Table of contents

Since the adoption of the 2015 Paris Agreement and the publication of the 2018 Special Report on Global Warming of 1.5°C of the Intergovernmental Panel on Climate Change, net zero targets have become a central feature in climate policy. This Perspective looks back at the scientific foundations of this recent policy development, the current state of play, and next frontiers for research on this topic.

Most climate change mitigation scenarios restricting global warming to 1.5 °C rely heavily on negative emissions technologies and practices (NETPs). Here we updated previous literature reviews and conducted an analysis to identify the most appealing NETPs. We evaluated 36 NETPs configurations considering their technical maturity, economic feasibility, greenhouse gas removal potential, resource use, and environmental impacts. We found multiple trade-offs among these indicators, which suggests that a regionalised portfolio of NETPs exploiting their complementary strengths is the way forward. Although no single NETP is superior to the others in terms of all the indicators simultaneously, we identified 16 Pareto-efficient NETPs. Among them, six are deemed particularly promising: forestation, soil carbon sequestration (SCS), enhanced weathering with olivine and three modalities of direct air carbon capture and storage (DACCS). While the co-benefits, lower costs and higher maturity levels of forestation and SCS can propel their rapid deployment, these NETPs require continuous monitoring to reduce unintended side-effects—most notably the release of the stored carbon. Enhanced weathering also shows an overall good performance and substantial co-benefits, but its risks—especially those concerning human health—should be further investigated prior to deployment. DACCS presents significantly fewer side-effects, mainly its substantial energy demand; early investments in this NETP could reduce costs and accelerate its scale-up. Our insights can help guide future research and plan for the sustainable scale-up of NETPs, which we must set into motion within this decade.

Concerns about the climate and local air impacts of emissions from the oil and gas supply chain have caused a reevaluation of natural gas' role in a low carbon future. In response, some producers, large purchasers, and investors have pushed to certify some gas deliveries as 'responsibly-sourced' or 'green', which could give rise to a differentiated gas market. Third-party oil and gas certifications have been under development for several years, however, their focus has historically been on a broader set of societal impacts and risks, and they have typically focused on the upstream sector. Recent advances have been focused on methane emissions and supply chains into the certification process. In this paper we provide a critical review of several prominent natural gas certification processes. We do so within a broader historical context of using third-party market certifications and labels to differentiate clean vs. dirty versions of commodities.

The original intention of daylight saving time (DST) was to save energy required for artificial lighting. This one-hour shift in working hours, however, also impacts the current and future heating and cooling demand of buildings, which is yet to be thoroughly investigated. Here, daylight saving time-induced heating and cooling demand of archetype offices across the United States are simulated for 15 cities for different representative concentration pathway (RCP) climate trajectories. DST reduces cooling more than it increases heating. Maximum savings of up to 5.9% for cooling and 4.4% increase in heating were simulated under current climatic conditions, noting that cooling dominates the buildings' demand during the DST period. Climate change increases future cooling demand, but does not significantly affect the combined (heating and cooling) potential of reducing energy demand when DST is introduced. However, the relative reduction (i.e. decrease in the percentage of total cooling demand) is smaller when considering climate change. The impact of DST on cooling and heating energy demand depends on the geographical location, which determines the amount and temporal pattern of cooling and heating demand. For the considered case studies, introducing DST with climate change generally resulted in overall combined savings with a maximum saving of 3% for Port Angeles, assuming an RCP 4.5 scenario. Policies that shift working hours need to be evaluated considering their impact on building energy demand and it is necessary to establish whether saving cooling or saving heating energy demand can achieve higher CO₂ emission reductions.

Motivated by ongoing partisan division in support of climate change policy, this paper investigates whether, among self-identifying liberals and conservatives, the mere presence of a non-transformative climate policy such as carbon capture and storage (CCS), lowers support for a renewable energy (RE) policy. To interrogate this question, we use a survey experiment asking 2374 respondents about their support for a RE policy when presented with the RE policy alone, and when presented alongside a CCS policy whose funding and implementation leverage independent funding sources. We find that among conservatives, the presence of a CCS policy lowers support for RE. Furthermore, despite the lack of apparent political party cues, when presented with the policy-pair, conservatives tend to view the RE policy in more partisan terms, specifically, less supported by Republicans. Additional experimental conditions with explicit party cues reinforce this interpretation. These findings suggest that the triggering of partisan perceptions even without explicit partisan cues—what we call political anchoring—might be a key impediment to bipartisan support of climate solutions in the U.S. context.

Surface ozone is an important pollutant causing damage to human health and ecosystems. Here, we find that the Arctic surface ozone during the 2020–2021 winter was evidently enhanced after the sudden stratospheric warming (SSW) onset based on reanalysis data and simulations of a state-of-the-art chemistry-climate model. Further analysis suggests that this enhancement of Arctic surface ozone is primarily a result of the strengthening of the stratosphere-to-troposphere transport associated with the SSW. It is found that the SSW leads to more ozone in the Arctic stratosphere and enhanced downward transport with SSW-related downdraft. The 2021 SSW may also lead to positive anomalies in surface ozone in the northern midlatitudes, which are associated with cold air outbreaks. Our results indicate that the SSW not only affects the weather and climate in the troposphere but may also affect the surface air quality.

With advances in artificial intelligence, machine learning-based models such as long short-term memory (LSTM) models have shown much promise in forecasting long-term runoff by mapping pathways between large-scale climate patterns and catchment runoff responses without considering physical processes. The recognition of key factors plays a vital role and thus affects the performance of the model. However, there is no conclusion on which recognition algorithm is the most suitable. To address this issue, an LSTM model combined with two attention mechanisms both in the input and hidden layers, namely AT-LSTM, is proposed for long-term runoff forecasting at Yichang and Pingshan stations in China. The added attention mechanisms automatically assign weights to 130 climate phenomenon indexes, avoiding the use of subjectively set recognition algorithms. Results show that the AT-LSTM model outperforms the Pearson's correlation based LSTM model in terms of four evaluation metrics for monthly runoff forecasting. Further, the set indirect runoff prediction method verifies that the AT-LSTM model also performs effectively in precipitation and potential evapotranspiration forecasting, and the indirect runoff prediction is inferior to the AT-LSTM model to establish a direct link between climate factors and runoff. Finally, four key factors related to runoff are identified by the attention mechanism and their impacts on runoff are analyzed on intra- and inter-annual scales. The proposed AT-LSTM model can effectively improve the accuracy of long-term forecasting and identify the dynamic influence of input factors.

A major change in winter sea surface heat loss between two key Mediterranean dense water formation sites, the North–West Mediterranean (NWMed) and the Aegean Sea, since 1950 is revealed using atmospheric reanalyses. The NWMed heat loss has weakened considerably (from −154 Wm⁻² in 1951–1985 to −137 Wm⁻² in 1986–2020) primarily because of reduced latent heat flux. This long-term weakening threatens continued dense water formation, and we show by evaluation of historical observations that winter-time ocean convection in the NWMed has declined by 40% from 1969 to 2018. Extension of the heat flux analysis reveals changes at other key dense water formation sites that favour an eastward shift in the locus of Mediterranean convection towards the Aegean Sea (where heat loss has remained unchanged at −172 Wm⁻²). The contrasting behaviour is due to differing time evolution of sea-air humidity and temperature gradients. These gradients have weakened in the NWMed due to more rapid warming of the air than the sea surface but remain near-constant in the Aegean. The different time evolution reflects the combined effects of global heating and atmospheric circulation changes which tend to offset heating in the Aegean but not the NWMed. The shift in heat loss has potentially significant consequences for dense water formation at these two sites and outflow to the Atlantic. Our observation of differential changes in heat loss has implications for temporal variations in the balance of convection elsewhere e.g. the Labrador-Irminger-Nordic Seas nexus of high latitude Atlantic dense water formation sites.

The observed temperature increase due to anthropogenic carbon emissions has impacted economies worldwide. National income levels in origin and destination countries influence international migration. Emigration is relatively low not only from high income countries but also from very poor regions, which is explained in current migration theory by credit constraints and lower average education levels, among other reasons. These relationships suggest a potential non-linear, indirect effect of climate change on migration through this indirect channel. Here we explore this effect through a counterfactual analysis using observational data and a simple model of migration. We show that a world without climate change would have seen less migration during the past 30 years, but that this effect is strongly reduced due to inhibited mobility. Our framework suggests that migration within the Global South has been strongly reduced because these countries have seen less economic growth than they would have experienced without climate change. Importantly, climate change has impacted international migration in the richer and poorer parts of the world very differently. In the future, climate change may keep increasing global migration as it slows down countries' transition across the middle-income range associated with the highest emigration rates.

The U.S. State of California has experienced frequent drought events, hotter temperatures and other disruptions to the climate system whose effects on ecosystems have been widely reported in recent decades. Studies primarily confined to specific vegetation communities or species, individual drought incidents, or analysis over a relatively short intervals, has limited our understanding of the broad-scale effects on tree cover and the spatiotemporal variability of effects across broader regions. We focused analysis on multi-annual land cover and land surface change to assess patterns and trends in tree cover loss in tree-dominated Californian ecoregions from 1986 to 2019. The top three years of total tree cover loss for the state were 2018 (1901 km²), 2015 (1556 km²), and 2008 (1549 km²). Overall, annual tree cover loss had upward trends. Tree cover loss rapidly surged later in the study period and was apparently driven by climate stress and wildfires. Underlying geographic variability was apparent in both non-fire and fire-related tree cover loss that sharply increased during hotter multi-year droughts. The increasingly hotter and drier climate conditions were associated with significant increases in fire-induced mortality. Our findings indicate that a possible effect of future hotter and drier climate would lead to further tree cover loss, thereby endangering California's ecosystem goods and services. Geographic variability in tree cover trends indicates that ecoregion-specific mitigation and adaptation strategies would be useful to conserve the region's forest resources. Such strategies may benefit from consideration of historical disturbances, ecoregion's sensitivity to disturbance types, as well as potential ecoregion-specific climate-vegetation-fire feedbacks.

Ordinary least squares linear regression (LR) has long been a popular choice among researchers interested in using historical data for estimating crop yield response to climate change. Today, the rapidly growing field of machine learning (ML) offers a wide range of advanced statistical tools that are increasingly being used for more accurate estimates of this relationship. This study compares LR to a popular ML technique called boosted regression trees (BRTs). We find that BRTs provide a significantly better prediction accuracy compared to various LR specifications, including those fitting quadratic and piece-wise linear functions. BRTs are also able to identify break points where the relationship between climate and yield undergoes significant shifts (for example, increasing yields with precipitation followed by a plateauing of the relationship beyond a certain point). Tests we performed with synthetically simulated climate and crop yield data showed that BRTs can automatically account for not only spatial variation in climate–yield relationships, but also interactions between different variables that affect crop yields. We then used both statistical techniques to estimate the influence of historical climate change on rice, wheat, and pearl millet in India. BRTs predicted a considerably smaller negative impact compared to LR. This may be an artifact of BRTs conflating time and climate variables, signaling a potential weakness of models with excessively flexible functional forms for inferring climate impacts on agriculture. Our findings thus suggest caution while interpreting the results from single-model analyses, especially in regions with highly varied climate and agricultural practices.

Arctic winter daily warming events have sparked growing interest, particularly in recent years, when Arctic daily temperatures have approached melting point several times. This analysis reveals that the impact of the El Niño-Southern Oscillation (ENSO) on the frequency of Arctic daily warming events experienced an obvious change around the late 1970s, which may be attributed to changes in ENSO intensity. Since the late 1970s, due to stronger ENSO intensities, ENSO has induced a stronger Rossby wave; then, El Niño (La Niña) has deepened (weakened) the Aleutian Low and strengthened anomalous northerlies (southerlies) over the North Pacific, thereby decreasing (increasing) the frequency of Arctic daily warming events. In contrast, before the late 1970s, the ENSO did not have an apparent direct impact on the frequency of Arctic daily warming events due to its weaker intensity. Our findings provide a potential relationship between the equator and the Arctic to improve the prediction accuracy of extreme Arctic daily warming events. By analyzing Coupled Model Intercomparison Project phase-6 models, we confirm that the potential relation may be strengthened under the global warming scenario.

The urban turbidity island (UTI) effect is an important research topic in urban climate studies. It is closely related to urban visibility and the health of urban residents; however, it has received little attention in previous research. This study analyzes the temporal and spatial distribution characteristics of the UTI effect through the combined use of satellite remote sensing and ground observation data. Specifically, absolute and relative urban turbidity island intensity (UTII_A and UTII_R) indices are proposed and calculated for 2000–2020 by using aerosol data products and atmospheric fine particle mass concentration inversion products, which are represented by aerosol optical depth (AOD), PM₁, PM_2.5, and PM₁₀. The results show the following: (a) there has been a clear footprint of the UTI effect in Beijing since 2000, generally consistent with trends of urban sprawl; (b) there are great differences in the interannual distribution of AOD, normalized AOD and PM values in urban and suburban areas; and (c) there are seasonal differences in the UTI distribution and air pollutant concentrations. The differences among indices between urban and suburban areas are mainly caused by heat island-induced air convection, complex structures in urban areas and regional weather conditions. Importantly, the interannual distribution of AOD and UTII_A of PM values decreased from 2014 to 2020, indicating that the government's air pollution control policy has significantly improved air quality. Analysis from this study could support the formulation of urban planning and control policies to guide human activities.

Black carbon (BC) is an important aerosol species due to its strong heating of the atmosphere accompanied by cooling of the Earth's surface, but its radiative forcing is poorly constrained by different regional size distributions due to uncertain reproductions of a morphologically simplified model. Here, we quantify the BC morphological effect on measuring the particle size using an aggregate model. We show that the size distributions of loose BC particles could account for up to 45% underestimation by morphological simplification, leading to up to 25% differences, by relying on a simplified model to estimate radiative forcing. We find that the BC particle size is remarkably amplified for looser and larger BC aggregates by angular scattering observations. We suggest that the BC morphological diversity can be neglected in forward scattering angles (<30°), which is a useful supplement to reduce the uncertainty of radiative forcing assessment.

Global temperature is projected to increase, which impacts the ecological process in northern mid- and high-latitude ecosystems, but the winter temperature change in ecosystems is among the least understood. Rice paddy represents a significant contributor to global anthropogenic CH₄ emissions and has a strong climate forcing feedback; however, the legacy effects of warming winter on CH₄ emissions in the subsequent growing season remain uncertain. Here, we conducted field and incubation experiments to determine the effects of winter soil temperature changes on CH₄ emissions in the subsequent growing season. First, in the 3 year field experiment, we continuously measured CH₄ emissions from the rice cropping system. The winter soil temperature and its variation showed significant differences over the 3 years. In the warming-winter year, the rice paddy accumulated less NH₄⁺–N and more dissolved organic carbon (DOC) in the soil during winter, resulting in high CH₄ emissions. Second, we incubated the paddy soils without flooding at three temperatures (5 °C, 15 °C, and 25 °C) for 4 weeks to simulate warming winter, and subsequently incubated at same temperature (25 °C) under submerged conditions for 4 weeks to simulate growing season. The result was consistent with field experiment, increased soil temperature significantly increased soil DOC content and decreased NH₄⁺–N content in 'winter season'. The CH₄ emissions in the subsequent 'growing season' increased by 190% and 468% when previous incubation temperature increased 10 °C and 20 °C. We showed strong and clear links between warming winter and CH₄ emissions in the subsequent growing season for the first time, suggesting that CH₄ related processes respond not only to warming during the growing season but also in the previous winter. Our findings indicate that nonuniform global warming causes a disproportionate increase in climate forcing feedback to emit more CH₄.

Air travel generates a substantial and growing share of global greenhouse gas emissions. Reduction efforts partly rely on estimates of emissions per passenger, which may be used for carbon budgets, offsets, or taxes. Aircraft emissions are typically allocated to individual passengers through space-based allocation dependent on seating arrangements by travel class. However, the operation of aircraft depends on profitability, which benefits from high fares from late bookings, often by business and high-income travellers. Fare-based allocation recognises the economic drivers of airline emissions by allocating the aircraft emissions proportionally to the paid airfares. In this article, we compare space-based passenger emissions, which differ only by class, with fare-based passenger emissions, which depend on the fare paid by the individual traveller. We extract space-based allocation factors from widely used emission calculators and derive fare-based allocation factors from airfares for domestic travel in the US. We find that the space-based allocation factors reflect the difference in average expenditure by travel class but not the difference in expenditure between travellers. With fare-based accounting, the most expensive economy trips have similar emissions to space-based premium trips, while less expensive premium trips have similar emissions to space-based economy trips. We find that a tax on fare-based instead of space-based emissions leads to a more evenly distributed impact on low-fare and high-fare travellers whilst achieving the same reduction in airline revenues. We conclude that fare-based emissions accounting better reflects the drivers of airline emissions and supports more equitable climate action.

Agricultural large-scale land acquisitions have been linked with enhanced deforestation and land use change. Yet the extent to which transnational agricultural large-scale land acquisitions (TALSLAs) contribute to—or merely correlate with—deforestation, and the expected biodiversity impacts of the intended land use changes across ecosystems, remains unclear. We examine 178 georeferenced TALSLA locations in 40 countries to address this gap. While forest cover within TALSLAs decreased by 17% between 2000 and 2018 and became more fragmented, the spatio-temporal patterns of deforestation varied substantially across regions. While deforestation rates within initially forested TALSLAs were 1.5 (Asia) to 2 times (Africa) higher than immediately surrounding areas, we detected no such difference in Europe and Latin America. Our findings suggest that, whereas TALSLAs may have accelerated forest loss in Asia, a different mechanism might emerge in Africa where TALSLAs target areas already experiencing elevated deforestation. Regarding biodiversity (here focused on vertebrate species), we find that nearly all (91%) studied deals will likely experience substantial losses in relative species richness (−14.1% on average within each deal)—with mixed outcomes for relative abundance—due to the intended land use transitions. We also find that 39% of TALSLAs fall at least partially within biodiversity hotspots, placing these areas at heightened risk of biodiversity loss. Taken together, these findings suggest distinct regional differences in the nature of the association between TALSLAs and forest loss and provide new evidence of TALSLAs as an emerging threat to biodiversity in the Global South.

Fire in the tropical peatland forests of Borneo is an environmental issue interactioning with climate change and deforestation, and the consequences have local and global implications. While research has shown that fire severity and frequency are expected to increase with climate change, there is conflicting model and observational data as to the effect of deforestation on precipitation, which is a key metric for fire risk. To better understand the changes in fire risk from deforestation and climate change we ran simulations of the climate scenario RCP8.5 with and without total deforestation using regional climate model RegCM4. The output was then used for calculations of the fire weather index. We find that annual temperature change from deforestation at elevations above 500 m is 53% of the change over the 21st century in RCP8.5. Fire risk is significantly affected by both climate change and deforestation, despite some increases in precipitation from deforestation. While the multi model dry season (June–August) mean increases in fire risk are larger from elevated atmospheric carbon dioxide, the increases in maximum fire risk are larger from deforestation. The altitude is a good predictor of fire risk change, with larger increases at more densely populated lower elevations where the peatlands are concentrated and smaller increases at higher elevations. Therefore, while deforestation generally causes a smaller increase in climate-related fire risk than climate change, its local control and heterogeneous effects compared to global carbon emissions makes it critical for climate mitigation policy. These high-resolution simulations provide a guide to the most vulnerable areas of Borneo from climatic increases in fire risk.

Storm surges are among the deadliest natural hazards, but understanding and prediction of year-to-year variability of storm surges is challenging. Here, we demonstrate that the interannual variability of observed storm surge levels can be explained and further predicted, through a process-based study in Hong Kong. We find that El Niño-Southern Oscillation (ENSO) exerts a compound impact on storm surge levels through modulating tropical cyclones (TCs) and other forcing factors. The occurrence frequencies of local and remote TCs are responsible for the remaining variability in storm surge levels after removing the ENSO effect. Finally, we show that a statistical prediction model formed by ENSO and TC indices has good skill for prediction of extreme storm surge levels. The analysis approach can be applied to other coastal regions where tropical storms and the climate variability are main contributors to storm surges. Our study gives new insight into identifying 'windows of opportunity' for successful prediction of storm surges on long-range timescales.

Unusual, persistent configurations of the North Atlantic jet stream affect the weather and climate over Europe. We focus on winter and on intraseasonal and seasonal time scales, and study persistent jet anomalies through the lens of large deviation theory using Coupled Model Intercomparison Project (CMIP6) simulations of the MPI-ESM-LR model and ERA5 reanalysis data. The configurations of interest are defined as long-lasting anomalies of a few months in jet latitude, speed or zonality. Our results show that persistent temperature and precipitation extremes over large European regions are anomalously frequent during the unusual, persistent jet configurations we identify. Furthermore, the relative increase in frequency of surface extremes is larger for more intense surface extremes and/or more extreme jet anomalies. This is relevant in the context of the predictability of these extremes. The highest extreme event frequencies at the surface are observed in case of precipitation over the Mediterranean and Western Europe during anomalously zonal and/or fast jet events, pointing to these jet anomalies matching rather homogeneous large scale atmospheric configurations with a clear surface footprint. Additionally, our results emphasise the usefulness of large deviation rate functions to estimate the frequency of occurrence of persistent jet anomalies. They therefore provide a tool to statistically describe long-lasting anomalies, much like extreme value theory may be used to investigate shorter-lived extreme events.

Wetlands protect downstream waters by filtering excess nitrogen (N) generated from agricultural and urban activities. Many small ephemeral wetlands, also known as geographically isolated wetlands (GIWs), are hotspots of N retention but have received fewer legal protections due to their apparent isolation from jurisdictional waters. Here, we hypothesize that the isolation of the GIWs make them more efficient N filters, especially when considering transient hydrologic dynamics. We use a reduced complexity model with 30 years of remotely sensed monthly wetland inundation levels in 3700 GIWs across eight wetlandscapes in the US to show how consideration of transient hydrologic dynamics can increase N retention estimates by up to 130%, with greater retention magnification for the smaller wetlands. This effect is more pronounced in semi-arid systems such as the prairies in North Dakota, where transient assumptions lead to 1.8 times more retention, compared to humid landscapes like the North Carolina Pocosins where transient assumptions only lead to 1.4 times more retention. Our results highlight how GIWs have an outsized role in retaining nutrients, and this service is enhanced due to their hydrologic disconnectivity which must be protected to maintain the integrity of downstream waters.

Meteorological observations provide essential data for weather forecasting and climate change studies. Whether the measured data can accurately support such applications closely relates to the representativeness of the data collected, which depends on both the scale of observation and the density of the measurement network. Precipitation presents in the form of events and is discontinuous both in time and space. Gauge observations of precipitation could provide fundamental data but have difficulty quantitatively assessing precipitation system scale. Therefore, assessments on the representativeness of precipitation at synoptic and climatological scales remain needed. Here, we show the first high-resolution map of the representativeness of precipitation over Mainland China based on the latest satellite data. Our results show that the daily precipitation spatial consistency is the highest in eastern China and lowest on the Tibetan Plateau. However, the pattern of the monthly spatial consistency is different and is the highest over Northeast China Plain, the Loess Plateau, and the Middle–Lower Yangtze Plain. Compared to the density of rain gauges, we find that the current national station network with ∼2400 stations still has difficulty supporting synoptic studies in western China. However, for climate change studies based on monthly data, the density of the national reference climatological station network is sufficient, except in the western Tibetan Plateau and deserts with no available stations. For climatological studies, the quality of precipitation gauge observations is more important than its spatial density. Our results could provide great practical significance for considering the layout of rain gauges.

Understanding contributions of climate and management intensifications to crop yield trends is essential to better adapt to climate changes and gauge future food security. Here we quantified the synergistic contributions of climate and management intensifications to maize yield trends from 1961 to 2017 in Iowa (United States) using a process-based modeling approach with a detailed climatic and agronomic observation database. We found that climate (management intensifications) contributes to approximately 10% (90%), 26% (74%), and 31% (69%) of the yield trends during 1961–2017, 1984–2013, and 1982–1998, respectively. However, the climate contributions show substantial decadal or multi-decadal variations, with the maximum decadal yield trends induced by temperature or radiation changes close to management intensifications induced trends while considerably larger than precipitation induced trends. Management intensifications can produce more yield gains with increased precipitation but greater losses of yields with increased temperature, with extreme drought conditions diminishing the yield gains, while radiation changes have little effect on yield gains from management intensifications. Under the management condition of recent years, the average trend at the higher warming level was about twice lower than that at the lower warming level, and the sensitivity of yield to warming temperature increased with management intensifications from 1961 to 2017. Due to such synergistic effects, management intensifications must account for global warming and incorporate climate adaptation strategies to secure future crop productions. Additional research is needed to understand how plausible adaptation strategies can mitigate synergistic effects from climate and management intensifications.

With the Glasgow Climate Pact 2021, the global community has committed explicitly to phasing down coal consumption. Yet the coal supply sector continues to develop new capacities, despite the risk of asset stranding. This article presents the first assessment of the implications of 1.5^∘C mitigation pathways for the coal mining industry. Based on open coal mine data and a new version of the open coal sector model COALMOD-World, the prospects for individual coal mining regions and their risk of early mine closures and asset stranding are analyzed. Results show that global cumulative production capacity from operating thermal coal mines exceed the remaining consumption values for 2020 through 2050 by more than 50%. This supply-consumption discrepancy would hit Russia and the USA especially hard, causing the stranding of around 80% of operating capacities in each case. But the early closure of operating coal mines would affect all of the world's major thermal coal producing regions, with most regions seeing more than three-fourths of their mine capacity closing early by 2030. Stranded assets from operating coal mines would total some USD₂₀₁₅ 120 to 150 billion until 2050, with an additional USD₂₀₁₅ 100 billion should currently proposed new coal mining projects be realized. If demand declines in accordance with 1.5^∘C pathways, new coal mines or mine extensions would be redundant in all coal regions. Although the stranded asset value of mines is relatively small compared to that of the coal power plant sector, early closures would especially affect workers and local communities. Thus, efforts are urgently needed to ensure a just transition in coal mining regions and to address excess operating and proposed coal supply capacities that continue to fuel global warming.

Despite the importance of carbon dioxide removal (CDR) in most climate change mitigation scenarios that limit warming to well below 2 °C, the study of CDR is still a nascent field with basic questions to be resolved. Crucially, it is not known how much CDR is currently deployed at a global scale, nor how that compares to mitigation scenario estimates. Here, we address this problem by developing an estimate of global current CDR activity. We draw on national greenhouse gas inventory data combined with CDR registries and commercial databases to estimate that global anthropogenic activity presently generates ∼1985 MtCO₂yr⁻¹ of atmospheric removals. Almost all of these—1983 MtCO₂yr⁻¹—are removals from land-use, land-use change and forestry. Non-land-management CDR projects such as bioenergy with carbon capture and storage, direct air capture with carbon capture and storage and biochar remove only about 2 MtCO₂yr⁻¹. We compare this estimate with Shared Socioeconomic Pathways projections of CDR deployed in 'well-below 2°C' mitigation pathways. In so doing we demonstrate current CDR deployment would need to grow exponentially to keep the world aligned with most 'well-below 2°C' scenarios, which see CDR deployment growing between 75% and 100% per year between 2020 and 2030, adding ∼300–2500 MtCO₂ in total CDR capacity. To conclude we discuss uncertainties related to our estimates, and suggest priorities for the future collection and management of CDR data, particularly related to the role of the land sink in generating CDR.

In July and August of 2022, unprecedented and long-lasting heatwaves attacked central and eastern China (CEC); and the most affected area was in the Yangtze River (YR) basin. The extreme heatwaves and associated drought and wildfire had significant social impacts, but the underlying mechanisms remain unknown. Observational analysis indicates that the heatwaves were regulated by anomalous anticyclone in the mid-upper troposphere over northern CEC. Specifically, the easterly anomalies at the southern flank of the anticyclone caused air isentropic sliding and transported low moist enthalpy (cold and dry) air to the YR basin, contributing to anomalous sinking motions and extreme heatwaves. In comparison, heatwaves were more serious in August than in July due to stronger upper-level anomalous anticyclone and associated easterlies. Importantly, different mechanisms were responsible for the heatwaves in the two months. In July, the relatively weaker anticyclonic anomaly over northern CEC was dominated by the forcing of diabatic heating over northwestern South Asia (NWSA), corresponding with the record-breaking rainfall in and around Pakistan. In August, a powerful anticyclonic condition for the CEC heatwaves originated from an extreme silk road pattern (SRP), superposing the effect of NWSA diabatic heating due to persistent downpour. We notice that another upstream anticyclonic node in the SRP also created heatwaves in Europe. Therefore, the CEC extreme heat was actually associated with other concurrent extremes over the Eurasian continent through large-scale atmospheric teleconnections in 2022.

Equity has become central in the academic and regulatory discourse shaping the future of residential-scale clean energy technologies in the United States, particularly rooftop solar. Here, we develop a holistic perspective on these issues by analyzing rooftop solar adoption trends using two alternative forecasting methods: an inside-view forecast based on historical solar adoption data, and an outside-view forecast based on adoption data for other emerging consumer technologies. We show how rooftop solar, like other emerging consumer technologies, has become more equitably adopted over time. We show that solar diffusion patterns are largely consistent with those of other technologies. Both forecasting methods suggest that clean energy technologies should be expected to become more equitably adopted over time. Policy could accelerate this process by supporting low-income adoption without unduly curbing overall diffusion.

Retrieving weather extremes from observations is critical for weather forecasting and climate impact studies. Statistical and machine learning methods are increasingly popular in the remote sensing community. However, these models act as regression tools when dealing with regression problems and as such, they are not always well-suited for the estimation of the extreme weather states. This study firstly introduces two error types that arise from such statistical methods: (a) 'dampening' refers to the reduction of the range of variability in the retrieved values, a natural behavior for regression models; (b) 'inflating' is the opposite effect (i.e. larger ranges) due to data pooling. We then introduce the concept of localization that intends to better take into account local conditions in the statistical model. Localization largely improves the retrievals of extreme states, and can be used both for retrieval at the pixel level or in image processing techniques. This approach is tested on the retrieval of land surface temperature using infrared atmospheric sounding interferometer observations: the dampening is reduced from 1.9 K to 1.6 K, and the inflating from 1.1 K to 0.5 K, respectively.

Central heating in North China produces severe air pollution, although the need for heating may be reduced by rising temperatures associated with climate change. The regional trend of mean heating length (HL) for North China was −0.32 d per year during 1961–2019. Compared with the 2010–2015 mean values, the start and end dates for central heating in the North China Plain (NCP) during 2050–2055 will delayed by 9 d and advanced by 12 d, respectively, under the Shared Socioeconomic Pathway 5–8.5 (SSP5-85), and by 5 and 8 d under the carbon-neutral (CN) scenario, based on Coupled Model Intercomparison Project phase 6 model simulations. Here we propose a flexible heating policy (FHP), such that HL is determined strictly by temperature, and the associated air pollution benefit of shortening HL are examined by a global 3D chemical transport model GEOS-Chem. The study focused on the year 2019 with the current goal of elimination of severe PM_2.5 pollution, and with the minimum HL estimated to provide up to a 24% reduction in severe PM_2.5 pollution (daily mean PM_2.5 > 150 μg m⁻³) over the NCP during periods of FHP implementation. For future CN policies, the NCP can achieve great air quality improvements by 2050, with more than 60% of days throughout the heating season with daily PM_2.5 concentrations of <10 μg m⁻³, and 95% with <35 μg m⁻³. Although the SSP5-85 scenario may lead to reduced HLs, pollutant emissions are likely much higher than under CN scenarios, with pollution days of PM_2.5> 100 μg m⁻³ still occurring frequently by 2050. Our results highlight that FHPs may effectively reduce severe PM_2.5 pollution, and China's carbon neutrality goals will play critical roles in mitigating air pollution and prolonged heating welfare during future heating season.

Forests are major terrestrial carbon (C) sinks and play a crucial role in climate change mitigation. Despite extensive studies on forest C sequestration, the relationship between seasonal C uptake and its allocation to woody biomass is poorly understood. Here we used a novel dendro-anatomical approach to investigate the relationships between climate variability, C uptake, and woody biomass growth in an 80 year-old eastern white pine (Pinus strobus) plantation forest in Ontario, Canada. We used eddy covariance (EC) gross primary productivity (GPP) from 2003–2018 and woody biomass estimated from chronologies of cell wall area (CWA, a proxy for C storage in individual wood cells) and ring wall area (RWA) for earlywood (EW) and latewood (LW) from 1970–2018. Warm temperatures in early spring and high precipitation in mid-spring and summer positively and strongly affected GPP, while high temperature and high vapor pressure deficit in the summer had a negative effect. From 2003 to 2018, there was a steady increase in both GPP and woody cell biomass. Moreover, we found strong positive correlations between GPP and CWA both in EW (May—July GPP, r= 0.65) and LW (July—August GPP, r = 0.89). Strong positive correlations were also found between GPP and RWA both in EW and LW (April—September, r =⩾ 0.79). All these associations were stronger than the association between annual GPP and tree-ring width (r = 0.61) used in previous studies. By increasing the resolution of tree-ring analysis to xylem-cell level, we captured intra-annual variability in biomass accumulation. We demonstrated a strong control of seasonal C assimilation (source) over C accumulation in woody biomass at this site. Coupling high-resolution EC fluxes (GPP) and wood anatomical measurements can help to reduce existing uncertainties on C source-sink relationships, opening new perspectives in the study of the C cycle in forests.

Armed conflict and economic growth are inherently coupled; armed conflict substantially reduces economic growth, while economic growth is strongly correlated with a reduction in the propensity of armed conflict. Here, we simulate the incidence of armed conflict and its effect on economic growth simultaneously along the economic pathways defined by the shared socioeconomic pathways (SSPs). We argue that gross domestic product per capita projections through the 21st century currently in use are too optimistic since they disregard the harm to growth caused by conflict. Our analysis indicates that the correction required to account for this is substantial—expected income is 25% lower on average across countries when taking conflict into account. The correction is particularly strong for the more pessimistic SSP3 and SSP4 where expected future incidence of armed conflict is high. There are strong regional patterns with countries with contemporaneous conflicts experiencing much higher conflict burdens and reduced economic growth by the end of the century. The implications of this research indicate that today's most marginalized societies will be substantially more vulnerable to the impact of climate change than indicated by existing income projections.

Methane (CH₄) emissions from the oil and natural gas (O&G) supply chain have been demonstrated to be one of the largest anthropogenic greenhouse gas emission sources ripe for mitigation to limit near-term climate warming. In recent years, exploration and production (E&P) operators have made public commitments to reducing their greenhouse gas emission intensity, yet little empirical information has been made available in the public domain to allow an accurate comparison of their emissions performance. In this study, we utilize a series of aircraft surveys of large CH₄ point source emissions (∼10¹–10⁴ kg CH₄ hr⁻¹) related to O&G production in the Permian Basin to enable comparison of company-level production-sector emission intensities. We calculate gas and total energy production normalized emission intensities for several of the largest E&P operators in the Permian Basin accounting for ∼85% of production within the flight region. We find differences of more than an order of magnitude in emission intensity across operators, with nearly half demonstrating a ⩾50% improvement in performance from 2019 to 2021. With the availability of such publicly attributed emissions data anticipated to increase in the future, we provide methodological insights and cautions to developing operator metrics from future empirical datasets.

Cocoa production has been identified as a major global driver of deforestation, but its precise contribution to deforestation dynamics in West Africa remains unclear. It is also unknown to what degree companies and international markets are able to trace their cocoa imports, and satisfy their sustainable sourcing commitments. Here, we use publicly-available remote-sensing and supply chain data for Côte d'Ivoire, the world's largest cocoa producer, to quantify cocoa-driven deforestation and trace 2019 cocoa exports and the associated deforestation from their department of origin, via trading companies, to international markets. We find 2.4 Mha of cocoa deforestation and degradation over 2000–2019, i.e. 125 000 ha y⁻¹, representing 45% of the total deforestation and forest degradation over that period. Only 43.6% (95% CI: 42.6%–44.7%) of exports can be traced back to a specific cooperative and department. The majority of cocoa (over 55%) thus remains untraced, either indirectly sourced from local intermediaries by major traders (23.9%, 95% CI: 22.9%–24.9%), or exported by untransparent traders—who disclose no information about their suppliers (32.4%). Traceability to farm lags further behind, and is insufficient to meet the EU due-diligence legislation's proposed requirement for geolocation of product origins. We estimate that trading companies in the Cocoa and Forests Initiative have mapped 40% of the total farms supplying them, representing only 22% of all Ivorian cocoa exports in 2019. We identify 838 000 hectares of deforestation over 2000–2015 associated with 2019 EU imports, 56% of this arising through untraced sourcing. We discuss issues of company- and state-led traceability systems, often presented as solutions to deforestation, and stress the need for transparency and for the sector to work beyond individual supply chains, at landscape-level, calling for collaboration, stronger regulatory policies, and investments to preserve the remaining stretches of forests in West Africa.

Andean glaciers have melted rapidly since the 1960s. While some melting is likely due to anthropogenic climate change driven by increasing greenhouse gases, deposition of light-absorbing particles such as black carbon (BC) may also play a role. We hypothesize that BC from fires in the Amazon Basin and elsewhere may be deposited on Andean glaciers, reducing the surface albedo and inducing further melting. Here we investigate the role of BC deposition on albedo changes in the Andes for 2014–2019 by combining atmospheric chemistry modeling with observations of BC in snow or ice at four mountain sites in Peru (Quelccaya, Huascarán, Yanapaccha, and Shallap) and at one site in Bolivia (Illimani). We find that annual mean ice BC concentrations simulated by the chemical transport model GEOS-Chem for 2014–2019 are roughly consistent with those observed at the site with the longest record, Huascarán, with overestimates of 15%–40%. Smoke from fires account for 20%–70% of total wet and dry deposition fluxes, depending on the site. The rest of BC deposited comes from fossil fuel combustion. Using a snow albedo model, we find that the annual mean radiative forcing from the deposition of smoke BC alone on snow ranges from +0.1 to +3.2 W m⁻² under clear-sky conditions, with corresponding average albedo reductions of 0.04%–1.1%. These ranges are dependent on site and snow grain size. This result implies a potentially significant climate impact of biomass burning in the Amazon on radiative forcing in the Andes.

Rising greenhouse gases (GHG) and decreasing anthropogenic ozone-depleting substances (ODS) are the main drivers of the stratospheric climate evolution in the 21st century. However, the coupling between stratospheric composition, radiation and dynamics is subject to many uncertainties, which is partly because of the simplistic representation of ozone (O₃) in many current climate models. Changes in ozone due to heterogeneous chemistry are known to be the largest during springtime in the Arctic, which is also a season with very active stratosphere–troposphere coupling. The focus of this study is to investigate the role of varying ozone levels driven by changing GHG and ODS for the Arctic polar cap stratosphere. We use two state-of-the-art chemistry-climate models with ocean coupling in two configurations (prescribed ozone fields vs. interactive ozone chemistry) for three different scenarios: preindustrial conditions—1 × CO₂, year 2000 conditions (peak anthropogenic ODS levels) and extreme future conditions—4 × CO₂. Our results show that in the upper and middle stratosphere CO₂ thermal cooling is the dominant effect determining the temperature response under 4 × CO₂, and outweighs warming effects of ozone by about a factor of ten. In contrast, in the lower stratosphere, the effects of O₃ warming and CO₂ cooling under 4 × CO₂ are largely offsetting each other. ODS driven variations in O₃ affect both the temperature mean and variability, and are responsible for the tight springtime coupling between composition and dynamics under year 2000 conditions in comparison to simulations under 1 × CO₂ or 4 × CO₂.

Future ocean acidification mainly depends on the continuous ocean uptake of CO₂ from the atmosphere. The trajectory of future atmospheric CO₂ is prescribed in traditional climate projections with Earth system models, leading to a small model spread and apparently low uncertainties for projected acidification, but a large spread in global warming. However, climate policies such as the Paris Agreement define climate targets in terms of global warming levels and as traditional simulations do not converge to a given warming level, they cannot be used to assess uncertainties in projected acidification. Here, we perform climate simulations that converge to given temperature levels using the Adaptive Emission Reduction Algorithm (AERA) with the Earth system model Bern3D-LPX at different setups with different Transient Climate Response to cumulative carbon Emissions (TCRE) and choices between reductions in CO₂ and non-CO₂ forcing agents. With these simulations, we demonstrate that uncertainties in surface ocean acidification are an order of magnitude larger than the usually reported inter-model uncertainties from simulations with prescribed atmospheric CO₂. Uncertainties in acidification at a given stabilized temperature are dominated by TCRE and the choice of emission reductions of non-CO₂ greenhouse gases (GHGs). High TCRE and relatively low reductions of non-CO₂ GHGs, for example, necessitate relatively strong reductions in CO₂ emissions and lead to relatively little ocean acidification at a given temperature level. The results suggest that choices between reducing emissions of CO₂ versus non-CO₂ agents should consider the economic costs and ecosystem damage of ocean acidification.

Ecosystems like coral reefs mitigate rising coastal flood risks, but investments into their conservation remain low relative to the investments into engineered risk-mitigation structures. One reason is that quantifying the risk-reduction benefits of coral reefs requires an estimate of their fragility to severe stresses. Engineered structures typically have associated fragility functions which predict the probability of exceeding a damage state with the increasing loading intensity imposed by a stressor, like a hurricane. Here, we propose a preliminary framework for capturing the fragility of coral reefs towards hurricanes in an analogous way to that of an engineered structure. We base our framework on Disturbance Response Monitoring data collected in the Florida Keys and Puerto Rico following hurricanes Irma and Maria. We first establish a qualitatively consistent correlation between hurricane impacts and coral mortality rates using two surveys of coral health. We focus specifically on stony coral mortality as a metric for reef damage, simplifying the effect of coral morphology into a single quantitative index at the site scale. To quantify the loading intensity of a hurricane, we propose a Hurricane Wind Exposure Time that captures spatial variations in the exposure of different coral reef sites to hurricane force winds. We ultimately derive a simple empirical fragility function for the Florida Keys and Puerto Rico to support side-by-side comparisons of the cost-effectiveness of a coral reef and engineered solutions to flood risk reduction in these regions.

Current national greenhouse gas (GHG) emissions accounts and mitigation targets are mostly based on territorial GHG accounting. While several analyses present future trajectories describing how nations could achieve emissions targets, there are relatively few analyses from the consumption-based perspective. Simultaneously, there is a broad literature on consumption-based carbon footprints of individuals and regions, but without connection to the remaining carbon budgets and associated mitigation pathways, nor to the current levels of human development. This study contributes to these debates by downscaling the 1.5-degree target to an individual scale for 152 countries, following the Intergovernmental Panel on Climate Change (IPCC's) shared socioeconomic pathway (SSP1-1.9) pathway. We compare the calculated limits to current carbon footprints and show how the individual carbon budget can be operationalized on a national and regional level using Africa, Europe, and the USA as examples. We show that while GHG emissions in Europe and the USA greatly exceed the budget, in Africa the budget allows even growth in the short and medium term, and the emission cuts later if the remaining carbon budget is equally allocated regardless of the historic emissions. Finally, we modify the planetary pressures adjusted human development index (HDI) with consumption-based carbon footprints to highlight how different accounting principles underscore the uneven development between nations. We find that the average carbon footprint of many highly developed nations is as much as seven times the climate-sustainable limit. Furthermore, these same nations perform poorly when measuring their development level with the consumption-based emissions updated planetary pressures HDI. However, in the majority of nations (80% of the global population) the average carbon footprint is near or below the climate-sustainable level, but not in any of the top HDI countries. Our findings highlight that stronger policy and swift changes are needed to bring the carbon footprints of the residents of affluent countries to a climate-sustainable level.

Predicting the vertical distribution of microplastics in the ocean surface mixed layer is necessary for extrapolating surface measurements and comparing observations across conditions. The competing mechanisms that control the vertical distribution are particle buoyancy, which is primarily a function of particle properties and drives microplastics to accumulate at the ocean surface, and turbulent mixing, which disperses microplastics throughout the mixed layer and depends on local hydrodynamics. In this study, we focused on the physical properties of microplastics collected within one vertical profile in the North Pacific. We measured the size, shape, and rise velocity of all microplastics collected, finding that average size and rise velocity decay with depth. In addition, we demonstrate how the vertical distribution of the microplastics depends on the rise velocity of the microplastics by segregating the data into three regimes based on a ratio of microplastic rise velocity and a characteristic turbulence velocity scale. Using an individual model for each regime, we can extrapolate the vertical distribution of microplastics to the bottom of the mixed layer and find the total concentration of microplastics. The total extrapolated concentration using the combined model results in 10× the concentration of the surface net alone and 47% more than a model which does not consider the different microplastic regimes. Finally, we discuss how the vertical distribution also depends on microplastic form, finding that lines are approximately well-mixed whereas the concentration of fragments decays with depth. These observations indicate the importance of considering the appropriate rise velocity regime when predicting the vertical distribution of microplastics.

We provide the magnitude of a worst case scenario for extreme sea levels (ESLs) along the global coastline by 2100. This worst case scenario for ESLs is calculated as a combination of sea surface height associated with storm surge and wave (100 year return period, the 95th percentile), high tide (the 95th percentile) and a low probability sea level rise scenario (the 95th percentile). Under these conditions, end-of-21st century ESLs have a 5% chance of exceeding 4.2 m (global coastal average), compared to 2.6 m during the baseline period (1980–2014). By 2100 almost 45% of the global coastline would experience ESLs above the global mean of 4.2 m, with up to 9–10 m for the East China Sea, Japan and North European coastal areas. Up to 86% of coastal locations would face ESLs above 3 m (100 year return period) by 2100, compared to 33% currently. Up to 90% of increases in magnitude of ESLs are driven by future sea level rise, compare to 10% associated with changes in storm surges and waves. By 2030–2040 the present-day 100 year return period for ESLs would be experienced at least once a year in tropical areas. This 100-fold increase in frequency will take place on all global coastlines by 2100.

Three-dimensional shallow benthic complexity (also known as benthic rugosity) reflects the physical conditions of shallow coral reefs environments and can be used to estimate fish biomass and coral cover on reefs. Spatially explicit data on benthic complexity could offer critical information for coral reef conservation and management. However, benthic complexity has not yet been mapped at a global scale. We mapped global shallow water benthic complexity to 20 m depth at a spatial resolution of 10 m using 22 000 Sentinel-2 satellite images and a globally applicable underwater algorithm. We quantified geographic variation of benthic complexity in shallow coral reef areas from individual reef to ocean basin scales. We found that shallow benthic complexity is unevenly distributed worldwide, with high benthic complexity regions found in areas known to have high levels of benthic biodiversity such as the Coral Triangle, Coral Sea, and Great Barrier Reef. Yet nearly 60% of detected coral reef regions (size = 61 156 km²) are not listed as protected under current marine protected plans. These unprotected regions include substantial reef areas of high benthic complexity that may harbor high levels of biodiversity. Our global coral reef benthic complexity map supports plans to improve marine protected areas, reef conservation, and management.

In combination with drastic emission reduction cuts, limiting global warming below 1.5 °C or 2 °C requires atmospheric carbon dioxide removal (CDR) of up to 16 GtCO₂ yr⁻¹ by 2050. Among CDR solutions, ocean afforestation through macroalgae cultivation is considered promising due to high rates of productivity and environmental co-benefits. We modify a high-resolution ocean biogeochemical model to simulate the consumption of dissolved inorganic carbon and macronutrients by idealised macroalgal cultivation in Exclusive Economic Zones. Under imposed macroalgal production of 0.5 PgC yr⁻¹ with no nutrient feedbacks, physicochemical processes are found to limit the enhancement in the ocean carbon sink to 0.39 PgC yr⁻¹ (1.43 GtCO₂ yr⁻¹), corresponding to CDR efficiency of 79%. Only 0.22 PgC yr⁻¹ (56%) of this air–sea carbon flux occurs in the regions of macroalgae cultivation, posing potential issues for measurement, reporting, and verification. When additional macronutrient limitations and feedbacks are simulated, the realised macroalgal production rate drops to 0.37 PgC yr⁻¹ and the enhancement in the air–sea carbon flux to 0.21 PgC yr⁻¹ (0.79 GtCO yr⁻¹), or 58% of the macroalgal net production. This decrease in CDR efficiency is a consequence of a deepening in the optimum depth of macroalgal production and a reduction in phytoplankton production due to reduced nitrate and phosphate availability. At regional scales, the decrease of phytoplankton productivity can even cause a net reduction in the oceanic carbon sink. Although additional modelling efforts are required, Eastern boundary upwelling systems and regions of the Northeast Pacific and the Southern Ocean are revealed as potentially promising locations for efficient macroalgae-based CDR. Despite the CDR potential of ocean afforestation, our simulations indicate potential negative impacts on marine food webs with reductions in phytoplankton primary production of up to −40 gC m⁻² yr⁻¹ in the eastern tropical Pacific.

Addressing questions of equitable contributions to emission reductions is important to facilitate ambitious global action on climate change within the ambit of the Paris Agreement. Several large developing regions with low historical contributions to global warming have a strong moral claim to a large proportion of the remaining carbon budget (RCB). However, this claim needs to be assessed in a context where the RCB consistent with the long-term temperature goal (LTTG) of the Paris Agreement is rapidly diminishing. Here we assess the potential tension between the moral claim to the remaining carbon space by large developing regions with low per capita emissions, and the collective obligation to achieve the goals of the Paris Agreement. Based on scenarios underlying the IPCC's 6th Assessment Report, we construct a suite of scenarios that combine the following elements: (a) two quantifications of a moral claim to the remaining carbon space by South Asia, and Africa, (b) a 'highest possible emission reduction' effort by developed regions (DRs), and (c) a corresponding range for other developing regions (ODR). We find that even the best effort by DRs cannot compensate for a unilateral claim to the remaining carbon space by South Asia and Africa. This would put the LTTG firmly out of reach unless ODRs cede their moral claim to emissions space and, like DRs, pursue highest possible emission reductions, which would also constitute an inequitable outcome. Furthermore, regions such as Latin America would need to provide large-scale negative emissions with potential risks and negative side effects. Our findings raise important questions of perspectives on equity in the context of the Paris Agreement including on the critical importance of climate finance. A failure to provide adequate levels of financial support to compensate large developing regions to emit less than their moral claim will put the Paris Agreement at risk.

Forest anthropogenic and natural stand-replacing disturbances are increasing worldwide due to global change. Many uncertainties regarding the regeneration and growth of these young forests remain within the context of changing climate. In this study, we investigate the effects of climate, tree species composition, and other landscape-scale environmental variables upon boreal forest regrowth following clearcut logging in eastern Canada. Our main objective was to predict the effects of future climate changes upon post-logging forest height regrowth at a subcontinental scale using high spatial resolution remote sensing data. We modeled forest canopy height (estimated from airborne laser scanning [LiDAR] data over 20 m resolution virtual plots) as a function of time elapsed since the last clearcut along with climate (i.e. temperature and moisture), tree species composition, and other environmental variables (e.g. topography and soil hydrology). Once trained and validated with ∼240 000 plots, the model that was developed in this study was used to predict potential post-logging canopy height regrowth at 20 m resolution across a 240 000 km² area following scenarios depicting a range of projected changes in temperature and moisture across the region for 2041–2070. Our results predict an overall beneficial, but limited effect of projected climate changes upon forest regrowth rates in our study area. Stimulatory effects of projected climate change were more pronounced for conifer forests, with growth rates increasing between +5% and +50% over the study area, while mixed and broadleaved forests recorded changes that mostly ranged from −5% to +35%. Predicted increased regrowth rates were mainly associated with increased temperature, while changes in climate moisture had a minor effect. We conclude that such growth gains could partially compensate for the inevitable increase in natural disturbances but should not allow any increase in harvested volumes.

Arctic hydrology is experiencing rapid changes including earlier snow melt, permafrost degradation, increasing active layer depth, and reduced river ice, all of which are expected to lead to changes in stream flow regimes. Recently, long-term (>60 years) climate reanalysis and river discharge observation data have become available. We utilized these data to assess long-term changes in discharge and their hydroclimatic drivers. River discharge during the cold season (October–April) increased by 10% per decade. The most widespread discharge increase occurred in April (15% per decade), the month of ice break-up for the majority of basins. In October, when river ice formation generally begins, average monthly discharge increased by 7% per decade. Long-term air temperature increases in October and April increased the number of days above freezing (+1.1 d per decade) resulting in increased snow ablation (20% per decade) and decreased snow water equivalent (−12% per decade). Compared to the historical period (1960–1989), mean April and October air temperature in the recent period (1990–2019) have greater correlation with monthly discharge from 0.33 to 0.68 and 0.0–0.48, respectively. This indicates that the recent increases in air temperature are directly related to these discharge changes. Ubiquitous increases in cold and shoulder-season discharge demonstrate the scale at which hydrologic and biogeochemical fluxes are being altered in the Arctic.

Soil respiration (R_s) is the largest carbon (C) flux from terrestrial ecosystems to the atmosphere and is of great significance to the global C budget. An increasing number of studies have assessed R_s through in situ observations and model estimates over the last decades, but the sources and pathways of soil carbon dioxide (CO₂) are not fully understood, and great uncertainty remains in R_s partitioning of soil CO₂ sources. Here, we compiled 236 paired observations that measured soil CO₂ fluxes after concurrently removal of living roots (and rhizosphere), litter, and both roots and litter in plant input manipulation experiments conducted at 14 forest sites to partition root + rhizosphere (R_r), litter (R_l) and soil organic matter-derived microbial respiration (R_m) in total soil CO₂ flux. We found that R_r, R_l and R_m accounted for 20.1%, 21.8% and 62.7% of the total R_s, respectively. Mean annual precipitation (MAP) was the most important factor driving R_r/R_s, R_l/R_s and R_m/R_s, and MAP was positively correlated with R_r/R_s and R_l/R_s but negatively correlated with R_m/R_s, suggesting a significant climatic control over the proportions of R_s components. Across all sites, the proportions of R_r/R_s and R_l/R_s increased but R_m/R_s decreased with the increase in soil CO₂ flux, suggesting that the proportions of root- and litter-derived soil CO₂ are generally higher in the tropics than in cold temperate and boreal forests. More accurate partitioning of R_r, R_l and R_m to elucidate different sources and pathways of soil CO₂ flux will provide important insights for the global R_s assessment and terrestrial C budget.

With the recurrence of high-impact extreme events and the growing public demands to understand the causes of the events, event attribution has emerged as a frontier of climate change research. Typically, an event attribution study focuses on one individual extreme event that has just occurred. Studies rarely examine human influence on multiple extreme events in different times of the past. Here we conduct a comprehensive attribution analysis on the four heaviest precipitation events in the Yangtze River Valley during the past 100 years. We start by defining extreme precipitation events as the heaviest precipitation over a fixed size area that is of direct relevance to flood preparedness and management. When examining the events over the historical time, we allow the precise location of the area to change in different years. By definition, four extremely strong events are identified, and they happened in the summer of 1931, 1954, 1998 and 2020. We find that the impacts of greenhouse gases (GHGs) and anthropogenic aerosols (AAs) on these events show clear difference in different time period. The impacts were negligible in the early period and became more and more discernible since the late 20th century. The GHGs have gradually increased the occurrence probability of extreme precipitaiton while the AAs have decreased the occurrence of extrem precipitation. These competing effects from the GHGs and AAs have led to a slight and then gradually increasing human influence on extreme precipitation over time. GHGs have exerted a larger influence on short-duration precipitation events while AAs have had a larger influence on monthly mean precipitation. The more extreme the precipitation event, the clearer the anthropogenic influence.

Climate actions have focused on CO2 mitigation and only some studies of China consider non-CO2 greenhouse gases (GHGs), which account for nearly 18% of gross GHG emissions. The economy-wide impact of mitigation covering CO2 and non-CO2 GHGs in China, has not been comprehensively studied and we develop a multi-sector dynamic model to compare the impact of CO2-only mitigation with a multi-GHG mitigation policy that also price non-CO2 GHGs. We find that the multi-GHG approach significantly reduces the marginal abatement cost and economic loss to reach the same level of GHG emissions (measures as 100 year global warming potential) compared to a CO2-only scenario. By 2060, multi-gas mitigation can reduce the tax rate by 15.44% and improve real gross domestic product (GDP) by 0.41%. The aggregate gain brought by multi-GHG mitigation are robust to various pathways and but vary across periods and sectors.

Restoration of native tropical forests is crucial for protecting biodiversity and ecosystem functions, such as carbon stock capacity. However, little is known about the contribution of early stages of forest regeneration to crop productivity through the enhancement of ecosystem services, such as crop pollination and pest control. Using data from 610 municipalities along the Brazilian Atlantic Forest (30 m spatial resolution), we evaluated if young regenerating forests (YRFs) (less than 20 years old) are positively associated with coffee yield and whether such a relationship depends on the amount of preserved forest in the surroundings of the coffee fields. We found that regenerating forest alone was not associated with variations in coffee yields. However, the presence of YRF (within a 500 m buffer) was positively related to higher coffee yields when the amount of preserved forest in a 2 km buffer is above a 20% threshold cover. These results further reinforce that regional coffee yields are influenced by changes in biodiversity-mediated ecosystem services, which are explained by the amount of mature forest in the surrounding of coffee fields. We argue that while regenerating fragments may contribute to increased connectivity between remnants of forest fragments and crop fields in landscapes with a minimum amount of forest (20%), older preserved forests (more than 20 years) are essential for sustaining pollinator and pest enemy's populations. These results highlight the potential time lag of at least 20 years of regenerating forests' in contributing to the provision of ecosystem services that affect coffee yields (e.g. pollination and pest control). We emphasize the need to implement public policies that promote ecosystem restoration and ensure the permanence of these new forests over time.

Conservation programs in low-income countries often have dual goals of protecting the environment and reducing poverty. This article discusses the tension between these two goals in payments for ecosystem services (PESs) programs. Participants who undertake a pro-environment behavior receive a payment, which can be decomposed into two parts: the amount that compensates them for the cost of changing their behavior and the extra amount that is a 'pure transfer' to them. To maximize the program's environmental benefits, a policy maker would like to set the pure transfer component to zero, yet the pure transfer is the only part of the payment that increases participants' economic well-being. In practice, PES programs pay out some pure transfers, and the extent of the anti-poverty benefits depends on whether the pure transfers are de facto targeted to the poor. I lay out these points and then illustrate them with data from a randomized trial of payments for forest protection in Uganda. I provide evidence that the economic gains from participation in PES are indeed larger for those with low costs to fulfill the program's conservation requirements. I also show that, in this context, poorer eligible households enjoyed more improvement in their economic well-being than richer ones did.

The exploitation of ecosystem services, through processes like agricultural production, is associated with myriad negative environmental impacts, which are felt by stakeholders on local, regional, and global scales. The varying type and scale of impacts leads naturally to fragmented and siloed approaches to mitigating externalities by diverse governmental and non-governmental institutions. However, policies designed to address a single impact may worsen other negative impacts. As a result, even when groups have the expertise to design policy solutions in one dimension, policies addressing single issues may conflict and result in less than ideal outcomes in combination. In this paper, we present a conceptual framework and examples of this kind of 'policy collision,' where policies produce mutual negative interference so that policies designed independently may fail to achieve their goals. We argue that an integrated systems perspective is needed to overcome this problem and present several positive examples where this has been put into practice. Policy collision provides a useful framework for determining how each colliding policy should be modified in improve outcomes.

We present a new application to recognize 218 species of cultivated crops on geo-tagged photos, 'Pl@ntNet Crops'. The application and underlying algorithms are developed using more than 750k photos voluntarily collected by Pl@ntNet users. The app is then enriched by data and photos coming from the European Union's (EU) Land Use and Coverage Area frame Survey (LUCAS). During five tri-annual LUCAS campaigns from 2006 to 2018, 242 476 close-up 'cover' photos of crops were collected. The survey protocol for these photos specified that 'the picture should be taken at a close distance, so that the structure of leaves can be clearly seen, as well as flowers or fruits'. This unique labelled data provides an opportunity to further generalize the Pl@ntNet computer vision algorithms to recognize crops and enlarge their geographic representivity across the EU. To include LUCAS cover photos, we semantically match Pl@ntNet species and LUCAS legends, predict the species on LUCAS cover photos with the existing Pl@ntNet algorithm, and consider the accuracy of the classification and the number of species enriched by the photos. By setting a threshold of $\gt$0.5 on the Pl@ntNet prediction probabilities, 70 170 LUCAS photos representing 101 species classified with an accuracy of 0.9 were added to the 'Crops' app. The thematic accuracy of the legacy LUCAS data was improved by distinguishing 218 species, opposed to the original 36 LUCAS levels. Official and publicly financed LUCAS datastreams can now be improved because of Pl@ntNet citizen science, photo collection, and deep learning model development. Further use of the app and policy-relevant workflows in the agri-food-environment domain are discussed.

As Arctic and boreal regions rapidly warm, the frequency and seasonal timing of hazardous driving conditions on all-season Arctic-boreal roads are likely to change. Because these roads link remote Arctic areas to the rest of the North American road system, climate change may substantially affect safety and quality of life for northern residents and commercial enterprises. To gain insight into future hazardous driving conditions, we built Random Forest models that predict the occurrence of hazardous driving conditions by linking snow, ice, and weather simulated by a spatially explicit modeling system (SnowModel) to archived road condition reports from two highly trafficked all-season northern roads: the Dalton Highway (Alaska, USA) and Dempster Highway (Yukon, Canada). We applied these models to downscaled future climate trajectories for the study period of 2006–2100. We estimated future trends in the frequency and timing of icy, wet-icy, and snowy road surfaces, blowing and drifting snow, and high winds. We found that as the climate warms, and the portion of the year when snow and ice occur becomes shorter, overall frequency of snow storms and ice- and snow-related driving hazards decreased. For example, the mean number of days per year when roads are covered in snow or ice decreased by 51 d (−21%) on the Dalton Highway between the 2006–2020 and 2081–2100 time periods. However, the intensity of storms was predicted to increase, resulting in higher mean annual storm wind speeds (Dalton +0.56 m s⁻¹ [+17%]) and snowfall totals (Dalton +0.3 cm [+36%]). Our models also predicted increasing frequency of wet-icy driving conditions during November, December, January, and February, when daylength is short and hazardous conditions may be more difficult to perceive. Our findings may help road managers and drivers adapt their expectations and behaviors to minimize accident risk on Arctic-boreal roads in the future.

Achieving sustainable development requires understanding how human behavior and the environment interact across spatial scales. In particular, knowing how to manage tradeoffs between the environment and the economy, or between one spatial scale and another, necessitates a modeling approach that allows these different components to interact. Existing integrated local and global analyses provide key insights, but often fail to capture 'meso-scale' phenomena that operate at scales between the local and the global, leading to erroneous predictions and a constrained scope of analysis. Meso-scale phenomena are difficult to model because of their complexity and computational challenges, where adding additional scales can increase model run-time exponentially. These additions, however, are necessary to make models that include sufficient detail for policy-makers to assess tradeoffs. Here, we synthesize research that explicitly includes meso-scale phenomena and assess where further efforts might be fruitful in improving our predictions and expanding the scope of questions that sustainability science can answer. We emphasize five categories of models relevant to sustainability science, including biophysical models, integrated assessment models, land-use change models, earth-economy models and spatial downscaling models. We outline the technical and methodological challenges present in these areas of research and discuss seven directions for future research that will improve coverage of meso-scale effects. Additionally, we provide a specific worked example that shows the challenges present, and possible solutions, for modeling meso-scale phenomena in integrated earth-economy models.

Estimating the impacts of climate change on the global carbon cycle relies on projections from Earth system models (ESMs). While ESMs currently project large warming in the high northern latitudes, the magnitude and sign of the future carbon balance of Arctic-Boreal ecosystems are highly uncertain. The new generation of increased complexity ESMs in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) is intended to improve future climate projections. Here, we benchmark the Coupled Model Intercomparison Project (CMIP) 5 and 6 (8 CMIP5 members and 12 CMIP6 members) with the International Land Model Benchmarking (ILAMB) tool over the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America. We show that the projected average net biome production (NBP) in 2100 from CMIP6 is higher than that from CMIP5 in the ABoVE domain, despite the model spread being slightly narrower. Overall, CMIP6 shows better agreement with contemporary observed carbon cycle variables (photosynthesis, respiration, biomass) than CMIP5, except for soil carbon and turnover time. Although both CMIP ensemble members project the ABoVE domain will remain a carbon sink by the end of the 21st century, the sink strength in CMIP6 increases with CO₂ emissions. CMIP5 and CMIP6 ensembles indicate a tipping point defined here as a negative inflection point in the NBP curve by 2050–2080 independently of the shared socioeconomic pathway (SSP) for CMIP6 or representative concentration pathway (RCP) for CMIP5. The model ensembles therefore suggest that, if the carbon sink strength keeps declining throughout the 21st century, the Arctic-Boreal ecosystems in North America may become a carbon source over the next century.

We present a data-driven typology framework for understanding patterns in drinking water accessibility across low- and middle-income countries. Further, we obtain novel typology-specific insights regarding the relationships between possible explanatory variables and typology outcomes. First, we conducted exploratory factor analysis to obtain a smaller set of interpretable factors from the initial set of 17 drinking water accessibility indicators from 73 countries. The resulting seven factors summarize the key drivers for water accessibility, and also serve as a vehicle for framing discussions on country outcomes. We clustered the countries based on their seven-dimensional water accessibility factor scores, referring to the resulting three clusters as 'typologies,' namely, Decentralized, Centralized and Hybrid. The typologies serve as a vehicle for analyzing water accessibility among countries with similar patterns, in contrast with geographically-based approaches. Finally, we fitted a decision tree classifier to analyze relationships between a country's typology membership and socioeconomic, geographic and transportation explanatory variables. We found that private car ownership, population density and per-capita gross domestic product are most relevant in predicting a country's drinking water accessibility typology.