Table of contents

The reduction of cold temperature constraints on photosynthesis in recent decades has led to extended growing seasons and increased plant productivity (greening) in significant parts of Polar, Arctic and Boreal regions, here called northern lands. However, most territories within these regions display stable productivity in recent years. Smaller portions of Arctic and Boreal regions show reduced productivity (browning). Summer drought and wildfires are the best documented drivers causing browning of continental areas. Yet factors like winter warming events dampening the greening effect of more maritime regions have remained elusive, least monitored and least understood. A Norway-US network project called ArcticBiomass was launched in 2013 to further reveal both positive and negative effects of climate change on biomass in Arctic and Boreal regions. This focus collection named Focus on Recent, Present and Future Arctic and Boreal Productivity and Biomass Changes includes 24 articles and is an important outcome of this work and addresses recent changes in phenology, biomass and productivity and the mechanisms. These mechanisms include former human interactions (legacies) and drivers that control such changes (both greening and browning), along with consequences for local, regional and global scale processes. We complete our synthesis by stressing remaining challenges and knowledge gaps, and provide an outlook on future needs and research questions in the study of climate and human driven interactions in terrestrial Arctic and Boreal ecosystems.

In many regions and at the planetary scale, human pressures on the environment exceed levels that natural systems can sustain. These pressures are caused by networks of human activities, which often extend across countries and continents due to global trade. This has led to an increasing requirement for methods that enable absolute environmental sustainability assessment (AESA) of anthropogenic systems and which have a basis in life cycle assessment (LCA). Such methods enable the comparison of environmental impacts of products, companies, nations, etc, with an assigned share of environmental carrying capacity for various impact categories. This study is the first systematic review of LCA-based AESA methods and their applications. After developing a framework for LCA-based AESA methods, we identified 45 relevant studies through an initial survey, database searches and citation analysis. We characterized these studies according to their intended application, impact categories, basis of carrying capacity estimates, spatial differentiation of environmental model and principles for assigning carrying capacity. We then characterized all method applications and synthesized their results. Based on this assessment, we present recommendations to practitioners on the selection and use of existing LCA-based AESA methods, as well as ways to perform assessments and communicate results to decision-makers. Furthermore, we identify future research priorities intended to extend coverage of all components of the proposed method framework, improve modeling and increase the applicability of methods.

Background: For decades, scientists have attempted to provide a sustainable development framework that integrates goals of environmental protection and human development. The Planetary Boundaries concept (PBc)—a framework to guide sustainable development—juxtaposes a 'safe operating space for humanity' and 'planetary boundaries', to achieve a goal that decades of research have yet to meet. We here investigate if PBc is sufficiently different to previous sustainability concepts to have the intended impact, and map how future sustainability concept developments might make a difference. Design: We build a genealogy of the research that is cited in and informs PBc. We analyze this genealogy with the support of two seminal and a new consumer-resource models, that provide simple and analytically tractable analogies to human-environment relationships. These models bring together environmental limits, minimum requirements for populations and relationships between resource-limited and waste-limited environments. Results: PBc is based on coherent knowledge about sustainability that has been in place in scientific and policy contexts since the 1980s. PBc represents the ultimate framing of limits to the use of the environment, as limits not to single resources, but to Holocene-like Earth system dynamics. Though seldom emphasized, the crux of the limits to sustainable environmental dynamics lies in waste (mis-)management, which sets where boundary values might be. Minimum requirements for populations are under-defined: it is the distribution of resources, opportunities and waste that shape what is a safe space and for whom. Discussion: We suggest that PBc is not different or innovative enough to break 'Cassandra's dilemma' and ensure scientific research effectively guides humanity towards sustainable development. For this, key issues of equality must be addressed, un-sustainability must be framed as a problem of today, rather than projected into the future, and scientific foundations of frameworks such as PBc must be broadened and diversified.

Environmental life cycle assessment (LCA) is increasingly being used to evaluate infrastructure products and to inform their funding, design and construction. As such, recognition of study limitations and consideration of uncertainty are needed; however, most infrastructure LCAs still report deterministic values. Compared to other LCA subfields, infrastructure LCA has developed relatively recently and lags in adopting uncertainty analysis. This paper presents four broad categories of infrastructure LCA uncertainty. These contain 11 drivers focusing on differences between infrastructure and manufactured products. Identified categories and drivers are: application of ISO 14040/14044 standards (functional unit, reference flow, boundaries of analysis); spatiotemporal realities underlying physical construction (geography, local context, manufacturing time); nature of the construction industry (repetition of production, scale, and division of responsibilities); and characteristics of infrastructure projects (agglomeration of other products, and recurring embodied energy). Infrastructure products are typically large, one-off projects with no two being exactly alike in terms of form, function, temporal or spatial context. As a result, strong variability between products is the norm and much of the uncertainty is irreducible. Given the inability to make significant changes to an infrastructure project ex-post and the unique nature of infrastructure, ex-ante analysis is of particular importance. This paper articulates the key drivers of infrastructure specific LCA uncertainty laying the foundation for future refinement of uncertainty consideration for infrastructure. As LCA becomes an increasingly influential tool in decision making for infrastructure, uncertainty analysis must be standard practice, or we risk undermining the fundamental goal of reduced real-world negative environmental impacts.

Food security will become increasingly challenged over the coming decades, and sustainable intensification is often touted as an ideal way to increase yields while limiting negative environmental impacts. Yet, the extent to which sustainable intensification can increase yields remains unclear. We systematically reviewed the literature to assess the extent to which sustainable intensification can increase yields across South Asia, a region that is expected to face some of the greatest food security challenges over the coming decades. We found that yield gains from sustainable intensification interventions were heterogeneous, and that the average yield gain across all studi es was 21%. Residue retention and the use of organic fertilizers were, in particular, associated with significant and positive yield gains, though the use of organic fertilizers was not always profitable, likely due to large subsidies provided for mineral fertilizers across South Asia. Our work also revealed biases in the current sustainable intensification literature, with research clustered in highly productive, irrigated, and commodity cropping systems, which do not represent large portions of agricultural systems across South Asia. Our results highlight that sustainable intensification interventions should play an important role in increasing food production across South Asia, but yield gains from these interventions are modest compared to estimated yield gaps across the region.

With climate change impacts being felt across Europe, governments have started to invest in designing and implementing adaptation actions. The means through which governments adapt is generally referred to as policy instruments. Although there is a large body of adaptation policy literature emerging, our comprehensive understanding of policy instruments is limited nor do we know much about how scholarship on adaptation is addressing critical questions about policy instrument choice and effectiveness to support policy practice. In this article we map academic scholarship on climate change adaptation policy instruments in Europe. Using systematic approaches, we identify 184 relevant articles published 2014–2019. Our findings show that research is heavily concentrated on a limited number of western-European countries, with hardly any insights from eastern Europe and smaller countries. Most studies do not connect climate change impacts and risks with policy instruments, making assessment of policy effectiveness difficult, if not impossible. We argue that expanding the geographical scope of future research and enhancing the diversity of study types across Europe is critical for advancing theories on climate change adaptation policy, as well as providing useful recommendations for policy makers to strengthen the solution space and accelerate climate change adaptation.

The Paris Agreement (PA) sets out to strengthen the global response to climate change, setting targets for mitigation, adaptation, and finance, and establishing mechanisms through which to achieve these targets. The effectiveness of the PA's mechanisms in achieving its targets, however, has been questioned. This review systematically maps the peer-reviewed literature on the PA, categorizing the available evidence on whether or not the 'Paris Regime' can be effective. We split our analysis into three methodologically distinct sections: first we categorize the literature according to the mechanisms being studied. We find a diverse body of literature, albeit with a clear focus on mitigation, and identify adaptation and capacity building to be clear gaps. Second, we carry out a content analysis, identifying common drivers of, barriers to, and recommendations for effectiveness. Here we find mixed evidence, with potential drivers often qualified by more concrete barriers. Thirdly, we use scientometrics to identify six research clusters. These cover loss and damage, finance, legal issues, international politics, experimental evidence, and studies on tracking progress on the PA's targets. We conclude with a narrative discussion of our findings, presenting three central themes. First, transparency is widely considered a precondition for the PA to be institutionally effective. However, a lack of clear reporting standards and comparable information renders the PA's transparency provisions ineffective. Second, environmental effectiveness relies on national ambition, of which there is currently too little. It remains unclear to which extent the Paris Regime structure itself can induce significant ratcheting-up of ambition. Finally, the PA facilitates the diffusion of norms, enables learning and the sharing of best practices. This production of shared norms provides the most promising avenue for overcoming the current lack of ambition. One of the primary successes of the PA is in providing a platform for the exchange of experiences and ideas.

Deforestation and associated changing landscapes are major components of environmental changes, with important implications for ecosystem functioning and biodiversity conservation. Tropical forests are hot spots of biodiversity and provide multiple goods and ecosystem services which benefit people in many ways. Forests also play an important role in health-related legends, myths, and fairy tales from all over the world, and are important sources of new potential emerging microbial threats to humans. Although plausibly numerous abundant microbial forms with a forest origin may exist, our systematic literature review shows that forest-derived infection studies are relatively unexplored, and both taxonomically and geographically biased. Since biodiversity has been associated with emergence of novel infectious diseases at macro-scale, we describe the main biogeographical patterns in the emerging infection-biodiversity-forest loss nexus. Then, we illustrate four fine-scale case studies to decipher the underlying processes of increased infection risk in changing forest clearing landscapes. Finally, we identify scientific challenges and regional management measures required to mitigate these important new emerging threats.

Understanding how climate change will affect global health is a defining challenge of this century. This is predicated, however, on our ability to combine climate and health data to investigate the ways in which variations in climate, weather, and health outcomes interact. There is growing evidence to support the value of place- and community-based monitoring and surveillance efforts, which can contribute to improving both the quality and equity of data collection needed to investigate and understand the impacts of climate change on health. The inclusion of multiple and diverse knowledge systems in climate-health surveillance presents many benefits, as well as challenges. We conducted a systematic review, synthesis, and confidence assessment of the published literature on integrated monitoring and surveillance systems for climate change and public health. We examined the inclusion of diverse knowledge systems in climate-health literature, focusing on: (1) analytical framing of integrated monitoring and surveillance system processes; (2) key contributions of Indigenous knowledge and local knowledge systems to integrated monitoring and surveillance systems processes; and (3) patterns of inclusion within these processes. In total, 24 studies met the inclusion criteria and were included for data extraction, appraisal, and analysis. Our findings indicate that the inclusion of diverse knowledge systems contributes to integrated climate-health monitoring and surveillance systems across multiple processes of detection, attribution, and action. These contributions include: the definition of meaningful problems; the collection of more responsive data; the reduction of selection and source biases; the processing and interpretation of more comprehensive datasets; the reduction of scale dependent biases; the development of multi-scale policy; long-term future planning; immediate decision making and prioritization of key issues; as well as creating effective knowledge-information-action pathways. The value of our findings and this review is to demonstrate how neither scientific, Indigenous, nor local knowledge systems alone will be able to contribute the breadth and depth of information necessary to detect, attribute, and inform action along pathways of climate-health impact. Rather, it is the divergence or discordance between the methodologies and evidences of different knowledge systems that can contribute uniquely to this understanding. We critically discuss the possibility of what we, mainly local communities and experts, stand to lose if these processes of inclusion are not equitable. We explore how to shift the existing patterns of inclusion into balance by ensuring the equity of contributions and justice of inclusion in these integrated monitoring and surveillance system processes.

Bomb cyclones are explosively intensifying extratropical cyclones that can cause severe damage to life and property. However, the poor ability of coarse-resolution climate models to simulate bomb cyclones, including underestimation of the frequency of bomb cyclones, remains a problem. In this study, the dependence of bomb cyclone characteristics on horizontal resolution from 135 to 18 km is investigated by analyzing the outputs of HighResMIP historical simulations of atmospheric general circulation models and four reanalysis datasets. Robust resolution dependence of bomb cyclone characteristics is identified for both the models and the reanalyses. Finer horizontal resolution significantly increases the frequency of bomb cyclones and reduces their average horizontal size. A regression analysis indicates that bomb cyclone frequency is roughly doubled from 140 km to 25 km resolution. The overall increase in bomb cyclone number is associated with a large increase in small bomb cyclones and a moderate decrease in large ones. Bomb cyclones in higher-resolution models are also accompanied by a higher maximum wind speed and more extreme wind events, which is probably related to the increased pressure gradients due to the smaller size of the bomb cyclones. These results imply that high-resolution models should be used for evaluating the impacts of bomb cyclones.

Many urban areas in Africa do not have sufficient monitoring programs to understand their air quality. This study uses visibility as a proxy for PM pollution to provide insight into PM air pollution in three East African cities: Addis Ababa, Nairobi and Kampala, from 1974 to 2018. Overall, a significant loss in East African visibility was observed since the 1970s, where Nairobi shows the greatest loss (60%), as compared to Kampala (56%) and Addis Ababa (34%). These changes are likely due to increased anthropogenic PM emissions. Correspondingly, PM pollution levels, in Kampala, Nairobi and Addis Ababa, are estimated to have increased by 162, 182 and 62%, respectively, since the 1970s to the current period.

Distinct variations in seasonal visibility are observed, which are largely explained by changing PM sources and sinks in rainy and dry seasons. Average PM hygroscopicity is investigated by comparing average visibilities under different RH conditions. It is observed that PM hygroscopicity has decreased over time in all three cities, which is consistent with increasing emissions of PM with hygroscopicity lower than the ambient background. A large urban increment in PM is observed, with poor visibility typically occurring when the wind brings air from densely populated urban areas.

To investigate the intersection between increasing pollution, population and economic growth, changes in pollution are compared to available population growth and GDP statistics. Significant positive correlations between increasing PM and national GDP (and city population) were found for all three study cities. These cities have undergone rapid increases in population and national GDP growth (driven predominantly by study cities' economies) during the study period. This has resulted in increased rates of citywide fuel use and motorization, which provides a direct link to increased PM emissions and thus visibility loss. The study suggests that socio-economic forecasts may enable future air quality projections.

China's urbanites continue to be exposed to high levels of air pollution. Such pollution exposure raises mortality risk, lowers the day-to-day sentiment of the population and lowers outdoor worker productivity. Using a unique set of data for Chinese judges, we document that local air pollution also lowers the productivity of high skilled government officials who work indoors. Our new evidence on the effects of air pollution highlights both the challenge that pollution poses for quality of life and workforce productivity and indicates that the Chinese urban elites gain co-benefits when their cities burn less fossil fuel.

Degradation in air quality could be a potential factor for decreasing solar photovoltaic (PV) power generation. However, our understandings of the potential of airborne particulate matter (PM) to reduce actual solar PV power generation remain unclear. This study quantifies attenuation impacts of airborne PM on solar PV power generation on cloudless days at Yeongam and Eunpyeong-gu power plants installed in the Republic of Korea. The reduction rate of solar PV power generation according to the substantial amount of PM is calculated by constructing multiple regression models based on actual solar PV power generation record, observed meteorological parameters, and measured PM2.5 and PM10 concentrations for 2015–2017. At both power plants, PM2.5 and PM10 commonly reduce solar PV power generation by more than 10% of the maximum capacity under the conditions of 'normal' air quality, 35 μg m⁻³ and 80 μg m⁻³ for PM2.5 and PM10, respectively. Moreover, the reduction rate of solar PV power generation exceeds 20% of the maximum capacity under 'bad' air quality, 75 μg m⁻³ and 150 μg m⁻³ for PM2.5 and PM10, respectively. Results show that the negative impacts of PM on solar PV power generation should be considered in the process of policymaking on target solar power generation in Korea, as well as in countries with high PM emissions.

Central Asia (CA) is a continental region that is sensitive to water conditions. Hence, drought has one of the primary effects on the vegetation activities in CA and could vary with climate change. However, it is still unclear how the drought vulnerability of vegetation differs among vegetation types and varies with drought scales in CA. Therefore, this paper studied the drought vulnerability of vegetation in CA from 1982–2015. Droughts were detected by using the standardized precipitation evapotranspiration index (SPEI), and the vegetation activities were represented by the Normalized Difference Vegetation Index (NDVI). Only the areas with no change in vegetation types were analyzed, in order to avoid interference with changes in land use. Results showed that both the duration and intensity of droughts were higher in the central, southwestern, and northeastern CA. The growing season (April–October) NDVI decreased by −0.0095 ± 0.0065 per decade in response to drying trends of 0.21 ± 0.22 unit aridity index per decade in these drought-concentrated regions. Forests and savannas/woody savannas were more vulnerable to drought from July–September, and their vulnerabilities were higher to droughts with longer time scales. Shrublands and grasslands were more vulnerable to drought from April–May and May–September, respectively, and the vulnerabilities during these months were higher for the droughts at 6–12 months scales. Twelve months was the optimal (most vulnerable) drought scale for the shrublands and grasslands and the secondary drought scale for the savannas/woody savannas. Further analysis of the vulnerability of vegetation to 12 months drought found that it generally increased with the increase of the drought magnitude (duration or intensity) to some peak values and then decreased. However, the vulnerability of forests and savannas/woody savannas increased with the drought intensity. Results would help for the drought risk assessment of vegetation in CA.

Both deforestation and El Niño events influence Borneo's climate, but their interaction is not well understood. Borneo's native forest cover decreased by 37.1% between 1980 and 2015 with large areas being replaced by oil palm and a mosaic of plantations and regrowth vegetation. The island is also affected by El Niño events, resulting in severe droughts and fires. Here, we used a high-resolution climate model to simulate and evaluate how deforestation and El Niño episodes interact during the 1980–2016 period. Simulations revealed that deforestation resulted in a warmer and drier climate with the most pronounced changes in the extensively deforested regions of eastern and southern Borneo. Deforestation-linked impacts were more pronounced under El Niño than neutral (non El Niño/La Niña) conditions. Changes in climate mainly corresponded with areas with the most deforestation. There was a significant increase in the frequency of hotter and drier climatic extremes, with the probability distribution of temperature, humidity and aridity shifting from narrow to a broadening distribution. For example, the frequency of 90th percentile of the hot temperatures (defined as average monthly temperatures >28.9 °C) during the dry season increased from 10% for neutral conditions for the 1980 forest cover to 22% for neutral conditions for the 2015 forest cover. For strong El Niño events, the frequency increased from 15.6% to 32.5%. Replacement of intact native forest with oil palm resulted in increased frequency of hot temperatures to 49% for neutral and 74% for El Niño conditions. Hotter and drier conditions are likely to increase tree mortality and forest flammability (and fire-driven deforestation). The continued reduction and fragmentation of Borneo's forests diminishes the ability to moderate regional climate impacted by larger scale and other regional/local human climate forcings.

The air temperature cooling impacts of infrastructure-based adaptation measures in expanding urban areas and under changing climatic conditions are not well understood. We present simulations conducted with the Weather Research and Forecasting (WRF) model, coupled to a multi-layer urban model that explicitly resolves pedestrian-level conditions. Our simulations dynamically downscale global climate projections, account for projected urban growth, and examine cooling impacts of extensive cool roof deployment in Atlanta, Detroit, and Phoenix (USA). The simulations focus on heatwave events that are representative of start-, middle-, and end-of-century climatic conditions. Extensive cool roof implementation is projected to cause a maximum city-averaged daytime air temperature cooling of 0.38 °C in Atlanta; 0.42 °C in Detroit; and 0.66 °C in Phoenix. We propose a means for practitioners to estimate the impact of cool roof treatments on pedestrian-level air temperature, for a chosen roof reflectivity, with a new metric called the Albedo Cooling Effectiveness (ACE). The ACE metric reveals that, on average, cool roofs in Phoenix are 11% more effective at lowering pedestrian-level air temperature than in Atlanta, and 30% more effective than in Detroit. Cool roofs remain similarly effective under future heatwaves relative to contemporary heatwaves for Atlanta and Detroit, with some indication of increased effectiveness under future heatwaves for Phoenix. By highlighting the underlying factors that drive cooling effectiveness in a trio of cities located in different climatic regions, we demonstrate a robust framework for estimating the pedestrian-level cooling impacts associated with reflective roofs without the need for computationally demanding simulations.

Bioenergy with carbon capture and storage (BECCS) is envisaged as a critical element of most deep decarbonisation pathways compatible with the Paris Agreement. Such a transformational upscaling—to 3–7 Gt CO₂/yr by 2050—requires an unprecedented technological, economic, socio-cultural and political effort, along with, crucially, transparent communication between all stakeholders. Integrated Assessment Models (IAMs) that underpin the 1.5 °C scenarios assessed by IPCC have played a critical role in building and assessing deep decarbonisation narratives. However, their high-level aggregation and their complexity can cause them to be perceived as non-transparent by stakeholders outside of the IAM community. This paper bridges this gap by offering a comprehensive assessment of BECCS assumptions as used in IAMs so as to open them to a wider audience. We focus on key assumptions that underpin five aspects of BECCS: biomass availability, BECCS technologies, CO₂ transport and storage infrastructure, BECCS costs, and wider system conditions which favour the deployment of BECCS. Through a structured review, we find that all IAMs communicate wider system assumptions and major cost assumptions transparently. This quality however fades as we dig deeper into modelling details. This is particularly true for sets of technological elements such as CO₂ transport and storage infrastructure, for which we found the least transparent assumptions. We also found that IAMs are less transparent on the completeness of their treatment of the five BECCS aspects we investigated, and not transparent regarding the inclusion and treatment of socio-cultural and institutional-regulatory dimensions of feasibility which are key BECCS elements as suggested by the IPCC. We conclude with a practical discussion around ways of increasing IAM transparency as a bridge between this community and stakeholders from other disciplines, policy decision makers, financiers, and the public.

Rising atmospheric CO₂ concentration ([CO₂]) enhances photosynthesis and reduces transpiration at the leaf, ecosystem, and global scale via the CO₂ fertilization effect. The CO₂ fertilization effect is among the most important processes for predicting the terrestrial carbon budget and future climate, yet it has been elusive to quantify. For evaluating the CO₂ fertilization effect on land photosynthesis and transpiration, we developed a technique that isolated this effect from other confounding effects, such as changes in climate, using a noisy time series of observed land-atmosphere CO₂ and water vapor exchange. Here, we evaluate the magnitude of this effect from 2000 to 2014 globally based on constraint optimization of gross primary productivity (GPP) and evapotranspiration in a canopy photosynthesis model over 104 global eddy-covariance stations. We found a consistent increase of GPP (0.138 ± 0.007% ppm⁻¹; percentile per rising ppm of [CO₂]) and a concomitant decrease in transpiration (−0.073% ± 0.006% ppm⁻¹) due to rising [CO₂]. Enhanced GPP from CO₂ fertilization after the baseline year 2000 is, on average, 1.2% of global GPP, 12.4 g C m⁻² yr⁻¹ or 1.8 Pg C yr⁻¹ at the years from 2001 to 2014. Our result demonstrates that the current increase in [CO₂] could potentially explain the recent land CO₂ sink at the global scale.

One of the most effective strategies to reduce the impacts of drought is by issuing a timely and targeted warning from month to seasons ahead to end users. Yet to accurately forecast the drought hazard on a sub-seasonal to seasonal time scale remains a challenge, and usually, meteorological drought is forecasted instead of hydrological drought, although the latter is more relevant for several impacted sectors. Therefore, we evaluate the hydro-meteorological drought forecast skill for the pan-European region using categorical drought classification method. The results show that the hydrological drought forecasts outperform the meteorological drought forecasts. Hydrological drought forecasts even show predictive power (area with perfect prediction > 50%) beyond two months ahead. Our study also concludes that dynamical forecasts, derived from seasonal forecasts, have higher predictability than ensemble streamflow predictions. The results suggest that further development of seasonal hydrological drought forecasting systems are beneficial, particularly important in the context of global warming, where drought hazard will become more frequent and severe in multiple regions in the world.

Among the major consequences of dam construction and operation are the deterioration of water quality and the increasing frequency of occurrence of harmful algae blooms in reservoirs and their tributaries. Former studies at Three Gorges Reservoir demonstrated that the Yangtze River main stream is the main source of nutrients and pollutants to connected tributary bays. Eutrophication and other water quality problems reported for the tributaries along Three Gorges Reservoir are likely a consequence of density-driven exchange flows. Past work has focused mainly on the influence of seasonal and daily flow regulation on exchange flows, less attention has been paid to hydrodynamic processes resulting from sub-daily discharge dynamics. High-frequency measurements of flow velocity and water level in a eutrophic tributary (Xiangxi River) of Three Gorges Reservoir revealed the persistent nature of bidirectional density currents within the bay. Superimposed on this mean flow, we observed ubiquitous flow oscillations with a period of approximately 2 h. The flow variations were associated with periodic water level fluctuations with increasing amplitude for increasing distance from the river mouth (up to ±0.1 m at a distance of 27.4 km from the river mouth). They were caused by a standing wave in the tributary bay, which was generated by rapid increase or decrease in discharge following peak-shaving operation modes at Three Gorges Dam. The high-frequency wave made up the largest contribution to the temporal variance of flow velocity in the tributary bay and represents a so far overlooked hydrodynamic feature of tributaries bays in large reservoirs.

Accelerating deforestation rates in Earth's tropical rainforests have dramatic impacts on local public health, agricultural productivity, and global climate change. We used satellite observations to quantify the local temperature changes in deforested patches of rainforests across the tropics and found local warming larger than that predicted from more than a century of climate change under a worst-case emissions scenario. We show that the most extreme warming is typically found in large patches of deforestation; the combined effects of deforestation and climate change on tropical temperatures present a uniquely difficult challenge to the long term public health, occupational safety, and economic security of tropical populations.

Indonesian peatlands are critical to the global carbon cycle, but they also support a large number of local economies. Intense forest clearing and draining in these peatlands is causing severe ecological and environmental impacts. Most studies highlighted increased carbon emission in the region through drought and large‐scale fires, further accelerating peatland degradation. Yet, little is known about the long-term impacts of human-induced disturbance on peatland hydrology in the tropics. Here we show that converting natural peat forests to plantations can significantly alter the hydrological system far worse than previously recognized, leading to amplified moisture stress and drought severity. This study quantified how human-induced changes to Indonesian peatlands have affected drought severity. Through field observations and modelling, we demonstrate that canalization doubled drought severity; logging and starting plantations even quadrupled drought severity. Recognizing the importance of peatlands to Indonesia, proper management, and rehabilitating peatlands remain the only viable option for continued plantation use.

The carbon intensity (CI) of biofuel's well-to-pump life cycle is calculated by life cycle analysis (LCA) to account for the energy/material inputs of the feedstock production and fuel conversion stages and the associated greenhouse gas (GHG) emissions during these stages. The LCA is used by the California Air Resources Board's Low Carbon Fuel Standard (LCFS) program to calculate CI and monetary credits are issued based on the difference between a given fuel's CI and a reference fuel's CI. Through the Tier 2 certification program under which individual fuel production facilities can submit their own CIs with their facility input data, the LCFS has driven innovative technologies to biofuel conversion facilities, resulting in substantial reductions in GHG emissions as compared to the baseline gasoline or diesel. A similar approach can be taken to allow feedstock petition in the LCFS so that lower-CI feedstock can be rewarded. Here we examined the potential for various agronomic practices to improve the GHG profiles of corn ethanol by performing feedstock-level CI analysis for the Midwestern United States. Our system boundary covers GHG emissions from the cradle-to-farm-gate activities (i.e. farm input manufacturing and feedstock production), along with the potential impacts of soil organic carbon change during feedstock production. We conducted scenario-based CI analysis of ethanol, coupled with regionalized inventory data, for various farming practices to manage corn fields, and identified key parameters affecting cradle-to-farm-gate GHG emissions. The results demonstrate large spatial variations in CI of ethanol due to farm input use and land management practices. In particular, adopting conservation tillage, reducing nitrogen fertilizer use, and implementing cover crops has the potential to reduce GHG emissions per unit corn produced when compared to a baseline scenario of corn–soybean rotation. This work shows a large potential emission offset opportunity by allowing feedstock producers a path to Tier 2 petitions that reward low-CI feedstocks and further reduce biofuels' CI. The prevalence of significant acreage that has not been optimized for CI suggests that policy changes that incentivize optimization of this parameter could provide significant additionality over current trends in farm efficiency and adoption of conservation practice.

The mixing between fresh and saline groundwater in beach aquifers promotes biogeochemical transformations that affect nutrient fluxes to the coastal ocean. We performed variable-density groundwater flow and reactive transport simulations with geostatistical representations of sedimentary structure to understand the influence of heterogeneity on groundwater dynamics and denitrification in intertidal mixing zones. Ensemble-averaged simulation results show that heterogeneity can enhance mixing between fresh and saline groundwater and increase residence time, resulting in up to 80% higher nitrate removal relative to equivalent effective homogeneous aquifer sediment. Denitrification hotspots form in high permeability structures where DOC and nitrate are readily supplied by convergent flow. The results provide a physical explanation for the formation of denitrification hotspots observed in beach aquifers and illustrate for the first time the influence of sediment heterogeneity on rates and spatial patterns of biogeochemical processes in intertidal aquifers that are critical mediators of land-sea solute fluxes along world coastlines.

Absolute-age dating horizons play a pillar role in the reconstruction of an ice core chronology. In the modern era, these have included the global fallout from massive volcanic eruptions, atmospheric and marine thermonuclear weapons testing and nuclear accidents. After the occurrence of the Fukushima Daiichi nuclear accident (FDNA) on March 11 2011, the simulation of the radioactivity from the FDNA by a dispersion model (HYSPLIT) shows that the nuclides reached the study area in late March, consistent with the ground measurements in Xi'an, Lanzhou and Urumqi. To investigate the deposition of radioactivity resulting from the FDNA, we collected snowpack samples from four glaciers (i.e. Glacier No. 1, Glacier No. 72, Qiyi and Shiyi glaciers, respectively) in northwestern China and analysed them for total β activity (TBA). The measured TBA in the FDNA layers were increased by two to four times, compared with the averages in the non-FDNA layers. We revisited Glacier No. 1 in 2018 and studied a much deeper snow-pit profile for the TBA, seven years after the first-time investigation into a relatively shallow snow pit in 2011. The TBA concentrated in a dust layer and became more significant in 2018 compared to that in 2011. We compared the TBA in Glacier No. 1 with that in the Muztagata glacier from the Chernobyl accident in 1986, and the depositions of radioactivity in the two High-Asian glaciers were comparable. We conclude that the FDNA formed a distinctly new lasting reference in the snow, which could help date the snow and ice in the Northern Hemisphere.

Kenya has experienced a decade of relative prosperity with consistent economic growth and minimal political tension. GDP is growing by 3% annually and poverty rates are declining. Despite these gains, Kenya still has a lot of ground to cover to achieve the Sustainable Development Goals (SDGs) by 2030. SDG7, which aims to 'Ensure access to affordable, reliable, sustainable and modern energy for all', exemplifies both Kenya's achievements and the challenges that remain. Access to grid-based electricity and LPG have grown rapidly. However, over 90% of Kenyans still rely on polluting fuels like wood, charcoal and/or kerosene for some or all of their cooking needs. Substantial effort is needed to ensure all Kenyans have access to clean cooking options by 2030. We present the results of a pilot study in which gasifier-based pellet stoves were introduced in 150 peri-urban households. The stoves include an internal fan that improves combustion efficiency and reduces emissions by 90%–99% relative to charcoal and fuelwood in traditional devices. A subset of participants received stoves with 'Pay-as-You-Cook' (PAYC) hardware, which relies on pre-paid RFID card to activate the stove's internal fan, allowing vendors to sell the stove below cost and recoup losses through pellet sales. We find that people were willing to include pellet stoves in their cooking routines and, in many cases, pellets displaced polluting fuels. We also find that PAYC hardware did not negatively impact adoption: PAYC users had higher daily rates of fuel consumption and reported higher willingness to pay for the stove than non-PAYC users. However, stoves were not used exclusively. Instead, people stacked pellets in combination with other cooking options, with pellets contributing to 12%–40% of their cooking needs (inter-quartile range). Though the project did not successfully overcome all of the barriers necessary to achieve long-term adoption of advanced pellet stoves, the results demonstrate that pellets could contribute to a portfolio of cleaner options.

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In a pilot project, clean-burning 'pay-as-you-cook' pellet stoves were stacked with LPG and polluting fuels.

Long-term plot-scale studies have found water limitation to be a key factor driving ecosystem productivity in the Rocky Mountains. Specifically, the intensity of early summer (the 'foresummer' period from May to June) drought conditions appears to impose critical controls on peak ecosystem productivity. This study aims to (1) assess the importance of early snowmelt and foresummer drought in controlling peak plant productivity, based on the historical Landsat normalized-difference vegetation index (NDVI) and climate data; (2) map the spatial heterogeneity of foresummer drought sensitivity; and (3) identify the environmental controls (e.g. geomorphology, elevation, geology, plant types) on drought sensitivity. Our domain (15 × 15 km) includes four drainages within the East Water watershed near Gothic, Colorado, USA. We define foresummer drought sensitivity based on the regression slopes of the annual peak NDVI against the June Palmer Drought Severity Index between 1992 and 2010. Results show that foresummer drought sensitivity is spatially heterogeneous, and primarily dependent on the plant type and elevation. In support of the plot-based studies, we find that years with earlier snowmelt and drier foresummer conditions lead to lower peak NDVI; particularly in the low-elevation regions. Using random forest analysis, we identify additional key controls related to surface energy exchanges (i.e. potential net radiation), hydrological processes (i.e. microtopography and slope), and underlying geology. This remote-sensing-based approach for quantifying foresummer drought sensitivity can be used to identify the regions that are vulnerable or resilient to climate perturbations, as well as to inform future sampling, characterization, and modeling studies.

Estimating baseline carbon stocks is a key step in designing forest carbon programs. While field inventories are resource-demanding, advances in predictive modeling are now providing globally coterminous datasets of carbon stocks at high spatial resolutions that may meet this data need. However, it remains unknown how well baseline carbon stock estimates derived from model data compare against conventional estimation approaches such as field inventories. Furthermore, it is unclear whether site-level management actions can be designed using predictive model data in place of field measurements. We examined these issues for the case of mangroves, which are among the most carbon dense ecosystems globally and are popular candidates for forest carbon programs. We compared baseline carbon stock estimates derived from predictive model outputs against estimates produced using the Intergovernmental Panel on Climate Change's (IPCC) three-tier methodological guidelines. We found that the predictive model estimates out-performed the IPCC's Tier 1 estimation approaches but were significantly different from estimates based on field inventories. Our findings help inform the use of predictive model data for designing mangrove forest policy and management actions.

Sugar is the second largest agro-based industry in India and has a major influence on the country's water, food, and energy security. In this paper, we use a nexus approach to assess India's interconnected water-food-energy challenges, with a specific focus on the political economy of the sugar industry in Maharashtra, one of the country's largest sugar producing states. Our work underscores three points. First, the governmental support of the sugar industry is likely to persist because policymakers are intricately tied to that industry. Entrenched political interests have continued policies that incentivize sugar production. As surplus sugar has been produced, the government introduced additional policies to reduce this excess and thereby protect the sugar industry. Second, although the sugar economy is important to India, sugar policies have had detrimental effects on both water and nutrition. Long-standing government support for sugarcane pricing and sales has expanded water-intensive sugarcane irrigation in low-rainfall areas in Maharashtra, which has reduced the state's freshwater resources and restricted irrigation of more nutritious crops. Despite its poor nutritional value, empty-calorie sugar has been subsidized through the public distribution system. Third, the Indian government is now promoting sugarcane-based ethanol production. This policy has the benefit of providing greater energy security and creating a new demand for surplus sugar in the Indian market. Our analysis shows that a national biofuel policy promoting the production of ethanol from sugarcane juice versus directly from molasses may help reduce subsidized sugar for human consumption without necessarily expanding water and land use for additional production of sugarcane.

The outbreak of Coronavirus Disease 2019 (COVID-19) in China in January 2020 prompted substantial control measures including social distancing measures, suspension of public transport and industry, and widespread cordon sanitaires ('lockdowns'), that have led to a decrease in industrial activity and air pollution emissions over a prolonged period. We use a 5 year dataset from China's air quality monitoring network to assess the impact of control measures on air pollution. Pollutant concentration time series are decomposed to account for the inter-annual trend, seasonal cycles and the effect of Lunar New Year, which coincided with the COVID-19 outbreak. Over 2015–2019, there were significant negative trends in particulate matter (PM_2.5, −6% yr⁻¹) and sulphur dioxide (SO₂, −12% yr⁻¹) and nitrogen dioxide (NO₂, −2.2% yr⁻¹) whereas there were positive trends in ozone (O₃, + 2.8% yr⁻¹). We quantify the change in air quality during the LNY holiday week, during which pollutant concentrations increase on LNY's day, followed by reduced concentrations in the rest of the week. After accounting for interannual trends and LNY we find NO₂ and PM concentrations were significantly lower during the lockdown period than would be expected, but there were no significant impacts on O₃. Largest reductions occurred in NO₂, with concentrations 27.0% lower on average across China, during the lockdown. Average concentrations of PM_2.5 and PM₁₀ across China were respectively 10.5% and 21.4% lower during the lockdown period. The largest reductions were in Hubei province, where NO₂ concentrations were 50.5% lower than expected during the lockdown. Concentrations of affected pollutants returned to expected levels during April, after control measures were relaxed.

As the Arctic warms at twice the global rate, radiative feedbacks from clouds will lead to compounding impacts on the surface energy budget that affect both regional and global weather, and climate. In a future warmer world, the Arctic is projected to become cloudier. However, the formation and evolution of Arctic clouds remain highly uncertain in part due to a limited understanding of current and future sources of ice nucleating particles (INPs). In particular, the sources and abundance of biologically-derived INPs are poorly characterized, yet they may be pivotal for cloud ice formation, especially at temperatures in which Arctic mixed-phase clouds (AMPCs) persist (i.e. >−15 °C). Here, we show for the first time that permafrost is a remarkably rich source of biologically-derived INPs, both heat labile (probably proteinaceous) and other organic INPs of biomolecular origin (41%–100% and 99%–100% of the total INPs, respectively). INP concentrations in 1000 to 30 000 year old permafrost were comparable to the most active of other Arctic and midlatitude soil sources (up to 10¹⁰ INPs per gram of soil). Thawing of permafrost—which promotes metabolic activity in microbes—and subsequent mobilization of those soils directly into the atmosphere or into lakes, rivers, and the ocean, suggests the intriguing possibility that increasing emissions of INPs from this hitherto overlooked reservoir could be widespread, and, in time, greatly impact Arctic cloud cloud glaciation and radiative properties. This discovery is timely given the rapidly-thawing permafrost in Alaska and across Earth's high latitudes. Since permafrost covers 15% of Northern Hemisphere land, this novel and prevalent INP source may become central to predictions of aerosol-cloud-precipitation interactions in AMPCs.

Over the 20th and 21st centuries, both anthropogenic greenhouse gas increases and changes in anthropogenic aerosols have affected rainfall in the Sahel. Using multiple characteristics of Sahel precipitation, we construct a multivariate fingerprint that allows us to distinguish between the model-predicted responses to greenhouse gases and anthropogenic aerosols. Models project the emergence of a detectable signal of aerosol forcing in the middle of the 20th century and a detectable signal of greenhouse gas forcing at the beginning of the 21st. However, the signals of both aerosol and greenhouse gas forcing in observations emerge earlier and are stronger than in the models, far stronger in the case of aerosols. The similarity between the response to aerosol forcing and the leading mode of internal variability makes it difficult to attribute this model-observation discrepancy to errors in the forcing, errors in the forced response, model inability to capture the amplitude of internal variability, or some combination of these. For greenhouse gases, however, the forced response is distinct from internal variability as estimated by models, and the observations are largely commensurate with the model projections.

Electricity consumption and greenhouse gas (GHG) emissions associated with wastewater flows from residential and commercial water use in three major cities of the United States are analyzed and compared for the period 2010–2018. Contributions of unit wastewater treatment processes and electricity sources to the overall emissions are considered. Tucson (Arizona), Denver (Colorado), and Washington, DC were chosen for their distinct locations, climatic conditions, raw water sources, wastewater treatment technologies, and electric power mixes. Denver experienced a 20% reduction in treated wastewater volumes per person despite a 16% increase in population. In Washington, DC, the reduction was 19%, corresponding to a 16% increase in population, and in Tucson 14% despite a population growth of 3%. The electricity intensity per volume of treated wastewater was higher in Tucson (1 kWh m⁻³) than in Washington, DC (0.7 kWh m⁻³) or Denver (0.5 kWh m⁻³). Tucson's GHG emissions per person were about six times higher compared to Denver and four times higher compared to Washington, DC. Wastewater treatment facilities in Denver and Washington, DC generated a quarter to third of their electricity needs from onsite biogas and lowered their GHG emissions by offsetting purchases from the grid, including coal-generated electricity. The higher GHG emission intensity in Tucson is a reflection of coal majority in the electricity mix in the period, gradually replaced with natural gas, solar, and biogas. In 2018, the GHG reduction was 20% when the share of solar electricity increased to 14% from zero in 2016. In the analysis period, reduced wastewater volumes relative to the 2010 baseline saved Denver 44 000 MWh, Washington, DC 11 000 MWh and Tucson 7000 MWh of electricity. As a result, Washington, DC managed to forgo 21 000 metric tons of CO_2-eq and Denver 34 000 metric tons, while Tucson's cumulative emissions increased by 22 000 metric tons of CO_2-eq. This study highlights the variability observed in water systems and the opportunities that exist with water savings to allow for wastewater generation reduction, recovering energy from onsite biogas, and using energy-efficient wastewater treatment technologies.

Water use efficiency (WUE) characterizes the relationship between water dissipation and carbon sequestration. Knowledge of WUE dynamics and its responses to complex climate controls are prerequisites for addressing the challenges of future climate change and human disturbance of wild lands. Owing to a lack of experimental observations and the complexity of quantifying the individual and interactive effects of different environmental factors, the mechanism of WUE dynamics and the spatiotemporal characteristics of WUE in Central Asian ecosystems remain unclear. Here, a specific Arid Ecosystem Model was used to assess WUE dynamics under environmental stresses, specifically isolating and identifying proprietary features from complex coupling effects, across different ecosystems in Central Asia from 1980 to 2014. WUE declined in southern Xinjiang but exhibited an upward trend in the Tianshan Mountains and northern Kazakhstan. Precipitation and CO₂ controlled WUE of 39% and 54% of Central Asia, respectively. The factor analysis showed that the negative effects of climate change were largely compensated by the CO₂ fertilization effect, and their interaction produced negative feedback to WUE. This resulted in inhibition of the CO₂ fertilization effect during long droughts. The negative effects of warming included increased water stress and enhanced evapotranspiration from vegetation. Based on variations in precipitation and net primary production, we determined that southern Xinjiang and the Turgay Plateau were environmentally vulnerable areas. Our study provides guidance regarding how ecologically fragile regions in Central Asia might cope with environmental pressures under extreme climate change in the future.

Changes in snow precipitation at high latitudes can significantly affect permafrost thermal conditions and thaw depth, potentially exposing more carbon-laden soil to microbial decomposition. A fully coupled process-based surface/subsurface thermal hydrology model with surface energy balance is used to analyze the impact of intra-annual variability in snow on permafrost thermal regime and the active layer thickness. In the four numerical scenarios considered, simulations were forced by the same meteorological data, except the snow precipitation, which was systematically altered to change timing of snowfall. The scenarios represent subtle shifts in snow timing, but the snow onset/melt days, the end of winter snowpack depth, and total annual snow precipitation are unchanged among scenarios. The simulations show that small shifts in the timing of snow accumulation can have significant effects on subsurface thermal conditions leading to active layer deepening and even talik formation when snowfall arrives earlier in the winter. The shifts in snow timing have a stronger impact on wetter regions, especially soil underneath small ponds, as compared to drained regions. This study highlights the importance of understanding potential changes in winter precipitation patterns for reliable projections of active-layer thickness in a changing Arctic climate.

Madden–Julian oscillation (MJO), the dominant mode of intraseasonal variability in the tropical troposphere, has recently been shown to have a great impact on Northern Hemisphere (NH) extratropical stratosphere. But the influence of the variability in the extratropical stratosphere on MJO is seldom reported. In this study, the influence of major, mid–winter NH stratospheric sudden warmings (SSWs) on the MJO is investigated using meteorological reanalysis datasets. Our analysis reveals that SSWs also exert considerable influence on tropical intraseasonal convection. The occurrences of MJO phases 6 and 7 significantly increase during around 20 d after the onset of SSWs, corresponding to enhanced convective activity over the equatorial Central and Western Pacific. Then in the following days, the coherent eastward propagation of tropical intraseasonal convection resembles the periodic variation in a typical MJO. These results suggest that the extratropical stratosphere affects the organized tropical intraseasonal convection, and variability of the tropical intraseasonal convection related to MJO can be better grasped by taking extratropical stratospheric variability into account. Considering the complex interaction between MJO and extratropical stratosphere, further work on comprehensive understanding of the relationship between SSWs and MJO is required in future studies.

Assessing the climate change impact (CCI) on weather conditions is important for addressing climate change and promoting sustainable development. This study used a weather preference index (WPI) as an indicator to evaluate the CCI on weather conditions in China under different scenarios from 2025 to 2100. First, we analyzed the change in the WPI in China from 1971 to 2013. Then, we estimated the trends in the WPI in China from 2025 to 2100 under different representative concentration pathways (RCPs) based on global climate models. We found that China's weather conditions improved from 1971 to 2013, as the national average WPI increased from 1.34 to 1.59 with a change rate of 0.03 per decade (0.03/10 a). Under all climate change scenarios, the weather conditions in China will deteriorate. The change rates of the WPI will be −0.19/10 a ∼ − 0.01/10 a. The number of people experiencing deteriorated weather conditions will be 0.71 billion ∼ 1.22 billion, accounting for 53.28% ∼ 91.58% of the total population in China. We also found that the area of the regions with deteriorated weather conditions under all three climate change scenarios will be 2.34 million km², accounting for 24.31% of China's total land area. At the same time, as the emissions concentrations increase from RCP2.6 to RCP8.5, the area of the regions with severely deteriorated weather conditions in China will increase from 0 to 3.27 million km². Therefore, we suggest that China needs to implement effective measures to address climate change in the future and focus on the mitigation of and adaptation to climate change in regions with deteriorated weather conditions.

Groundwater is a critical freshwater resource for irrigation in the California Central Valley, particularly in times of drought. Groundwater depth has dropped rapidly in California's overdrafted basins, but irregular monitoring across space and time limits the accuracy of the groundwater depth projections in the Groundwater Sustainability Plans required by the California Sustainable Groundwater Management Act (SGMA). This work constructs a Bayesian hierarchical model for predicting groundwater depth from sparse monitoring data in three Central Valley counties. We apply this model to generate 300 m resolution monthly groundwater depth estimates for drought years 2013–2015, and compare our smoothed groundwater depth map to smoothed rasterized maps published by the CA Department of Water Resources. Finally, we quantify uncertainty in groundwater depth predictions that are made by imputing missing well data and interpolating predictions across the study domain, which is helpful in directing future sampling efforts towards areas with high uncertainty. The BHM model accurately captures the spatiotemporal pattern in groundwater depth, as evidenced by 94.54% of withheld test samples' true depth being covered by the 95% prediction interval drawn from the BHM posterior distribution. The model converged despite a very sparse dataset, demonstrating broad applicability for evaluating changes in regional groundwater depth as required by SGMA. Depth prediction intervals can also help prioritize future groundwater depth sampling activity and increase the utility of groundwater depth maps in total storage predictions by enabling sensitivity analysis.

Conflicts between agricultural intensification and the increasing demand for clean water resources are growing worldwide. This study sought to understand how the negative consequences of agricultural expansion in fragile hilly watersheds can be mitigated by ecologically based engineering practices. We analyzed long-term and seasonal water quality trends in two sub-watersheds of the Tianmu Lake watershed in Eastern China. The Zhongtian and nearby Zhucao sub-watersheds are very similar in terms of climate, topography and other features that can influence water quality. Both are experiencing rapid expansion of tea plantations, but the Zhongtian River contains an engineered system of overflow dams and cascade wetlands that is absent from the Zhucao River. The multi-year averaged reduction (2009–2018) of total nitrogen (TN) and total phosphorus (TP) from upstream to downstream reaches was 10%–15% greater in the engineered Zhongtian River compared to the non-engineered and free flowing Zhucao River, which has no interventions to reduce nutrient concentrations. Average annual reductions in TN, TP, and total suspended solids (SS) downstream of the engineered system reached 0.5%–4.0% of their multi-year averaged concentrations over this time interval. These reductions occurred despite a 2.3-fold expansion of tea plantation area in the engineered watershed, which contrasts with deteriorating water quality in the non-engineered watershed that had a 0.4-fold expansion of tea plantation area. Our results underscore the value of such engineered systems to improve water quality and help reconcile competing advantages of agricultural development and environmental protection in hilly watersheds, where there is limited in-stream processing of nutrients and the effects of human activities are substantial.

The environmental benefits of plug-in hybrid electric vehicles (PHEVs) are closely related to the driving and charging behavior of vehicle owners. It is often wrongly assumed that PHEV drivers plug-in once per day. Using data from drivers of the vehicles we show this is not the case and that some drivers rarely charge their PHEV. If the vehicle is not plugged-in regularly, the vehicle will drive fewer electric miles and more gasoline miles, thereby losing out on potential emission savings. Analyzing 30-day charging behavior of 5418 PHEV owners using a logistic regression model, we explore the factors that influence driver's decisions to not charge their vehicle. Several factors play a role in drivers' decision to plug-in their PHEV or not, including vehicle characteristics and the availability and cost of charging at various locations. Higher home electricity prices, lower electric driving range, lower electric motor power to vehicle weight ratios, lower potential cost savings from charging, and living in an apartment or condo, among other factors are related to not plugging in a PHEV. The findings have important implications in terms of future policy and vehicle design including which PHEVs policymakers should incentivize and what measures can encourage PHEV owners to plug-in their vehicles to help realize the environmental benefits of the technology.

Many experts see carbon pricing as an effective way to reduce emissions of greenhouses gases; however, political and public support for carbon pricing has faltered. Recent research indicates that revenue recycling and policy design options may induce public support for carbon pricing, but does not examine change in support as a result of revenue use or possible heterogeneity in these inducements across partisan groups. Does support for a carbon tax shift significantly once revenue uses are discussed? Do conservatives and Republicans and liberals and Democrats respond to different revenue reuse options when formulating opinions about carbon taxation? This study employs a survey experiment to examine these questions. Key results indicate that support shifts are largest when the revenue would be refunded and conservatives and Republicans are responsive to different revenue usage options. Specifically, conservatives and Republicans are more supportive of a carbon tax when revenues go towards a tax rebate or deficit reduction. While the differences are relatively small and variable (uncertain), these results provide suggestive insight into the policy design options that may induce a bipartisan basis of public support for carbon taxation policies.

Lake area change in the Tibetan plateau is an important indicator for climate change assessment. To overcome the temporal inconsistency of optical remote sensing-based lake area detections, a land surface temperature (LST)-based detection scheme was proposed by utilizing the big difference between land and water surface temperatures. A trend test conducted by the Mann–Kendall (MK) method was successfully applied to investigate lake area variation from 2000 to 2018 with the use of the annual mean temperature information derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST daily product. A comparison with the monitoring results from Landsat images indicates the proposed method can provide spatial distributions of lake area change with high accuracy. More importantly, the temporal variation of annual mean LST provides a special way to detect the abrupt change year (ACY) in lake area.The ACYs of most lakes mainly occur from 2004 to 2012. For an individual lake, the ACY offers vital information about the lake area change process. In summary, this work demonstrates the good potential of the LST-based method for lake area monitoring and assessment.

Urban particle pollution is affected by not only the emissions of pollutants and secondary aerosol formation through atmospheric chemistry on a local scale, but also the regional transport of particles and precursor gases from highly polluted upwind areas. However, this regional impact on urban particle formation is not well understood. Wintertime haze events occur at Seoul, Korea through the combination of regional transport from China and local formation at Seoul. We perform thermodynamic model simulations based on inorganic component measurements of haze particles collected at Seoul and Deokjeok Island (upwind background). Results suggest that in the downwind area (Seoul) the local formation of sulfates increases the mass concentrations of transported particles through the gas–particle partitioning of semivolatile nitric acid (HNO₃) and ammonia (NH₃). Therefore, this synergetic effect of the local sulfate formation on urban haze with regional transport must be considered in implementing effective particle reduction controls for urban sustainability.

Conservation provisions of US farm bills since 1985 have been aimed at mitigating negative environmental impacts of US agriculture. One of the long term goals has been to protect against soil erosion, with a focus specifically on highly erodible land (HEL). Conservation Compliance (CC) mandates that, in order to receive federal subsidies, farmers who plant annual crops on HEL must implement a conservation plan, with practices such as rotating crops and no-till farming. When crop prices increase, however, the incentives not to follow the plan increase, as conservation activities can reduce farmers' profits. This study is the first to assess the performance of conservation compliance between 2007 and 2019, a period of historically high and variable crop prices, using geographical information system tools and crop data in a critical agricultural production region, the US Corn Belt. Our results indicate there was a substantial increase in continuous corn on HEL, a proxy measure for non-compliance, in several portions of the study area in correspondence with higher crop prices following the 2007 Energy Bill. This mirrored the change in crop rotations on all cropland. The increase was positively correlated with both absolute and relative corn prices. While at the height of absolute and relative corn prices there were increases in continuous corn on HEL everywhere across the study region except parts of Missouri, some of the largest changes occurred in environmentally sensitive regions and areas which use irrigation, thereby potentially creating disproportionate environmental impacts. Similar changes in continuous corn also occurred in all cropland in the region, indicating that mandatory conservation programs are as vulnerable to periods of high crop prices as voluntary programs. Better monitoring for both CC and other conservation programs is critical to ensure the policies work as intended.

The role of climate and aclimatic factors on species distribution has been debated widely among ecologists and conservationists. It is often difficult to attribute empirically observed changes in species distribution to climatic or aclimatic factors. Giant pandas (A. melanoleuca) provide a rare opportunity to study the impact of climatic and aclimatic factors, particularly the food sources on predicting the distribution changes in the recent decade, as well-documented information on both giant panda and bamboos exist. Here, we ask how the climate metrics compare to the bamboo suitability metric in predicting the giant panda occurrences outside the central areas in the Qinling Mountains during the past decade. We also seek to understand the relative importance of different landscape-level variables in predicting giant panda emigration outside areas of high giant panda densities. We utilize data from the 3rd and 4th National Giant Panda Surveys (NGPSs) for our analysis. We evaluate the performance of the species distribution models trained by climate, bamboo suitability, and the combination of the two. We then at 4 spatial scales identify the optimal models for predicting giant panda emigration between the 3rd and the 4th NGPSs using a list of landscape-level environmental variables. Our results show that the models utilizing the bamboo suitability alone consistently outperform the bioclimatic and the combined models; the distance to high giant panda density core area and bamboo suitability show high importance in predicting expansion probability across all four scales. Our results also suggest that the extrapolated bamboo distribution using bamboo occurrence data can provide a practical and more reliable alternative to predict potential expansion and emigration of giant panda along the range edge. It suggests that restoring bamboo forests within the vicinity of high giant panda density areas is likely a more reliable strategy for supporting shifting giant panda populations.

Large-scale urban expansion worldwide has exerted great impacts on cropland and its net primary productivity (NPP), which can affect whether food security and sustainable development goals will be met at global and local scales. Although important, the impacts at the global scale over the last 25 years remain unclear. Based on the latest long-term dynamic urban expansion data, this study analyzed global urban expansion and its impacts on cropland NPP from 1992 to 2016 at multiple scales. The results showed that the expansion of urban land occupied a total of 159 170 km² of cropland, accounting for 45.9% of the total expanded urban area. The cropland NPP decreased by 58.71 (56.52 ∼ 59.81, 95% confidence interval) TgC as a result of urban expansion, which represents approximately 0.42% (0.40% ∼ 0.43%) of the multiyear average of total cropland NPP from 2000 to 2015. If the cropland NPP losses were converted to the grain production (i.e. 1.44 × 10⁷ tons), it is equivalent to the minimum annual food intake demands for at least 36 million people. More importantly, urban expansion is exacerbating the risk of food security in developing countries in Asia and Africa, such as China, Vietnam and Egypt. In the future, these countries should balance urban expansion with cropland protection by strictly restricting the occupation of cropland and encouraging smart urban growth.

Past studies have concluded that climate models of previous generations tended to underestimate the large warming trend that has been observed in summer over western Europe in the last few decades. The causes of this systematic error are still not clear. Here, we investigate this issue with a new generation of climate models and systematically explore the role of large-scale circulation in that context.

As an ensemble, climate models in this study warm less over western Europe and warm more over eastern Europe than observed on the 1951–2014 period, but it is difficult to conclude this is directly due to systematic errors given the large potential impact of internal variability. These differences in temperature trends are explained to an important extent by an anti-correlation of sea level pressure trends over the North Atlantic / Europe domain between models and observations. The observed trend tends to warm (cool) western (eastern) Europe but the simulated trends generally have the opposite effect, both in new generation and past generation climate models. The differences between observed and simulated sea level pressure trends are likely the result of systematic model errors, which might also impact future climate projections. Neither a higher resolution nor the realistic representation of the evolution of sea surface temperature and sea ice leads to a better simulation of sea level pressure trends.

The restoration of ecosystems provides an important opportunity to improve the provision of ecosystem services. Achieving the maximum possible benefits from restoration with a limited budget requires knowing which places if restored would produce the best combination of improved ecosystem services. Using an ecosystem services assessment and optimization algorithm, we find choices that generate maximum benefits from ecosystem restoration. We applied a set of weights to integrate multiple services into a unified approach and find the optimal land restoration option given those weights. We then systematically vary the weights to find a Pareto frontier that shows potentially optimal choices and illustrates trade-offs among services. We applied this process to evaluate optimal restoration on Hainan Island, China, a tropical island characterized by multiple ecosystem service hotspots and conditions of poverty. We analyzed restoration opportunities with the goal of increasing a provisioning service, plantation revenue, and several water-related ecosystem services that contribute to improved water quality and flood mitigation. We found obvious spatial inconsistencies in the optimal location for maximizing separate services and tradeoffs in the provision of these services. Optimized land-use patterns greatly out-performed the non-target restoration scheme. When explicit consideration of the importance of poverty alleviation was taken into account, the location of the prioritized areas shifted and trade-offs among services varied. Our study emphasizes the importance of integrating social concerns into land-use planning to mitigate conflicts and improve equity, especially in the areas where poverty and hotspots of biodiversity and ecosystem services are highly geographically coincident.

Given the current confirmed permafrost degradation and its considerable impacts on ecosystems, water resources, infrastructure and climate, there is great interest in understanding the causes of permafrost degradation. Using the surface frost index (SFI) model and multimodel data from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), this study, for the first time, investigates external anthropogenic and natural forcing impacts on historical (1921–2005) near-surface permafrost change in the Northern Hemisphere. The results show that anthropogenic greenhouse gas (GHG) forcing produces a significant decrease in the area of near-surface permafrost distribution at a rate of 0.46 × 10⁶ km² decade⁻¹, similar to observations and the historical simulation (ALL). Anthropogenic aerosol (AA) forcing yields an increase in near-surface permafrost distribution area at a rate of 0.25 × 10⁶ km² decade⁻¹. Under natural (NAT) forcing, there is a weak trend and distinct decadal variability in near-surface permafrost area. The effects of ALL and GHG forcings are detectable in the observed change in historical near-surface permafrost area, but the effects of NAT and AA forcings are not detected using the optimal fingerprint methods. This indicates that the observed near-surface permafrost degradation can be largely attributed to GHG-induced warming, which has decreased the near-surface permafrost area in the Northern Hemisphere by approximately 0. 21 × 10⁶ km² decade⁻¹ on average over the study period, according to the attribution analysis.

Renewable natural gas (RNG) is a fuel comprised of essentially pure methane, usually derived from climate-neutral (e.g. biogenic or captured) carbon dioxide (CO₂). RNG is proposed as a climate friendly direct substitute for fossil natural gas (FNG), with the goal of enabling diverse natural gas users to continue operating without substantial infrastructure overhauls. The assumption that such substitution is climate friendly relies on a major condition that is unlikely to be met: namely, that RNG is manufactured from waste methane that would otherwise have been emitted to the atmosphere. In practice, capturable waste methane is extremely limited and is more likely to be diverted from a flare than from direct atmospheric release in a climate-conscious policy context, which means that RNG systems need to be more destructively efficient than a flare to provide climate benefits versus the likely alternative management strategy. Assuming demand levels consistent with the goal of using existing FNG infrastructure, RNG is likely to be derived from methane that is either intentionally produced or diverted from a flare, so essentially any methane leakage is climate additional. Further, in a decarbonizing system, RNG will likely compete with lower-emissions resources than FNG and thus provides fewer net emissions benefits over time. Anticipated leakage is climatically significant: literature estimates for methane leakage from biogas production and upgrading facilities suggest that leakage is in the 2%–4% range (mass basis), up to as much as 15%. Policy makers should consider that under reasonable leakage and demand assumptions, RNG could be climate intensive.

The northern polar region possesses the most extensive cold airmass on Northern Hemisphere. The generation of this polar cold airmass and its outflow to lower latitudes play an important role in the climate system in terms of mass and heat exchanges. However, long-term changes in the polar cold airmass, especially in summer, and the climatic effects on mid-high latitudes are still unclear. Using an isentropic approach, we quantitatively show that the polar cold airmass amount has decreased rapidly since the 1980s, with a decade lagging behind the global warming. The equatorward flux of the cold airmass has also weakened, trapping the cold airmass in its source region. These profound changes in the cold airmass coincide with a period characterized by rapid surface warming and increasingly frequent heat waves in recent three decades over the mid-high latitude continents. Owing to regional differences in the cold airmass reduction, Europe and North America have experienced a surface warming faster than the Northern Hemisphere mean. Furthermore, such a long-term trend in the polar cold airmass can be attributed to Arctic sea ice loss and internal decadal variability of sea surface temperature in high-latitude oceans. Our results highlight that the isentropic analysis of cold airmass may serve a good detection of the climate change at polar region and mid-high latitudes.

Rapid intensification (RI) refers to a significant increase in tropical cyclone (TC) intensity over a short period of time. A TC can also undergo multiple RI events during its lifetime, and these RI events pose a significant challenge for operational forecasting. The long-term tendency in RI magnitude of TCs over the western North Pacific is investigated in this study. During 1979–2018, a significant increasing trend is found in RI magnitude, which primarily results from the significant increasing number of strong RI events, defined as 24 h intensity increases of at least 50 kt. Furthermore, there are significantly more (slightly fewer) strong RI occurrences west (east) of 155°E in 1999–2018 than in 1979–1998. Significant increases in strong RI occurrences are located over the region bounded by 10°∼20°N, 120°∼150°E. These changes are likely induced by the warming ocean but appear uncorrelated with changes in the atmospheric environment. By contrast, there are slight decreases in strong RI occurrences over the region bounded by 12.5°∼22.5°N, 155°∼170°E, likely due to the offset between RI-favorable influences of the warming ocean and RI-unfavorable influences of increasing vertical wind shear (VWS).

The aim of this research is to address the challenge of achieving more equitable social outcomes through a reduction and fairer allocation of environmental burdens, and in doing so, contributing to national sustainable development policy. This novel study demonstrates the nature of societal outcomes through the lens of inequity with respect to lifestyle related environmental footprints and stakeholder preferences. Footprints are derived using input-output analysis, while environmental issue preferences and potential remedial actions are identified using a national survey. To highlight the value of the broadly applicable framework, here we demonstrate a case study of Japan, which is interesting due to shifting demographics engendering an aging, shrinking population. Key findings include that the mitigation of environmental footprints in line with household preferences can positively influence both societal equity outcomes and contribute to closing the gap between rich and poor. Importantly, broad participation, i.e. participation irrespective of income level, is shown to be more effective than participation from a single sector. These findings can assist policymakers to develop policies which are responsive to societal preferences and demographic trends while also furthering the debate toward clarifying norms for acceptable levels of social equity.

In the transition to a renewable energy system, the occurrence of low-wind-power events receives increasing attention. We analyze the frequency and duration of such events for onshore wind power in Germany, based on 40 years of reanalysis data and open software. We find that low-wind-power events are less frequent in winter than in summer, but the maximum duration is distributed more evenly between months. While short events are frequent, very long events are much rarer. Every year, a period of around five consecutive days with an average wind capacity factor below 10% occurs, and every ten years a respective period of nearly eight days. These durations decrease if only winter months are considered. The longest event in the data lasts nearly ten days. We conclude that public concerns about low-wind-power events in winter may be overrated, but recommend that modeling studies consider multiple weather years to properly account for such events.

Understanding how climate change and demographic factors may shape future population exposure to viruses such as Zika, dengue, or chikungunya, transmitted by Aedes mosquitoes is essential to improving public health preparedness. In this study, we combine projections of cumulative monthly Aedes-borne virus transmission risk with spatially explicit population projections for vulnerable demographic groups to explore future county-level population exposure across the conterminous United States. We employ a scenario matrix—combinations of climate scenarios (Representative Concentration Pathways) and socioeconomic scenarios (Shared Socioeconomic Pathways)—to assess the full range of uncertainty in emissions, socioeconomic development, and demographic change. Human exposure is projected to increase under most scenarios, up to + 177% at the national scale in 2080 under SSP5*RCP8.5 relative to a historical baseline. Projected exposure changes are predominantly driven by population changes in vulnerable demographic groups, although climate change is also important, particularly in the western region where future exposure would be about 30% lower under RCP2.6 compared to RCP8.5. The results emphasize the crucial role that socioeconomic and demographic change play in shaping future population vulnerability and exposure to Aedes-borne virus transmission risk in the United States, and underline the importance of including socioeconomic scenarios in projections of climate-related vector-borne disease impacts.

High-resolution ocean general circulation model (OGCM) simulations are employed to investigate interannual variability of the upper-ocean temperature in the tropical Indian Ocean (20°S–20°N). The seasonal cycle and interannual variability in the upper-ocean temperature in the tropical Indian Ocean in the forced ocean simulation are in good agreement with available observation and reanalysis products. Two further sensitivity OGCM simulations are used to separate the relative contributions of heat flux and wind stress. The comparison of the model simulations reveals the depth-dependent influences of heat flux and wind stress on the ocean temperature variability in the tropical Indian Ocean. Generally, heat flux dominates the temperature variability in the top 30 m, while wind stress contributes most strongly to the subsurface temperature variability below 30 m. This implies that a transition depth should exist at each location, where the dominant control of the ocean temperature variability switched from heat flux to wind stress. We define the depth of this transition point as the 'crossing depth' and make use of this concept to better understand the depth-dependent impacts of the heat flux and wind stress on the upper-ocean temperature variability in the tropical Indian Ocean. The crossing depth tends to be shallower in the southern tropical Indian Ocean (20°S-EQ), including the Seychelles-Chagos Thermocline Ridge (SCTR) and the eastern part of the Indian Ocean Dipole (IOD), suggesting the dominance of forcing due to wind stress and the resulting ocean dynamical processes in the temperature variability in those regions. The crossing depth also shows prominent seasonal variability in the southern tropical Indian Ocean. In the SCTR, the variability of the subsurface temperature forced by the wind stress dominates largely in boreal winter and spring, resulting in the shallow crossing depth in these seasons. In contrast, the intensified subsurface temperature variability with shallow crossing depth in the eastern part of the IOD is seen during boreal autumn. Overall, our results suggest that the two regions within the tropical Indian Ocean, the SCTR and the eastern part of the IOD, are the primary locations where the ocean dynamics due to wind-stress forcing control the upper-ocean temperature variability.

A life-cycle assessment approach is used to analyze the energy demand and greenhouse gas emissions associated with potable water usage trends in three major cities of the United States in different regions and climates and relying on different types of raw water sources. Between 2011 and 2016, a decreasing trend in per-person water consumption is observed despite growing populations. The per-person water consumption decreased by 10% in Tucson (Arizona) and Washington, DC, and by 16% in Denver (Colorado). Leveraging certain distinctive water and electricity supply characteristics of the case study cities can provide insights into potential interventions and cross-comparison for generalizing trends. In Tucson, potable water production is the most energy intensive and electricity is produced mainly from coal. The greenhouse gas emissions of the per-person water consumption in Tucson are about five times higher compared to Denver and Washington, DC, thus water savings in Tucson should be particularly pursued. GHG emissions decreased in the period by even higher percentages than water use: 15%, 14% and 27% between 2011 and 2016 for Tucson, Washington, DC, and Denver, respectively. In 2015, just four years' worth of forgone GHG emissions in Tucson were somewhat higher than the total GHG emissions associated with water consumption in all of Washington, DC, a city with the same population size as Tucson. Results show that cities should prioritize promotion of water savings to decrease the average per-person water consumption because it can be achieved and can compensate for increases in population. Lower greenhouse gas emissions can be attained in tandem with the local electric power industry.

We analyze public interest in pesticides and plant protection products over time utilizing Google Trends data for Switzerland from 2011 until 2019. We find that the aggregated public interest in pesticides and plant protection products has increased substantially in recent years, especially since 2017. This trend can be attributed to search terms related to pesticides, while the interest in search terms related to plant protection products remained stable. Since the term 'pesticides' has a more negative connotation than 'plant protection products', the increased public interest might be linked to a higher negative perception of environment and human health impacts of pesticides. We also find evidence which supports the hypothesis that growing public concerns on pesticide use contributed to the launches of two popular initiatives aiming to restrict pesticide use in Switzerland. At the same time, our results support that the launch of these initiatives amplified public concerns regarding pesticides. We conclude that Google Trends is a useful tool for the timely detection of ongoing environmental and agricultural discussions, which might otherwise be unobserved. Therefore, it can generate helpful insights and contribute to agricultural policy problem framing.

The Arabian Gulf region experiences regular thermally driven sea breeze circulations which occur all year round, penetrating hundreds of kilometres inland. As a sea breeze front moves inland, substantial electric fields are generated by separation of charged desert dust. In the first surface electric field measurements made in the United Arab Emirates (UAE), consistent and repeatable substantial electric field changes with magnitudes up to 7 kV m⁻¹ have been detected at Al Ain (170 km from the western coast), during 80 separate sea breeze events in 2018. Every sea breeze frontal passage shows the same characteristic signature of a transient maximum peak in electric field lasting tens of minutes. Electric field changes during these events were always negative (i.e. enhancing the existing negative 'fair weather' electric field), in contrast to many other reported observations in dust storms in which conditions were less repeatable. The regular and substantial dust electrification found demonstrates that accurate representation of dust in climate and weather models requires electrical effects to be addressed, both in the generation process, and by considering aggregates in radiative transfer calculations as electrically aligned rather than randomly ordered. Furthermore, satellite aerosol retrievals are affected by the changed attenuation of electromagnetic radiation when dust particles are charged, for which corrections may be needed.

Probabilistic seasonal rainfall forecasting is of great importance for stakeholders such as farmers and policymakers to assist in developing risk management strategies and to inform decisions. In practice, there are two kinds of commonly used tools, dynamical models and statistical models, to provide probabilistic seasonal rainfall forecasts. Dynamical models are based on physical processes but are usually expensive to operate and implement, and rely overly on initial conditions. Statistical models are easy to implement but are usually based on simple or linear relationships between observed variables. Recently, machine learning techniques have been widely used in climate projection and perform well in reproducing historical climate. For these reasons, we conducted a case study in Australia by developing a machine learning-based probabilistic seasonal rainfall forecasting model using multiple large-scale climate indices from the Pacific, Indian and Southern Oceans. Rainfall probabilities of exceeding the climatological median for upcoming seasons from 2011 to 2018 were successively forecasted using multiple climate indices of precedent six months. The performance of the model was evaluated by comparing it with an officially used forecasting model, the SOI (Southern Oscillation Index) phase model (SP) operated by Queensland government in Australia. Results indicated that the random forest (RF) model outperformed the SP model in terms of both distinct forecasts and forecasting accuracy. The RF model increased the percentages of distinct forecasts to 64.9% for spring, to 71.5% for summer, to 65.8% for autumn, and to 63.9% for winter, 1.4 ∼ 3.2 times of the values from the SP model. Forecasting accuracy was also greatly increased by 28%, 167%, 219%, and 76% for four seasons respectively, compared to the SP model. The proposed rainfall forecasting model is based on readily available data, and we believe it can be easily extended to other regions to provide seasonal rainfall outlooks.

The effect of weather on public transport usage in Berlin is analysed. The number of single and day tickets sold is used as a proxy for the number of occasional public transport users. Analysing more than three years of hourly ticket sale data, it is shown that the most important factor influencing ticket sales is temperature. Temperatures below −5^°C lead to an increase in ticket sales by up to 30% on working days, while on hot days (> 28^°C) passenger numbers drop by up to 5%. Precipitation increases the number of sales on working days by up to 5%. On weekends, the lowest ticket-sale numbers are associated with wet and either very cold or very hot conditions. Another factor influencing ticket sales is sunshine duration, while wind and snowfall do not seem to play a role for ticket sales in Berlin. It is demonstrated that it is possible to predict ticket sales depending on date, time and weather conditions using a statistical model.

On designated public transport routes the effect of weather on passenger numbers can be much stronger than the district average. This is shown for the example of a bus route to a public beach. With each degree of temperature increase, passenger numbers on this line go up by approximately 30%.

Atmospheric carbon dioxide (CO₂) inversions for estimating natural carbon fluxes typically do not allow for adjustment of fossil fuel CO₂ emissions, despite significant uncertainties in emission inventories and inadequacies in the specification of international bunker emissions in inversions. Also, most inversions place CO₂ release from fossil fuel combustion and biospheric sources entirely at the surface. However, a non-negligible portion of the emissions actually occurs in the form of reduced carbon species, which are eventually oxidized to CO₂ downwind. Omission of this 'chemical pump' can result in a significant redistribution of the inferred total carbon fluxes among regions. We assess the impacts of different prescriptions of fossil fuel emissions and accounting for the chemical pump on flux estimation, with a novel aspect of conducting both satellite CO₂ observation-based and surface in situ-based inversions. We apply 3-D carbon monoxide (CO) loss rates archived from a state-of-the-art GEOS chemistry and climate model simulation in a forward transport model run to simulate the distribution of CO₂ originating from oxidation of carbon species. We also subtract amounts from the prior surface CO₂ fluxes that are actually emitted in the form of fossil and biospheric CO, methane, and non-methane volatile organic compounds (VOCs). We find that the posterior large-scale fluxes are generally insensitive to the finer-scale spatial differences between the ODIAC and CDIAC fossil fuel CO₂ gridded datasets and assumptions about international bunker emissions. However, accounting for 3-D chemical CO₂ production and the surface correction shifts the global carbon sink, e.g. from land to ocean and from the tropics to the north, with a magnitude and even direction that depend on assumptions about the surface correction. A GOSAT satellite-based inversion is more sensitive to the chemical pump than one using in situ observations, exhibiting substantial flux impacts of 0.28, 0.53, and −0.47 Pg C yr⁻¹ over tropical land, global land, and oceans, due to differences in the horizontal and vertical sampling of the two observation types. Overall, the biases from neglecting the chemical pump appear to be minor relative to the flux estimate uncertainties and the differences between the in situ and GOSAT inversions, but their relative importance will grow in the future as observational coverage further increases and satellite retrieval biases decrease.

Extreme precipitation is one of the most devastating forms of atmospheric phenomenon, causing severe damage worldwide, and is likely to intensify in strength and occurrence in a warming climate. This contribution gives an overview of the potential and challenges associated with using weather radar data to investigate extreme precipitation. We illustrate this by presenting radar data sets for Germany, the U.S. and the UK that resolve small-scale heavy rainfall events of just a few km² with return periods of 5 years or more. Current challenges such as relatively short radar records and radar-based QPE uncertainty are discussed. An example from a precipitation climatology derived from the German weather radar network with spatial resolution of 1 km reveals the necessity of radars for observing short-term (1–6 h) extreme precipitation. Only 17.3% of hourly heavy precipitation events that occurred in Germany from 2001 to 2018 were captured by the rain gauge station network, while 81.8% of daily events were observed. This is underlined by a similar study using data from the UK radar network for 2014. Only 36.6% (52%) of heavy hourly (daily) rain events detected by the radar network were also captured by precipitation gauging stations. Implications for the monitoring of hydrologic extremes are demonstrated over the U.S. with a continental-scale radar-based reanalysis. Hydrologic extremes are documented over ∼1000 times more locations than stream gauges, including in the majority of ungauged basins. This underlines the importance of high-resolution weather radar observations for resolving small-scale rainfall events, and the necessity of radar-based climatological data sets for understanding the small-scale and high-temporal resolution characteristics of extreme precipitation.

Synergies and trade-offs exist between climate mitigation actions and target indicators of the Sustainable Development Goals (SDGs). Some studies have assessed such relationships, but the degree of such interaction remains poorly understood. Here, we show the SDG implications associated with CO₂ emissions reductions. We developed 'marginal SDG-emissions-reduction values (MSVs)', which represent the marginal impacts on SDG indicators caused by a unit CO₂ emissions reduction. This metric is applicable to national assessments and was applied to Asia. We found clear relationships between CO₂ emissions reduction rates and many SDG targets. For instance, 1% reduction of CO₂ can avoid 0.57% of air pollution-related premature deaths (SDG3), whereas the mean species richness (SDG15) is decreased by 0.026% with the same reduction (not including climate change impacts). Our findings are useful for assessing the SDG implications associated with CO₂ emissions reduction targets, which will help inform national climate policies.

The past decade includes some of the most extensive boreal forest fires in the historical record. Warming temperatures, changing precipitation patterns, the desiccation of thick organic soil layers, and increased ignition from lightning all contribute to a combustive combination. Smoke aerosols travel thousands of kilometers, before blanketing the surfaces on which they fall, such as the Juneau Icefield. However, many aerosols found in smoke plumes are also produced by other processes and therefore can be ambiguous indicators of fire activity. Here, we use the monosaccharide anhydrides levoglucosan, mannosan, and galactosan as specific indicators of biomass burning to unambiguously demonstrate that fire aerosols reach the Juneau Icefield and are integrated into the snowpack. Back trajectories and satellite observations demonstrate that smoke plumes originating in central Alaska and eastern Siberia affect the Juneau Icefield. These regional sources of fire differ from other combustion aerosols deposited on the Juneau Icefield, such as black carbon, that originate from local fossil fuel burning. Ratios of levoglucosan/mannosan (L/M) and levoglucosan/(mannosan + galactosan) (L/(M + G)) demonstrate that while the majority of fire aerosols reaching the Juneau Icefield originate from softwood burning, grasslands and hardwood forests are also sources. The presence of these hardwoods suggests that fire aerosols may reach the Juneau Icefield from locations as far away as East Asia.

Vegetation is responding to climate change, which is especially prominent in the Arctic. Vegetation change is manifest in different ways and varies regionally, depending on the characteristics of the investigated area. Although vegetation in some Arctic areas has been thoroughly investigated, central Chukotka (NE Siberia) with its highly diverse vegetation, mountainous landscape and deciduous needle-leaf treeline remains poorly explored, despite showing strong greening in remote-sensing products. Here we quantify recent vegetation compositional changes in central Chukotka over 15 years between 2000/2001/2002 and 2016/2017. We numerically related field-derived information on foliage projective cover (percentage cover) of different plant taxa from 52 vegetation plots to remote-sensing derived (Landsat) spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Snow Index (NDSI)) using constrained ordination. Clustering of ordination scores resulted in four land-cover classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra and (4) prostrate herb tundra and barren areas. We produced land-cover maps for early (2000, 2001 or 2002) and recent (2016 or 2017) time-slices for four focus regions along the tundra-taiga vegetation gradient. Transition from graminoid tundra to forest tundra and shrub tundra is interpreted as shrubification and amounts to 20% area increase in the tundra-taiga zone and 40% area increase in the northern taiga. Major contributors of shrubification are alder, dwarf birch and some species of the heather family. Land-cover change from the forest tundra and shrub tundra class to the larch closed-canopy forest class is interpreted as tree infilling and is notable in the northern taiga. We find almost no land-cover changes in the present treeless tundra.

In the context of growing societal demands for land-based products, crop production can be increased through expanding cropland or intensifying production on cultivated land. Intensification can allow sparing land for nature, but it can also drive further expansion of cropland, i.e. a rebound effect. Conversely, constraints on cropland expansion may induce intensification. We tested these hypotheses by investigating the bidirectional relationships between changes in cropland area and intensity, using a global cross-country panel dataset over 55 years, from 1961 to 2016. We used a cointegration approach with additional tests to disentangle long- and short-run causal relations between variables, and total factor productivity and yields as two measures of intensification. Over the long run we found support for the induced intensification thesis for low-income countries. In the short run, intensification resulted in a rebound effect in middle-income countries, which include many key agricultural producers strongly competitive in global agricultural commodity markets. This rebound effect manifested for commodities with high price-elasticity of demand, including rubber, flex crops (sugarcane, oil palm and soybean), and tropical fruits. Over the long run, strong rebound effects remained for key commodities such as flex crops and rubber. The intensification of staple cereals such as wheat and rice resulted in significant land sparing. Intensification in low-income countries, driven by increases in total factor productivity, was associated with a stronger rebound effect than yields increases. Agglomeration economies may drive yield increases for key tropical commodity crops. Our study design enables the analysis of other complex long- and short-run causal dynamics in land and social-ecological systems.