM) simulations following the SSP1-2.6 and SSP5-8.5 climate change scenarios extended to the year 2300 to reveal that anthropogenic CO₂ begins to outgas in the subtropical gyres of both hemispheres during the summer months of the 21st century. In 2100, about 53% of the surface ocean experience outgassing at least one month in a year in SSP1-2.6, against 37% in SSP5-8.5. After 2100, this fraction keeps increasing, reaching 63% by 2300 in SSP5-8.5 while stabilizing at 55% in SSP1-2.6. This outgassing pattern is driven by the rapid increase in oceanic pCO₂, faster than the atmospheric pCO₂, due to the combined effect of both rapid warming and long-term accumulation of anthropogenic carbon in these regions. These findings call for increased observation efforts in these areas, particularly in the subtropical gyres of the Southern Hemisphere, in order to detect future release of anthropogenic carbon and accurately constrain the future carbon budget.

Plastic environmental pollution is threatening water resources, aquatic ecosystems, and human wellbeing but is still highly uncertain with global fluxes to sea of 0.4–13 Mt\yr, and up to 517 Mt of mismanaged plastics on land. Catchment modelling tools are required to challenge current knowledge, simulate impacts of management initiatives, and complement global and observation-based studies. Here we present the first spatiotemporally explicit model for mismanaged plastic mobilization and transport from land to sea from the INtegrated CAtchment (INCA) family. INCA-Macroplastics encompasses all components of the catchment, is driven by available data (weather, population, solid waste) and enables calibration and validation against diverse observations (river monitoring, household surveys). INCA-Macroplastics was applied to the Imus River, Philippines, one of World's most polluted rivers. Given large uncertainties on catchment plastic retention, two calibrations and two emission scenarios were developed to describe catchment plastic fluxes, residence time and stocks over 1990–2020. Plastic fluxes to the sea are highly variable over years and seasons (55–75% exported during the wet season) and have increased exponentially over 1990–2020 from 5–100 to 2000–15000 tons\yr. INCA-Macroplastics is the first model handling plastic accumulation on land and highlights the importance of extreme flooding events in mobilizing and transporting legacy plastics. Model outputs explicitly show that current land plastic pollution can impact fluxes to the ocean for up to 30 years into the future. INCA-Macroplastics is useful to provide tailored recommendations for local monitoring, testing waste management scenarios and pointing towards future research avenues.

Using a metaphor based on a historical debate between socialist and free-market economists, Salliou and Stritih (Environ. Res. Lett.18 151001) advocate for decentralizing environmental management to harness emergent complexity and promote ecosystem health. Concerningly, however, their account seems to leave little room for top-down processes like government-led sustainability programs or centrally-planned conservation initiatives, the cornerstone of the post-2020 biodiversity framework. While we appreciate their call for humbleness, we offer a few words in defense of planning. Drawing on evidence from ecology, economics, and systems theory, we argue that (1) more complexity is not always better; (2) even if it were, mimicking minimally-regulated markets is probably not the best way to get it; and (3) sophisticated decision support tools can support humble planning under uncertainty. We sketch a re-interpretation of the socialist calculation debate that highlights the role of synthesis and theoretical pluralism. Rather than abandoning big-picture thinking, scientists must continue the difficult work of strengthening connections between and across multiple social, ecological, and policy scales.

A paper modeling future CO₂ fertilization of oil palm (OP) resulting in higher palm oil yields is a significant advance. However, climate and disease effects on OP are discussed herein inferring that CO₂ fertilization will not occur significantly. It is important that logical assessments of future climate effects on the palm oil industry occur.