Table of contents

This ERL focus collection has published 17 papers that have advanced our understanding of different dimensions of warming-induced tree mortality. Here we summarize these focus collection papers, organized by four topics related to tree mortality: pathogens, droughts/heat waves, fire/bark beetles, and teleconnections/air pollution. This focus collection illustrates a variety of methods in measuring and modeling tree-mortality, and adds significant new research findings into the scientific literature on tree mortality from hotter droughts. Some of these results also are useful for policymakers and forest managers in addressing amplified forest stress and tree mortality as a result of increasingly severe warming-induced climate and weather extremes.

Fossil CO₂ emissions in 2021 grew an estimated 4.2% (3.5%–4.8%) to 36.2 billion metric tons compared with 2020, pushing global emissions back close to 2019 levels (36.7 Gt CO₂).

The interacting effects of multiple hazards pose a substantial challenge to poverty reduction and national development. Yet, social vulnerability to multiple hazards is a relatively understudied, though growing concern. The impacts of climate hazards in particular, leave increasingly large populations becoming more exposed and susceptible to the devastating effects of repeat, chronic and sequential natural hazards. Multi-hazard research has focused on the physical aspects of natural hazards, giving less attention to the social facets of human-hazard interaction. Further, there is no single conceptualization of 'multi-hazard'. This systematic review utilizes correlations and hierarchical clustering to determine how social vulnerability is assessed in the context of the three most common classifications of 'multi-hazard': aggregate, cascading and compound. Results reveal these classifications of 'multi-hazard' each focus on different aspects of social vulnerability. Studies in the aggregate classification of multi-hazard were more likely to represent social vulnerability as an outcome of hazard events, while those in the cascading and compound classifications more often addressed social vulnerability as a preexisting condition. Further, knowledge of social vulnerability to multi-hazards comes mainly from the aggregate classification and the mitigation phase of the disaster cycle. The difference in perspectives of social vulnerability covered, and limited context in which multi-hazard studies of social vulnerability have been applied, mean a full understanding of social vulnerability remains elusive. We argue that research should focus on the cascading and compound classifications of multi-hazards, which are more suited to interrogating how human-(multi)hazard interactions shape social vulnerability.

Climate-related risks in Central and South America have received increased attention and concern in science and policy, but an up-to-date comprehensive review and synthesis of risks and adaptation potential is currently missing. For this paper we evaluated over 200 peer-reviewed articles and grey literature documents published since 2012. We found that climate change in Central and South America during the 21st century may increase the risk to severe levels for the following topical risk clusters: (a) Food insecurity; (b) Floods and landslides; (c) Water scarcity; (d) Epidemics of vector-borne diseases; (e) Amazon Forest biome shift; (f). Coral bleaching; (g) Coastal risks of sea level rise, storm surges and erosion; (h) Systemic failure due to cascading impacts of hazards and epidemics. Our synthesis also identified feasible adaptation measures for each risk. The impacts of the risks will be heterogeneous throughout the region, with rural communities, Indigenous peoples, Afro-Latin Americans, women, disabled people, and migrants identified as being the most severely affected. We refer to a number of adaptation options for each risk. However, unabated climate change together with low adaptive capacity will strictly limit adaptation options. Immediate strengthening of policies for building adaptive capacity and increase of research on the risk-adaptation nexus in Central and South America are paramount. Our findings might contribute to guide the adjustment and emphasis of adaptation policies and climate risk management strategies from local to national level.

The negative impact of plastic accumulation in aquatic ecosystems is a known and undeniable problem. However, while many of the scientific community's countermeasures against such accumulation target the effects of the most common commodity plastics, the consequences of so-called 'biodegradable' plastics in those ecosystems are seldom discussed. After all, though their alleged biodegradability sustains the widespread belief that they are harmless to the environment, because a material's fate determines its classification as biodegradable or not, many plastics classified as biodegradable do not in fact meet the required norms and standards of biodegradability in aquatic ecosystems. Furthermore, during the past five years, the scientific community has shown that the degradation of such plastics can generate bio-microplastics that have effects similar to or worse than those of conventional microplastics (MPs). Against that background, this review details the latest findings regarding how biodegradable plastics can influence aquatic ecosystems and thus cause adverse health effects in living organisms and/or act as vectors of chemical pollutants. Beyond that, it identifies the key aspects of such trends to be investigated in greater depth, including the need to consider a wider variety of biodegradable plastics and to develop systematic methods that allow quantifying and identifying the remains of those pollutants in living species. Other aspects worth considering include the arrival and mobilisation dynamics of MPs in oceans. The ways in which small animals fed by filtering (e.g. red crabs and other zooplankton organisms) move MPs through the water column and into food webs also merit attention, for those MPs are ingested by numerous species at different trophic levels, at which point bioaccumulation in tissues has to be considered as a factor of toxicity. This review closes with a series of recommendations and perspectives for future studies on 'biodegradable plastics' in aquatic ecosystems.

Although agriculture is enjoying booming development it is facing increasingly serious environmental pressures. With increase in the scale of fruit planting, inorganic mineral elements are becoming one of the main sources of non-point pollution. How to achieve sustainable production in agriculture is an issue that needs urgent attention in current rural development. In this paper, based on the micro-production data of peach farmers in 18 prefecture-level provinces, we introduce fine management techniques into the production function to analyze the effects of different techniques and further explore the influence of fine management techniques on fertilizer efficiency. Our findings show that with no change in the degree of investment in fine management techniques the increase in use of chemical fertilizers and pesticides has not only made little contribution to increasing profits but has also resulted in excessive investment in fertilizers that damage the environment. Notably, fine management techniques exerting positive effects on the application efficiency of mineral elements could be an efficient and sustainable way to ease the conflict between environment and profit. However, such techniques are used rarely in practice due to the lack of economic incentives. A brief review of the main measures, such as timely updating of market information, agricultural product branding and socialized services, is offered.

Forests are considered important in the mitigation of climate change. Biophysical effects of afforestation and deforestation on land surface temperature (LST) have been extensively documented. As a fundamental variable of forest structure, however, few studies have investigated the biophysical feedback of forest canopy height (FCH) changes on LST at large scale. This study is designed to investigate the impact of FCH changes on local land LST and clarify the biophysical processes controlling LST change from 2003 to 2005 over the contiguous United States, based on satellite observations. To this end, one satellite-based FCH product is selected, and the space-for-time approach, together with the energy balance equation, is applied. Results show that for different forest types, namely evergreen forest (EF), deciduous forest (DF), and mixed forest (MF), taller forests present a greater net cooling effect (0.056–0.448 K) than shorter forests at annual scale. The increase in net radiation and sensible heat flux was less than the increase in the latent heat flux when FCH classes converted from shorter to taller, resulting in annual net cooling effects. Furthermore, the cooling effect of EF is stronger than that of DF and MF, whether for tall, medium, or short FCH classes. Multiple regression analysis reveals that the changes in biophysical components can effectively explain the LST change during the growing season. Our findings provide a new insight for forest management decision-making with the purpose of mitigating climate warming.

Northwestern China (NWC) is among the major global hotspots undergoing massive terrestrial water storage (TWS) depletion. Yet driver(s) underlying such region-wide depletion remain controversial, i.e. warming-induced glaciermelting versus anthropogenic activities. Reconciling this controversy is the core initial step to guide policymaking to combat the dual challenges in agriculture production and water scarcity in the vastly dry NWC toward sustainable development. Utilizing diverse observations, we found persistent cropland expansion by >1.2 × 10⁴ km² since 2003, leading to growth of 59.9% in irrigated area and 19.5% in agricultural water use, despite a steady enhancement in irrigation efficiency. Correspondingly, a substantially faster evapotranspiration (ET) increase occurred in crop expansion areas, whereas precipitation exhibited no long-term trend. Counterfactual analyses suggest that the region-wide TWS depletion is unlikely to have occurred without an increase in crop expansion-driven ET even in the presence of glaciermelting. These findings imply that sustainable water management is critically needed to ensure agriculture and water security in NWC.

Environmental risk factors for psychiatric health are poorly identified. We examined the association between air pollution and psychiatric symptoms, which are often precursors to the development of psychiatric disorders. This study included 570 participants in the US Veterans Administration (VA) Normative Aging Study (NAS) and 1114 visits (defined as an onsite follow-up at the VA with physical examination and questionnaires) from 2000 to 2014 with information on the brief symptom inventory (BSI) to assess their psychiatric symptom levels. Differences in the three BSI global measures (global severity index (GSI), positive symptom distress index (PSDI) and positive symptom total (PST)) were reported per interquartile (IQR) increase of residential address-specific air pollutants levels (fine particulate matter—PM_2.5, ozone—O₃, nitrogen dioxide—NO₂) at averages of one week, four weeks, eight weeks and one year prior to the visit, using generalized additive mixed effects models. We also evaluated modification by neighborhood factors. On average, among the NAS sample (average age: 72.4 years (standard deviation: 6.7 years)), an IQR increase in one and four week averages of NO₂ before a visit was associated with a PSDI T score (indicator for psychiatric symptom intensity) increase of 1.60 (95% confidence interval (CI): 0.31, 2.89), 1.71 (95% CI: 0.18, 3.23), respectively. Similarly, for each IQR increase in one and four week averages of ozone before a visit, the PSDI T-score increased by 1.66 (95% CI: 0.68, 2.65), and 1.36 (95% CI: 0.23, 2.49), respectively. Stronger associations were observed for ozone and PSDI in low house-value and low household income areas. No associations were found for PM_2.5. Exposure to gaseous air pollutants was associated with a higher intensity of psychiatric symptoms among a cohort of older men, particularly in communities with lower socio-economic or housing conditions.

Historic land alterations and agricultural intensification have resulted in legacy phosphorus (P) accumulations within lakes and reservoirs. Internal loading from such legacy stores can be a major driver of future water quality degradation. Yet, little is known about the magnitude and spatial patterns of legacy P accumulation in lentic systems, and how watershed disturbance trajectories drive these patterns. Here, we used a meta-analysis of 113 paleolimnological studies across 124 lakes and four reservoirs (referred here on as lakes) in 20 countries to quantify the linkages between the 100 year trajectories of P concentrations in lake sediments, watershed inputs, and lake morphology. We find five distinct clusters for lake sediment P trajectories, with lakes in the developing and developed world showing distinctly different patterns. Lakes in the developed world (Europe and North America) with early agricultural intensification had the highest sediment P concentrations (1176–1628 mg kg⁻¹), with a peak between the 1970–1980s and a decline since then, while lakes in the developing world, specifically China, documented monotonically increasing sediment P concentrations (857–1603 mg kg⁻¹). Sediment P trajectories reflected watershed disturbance patterns and were driven by a combination of anthropogenic drivers (fertilizer input and population density) and lake morphology (watershed to lake area ratio). Specifically, we found the largest legacy accumulation rates to occur in shallow lakes experiencing long-term land-use disturbances. These links between land-use change and P accumulation in lentic systems can provide insights about inland water quality response and help to develop robust predictive models useful for resource managers and decision-makers.

Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability (FDP) caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of FDP for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events (n = 71 434) and a suite of geospatial predictors (e.g. flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a FDP for each 100 m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g. forest) resulting in higher error rates than others (e.g. wetlands). Our results identified FDP hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating FDP estimates across the nation.

Major Arctic rivers are undergoing changes due to climate warming with higher discharge and increased amounts of solutes and organic carbon (OC) draining into rivers and coastal seas. Permafrost thaw mobilizes previously frozen OC to the fluvial network where it can be degraded into greenhouse gases and emitted to the atmosphere. Degradation of OC during downstream transport, especially of the particulate OC (POC), is however poorly characterized. Here, we quantified POC degradation in the Kolyma River, the largest river system underlain with continuous permafrost, during 9–15 d whole-water incubations (containing POC and dissolved OC—DOC) during two seasons: spring freshet (early June) and late summer (end of July). Furthermore, we examined interactions between dissolved and particulate phases using parallel incubations of filtered water (only DOC). We measured OC concentrations and carbon isotopes (δ¹³C, Δ¹⁴C) to define carbon losses and to characterize OC composition, respectively. We found that both POC composition and biodegradability differs greatly between seasons. During summer, POC was predominantly autochthonous (47%–95%) and degraded rapidly (∼33% loss) whereas freshet POC was largely of allochthonous origin (77%–96%) and less degradable. Gains in POC concentrations (up to 31%) were observed in freshet waters that could be attributed to flocculation and adsorption of DOC to particles. The demonstrated DOC flocculation and adsorption to POC indicates that the fate and dynamics of the substantially-sized DOC pool may shift from degradation to settling, depending on season and POC concentrations—the latter potentially acting to attenuate greenhouse gas emissions from fluvial systems. We finally note that DOC incubations without POC present may yield degradation estimates that do not reflect degradation in the in situ river conditions, and that interaction between dissolved and particulate phases may be important to consider when determining fluvial carbon dynamics and feedbacks under a changing climate.

Land aridity is often characterized by the aridity index (AI), which does not account for land surface water-energy interactions that are crucially important in determining regional climate. Such interactions can be captured by the evaporative fraction (EF, ratio of evapotranspiration to available energy) regimes. As EF is subject to energy and water limitations in humid and dry areas, respectively, EF regimes may be used to characterize land aridity to account for the influence of complex land characteristics and their impact on water availability. Here, we propose a simple framework to characterize land aridity by statistically ranking the coupling strength between EF and surface energy and water terms. The framework is demonstrated using gridded data and compared with AI over the U.S. and China. Results show that regionalization of aridity zones based on EF regimes and a two-tiered classification scheme may provide information such as surface energy and water variability complementary to the background aridity depicted by AI, with implications for extreme events.

We provide a methodology to estimate possible extreme changes in seasonal rainfall for the coming decades. We demonstrate this methodology using Indian summer monsoon rainfall as an example. We use an ensemble of 1669 realizations of Indian summer monsoon rainfall from selected seasonal prediction systems to estimate internal variability and show how it can exacerbate or alleviate forced climate change. Our estimates show that for the next decade there is a ∼60% chance of wetting trends, whereas the chance of drying is ∼40%. Wetting trends are systematically more favoured than drying with the increasing length of the period. However, internal variability can easily negate or overwhelm the wetting trends to give temporary drying trends in rainfall. This provides a quantitative explanation for the varying trends in the past observational record of rainfall over India. We also quantify the likelihood of extreme trends and show that there is at least a 1% chance that monsoon rainfall could increase or decrease by one fifth over the next decade and that more extreme trends, though unlikely, are possible. We find that monsoon rainfall trends are influenced by trends in sea-surface temperatures over the Niño3.4 region and tropical Indian Ocean, and ∼1.5° cooling or warming of these regions can approximately double or negate the influence of climate change on rainfall over the next two decades. We also investigate the time-of-emergence of climate change signals in rainfall trends and find that it is unlikely for a climate change signal to emerge by the year 2050 due to the large internal variability of monsoon rainfall. The estimates of extreme rainfall change provided here could be useful for governments to prepare for worst-case scenarios and therefore aid disaster preparedness and decision-making.

The 2020 wildfire season (May through December) in the United States was exceptionally active, with the National Interagency Fire Center reporting over 10 million acres ($\gt$40 000 km²) burned. During the September 2020 wildfire events, large concentrations of smoke particulates were emitted into the atmosphere. As a result, smoke was responsible for ∼10%–30% reduction in solar power production during peak hours as recorded by the California Independent System Operator (CAISO) sites. In this study, we focus on a 9 d period in September when wildfire smoke had a profound impact on solar energy production. During the smoke episodes, hour-ahead forecasts utilized by CAISO did not include the effects of smoke and therefore overestimated the expected power production by ∼10%–50%. Here we use multiple observational networks and a numerical weather prediction (NWP) model to show that the wildfire events of 2020 had a significantly detrimental influence on solar energy production due to high aerosol loading. We find that including the contribution of biomass burning particles greatly improves the day-ahead solar energy bias forecast of both global horizontal irradiance and direct normal irradiance by nearly ∼50%. Our results suggest that a more comprehensive treatment of aerosols, including biomass burning aerosols, in NWP models may be an important consideration for energy grid balancing, in addition to solar resource assessment, as solar power reliance increases.

Continued investment in coal embroils regions in coal lock-ins, creating dependence and vested interests around coal and thereby limiting the speed and potential to switch to cleaner energy. In India, four states contribute 70% of coal production, with regions surrounding mines also housing significant operating and under-construction coal power stations. On the other hand, states in the west and south of India dominate current and near-term renewable energy capacity growth, broadly following patterns of highest resource potentials. We show that following current policies, by the end of the decade, coal-bearing states will likely sink deeper into carbon lock-ins, while the rest of the country, especially western and southern states could become increasingly decarbonised. Even in decarbonisation scenarios, gains from job and value creation in the clean energy sector might primarily take place away from existing coal regions, raising equity concerns, and ultimately putting the political feasibility of such a scenario in question. We suggest that policies aiming at higher renewable installations (mostly solar due to better potentials) in coal-bearing states, although not a one-to-one panacea, could provide an early break from lock-ins and into a just transition. This may, however, require a dedicated program and imply a small mark-up in power system costs. They would, however, help for medium-term diversification and job creation in all regions which will be key for assuring political support for the transition.

Evidence suggests that the response of rainfed crops to dry or wet years is modulated by soil texture. This is a central tenet for certain agronomic operations in water-limited regions that rely on spatial distribution of soil texture for guiding precision agriculture. In contrast, natural vegetation in climatic equilibrium evolves to form a dynamic assemblage of traits and species adapted to local climatic conditions, primarily precipitation in water-limited regions. For undisturbed landscapes, we hypothesize that natural vegetation responds to rainfall anomalies irrespectively of local soil texture whereas rainfed crops are expected to respond to texture-mediated plant available water. Earth system models (ESMs) often quantify vegetation response to drought and water stress based on traditional agronomic concepts despite fundamental differences in composition and traits of natural vegetation and crops. We seek to test the hypothesis above at local and regional scales to differentiate natural vegetation and rainfed crops response to rainfall anomalies across soil types and better link them to water and carbon cycles. We employed field observations and remote sensing data to systematically examine the response of natural and rainfed cropped vegetation across biomes and scales. At local scales (field to ∼0.1 km), we used crop yields from literature data and natural vegetation productivity as gross primary productivity (GPP) from adjacent FLUXNET sites. At regional scales (∼10² km), we rely exclusively on remote-sensing-based GPP. Results confirm a lack of response of natural vegetation productivity to soil texture across biomes and rainfall anomalies at all scales. In contrast, crop yields at field scale exhibit correlation with soil texture in dry years (in agreement with conventional agronomic practices). These results support the hypothesis that natural vegetation is decoupled from soil texture, whereas rainfed crops retain dependency on soil texture in dry years. However, the observed correlation of crops with soil texture becomes obscured at larger scales by spatial variation of topography, rainfall, and uncertainty in soil texture and GPP values. The study provides new insights into what natural vegetation's climatic equilibrium might mean and reveals the role of scale in expressing such sensitivities in ESMs.

Global warming has pronounced effects on tundra vegetation, and rising mean temperatures increase plant growth potential across the Arctic biome. Herbivores may counteract the warming impacts by reducing plant growth, but the strength of this effect may depend on prevailing regional climatic conditions. To study how ungulates interact with temperature to influence growth of tundra shrubs across the Arctic tundra biome, we assembled dendroecological data from 20 sites, comprising 1153 individual shrubs and 223 63 annual growth rings. Evidence for ungulates suppressing shrub radial growth was only observed at intermediate summer temperatures (6.5 °C–9 °C), and even at these temperatures the effect was not strong. Multiple factors, including forage preferences and landscape use by the ungulates, and favourable climatic conditions enabling effective compensatory growth of shrubs, may weaken the effects of ungulates on shrubs, possibly explaining the weakness of observed ungulate effects. Earlier local studies have shown that ungulates may counteract the impacts of warming on tundra shrub growth, but we demonstrate that ungulates' potential to suppress shrub radial growth is not always evident, and may be limited to certain climatic conditions.

Fine particulate matter (PM_2.5) is widely concerned for its harmful impacts on global environment and human health, making air pollution monitoring so crucial and indispensable. As the world's first open, real-time, and historical air quality platform, OpenAQ collects and provides government measurement and research-level data from various channels. However, despite OpenAQ's innovation in providing us with ground-measured PM_2.5 worldwide, we find significant data gaps in time series for most of the sites. The incompleteness of the data directly affects the public perception of PM_2.5 concentration levels and hinders the progress of research related to air pollution. To address these issues, a two-step hybrid model named ST-SILM, i.e. spatio-temporal model with single exponential smoothing-inverse distance weighted (SES-IDW) and long short-term memory (LSTM), is proposed to repair the missing data from PM_2.5 sites worldwide collected from OpenAQ from 2017 to 2019. Both spatio-temporal correlation and neighborhood fields are considered and established in the model. To be specific, SES-IDW were firstly used to repair missing values, and secondly, the LSTM network was employed to reconstruct the time series of continuous missing data. After the global ground-measured PM_2.5 was reconstructed, the light gradient boosting machine model was applied to remote sensing estimation of the original ground-measured PM_2.5 and of the reconstructed ground-measured PM_2.5 to further verify the performance of ST-SILM. Experiment results show that the estimation accuracy of the reconstructed dataset is better (R² from 2017 to 2019 increased by 0.02, 0.02, and 0.01 compared with the original dataset). Therefore, it is concluded that the proposed model can effectively reconstruct data from PM_2.5 sites worldwide.

As demand for avocado climbs, avocado production in Michoacán—Mexico's biggest avocado growing region—expands into new places. We use a spatial probit model to project the geographic distribution of likely future avocado expansion and analyze those results to determine (a) threats to specific forest types and (b) how the distribution of avocado is shifting spatially under current and future climate scenarios. Our results suggest that avocado expansion in Michoacán is strongly driven by distance to existing agriculture, roads, and localities, as well as the dwindling availability of Andosol soils. As future expansion ensues, it presents risk of forest loss across various forest types, with pine-oak forest, mesophilic montane forest, and oyamel fir forest being of particular concern. Moreover, our results suggest that avocado production will occupy wider ranges in terms of temperature, precipitation, slope steepness and soil. The model predicts that climate change will alter the spatial distribution of avocado plantings, expanding into forest types at lower and at higher elevations. Forest loss threatens ecosystem degradation, and a wider avocado crop production footprint could lead to orchard establishment into dwindling forests that host a high diversity of native oaks and charismatic species, including the monarch butterfly.

In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 h. Using land surface temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively active parts of mesoscale convective systems (MCSs) from spatial filtering of cloud-top temperature imagery. We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For a Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6–18 h, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics.

Climate change is expected to lead to changes in seasonal temperature-related mortality. However, this impact on health risk does not necessarily scale linearly with increasing temperature. By examining changes in risk relative to degrees of global warming, we show that there is a delayed emergence of the increase in summer mean mortality risk in England and Wales. Due to the relatively mild summer mean temperatures under the current climate and the non-linearity of the exposure–response relationships, minimal changes in summer mean risk are expected at lower levels of warming and an escalation in risk is projected beyond 2.5 °C of global warming relative to pre-industrial levels. In contrast, a 42% increase in mortality risk during summer heat extremes is already expected by 2 °C global warming. Winter attributable mortalities, on the other hand, are projected to decrease largely linearly with global warming in England and Wales.

Large-scale agricultural activities can exacerbate global climate change. In the past three decades, over 5 Mha of cultivated land have been equipped with water-saving techniques (WSTs) in Northwest China to cope with water scarcity. However, the effect of WSTs on local climate and its mechanisms are not yet understood. Here, we have quantified the local climatic effect by comparing temperature and humidity at controlled and irrigated sites before and after the large-scale implementation of WST. Results show that the substantial reduction in irrigation water use has led to an average increase of 0.3 °C in growing-season temperature and reduced relative humidity by 2%. Near-surface air temperature responds nonlinearly to percentage area of WST and a threshold value of 40% is found before any noticeable warming effect over the study area. Moreover, it is found that regions with relatively humid climates respond more significantly to WST. This study reveals the mechanism of WST on near-surface climate and highlights the importance of incorporating this feedback into sustainable water management and land-surface models for assessing the impact of irrigated agriculture on regional climate change.

The reduced emissions in deforestation and degradation (REDD+) initiative uses payments for ecosystem services as incentives for developing countries to manage and protect their forests. REDD+ initiatives also prioritize social (and environmental) co-benefits aimed at improving the livelihoods of communities that are dependent on forests. Despite the incorporation of co-benefits into REDD+ goals, carbon sequestration remains the primary metric for which countries can receive payments from REDD+, but after more than 10 years of REDD+, many site-specific programs have failed to complete the carbon verification process. Here, we examine whether the REDD+ social co-benefits alone are sufficient to have slowed deforestation in the absence of carbon payments on Pemba, Tanzania. Using satellite imagery (Landsat archive), we quantified forest cover change for the period before (2001–2010) and after (2010–2018) the launch in 2010–2011 of Pemba island's REDD+ readiness project. We then compared rates of forest cover change between shehia (administrative units) that were part of REDD+ readiness intervention and those that were not, adjusting for confounding variables and the non-random selection of REDD+ shehia with a statistical matching procedure. Despite considerable variation in forest outcomes among shehia, the associated co-benefits with the Pemba REDD+ project had no discernible effect on forest cover change. Likewise, we did not detect an effect of socioecological covariates on forest cover change across all shehia, though island-wide human population growth since 2012 may have played a role. These findings are unsurprising given the failure to secure carbon payments on Pemba and indicate that co-benefits alone are insufficient to reduce deforestation. We conclude that better oversight of all-involved parties is needed to ensure that REDD+ interventions satisfactorily conclude the process of securing a mechanism for carbon payments, if slowing deforestation is to be achieved.

Ozone (O₃) is one of the most critical pollutants affecting air quality in China in recent years. In this study, different impacts of the El Niño–Southern Oscillation (ENSO) warm/cold phases on summertime tropospheric O₃ over China are examined based on model simulations, ground measurements, and reanalysis data. Summertime surface O₃ concentrations in China show a positive correlation with ENSO index during years 1990–2019, with the largest increases by 20% over southern China in El Niño (warm phase) relative to La Niña (cold phase) years. The ENSO modulation extends to the middle and even upper troposphere. Our analysis indicates that O₃ flux convergence associated with weakened southerlies is the primary reason for the increase in tropospheric O₃ over southern China. In addition, the O₃ increase during El Niño years is mainly from domestic emissions in China. This study highlights the potential significance of ENSO in modulating tropospheric O₃ concentrations in China, with great implications for O₃ pollution mitigation.

Providing sufficient, safe, and reliable drinking water is a growing challenge as water supplies become more scarce and uncertain. Meanwhile, water utilities in the United States lose approximately 17% of their delivered water to leaks each year. Using data from over 800 utilities across four U.S. states, California, Georgia, Tennessee, and Texas, we characterize the heterogeneity in water losses across the U.S., develop a model to assess the economically efficient level of losses, and use this model to compare the net benefits of several proposed water loss regulations and modeling approaches. Combining economic and engineering principles, our model shows that for the median utility, it is economically efficient to reduce water losses by 34.7%, or 100 acre-feet (AF) per year. The median cost of water savings from leak management is $277/AF, which falls well below the cost of traditional water management tools. However, the optimal level of water losses strongly depends on utility-specific characteristics, leading to large differences in the potential for cost-effective leak reduction across utilities. We show that water loss management can lead to water savings that generate net economic benefits, but only if management approaches incorporate economic and engineering principles.

Global food production and international trade are rapidly expanding and drive increasing agricultural globalization and specialization. Following production patterns, network properties and added-value chains, exportable surpluses of countries can offset food and feed deficits in other countries. However, production and trade patterns are barely addressed in the scientific literature as two interactive components of global agriculture. Integrated analysis of the temporal dynamics and distribution patterns of production and trade among countries can help addressing future food security challenges in view of ongoing trends. Here, we analyse the interdependent patterns of global agricultural production and trade from 1986 to 2016. We classify total production and trade mass into six product categories—cereals, oilcrops, meat, fruits and vegetables, coffee and cocoa. We estimate reexports in global trade by assessing mass balances of production, imports and exports per country. We show that global trade and reexports increase exponentially faster than production and that production and trade are highly centralized among a small number of countries. For most agricultural categories, the centralization of flows has increased in time for production and net exports, and has decreased for net imports and reexports. Accordingly, a growing number of deficient countries are sustained by a decreasing number of top-producing countries. In parallel, reexport routes are increasingly dominated by long-industrialized countries besides the increase in time in the number of reexporting countries. We discuss the interdependencies between global agricultural production and trade patterns. We highlight the drivers and implications of the observed trends for food security challenges.

The Tibetan Plateau (TP) is known as one of the sentinels of global climate change. Substantial winter warming over the TP will likely lead, directly or indirectly, to a series of geological disasters such as snow and glacial avalanches. Hence, for better adaptation to climate change, it is vital to project the future change in winter temperature over the TP. However, the current state-of-the-art climate models involved in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) still produce strong cold biases over most parts of the TP in their historical simulations. On the basis of selecting the optimal models, here we use the statistical downscaling method to constrain the projected winter temperature in CMIP6 models. The results show that the regions with the strongest winter warming over the TP will be near the Himalayas and the densely populated eastern regions. The constrained warming magnitude is much greater than that in the ensemble mean of the original 32 CMIP6 models or six best models over these regions. Therefore, early warning and forecasting services should be strengthened for the future temperature over these regions. Moreover, the long-term spatial warming varies greatly under four different future emission scenarios. Under the most severe scenario, the increase in winter temperature near the Himalayas exceeds 10 °C, which will greatly destabilize glaciers in the region, while the increase is only 4 °C–6 °C under the weakest scenario. Therefore, it is urgent to reduce greenhouse gas emissions to control the future temperature increase at hotspots of climate vulnerability such as the TP.

Summer heat extremes in the UK pose a risk to health (amongst other sectors) and this is exacerbated by localised socio-economic factors that contribute to vulnerability. Here, regional climate model simulations from the UK Climate Projections are used to assess how different elements of extreme heat will vary across the UK in the future under global mean surface temperature warming levels of +1.5 °C, +2.0 °C and +3.0 °C above pre-industrial. Heat stress metrics incorporating daily maximum and minimum temperature, temperature variability and vapour pressure are included. These show qualitatively similar spatial patterns for the recent past, with the most pronounced heat hazards found in south-eastern regions of the UK. Projected heat hazard changes across the UK are not homogeneous, with southern regions (e.g. Greater London, South East) showing greater increases in maximum temperatures and northern regions (e.g. Scotland and Northern Ireland) showing greater increases in humidity. With +3.0 °C warming, the relative change in combined heat hazards is found to be greatest in the south-western UK, however, in absolute terms, south-eastern regions will still experience the greatest hazards. When combined with socio-economic factors, hotspots of high heat stress risk emerge in parts of London, the Midlands and eastern England along with southern and eastern coastal regions. Weighting of different heat risk factors is subjective and to this end we have developed and made available an interactive app which allows users to assess sensitivities and uncertainties in the projected UK heat risk.

The COVID-19 pandemic has reduced travel but led to an increase in household food and energy consumption. Previous studies have explored the changes in household consumption of food and energy during the pandemic; however, the economy-wide environmental implications of these changes have not been investigated. This study addresses the knowledge gap by estimating the life cycle environmental impacts of U.S. households during the pandemic using a hybrid life cycle assessment. The results revealed that the reduction in travel outweighed the increase in household energy consumption, leading to a nationwide decrease in life cycle greenhouse gas emissions (−255 Mton CO₂ eq), energy use (−4.46 EJ), smog formation (−9.17 Mton O₃ eq), minerals and metal use (−16.1 Mton), commercial wastes (−8.31 Mton), and acidification (−226 kton SO₂ eq). However, U.S. households had more life cycle freshwater withdrawals (+8.6 Gton) and slightly higher eutrophication (+0.2%), ozone depletion (+0.7%), and freshwater ecotoxicity (+2.1%) caused by increased household energy and food consumption. This study also demonstrated the environmental trade-offs between decreased food services and increased food consumption at home, resulting in diverse trends for food-related life cycle environmental impacts.

A number of studies have noted that the use of distinct reference periods when comparing indices measuring the frequency of days exceeding a particular temperature percentile threshold leads to apparently different behaviour. We show that these differences arise because of the interplay between the increasing temperatures and the choice of reference period. The time series of the indicators calculated using the different reference periods are offset, as expected, but also diverge. Linear trends calculated over the same period from the same underlying data but where different reference periods have been used are substantially different if a change in climatological conditions has occurred between the two reference periods. We show this not only occurs in our simple empirical approach, but also for the averages of gridded observational and reanalysis datasets and also at a station level. This has implications for data set comparisons using trends in temperature percentile indices that are based on different reference periods. It also has implications for updates to standard reference periods used to monitor the climate.

Transparency in global value chains of materials, fuels, and food is critical for the implementation of sustainability policies. Such policies should be led by the G20, who represent more than 80% of global material, fuel, and food consumption. Multi-regional input–output analysis plays an important role for consumption-based assessment, including supply chains and their environmental impacts. However, previous accounting schemes were unable to fully assess the impacts of materials, fuels, and food. To close this gap, we provide an improved method to map key aspects of sustainability along value chains of materials, fuels, and food. The results show that the rise in global coal-related greenhouse gas (GHG) emissions between 1995 and 2015 was driven by the G20's metals and construction materials industry. In 2015, the G20 accounted for 96% of global coal-related GHG emissions, of which almost half was from the extraction and processing of metals and construction materials in China and India. Major drivers include China's rising infrastructure and exports of metals embodied in machinery, transport, and electronics consumed by other G20 members. In 2015, the vast majority (70%–95%) of the GHG emissions of metals consumed by the EU, USA, Canada, Australia, and other G20 members were emitted abroad, mostly in China. In contrast, hotspots in the impact displacement of water stress, land-use related biodiversity loss, and low-paid workforce involve the G20's food imports from non-G20 members. Particularly high-income members have contributed to the G20's rising environmental footprints by their increasing demand for materials, food, and fuels extracted and processed in lower-income regions with less strict environmental policies, higher water stress, and more biodiversity loss. Our results underline the G20's importance of switching to renewable energy, substituting high-impact materials, improving supply chains, and using site-specific competitive advantages to reduce impacts on water and ecosystems.

Residential rooftop solar is slated to play a significant role in the changing US electric grid in the coming decades. However, concerns have emerged that the benefits of rooftop solar deployment are inequitably distributed across demographic groups. Previous work has highlighted inequity in national solar adopter deployment and income trends. We leverage a dataset of US solar adopter household income estimates—unique in its size and resolution—to analyze differences in adoption equity at the local level and identify those conditions that yield more equitable solar adoption, with implications for policy strategies to reduce inequities in solar adoption. The solar inequities observed at the national and state levels also exist at more granular levels, but not uniformly so; some US census tracts exhibit less solar inequity than others. Some demographic, solar system, and market characteristics robustly lead to more equitable solar adoption. Our findings suggest that while solar adoption inequity is frequently attributed to the relatively high costs of solar adoption, costs may become less relevant as solar prices decline. Results also indicate that racial diversity and education levels affect solar adoption patterns at a local level. Finally, we find that solar adoption is more equitable in census tracts served by specific types of installers. Future research and policy can explore ways to leverage these findings to accelerate the transition to equitable solar adoption.

Trees are among the most important urban land covers, and their effects on local thermal environments have been extensively evaluated by using the concept of urban trees' cooling efficiency (CE), defined as the magnitude of land surface temperature (LST) reduction by per 1% increase in fractional tree cover (FTC). Existing studies provide quantitative knowledge of the CE at local and regional scales, but global-scale analyses are still lacking. Therefore, this study fills this research gap through investigating the spatiotemporal pattern of CE in 510 global cities. CE is quantified by the opposite value of the regression coefficient of FTC (i.e. CE =−∂LST/∂FTC) in a multiple linear regression model, where LST is the dependent variable and FTC, surface elevation, and nighttime light intensity are the independent variables. Results show that daytime LST decreases greatly with increasing FTC in most cities, and the globally averaged annual daytime CE reaches 0.063 °C %⁻¹, while at night, the effect of urban trees on LST weakens a lot, with an annual average CE of only 0.007 °C %⁻¹ across global cities. CE varies markedly among cities and tends to be higher in hot and dry cities, which can be attributed to the significant nonlinear relation between CE and climatic conditions, in that the increase in temperature and the decrease in humidity can enhance vapor pressure deficit and further promote the heat dissipation by plant transpiration. As expected, CE shows a distinct seasonal variation, generally characterized as being higher in summer and lower in winter. In addition, our results suggest that previous studies based on a bivariate linear regression model have overestimated CE, especially at night when trees' activities are weak. This global-scale study provides new insights into the mitigation of urban thermal stress from the perspective of increasing urban greenery.

Arctic warming and its association with the mid-latitudes has been a hot topic over the past two decades. Although many studies have explored these issues, it is not clear how their linkage has changed over time. The results show that winter low tropospheric temperatures in Asia experienced two phases over the past two decades. Phase I (2007/2008–2012/2013) was characterized by a warm Arctic and cold Eurasia, and phase II by a warm Arctic and warm Eurasia (2013/2014–2018/2019). A strengthened association in winter temperature between the Arctic and Asia occurred during phase I, followed by a weakened linkage during phase II. Simulation experiments forced by observed Arctic sea ice variability largely reproduce observed patterns, suggesting that Arctic sea ice loss contributes to phasic (or low-frequency) variations in winter atmosphere and makes the Arctic–Asia temperature association fluctuate over time. The weakening of the Arctic–Asia linkage post-2012/2013 was associated with amplified and expanded Arctic warming. The corresponding anomalies in sea level pressure resembled a positive-phase North Atlantic Oscillation during phase II. This study implies that the phasic warm Arctic—cold Eurasia and warm Arctic—warm Eurasia patterns would alternately happen in the context of Arctic sea ice loss, which increases the difficulty in correctly predicting Asian winter temperature.

Electrification can reduce the greenhouse gas (GHG) emissions of light-duty vehicles. Previous studies have focused on comparing battery electric vehicle (BEV) sedans to their conventional internal combustion engine vehicle (ICEV) or hybrid electric vehicle (HEV) counterparts. We extend the analysis to different vehicle classes by conducting a cradle-to-grave life cycle GHG assessment of model year 2020 ICEV, HEV, and BEV sedans, sports utility vehicles (SUVs), and pickup trucks in the United States. We show that the proportional emissions benefit of electrification is approximately independent of vehicle class. For sedans, SUVs, and pickup trucks we find HEVs and BEVs have approximately 28% and 64% lower cradle-to-grave life cycle emissions, respectively, than ICEVs in our base case model. This results in a lifetime BEV over ICEV GHG emissions benefit of approximately 45 tonnes CO₂e for sedans, 56 tonnes CO₂e for SUVs, and 74 tonnes CO₂e for pickup trucks. The benefits of electrification remain significant with increased battery size, reduced BEV lifetime, and across a variety of drive cycles and decarbonization scenarios. However, there is substantial variation in emissions based on where and when a vehicle is charged and operated, due to the impact of ambient temperature on fuel economy and the spatiotemporal variability in grid carbon intensity across the United States. Regionally, BEV pickup GHG emissions are 13%–118% of their ICEV counterparts and 14%–134% of their HEV counterparts across U.S. counties. BEVs have lower GHG emissions than HEVs in 95%–96% of counties and lower GHG emissions than ICEVs in 98%–99% of counties. As consumers migrate from ICEVs and HEVs to BEVs, accounting for these spatiotemporal factors and the wide range of available vehicle classes is an important consideration for electric vehicle deployment, operation, policymaking, and planning.

The Arctic is warming two to three times faster than the global average, and the role of aerosols is not well constrained. Aerosol number concentrations can be very low in remote environments, rendering local cloud radiative properties highly sensitive to available aerosol. The composition and sources of the climate-relevant aerosols, affecting Arctic cloud formation and altering their microphysics, remain largely elusive due to a lack of harmonized concurrent multi-component, multi-site, and multi-season observations. Here, we present a dataset on the overall chemical composition and seasonal variability of the Arctic total particulate matter (with a size cut at 10 μm, PM₁₀, or without any size cut) at eight observatories representing all Arctic sectors. Our holistic observational approach includes the Russian Arctic, a significant emission source area with less dedicated aerosol monitoring, and extends beyond the more traditionally studied summer period and black carbon/sulfate or fine-mode pollutants. The major airborne Arctic PM components in terms of dry mass are sea salt, secondary (non-sea-salt, nss) sulfate, and organic aerosol (OA), with minor contributions from elemental carbon (EC) and ammonium. We observe substantial spatiotemporal variability in component ratios, such as EC/OA, ammonium/nss-sulfate and OA/nss-sulfate, and fractional contributions to PM. When combined with component-specific back-trajectory analysis to identify marine or terrestrial origins, as well as the companion study by Moschos et al 2022 Nat. Geosci. focusing on OA, the composition analysis provides policy-guiding observational insights into sector-based differences in natural and anthropogenic Arctic aerosol sources. In this regard, we first reveal major source regions of inner-Arctic sea salt, biogenic sulfate, and natural organics, and highlight an underappreciated wintertime source of primary carbonaceous aerosols (EC and OA) in West Siberia, potentially associated with the oil and gas sector. The presented dataset can assist in reducing uncertainties in modelling pan-Arctic aerosol-climate interactions, as the major contributors to yearly aerosol mass can be constrained. These models can then be used to predict the future evolution of individual inner-Arctic atmospheric PM components in light of current and emerging pollution mitigation measures and improved region-specific emission inventories.

Understanding the motivation to adopt personal household adaptation behaviors in the face of climate change-related hazards is essential for developing and implementing behaviorally realistic interventions that promote well-being and health. Escalating extreme weather events increase the number of those directly exposed and adversely impacted by climate change. But do people attribute these negative events to climate change? Such subjective attribution may be a cognitive process whereby the experience of negative climate-change-related events may increase risk perceptions and motivate people to act. Here we surveyed a representative sample of 1846 residents of Florida and Texas, many of whom had been repeatedly exposed to hurricanes on the Gulf Coast, facing the 2020 Atlantic hurricane season. We assessed prior hurricane negative personal experiences, climate-change-related subjective attribution (for hurricanes), risk appraisal (perceived probability and severity of a hurricane threat), hurricane adaptation appraisal (perceived efficacy of adaptation measures and self-efficacy to address the threat of hurricanes), and self-reported hurricane personal household adaptation. Our findings suggest that prior hurricane negative personal experiences and subjective attribution are associated with greater hurricane risk appraisal. Hurricane subjective attribution moderated the relationship between hurricane negative personal experiences and risk appraisal; in turn, negative hurricane personal experiences, hurricane risk appraisal, and adaptation appraisal were positively associated with self-reported hurricane personal adaptation behaviors. Subjective attribution may be associated with elevated perceived risk for specific climate hazards. Communications that help people understand the link between their negative personal experiences (e.g. hurricanes) and climate change may help guide risk perceptions and motivate protective actions, particularly in areas with repeated exposure to threats.

Considerable financial resources are allocated for measuring ambient air pollution in the United States, yet the locations for these monitoring sites may not be optimized to capture the full extent of current pollution variability. Prior research on best sensor placement for monitoring fine particulate matter (PM_2.5) pollution is scarce: most studies do not span areas larger than a medium-sized city or examine timescales longer than 1 week. Here we present a pilot study using multiresolution dynamic mode decomposition (mrDMD) to identify the optimal placement of PM_2.5 sensors from 2000 to 2016 over the contiguous United States. This novel approach incorporates the variation of PM_2.5 on timescales ranging from 1 d to over a decade to capture air pollution variability. We find that the mrDMD algorithm identifies more high-priority sensor locations in the western United States than those expected along the eastern coast, where a large number of Environmental Protection Agency (EPA) PM_2.5 monitors currently reside. Specifically, 53% of mrDMD optimized sensor locations are west of the 100th meridian, compared to only 32% in the current EPA network. The mrDMD sensor locations can capture PM_2.5 from wildfires and high pollution events, with particularly high skill in the west. These results suggest significant gaps in the current EPA monitoring network in the San Joaquin Valley in California, northern California, and in the Pacific Northwest (Idaho, and Eastern Washington and Oregon). Our framework diagnoses where to place air quality sensors so that they can best monitor smoke from wildfires. Our framework may also be applied to urban areas for equitable placement of PM_2.5 monitors.

Food supply chains are essential for distributing goods from production to consumption points. These complex supply chains are important for food security and availability. Recent research has developed novel methods to estimate food flows with high spatial resolution, but we do not currently understand how fine-grained food supply chains vary in time. In this study, we use an improved version of the Food Flow Model to estimate food flows (kg) between all county pairs across all food commodity groups for the years 2007, 2012, and 2017 (which requires estimating 206.3 million links). We then determine the core counties to the US food flow networks through time with a multi-criteria decision analysis technique. Our estimates of county-to-county food flows in time are freely available with this paper and could be useful for future research, policy, and decision-making.

Increasing renewable energy use is an essential strategy for mitigating climate change. Nevertheless, the sensitivity of renewable energy to climatic conditions means that the energy system's vulnerability to climate change can also become larger. In this research, we used two integrated assessment models and data from four climate models to analyse climate change impacts on primary energy use at a global and regional scale under a low-level (RCP2.6) and a medium-level (RCP6.0) climate change scenario. The impacts are analysed on the energy system focusing on four renewable sources (wind, solar, hydropower, and biomass). Globally, small climate impacts on renewable primary energy use are found in both models (5% for RCP2.6 and 6% for RCP6.0). These impacts lead to a decrease in the use of fossil sources for most regions, especially for North America and Europe under the RCP60 scenario. Overall, IMAGE and GCAM provide a similar signal impact response for most regions. E.g. in Asia (excluding China and India), climate change induces an increase in wind and hydropower use under the RCP6.0 scenarios; however, for India, a decrease in solar energy use can be expected under both scenarios and models.

The Amazon is hypothesized to reach an irreversible 'tipping point' when deforestation slows the hydrological cycle sufficiently that tropical forest ecosystems cannot be sustained. However, inception of such a tipping point has not been supported by observations and the relevant links between deforestation and atmospheric moisture recycling are poorly understood. Here we show that reduction in evapotranspiration from 20 years of deforestation dried the atmosphere persistently and caused moisture decoupling, i.e. an opposite sign of moisture change between the lower and middle troposphere. Increased deforestation exacerbated the lower troposphere drying and caused it to penetrate deeper into the middle troposphere in the dry and transition seasons over monsoon forests and savannas. Deforestation induced warming-enhanced buoyant updrafts, elevated hot and dry air and thereby reduced downward mixing of water supplies from the tropical Atlantic that normally moisten the Amazon forests. The severe atmospheric desiccation in the southern and eastern Amazon cannot be compensated by enhanced water supplies from the Atlantic Ocean, demonstrating an irreversible transition in Amazon hydrological cycle exacerbated by rapid deforestation. The more recent drying through the seasons over rainforests and during the wet season over the transition zones from rainforests to monsoon forests and savannas, however, suggests a window of opportunity for preventing ecosystem collapse with forest conservation.

Internal waves (IWs) mitigate thermal stress and provide refugia for corals against increasingly frequent mass bleaching. However, climate events may bring uncertainty regarding the resistance of such refugia. Here, using in situ observation data in the Andaman Sea (AS), we conduct a case study in which a monsoon anomaly associated with an El Niño event threatens IW coral refugia. IW cooling in the AS coral reefs is modulated by the thermocline depth variation, which is driven, to a significant extent, by Kelvin wave signals from the equator. In the pre-monsoon period, distinct variations in IW cooling and surface heating form a time window of quickly-growing cumulative heat exposure. The El Niño induces a typical two-week delay of summer monsoon onset, which prolongs the duration of thermal stress growth and brings severe bleaching risk to corals. As global warming increases the frequency of extreme El Niño events, IW coral refugia will face great challenges in the future.

Contrails are potentially the largest contributor to aviation-attributable climate change, but estimates of their coverage are highly uncertain. No study has provided observation-based continental-scale estimates of the diurnal, seasonal, and regional variability in contrail coverage. We present contrail coverage estimates for the years 2018, 2019 and 2020 for the contiguous United States, derived by developing and applying a deep learning algorithm to over 100 000 satellite images. We estimate that contrails covered an area the size of Massachusetts and Connecticut combined in the years 2018 and 2019. Comparing 2019 and 2020, we quantify a 35.8% reduction in distance flown above 8 km altitude and an associated reduction in contrail coverage of 22.3%. We also find that the diurnal pattern in contrail coverage aligns with that of flight traffic, but that the amount of contrail coverage per distance flown decreases in the afternoon.

Differentiating spatial–temporal hydropower risk triggered by climate change is crucial to climate adaptation and hydropower programming. In this research, we use a fixed-effect model on 5082 plants in China to estimate how the revenue of hydropower plants responded to climate change over 16 years, and project the revenue change and fit the damage function driven by 42 climate realizations. Results show that the revenue change of the hydropower sector demonstrates substantial regional variation and would reduce by 9.34% ± 1.21% (mean ± s.d.) yr⁻¹ on average under RCP 8.5 by 2090s as compared to 2013, about four times larger than that under RCP4.5. Carbon leakage caused by thermal power substitution reaches 467.56 ± 202.63 (112.49 ± 227.45) Mt CO₂e under RCP8.5 (RCP4.5). Different climatic conditions manifest locally, and different climate resilience makes the response function regionally heterogeneous. Southwest China is identified as the priority region for adaptation through integrated evaluation of historical climate sensitivity, future climate variability, and regional hydropower importance, informing more adaptation and investment needs of further hydropower development in the area.

The surface energy balance from canopy to landscape scales in crop fields plays an essential role in surface–atmosphere interactions, and it is strongly influenced by the management strategies and field practices of farmers. To characterize how different agricultural practices of farmers affect the microenvironment in perennial crop fields, long-term observation of the radiation budget and energy components under different field practices was undertaken in two neighboring tea fields with different management strategies (a conventional operation and an organic-certified field managed by different farmers) in northern Taiwan. The results showed that the difference in the radiation budget in these two tea fields was minor (only 1% for net radiation), but the differences in the energy components were more significant (sensible heat was 10% lower and latent heat was 25% higher in the organic-certified field than in the conventional field) due to highly distinct practices in these two fields. This finding implies that the organic-certified application could lower the partitioning of sensible heat flux and increase the latent heat flux, thereby reducing the temperature variation and decreasing the vapor pressure deficit. The organic-certified field reduced the surface heating in terms of the long-term energy patterns. This study's findings also indicate that field practices in a conventional field can increase the sensible heat flux (51.5% at noon time) on short-term time scales, compared with only 9.6% in an organic-certified field. Furthermore, this study offers a comprehensive understanding of tea field practices, a scientific basis for in-field water conservation, and a quantitative analysis for modeling from micro to regional scales.

Compared with independent hot days or nights, compound hot extremes have more adverse effects on society. In this study, hot extremes are categorized into three types: independent hot days, independent hot nights and compound hot events combining daytime and nighttime hot extremes based on daily maximum and minimum temperatures. Using observations from the gridded dataset CN05.1 and experiments undertaken with 22 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, we analyze the observed changes in summer hot extremes and compare them with model simulations over China between 1961 and 2014 and then conduct detection and attribution analyses of changes in compound hot events between 1965 and 2014 utilizing an optimal fingerprinting method. The results show that clear upward trends in the frequency and intensity of the three types of hot extremes are observed over China, with the largest trend occurring in hot nights for frequency and in compound hot events for intensity. The CMIP6 multimodel mean responses to all forcings agree well with the observed changes in the frequency and intensity of the three types of hot extremes. Anthropogenic (ANT) forcing can be robustly detected and separated from the response to natural (NAT) forcing in the frequency and intensity trends of compound hot events over China, and the attributable contribution of ANT forcing is estimated to be much larger than that of NAT forcing. Further analyses on the model responses to NAT, greenhouse gas (GHG) and ANT aerosol (AER) forcings indicate that GHG forcing is detectable in the observed increased frequency of compound hot events. By contrast, NAT and AER forcings cannot be detected, and their effects on the observed changes in compound hot events over China are generally negligible.

Residential relocation following environmental disasters is an increasingly necessary climate change adaptation measure. However, relocation is among the costliest individual-level adaptation measures, meaning that it may be cost prohibitive for disadvantaged groups. As climate change continues to worsen, it is important to better understand how existing socioeconomic inequalities affect climate migration and how they may be offset. In this study we use network regression models to look at how internal migration patterns in the United States vary by disaster-related property damage, household income, and local-level disaster resilience. Our results show that post-disaster migration patterns vary considerably by the income level of sending and receiving counties, which suggests that income-based inequality impacts both individuals' access to relocation and the ability of disaster-afflicted areas to rebuild. We further find evidence that income-based inequality in post-disaster outmigration is attenuated in areas with higher disaster resilience, not due to increased relocation out of poorer areas but instead because there is decreased relocation from richer ones. This finding suggests that, as climate adaptation measures, relocation and resilience-building are substitutes, with the implication that resilience incentivizes in situ adaptation, which can be a long term drain on individual wellbeing and climate adaptation resources.

Quantifying the contribution of natural ecosystems on air quality regulation can help to lay the foundation for ecological construction, and to promote the sustainable development of natural ecosystems. To identify the spatio-temporal dynamic changes of natural vegetation regulation on SO₂ absorption and the underlying mechanism of these changes in Qinghai Province, an important ecological barrier and the unique natural ecosystems, the Biome-BGC model was improved to simulate the canopy conductance to SO₂ and leaf area index (LAI) on the daily scale, and then the SO₂ absorption by vegetation was estimated coupling SO₂ concentration from satellite data. Our results showed that the annual average SO₂ absorption of the natural ecosystems in Qinghai Province was 4.74 × 10⁴ tons yr⁻¹ from 2005 to 2018, accounting for about 40% of the total emissions. Spatially, the ecosystem service of SO₂ absorption gradually decreased from southeast to northwest, and varied from 0 in Haixi state to 14.37 kg SO₂ ha⁻¹ yr⁻¹ in Haibei state. The annual average SO₂ absorption in unit area was 0.68 kg SO₂ ha⁻¹ yr⁻¹, and significantly higher SO₂ absorption was observed in summer with 0.45 kg SO₂ ha⁻¹ quarterly. The canopy conductance and LAI controlled by climate variables would be the dominant driving factors for the variation of SO₂ absorption for natural ecosystems. The sensitivity analysis showed that SO₂ concentration contributed more to the uncertainties of SO₂ absorption than the conductance in this study. Our results could provide powerful supports for realistic eco-environmental policy and decision making.

Forest restoration is increasingly applied as a climate change mitigation measure. Apart from sequestering carbon, the large-scale addition of trees on Earth may enhance global precipitation levels. Here we estimate the global precipitation effects of the global forest potential by estimating its effects on evaporation and simulating the downwind precipitation effect of the moisture added to the atmosphere. We find that maximum forestation would on average increase evaporation by 0.6 mm d⁻¹ and that two-thirds of that additional evaporation would rain out over land, especially during the growing season. Next, by excluding natural grasslands and prioritizing precipitation enhancement above areas that are projected to become drier due to global climate change, we establish where on Earth forest restoration would have the greatest precipitation benefits. Our results thus provide a first step towards forest restoration programs as double climate-change mitigation efforts.

Flooding in the Yangtze River Basin could severely damage socio-economic development, river ecosystems, food security, hydropower production and transportation in China. The Yangtze River Basin accounts for approximately 30% of China's gross domestic product (GDP) and is an engine for the country's rapid economic growth. One commonly held belief is that climate change has intensified extreme flood events, leading to increasing economic damage in the Yangtze River. Here, we quantitatively attributed economic exposure to climate change (i.e. climate-induced changes in weather-related events) and GDP growth, and assessed benefits, i.e. the reduction in economic exposure, from flood defence dikes of varying heights. To do this, we developed a framework by combing a large scale hydrological model, a hydraulic model, and long-term GDP data. We find that climate-induced changes in flood inundation area and resulted economic exposure were decreasing overall, whereas GDP growth drove the increases of potential economic exposure to floods. We also reveal that the basin average flood defence dikes should be at least approximately 3.5 m high to achieve an about ten-year average flood occurrence. Our results have significant policy and socioeconomic development implications.

Invasive species have become a global problem owing to their wide-ranging adverse effects. With intensifying climate change and artificial impacts (human-mediated disturbances), which exacerbate the adverse effects of invasive species, there is an urgent need to implement strategies for the management of these species. Various removal policies have been implemented globally to manage the common ragweed (Ambrosia artemisiifolia var. elatior (L.,) Decs) owing to its high tendency to 'spread'. Several studies on the control method, application of spatial perspective, and optimization have been conducted to establish and evaluate management strategies using different spatial models. Although each of these methods is essential for improving control efficiency, an integrated form of study is needed to determine the practicality of various policies. In this study, we developed an integrated spatial model using the species distribution model BIOMOD2, land change model LCM, dispersal model MigClim, and optimization model prioritizr, to construct an evaluation methodology. For modelling an optimal invasive species removal policy under climate change and human-mediated disturbances (2011–2079), we created two strategies from a spatial perspective, outside-in and inside-out, with the former entailing removal from the low-density outliers to the high-density centre of the colonized area and the latter processing in the opposite direction. The optimal removal sites for each strategy were set for each removal rate. Subsequently, a novel index, 'removal effect index', was proposed for the evaluation, in time series. The results indicate that the removal effect of the outside-in strategy was more effective, and the newly dispersed sites were efficiently removed. Furthermore, it was verified that with the implementation of the outside-in strategy having a removal rate of 65% by the 2070s, the species would be completely eradicated. Thus, this study is expected to help improve the efficiency of policy implementation for invasive species.

Akin to respiratory tract infection diseases, climatic conditions may significantly influence the COVID-19 epidemic. Since the beginning of the COVID-19 pandemic, significant efforts have been made to explore the relationship between climatic condition and growth in number of COVID-19 cases. Contentious findings of either positive, negative, or no association with climatic conditions have been reported in many studies based on some early data on COVID-19 cases over a shorter time span. We integrate COVID-19 datasets with long meteorological time series of 29 countries to explore cross-country variation in COVID-19 cases and death rates with respect to temperature and relative humidity. Our empirical study reveals that temperature and relative humidity jointly influence the growth of COVID-19 cases and death rates. We generate predictive scenarios for changes in daily cases and death rates under different combinations of temperature and relative humidity. Low temperature with low humidity in a temperate climate and high temperature with high humidity in a hot and humid climate are found to surge the growth of COVID-19 cases and death rates. These relationships and our predictive scenarios can be applied to generate early warning for any future outbreak to adopt stringency policies, kick-start economic activities, prepare healthcare service plans, and target vaccination coverage.

As the basis of food and fiber production, gross primary production (GPP) plays a critical role in the growth of vegetation. Understanding the response of GPP to climate extremes is important for ensuring food security under ongoing global warming. Plenty of evidence shows that the recent widespread dry or hot events across the globe have significant influences on GPP, yet little is known about their joint impacts. Here, we reveal a high risk of compound dry and hot events globally, in response to the strong negative dependence of precipitation and temperature, which leads to a substantial negative impact on GPP for both crop and pasture ecosystems. Using a meta-Gaussian model, we show that the probability of a reduction in global terrestrial GPP increases significantly under compound dry and hot conditions relative to their individual counterparts. Further, the risk of GPP reductions increases with the intensified severity of compound dry and hot events across the globe. These results unravel the sensitivity of GPP to compound dry and hot conditions and highlight the need to account for the influence of compound events when assessing the carbon budget.

The conversion of natural land cover into human-dominated land use systems has significant impacts on the environment. Global mapping and monitoring of human-dominated land use extent via satellites provides an empirical basis for assessing land use pressures. Here, we present a novel 2019 global land cover, land use, and ecozone map derived from Landsat satellite imagery and topographical data using derived image feature spaces and algorithms suited per theme. From the map, we estimate the spatial extent and dispersion of land use disaggregated by climate domain and ecozone, where dispersion is the mean distance of land use to all land within a subregion. We find that percent of area under land use and distance to land use follow a power law that depicts an increasingly random spatial distribution of land use as it extends across lands of comparable development potential. For highly developed climate/ecozones, such as temperate and sub-tropical terra firma vegetation on low slopes, area under land use is contiguous and remnant natural land cover have low areal extent and high fragmentation. The tropics generally have the greatest potential for land use expansion, particularly in South America. An exception is Asian humid tropical terra firma vegetated lowland, which has land use intensities comparable to that of temperate breadbaskets such as the United States' corn belt. Wetland extent is inversely proportional to land use extent within climate domains, indicating historical wetland loss for temperate, sub-tropical, and dry tropical biomes. Results highlight the need for planning efforts to preserve natural systems and associated ecosystem services. The demonstrated methods will be implemented operationally in quantifying global land change, enabling a monitoring framework for systematic assessments of the appropriation and restoration of natural land cover.

Assessments of changes in landscape patterns and functions during urban development need to factor urban fringes (UPs) as part of the overall social-environmental system, especially in regions with poor transportation systems where urban functions depend heavily on surrounding suburbs. In this study, we use net primary production (NPP) as an integrative measure to delineate UPs and to measure the expansion in 15 urban areas in the remote Qinghai-Tibet Plateau. Using a logistic curve fitting model based on NPP to delineate differences between the UF and rural landscapes, we explore how NPP-inferred UF expansions may have changed with increase in urban population and the secondary and tertiary industrial production. The UF width (area) was 17.4 km (950.67 km²) in 2000 but increased to 27.0 km (2289.06 km²) in 2019 for Lhasa. For Xining, this was from 28.0 km (2461.76 km²) to 36.0 km (4069.44 km²) during 2000–2019. For the prefecture-level cities, the rate increased from 2–16 km (12.56–803.84 km²) to 7–17 km (153.86–907.46 km²). More importantly, the ratio between UF width and population during the five study periods showed a linear decreasing trend, but an exponential decrease with economic measures. The urban expansion due to population increase changed from 26 m in 2000 to 21 m in 2019 for every increase of 1000 residents, while the expansion due to economic changes was significantly reduced from 732 m per billion RMB (Ren Min Bi) in 2000 to 52 m per billion RMB in 2019. We confirm a hypothesis that the ratio of expansion of UFs was more dependent on economic growth in early stages of urbanization than in later stages, whereas urban population promoted expansions over the entire study period.

The success of market-based mechanisms in reducing conflicts and internalizing externalities depends on their ability to clarify property rights amongst heterogenous resource users. We investigate the effectiveness of novel markets in achieving their goals using the case study of grazing markets in Laikipia County, Kenya. In this system, sheep- and goat (shoat)- and cattle-rearing pastoralists negotiate land access for cattle with neighboring cattle ranchers. Using data on pastoralists' livestock and contracting preferences and a model of pastoral herd management, we show that contracting for cattle grazing access on private property alters relative input shadow prices for grazing resources in communal pastoral lands, ultimately resulting in relieved cattle grazing pressure. However, the permitting process is less attractive to pastoralists who prefer rearing shoats instead of cattle. These shoat-rearing pastoralists instead fill some of the vacated space with shoats instead of purchasing permits themselves. This leakage offsets some of the conservation benefits arising from the contracting program and results in a greater share of shoats in the communal herd mix. Approximately 0.59 cows' worth of free space persists on the commons per permit sold, indicating reduced grazing pressure, but this represents a small proportion (3.8%) of the total livestock in the system. The narrow introduction of the cattle-focused permit market and lack of strong management institutions on the commons dampen the permitting program's conservation benefits, necessitating further interventions. Alleviating these factors and dramatically scaling up the program has the potential to turn the permitting system into a successful conservation tool.

Recent discussions of healthy and sustainable diets encourage increased consumption of plants and decreased consumption of animal-source foods (ASFs) for both human and environmental health. Seafood is often peripheral in these discussions. This paper examines the relative environmental costs of sourcing key nutrients from different kinds of seafood, other ASFs, and a range of plant-based foods. We linked a nutrient richness index for different foods to life cycle assessments of greenhouse gas (GHG) emissions in the production of these foods to evaluate nutritional benefits relative to this key indicator of environmental impacts. The lowest GHG emissions to meet average nutrient requirement values were found in grains, tubers, roots, seeds, wild-caught small pelagic fish, farmed carp and bivalve shellfish. The highest GHG emissions per nutrient supply are in beef, lamb, wild-caught prawns, farmed crustaceans, and pork. Among ASFs, some fish and shellfish have GHG emissions at least as low as plants and merit inclusion in food systems policymaking for their potential to support a healthy, sustainable diet. However, other aquatic species and production methods deliver nutrition to diets at environmental costs at least as high as land-based meat production. It is important to disaggregate seafood by species and production method in 'planetary health diet' advice.

Many dynamical systems experience sudden shifts in behaviour known as tipping points or critical transitions, often preceded by the 'critical slowing down' (CSD) phenomenon whereby the recovery times of a system increase as the tipping point is approached. Many attempts have been made to find a tipping point indicator: a proxy for CSD, such that a change in the indicator acts as an early warning signal. Several generic tipping point indicators have been suggested, these include the power spectrum (PS) scaling exponent whose use as an indicator has previously been justified by its relationship to the well-established detrended fluctuation analysis (DFA) exponent. In this paper we justify the use of the PS indicator analytically, by considering a mathematical formulation of the CSD phenomenon. We assess the usefulness of estimating the PS scaling exponent in a tipping point context when the PS does not exhibit power-law scaling, or changes over time. In addition we show that this method is robust against trends and oscillations in the time series, making it a good candidate for studying resilience of systems with periodic oscillations which are observed in ecology and geophysics.

Increases in climate hazards and their impacts mark one of the major challenges of climate change. Situations in which hazards occur close enough to one another to result in amplified impacts, because systems are insufficiently resilient or because hazards themselves are made more severe, are of special concern. We consider projected changes in such compounding hazards using the Max Planck Institute Grand Ensemble under a moderate (RCP4.5) emissions scenario, which produces warming of about 2.25 °C between pre-industrial (1851–1880) and 2100. We find that extreme heat events occurring on three or more consecutive days increase in frequency by 100%–300%, and consecutive extreme precipitation events increase in most regions, nearly doubling for some. The chance of concurrent heat and drought leading to simultaneous maize failures in three or more breadbasket regions approximately doubles, while interannual wet-dry oscillations become at least 20% more likely across much of the subtropics. Our results highlight the importance of taking compounding climate extremes into account when looking at possible tipping points of socio-environmental systems.

More than a century of land-use changes and intensive agriculture across the Mississippi River Basin (MRB) has led to a degradation of soil and water resources. Nitrogen (N) leaching from the excess application of fertilizers has been implicated in algal blooms and the development of large, coastal 'dead zones'. It is, however, increasingly recognized that water quality today is a function not only of the current-year inputs but also of legacy N within the watershed—legacy that has accumulated in soil and groundwater over decades of high-input agricultural practices. Although attempts have been made to quantify the extent to which soil organic nitrogen (SON) is being sequestered in agricultural soils with intensive fertilization, improved residue management, and the adoption of conservation tillage practices, the controls on accumulation dynamics as well as linkages between legacy N accumulation and water quality remain unclear. Here, we have used the process-based model CENTURY to quantify accumulation and depletion trajectories for soil N across a range of climate and soil types characteristic of the MRB. The model was calibrated against crop yield data and soil N accumulation data from a long-term field site. Model runs highlighted that under current management scenarios, N accumulation is greatest in regions with the highest crop yield, and this can be attributed to the higher residue rates with greater yields. We thus find that humans, through management practices, have homogenized spatial patterns of SON across the landscape by increasing SON magnitudes in warmer and drier regions. Results also suggest a regime shift in the relationship between soil organic N and N mineralization fluxes, such that N fluxes are greater now than in the 1930s, despite similar soil organic N magnitudes, mainly due to higher proportions of labile, unprotected soil organic matter. This regime shift leads to elevated N leaching to tiles and groundwater in landscapes under intensive agriculture.

Recent studies demonstrated the difficulties to explain observed tropospheric nitrogen dioxide (NO₂) variabilities over the United States and Europe, but thorough analysis for the impacts on tropospheric NO₂ in China is still lacking. Here we provide a comparative analysis for the observed and modeled (Goddard Earth Observing System-Chem) tropospheric NO₂ in early 2020 in China. Both ozone monitoring instrument and surface NO₂ measurements show marked decreases in NO₂ abundances due to the 2019 novel coronavirus (COVID-19) controls. However, we find a large discrepancy between observed and modeled NO₂ changes over highly polluted provinces: the observed reductions in tropospheric NO₂ columns are about 40% lower than those in surface NO₂ concentrations. By contrast, the modeled reductions in tropospheric NO₂ columns are about two times higher than those in surface NO₂ concentrations. This discrepancy could be driven by the combined effects from uncertainties in simulations and observations, associated with possible inaccurate simulations of lower tropospheric NO₂, larger uncertainties in the modeled interannual variabilities of NO₂ columns, as well as insufficient consideration of aerosol effects and a priori NO₂ variability in satellite retrievals. In addition, our analysis suggests a small influence from free tropospheric NO₂ backgrounds in E. China in winter. This work demonstrates the challenge to interpret wintertime tropospheric NO₂ changes in China, highlighting the importance of integrating surface NO₂ observations to provide better analysis for NO₂ variabilities.

The Science Based Targets initiative has published a Comment to our study (Bjørn et al 2021 Environ. Res. Lett.16 054019). We see the Comment as an important step towards addressing our study's call for more systematic presentation of methods for setting science-based targets and increased transparency behind the initiative's method recommendations. We also agree with some of the Comment's points of criticism of our study and the related nuances introduced. Yet, we find other points to be inaccurate or misdirected. Here, we reply to the Comment by clarifying misunderstandings on our study's aims, providing additional methodological details, and elaborating on our perspectives.

A study from Bjørn et al (2021) suggests that methods to allocate emissions to companies proportionally to their economic growth are consistent with equity-related principles and are effective at conserving a global emissions budget while the science based targets initiative's (SBTi's) absolute contraction approach (ACA) fulfills neither qualification. Here we identify four areas of concern with the study and propose a more comprehensive approach to science based targets (SBT) method evaluation. We respond that first, the authors' method characterization does not differentiate between the emissions allocation that occurs in mitigation scenarios and that which is normatively caused by method formulae, and it misinterprets the drivers of emissions allocation in scenarios. Second, we note that the authors evaluate a method formula for ACA that does not match its use by the SBTi. Third, we acknowledge that allocating emissions based on economic growth can yield incoherent results by comparison to published climate change mitigation scenarios and suggest the authors also evaluate whether methods are effective at conserving sub-global emissions budgets. Fourth, we observe that although the study is framed as an evaluation of SBT methods, it relies almost entirely on assessments of one characteristic. We conclude by proposing a set of principles that should be met by effective SBT methods and a high-level assessment of SBT methods against these principles.