Table of contents

Effective solutions require integration of improved data, technology innovations, community engagement, and environmental justice.

Climate change adaptation responses are being developed and delivered in many parts of the world in the absence of detailed knowledge of their effects on public health. Here we present the results of a systematic review of peer-reviewed literature reporting the effects on health of climate change adaptation responses in low- and middle-income countries (LMICs). The review used the 'Global Adaptation Mapping Initiative' database (comprising 1682 publications related to climate change adaptation responses) that was constructed through systematic literature searches in Scopus, Web of Science and Google Scholar (2013–2020). For this study, further screening was performed to identify studies from LMICs reporting the effects on human health of climate change adaptation responses. Studies were categorised by study design and data were extracted on geographic region, population under investigation, type of adaptation response and reported health effects. The review identified 99 studies (1117 reported outcomes), reporting evidence from 66 LMICs. Only two studies were ex ante formal evaluations of climate change adaptation responses. Papers reported adaptation responses related to flooding, rainfall, drought and extreme heat, predominantly through behaviour change, and infrastructural and technological improvements. Reported (direct and intermediate) health outcomes included reduction in infectious disease incidence, improved access to water/sanitation and improved food security. All-cause mortality was rarely reported, and no papers were identified reporting on maternal and child health. Reported maladaptations were predominantly related to widening of inequalities and unforeseen co-harms. Reporting and publication-bias seems likely with only 3.5% of all 1117 health outcomes reported to be negative. Our review identified some evidence that climate change adaptation responses may have benefits for human health but the overall paucity of evidence is concerning and represents a major missed opportunity for learning. There is an urgent need for greater focus on the funding, design, evaluation and standardised reporting of the effects on health of climate change adaptation responses to enable evidence-based policy action.

Worldwide, Mediterranean cropping systems face the complex challenge of producing enough high-quality food while preserving the quantity and quality of scarce water for people and agriculture in the context of climate change. While good management of nitrogen (N) is paramount to achieving this objective, the efficient strategies developed for temperate systems are often not adapted to the specificities of Mediterranean systems. In this work, we combine original data with a thorough literature review to highlight the most relevant drivers of N dynamics in these semi-arid systems. To do so, we provide an analysis at nested scales combining a bottom-up approach from the field scale, with a top-down approach considering the agro-food system where cropping systems are inserted. We analyze the structural changes in the agro-food systems affecting total N entering the territory, the contrasting response of yields to N availability under rainfed and irrigated conditions in a precipitation gradient, the interaction between N management and climate change adaptation, the main drivers affecting the release of Nr compounds (nitrate, ammonia, nitric oxide and nitrous oxide) compared with temperate systems and finally, the behavior of N once exported to highly regulated river networks. We conclude that sustainable N management in Mediterranean cropping systems requires the specific adaptation of practices to particular local agro-environmental characteristics with special emphasis on water availability for rainfed and irrigated systems. This approach should also include a systemic analysis of N input into the territory that is driven by the configuration of the agro-food system.

The normative concepts of equity and justice are rising narratives within global climate change discourse. Despite growing considerations of climate equity and justice within the adaptation literature, the extent to which adaptation research has worked to empirically assess and operationalize concepts of equity and justice in practice remains unclear. We employ a systematic mapping approach to examine how equity and justice are defined and understood within empirical climate change adaptation research, and how extensively they are being assessed within adaptation literature. Structuring our work using a conceptual approach focusing on distributional, recognition, procedural, and capability approaches to justice, we document and review articles that included empirical assessments from searches performed in Web of Science™, Scopus®, and Google Scholar™ databases. Our results highlight that greater attention in the literature is given to certain aspects of justice (e.g. distributive and procedural justice concerns) on certain topics such as climate policy and adaptation finance. Most of the included papers scored highly according to our criteria on their empirical assessment of equity and justice. The lowest scores were found for the methodological rigor of assessments. We find limited research on empirical equity and justice assessment and call for a multiscale and holistic approach to justice to address this research gap.

Considering the feasibility and effectiveness of adaptation options is essential for guiding responses to climate change that reduce risk. Here, we assessed the feasibility of adaptation options for the African context. Using the Global Adaptation Mapping Initiative, a stocktake of adaptation-related responses to climate change from the peer-reviewed literature in 2013–2020, we found 827 records of adaptation actions in Africa. We categorised and evaluated 24 adaptation options and for each option, six dimensions of feasibility were considered: economic, environmental, social, institutional, technological, and evidence of effectiveness. Over half (51%) of all adaptation actions were reported in the food sector where sustainable water management (SWM) was the most reported option. The fewest actions were reported for cities (5%). The majority of actions (53%) were recorded in just six countries: Ghana, Ethiopia, Kenya, Tanzania, Nigeria and South Africa. Encouragingly, effectiveness was assessed as medium or high for 95% of adaptation options. However, no options had high feasibility on any other dimension. Technological and institutional factors present major barriers to implementation. Crop management, SWM, sustainable agricultural practices, agroforestry, livelihood diversification, ecosystem governance and planning, health governance and planning, infrastructure and built environment, all had moderate feasibility across three or more dimensions. Human migration has low feasibility but high potential for risk reduction. Major knowledge gaps exist for environmental feasibility, for assessing adaptation limits at increasing levels of climate hazard, for economic trade-offs and synergies, and for Central and Northern Africa. Our results highlight sectors where enablers for adaptation can be increased. Future assessments can apply the method established here to extend findings to other national and local levels.

Global greenhouse gas (GHG) emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review, we synthesise the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of GHG emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Overall, the literature and data emphasise that progress towards reducing GHG emissions has been limited. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene.

Climate change is increasingly recognized as a threat to global peace and security. This paper intends to provide a better understanding of the nature of interactions between climate change and events that undermine peace through a systematic review of recent literature. It highlights major methodological approaches adopted in the literature, elaborates on the geographic focus of the research at the nexus of climate change and peace, and provides further information on how various climatic stressors, such as extreme temperature, floods, sea-level rise, storms, and water stress may be linked to different events that undermine peace (e.g. civil conflict, crime, intercommunal violence, interstate conflict, political conflict, and social conflict) through direct and indirect pathways. Results confirm previous findings that statistical techniques and qualitative case studies are dominant methods in climate-conflict research but show that there has been an increase in the geographic information system based risk analyses and qualitative comparative analyses in the recent years. In line with previous reviews, results show that the literature is mainly focused on certain regions of the world and several major regions that have experienced numerous conflicts over the past few years and/or are vulnerable to adverse climatic events are understudied. However, a new finding is that, in the past few years, there has been an increasing focus on Asia, which contrasts with previous reviews that show an African focus in the literature. Also, there is an unbalanced attention to different climatic stressors and peace-related events. Interactions between water stress/extreme temperature and civil and interstate conflicts have received more attention. A major finding is that, only under certain conditions climatic stressors may act as driving forces or aggravating factors. In fact, there is a strong consensus that climate change is less likely to undermine peace in isolation from a wide range of contextual socio-economic and institutional factors such as political instability, poor governance, poverty, homogeneous livelihood structures, and ethnic fractionalization. However, such contextual factors can contribute to undermining peace via either direct or indirect pathways. The former may occur through direct psychological/physiological effects of climatic impacts or via competition over scarce resources. In contrast, in indirect pathways climate change may lead to conflict through diminishing livelihood capacities and/or inducing migration. In addition to synthesizing literature on contextual factors and direct/indirect pathways, the review identifies gaps that need further research.

Private lands are increasingly targeted for ecological restoration and conservation initiatives in high-income countries. However, the fragmented nature of private land tenure, the large number of landowners and their heterogeneous profiles can pose significant challenges for conservation initiatives. This can lead to a range in landowners' attitudes toward conservation initiatives, with some initiatives being received with resistance, and others with consent and participation. Most research dealing with social outcomes of conservation or restoration initiatives on private lands addresses regionally specific case studies, but few studies have attempted to derive general trends. To fill this gap, we performed a systematic literature review of conservation measures on private lands to develop a comprehensive typology of factors influencing the acceptance of conservation initiatives on private lands. Our results show that conservation agents (typically government agencies or NGOs), despite their limited power over individual factors of private landowners, can seek to encourage both the adoption and perceptions of conservation initiatives on private land through improving institutional interactions. We propose six recommendations to help support and design conservation programs on private lands and to identify intervention levers that may be acted upon to improve the social acceptance of such conservation initiatives.

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community.

Low-carbon lifestyles are key to climate change mitigation, biodiversity conservation, and keeping the Earth in a safe operating space. Understanding the global feasibility and drivers of low-carbon lifestyles requires large scale data covering various countries, demographic and socioeconomic groups. In this study, we use the audience segmentation data from Facebook's advertising platform to analyse the extent and drivers of interest in sustainable lifestyles, plant-based diets in particular, at a global level. We show that formal education level is the most important factor affecting vegetarianism interest, and it creates a sharper difference in low-income countries. Gender is a strong distinguishing factor, followed by national gross domestic product per capita and age. These findings enable upscaling local empirical studies to a global level with confidence for integrated assessments of low-carbon lifestyles. Future studies can expand this analysis of social media audience data to other consumption areas, such as household energy demand, and can also contribute to quantifying the psychosocial drivers of low-carbon lifestyles, such as personal and social norms.

Land–atmosphere interactions have an important influence on Amazon precipitation (P), but evaluation of these processes in climate models has so far been limited. We analysed relationships between Amazon P and evapotranspiration (ET) in the 5th Coupled Model Intercomparison Project models to evaluate controls on surface moisture fluxes and assess the credibility of regional P projections. We found that only 13 out of 38 models captured an energy limitation on Amazon ET, in agreement with observations, while 20 models instead showed Amazon ET is limited by water availability. Models that misrepresented controls on ET over the historical period projected both large increases and decreases in Amazon P by 2100, likely amplified by unrealistic land–atmosphere interactions. In contrast, large future changes in annual and seasonal-scale Amazon P were suppressed in models that simulated realistic controls on ET, due to modulating land–atmosphere interactions. By discounting projections from models that simulated unrealistic ET controls, our analysis halved uncertainty in basin-wide future P change. The ensemble mean of plausible models showed a robust drying signal over the eastern Amazon and in the dry season, and P increases in the west. Finally, we showed that factors controlling Amazon ET evolve over time in realistic models, reducing climate stability and leaving the region vulnerable to further change.

With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.

Brazil is currently the largest contributor of land use and land cover change (LULCC) carbon dioxide net emissions worldwide, representing 17%–29% of the global total. There is, however, a lack of agreement among different methodologies on the magnitude and trends in LULCC emissions and their geographic distribution. Here we perform an evaluation of LULCC datasets for Brazil, including those used in the annual global carbon budget (GCB), and national Brazilian assessments over the period 2000–2018. Results show that the latest global HYDE 3.3 LULCC dataset, based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps, can represent the observed spatial variation in LULCC over the last decades, representing an improvement on the HYDE 3.2 data previously used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than estimates based on MapBiomas. We use HYDE 3.3 and MapBiomas as input to a global bookkeeping model (bookkeeping of land use emission, BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine Brazil's LULCC emissions over the period 2000–2019. Results show mean annual LULCC emissions of 0.1–0.4 PgC yr⁻¹, compared with 0.1–0.24 PgC yr⁻¹ reported by the Greenhouse Gas Emissions Estimation System of land use changes and forest sector (SEEG/LULUCF) and by FAO in its latest assessment of deforestation emissions in Brazil. Both JULES-ES and BLUE now simulate a slowdown in emissions after 2004 (−0.006 and −0.004 PgC yr⁻² with HYDE 3.3, −0.014 and −0.016 PgC yr⁻² with MapBiomas, respectively), in agreement with the Brazilian INPE-EM, global Houghton and Nassikas book-keeping models, FAO and as reported in the 4th national greenhouse gas inventories. The inclusion of Earth observation data has improved spatial representation of LULCC in HYDE and thus model capability to simulate Brazil's LULCC emissions. This will likely contribute to reduce uncertainty in global LULCC emissions, and thus better constrains GCB assessments.

Reservoir operation causes spatiotemporal variations in outflow, which influence the dynamics of downstream aquatic communities. However, empirical evidence of community responses to hydrological alteration remains limited for dam-regulated rivers. This study focused on quantifying the streamflow disturbance to multi-population dynamics in downstream of the China's Danjiangkou Reservoir. First, the stochastic population dynamics model (PDM) was used to simulate aquatic community dynamics. Then, the flow–ecology relationship was established to identify community response to reservoir outflow. Third, two novel ecological indicators, stable time (ST) and coefficient of variation at stable time (CVST), were proposed to evaluate the resilience and resistance of multi-population systems, respectively. Finally, the reservoir operating rule curves were optimized by considering tradeoffs between socioeconomic and ecological objectives. The coevolution processes of multi-population systems (fish, phytoplankton, zooplankton, zoobenthos, and macrophytes) were simulated by stochastic PDMs. The population densities of stable states showed continuous downward trends with increasing degree of hydrological alteration for multi-population systems, and aquatic community systems could be destroyed when alteration reached its acceptable maximum. The greater the degree of hydrological alteration, the longer the recovery time from an unstable to a stable state, and the weaker resistance for each population system. The resilience and resistance of downstream multi-population systems were enhanced by optimizing reservoir outflow. The optimization results illustrated that the performances of the multiple objectives of water supply, hydropower generation, and ST were improved by 2.37%, 2.40%, and 2.67%, respectively, whereas the performance of CVST was the same as the conventional operation. The flow–ecology relationship provided an approach to quantify the impacts of reservoir outflow on an aquatic community, which is helpful in guiding ecological flow strategies.

Sustainable forest management and harvested wood products together can create a growing carbon sink by storing carbon in long-lived products. The role of wood products in climate change mitigation has been studied from several perspectives, but not yet from a consumer's view. In this study, we examine the impact of wooden housing on consumer carbon footprints in Finland. We use the 2016 Finnish Household Budget Survey and Exiobase 2015, a global multi-regional input-output model. The sample size is 3700 households, of which 45% live in a wooden house. We find that residents of wooden houses have a 12(±3)% (950 kg CO₂-eq/year) lower carbon footprint on average than residents of non-wooden houses, when income, household type, education of the main income provider, age of the house, owner-occupancy and urban zone are controlled in regression analysis. This is not fully explained by the impact of the construction material, which suggests that the residents of wooden houses may have some features in their lifestyles that lower their carbon footprints further. In addition, we find that an investment in a new wooden house in an urban area has a strong reducing impact on a consumer's carbon footprint, while investments in other types of housing have a weaker or no reducing impact. Our findings support wooden housing as a meaningful sustainable consumption choice.

Based on unfiltered daily Japanese 55 year reanalysis covering the 60 winters in 1958–2018, a new teleconnection pattern called the zonal North Pacific Oscillation (ZNPO) pattern has been detected. The ZNPO pattern describes a mass oscillation in the troposphere between the eastern and western North Pacific, persisting for a week or so. It is shown that the ZNPO pattern is a high-impact teleconnection pattern that brings the wintertime North Pacific and North America severe weather and hydroclimate events. It may cause rapid surface air temperature drop or rise over the northern North Pacific and North America, remarkable sea ice concentration anomalies over the northeastern Bering Sea, and strong convective anomalies in the lower troposphere over the eastern and western midlatitude North Pacific. The ZNPO pattern arises from two westward-moving geopotential height disturbances over the North Pacific and North America and is driven mainly by baroclinic energy conversion and feedback forcing by transient eddies. The baroclinic energy conversion acts to overcome the available potential energy (APE) loss caused by the heat flux of transient eddies and at the same time acts as a major kinetic energy (KE) source to maintain the ZNPO pattern. The barotropic feedback forcing by transient eddies acts as a major KE source to drive the ZNPO pattern during the growing stage of the ZNPO pattern and as a major KE sink to heavily damp the ZNPO pattern during the decaying stage.

Agricultural landscapes across the planet have replaced natural habitat with crop production that is less diverse at field and landscape scales. Loss of cropland heterogeneity can increase pest colonization rates and decrease predation rates, thereby exacerbating pest pressure and leading to increased use of pesticides. Linking landscape pattern, crop pest pressure, and pesticide use is emerging as critical step for understanding the benefits, and potential trade-offs, of diversified agriculture. We advance this work by exploring how cropland heterogeneity drives pesticide use, and whether this effect is modified by pesticide class (i.e. fungicide, herbicide or insecticide). We focus on a diverse growing region, California's Central Valley, and use spatial auto-regressive models to test for consistent class-based differences in the relationship between pesticide use and cropland heterogeneity (i.e. mean field size and landscape-level crop diversity). We find reduced pesticide use, in terms of both frequency and intensity of application, in diversified, spatially-heterogenous landscapes. Additionally, we see (a) more consistent responses of fungicides and insecticides to landscape pattern, (b) pesticide use increases as cropland becomes more homogenous with respect to crop identity, and (c) this effect is more consistent for perennial crops than annual crops. The modifying influence of pesticide class is largely consistent with expectations from ecological theory. Our results support increasing focus on whether enhancing the heterogeneity of the crop mosaic itself can benefit biodiversity, ecosystem services, and human well-being.

Ensuring the environmental integrity of internationally transferred mitigation outcomes, whether through offset arrangements, a market mechanism or non-market approaches, is a priority for the implementation of Article 6 of the Paris Agreement. Any conventional transferred mitigation outcome, such as an offset agreement, that involves exchanging greenhouse gases with different lifetimes can increase global warming on some timescales. We show that a simple 'do no harm' principle regarding the choice of metrics to use in such transactions can be used to guard against this, noting that it may also be applicable in other contexts such as voluntary and compliance carbon markets. We also show that both approximate and exact 'warming equivalent' exchanges are possible, but present challenges of implementation in any conventional market. Warming-equivalent emissions may, however, be useful in formulating warming budgets in a two-basket approach to mitigation and in reporting contributions to warming in the context of the global stocktake.

In Spring 2020, COVID-19 led to an unprecedented halt in public and economic life across the globe. In an otherwise tragic time, this provides a unique natural experiment to investigate the environmental impact of such a (temporary) 'de-globalization'. Here, we estimate the medium-run impact of a battery of COVID-19 related lockdown measures on air quality across 162 countries, going beyond the existing short-run estimates from a limited number of countries. In doing so, we leverage a new dataset categorizing lockdown measures and tracking their implementation and release, extending to 31 August 2020. We find that domestic and international lockdown measures overall led to a decline in PM2.5 pollution by 45% and 35%, respectively. This substantial impact persists in the medium-run, even as lockdowns are lifted, there is, however, substantial heterogeneity across different types of lockdown measures, different countries, and different sources of pollution. We show that some country trajectories are much more appealing (with fewer COVID-19 casualties, less economic downturn and bigger pollution reductions) than others. Our results have important policy implications and highlight the potential to 'build back better' a sustainable economy where pollution can be curbed in a less economically costly way than during the COVID-19 pandemic.

The U.S. Department of Agriculture's Conservation Reserve Program (CRP) is one of the largest private lands conservation programs in the United States, establishing perennial vegetation on environmentally sensitive lands formerly in agricultural production. Over its 35 year existence, the CRP has evolved to include diverse conservation practices (CPs) while concomitantly meeting its core goals of reducing soil erosion, improving water quality, and providing wildlife habitat. Ongoing threats to grasslands and decreased CRP acreage highlighted the need for a national evaluation of the effectiveness in providing the program's intended benefits. To address this need, we conducted edge-of-field surveys of erosional features, vegetation, and soil cover on 1 786 fields across 10 CPs and 14 central and western states from 2016 to 2018. We grouped practices into three types (grassland, wetland, and wildlife) and states into six regions for analysis. Across practice types, ⩾99% of fields had no evidence of rills, gullies, or pedestaling from erosion, and 91% of fields had <20% bare soil cover, with region being the strongest predictor of bare soil cover. Seventy-nine percent of fields had ⩾50% grass cover, with cover differing by practice type and region. Native grass species were present on more fields in wildlife and wetland practices compared to grassland practices. Forb cover >50% and native forb presence occurred most frequently in wildlife practices, with region being the strongest driver of differences. Federally listed noxious grass and forb species occurred on 23% and 61% of fields, respectively, but tended to constitute a small portion of cover in the field. Estimates from edge-of-field surveys and in-field validation sampling were strongly correlated, demonstrating the utility of the edge-of-field surveys. Our results provide the first national-level assessment of CRP establishment in three decades, confirming that enrolled wildlife and wetland practices often have diverse perennial vegetation cover and very few erosional features.

Numerous attempts have been made to understand the connection between extreme weather and Arctic amplification (AA), and it is still disputed whether the mechanism is the elongation and deceleration of planetary-scale waves. In this study, we provide further evidence that the Arctic's rapid warming is influencing extreme precipitation in the Northern Hermisphere based on observation and model outputs, and elucidate the underlying dynamic mechanisms. We find that AA has a significant positive correlation with extreme precipitation, both in the past (1901–2018) and in the future (by 2100). Moreover, observations reveal that, with the enhancement of AA since the mid-1980s, the planetary-scale wave amplitude has increased significantly at 0.66°/decade. This is associated with a negative Northern Hemisphere annular mode and an increased duration of weather patterns, resulting in more extreme precipitation events. Under Shared Socioeconomic Pathways SSP585 scenario, extreme precipitation increases by 8.7% along with wave amplitude increase of 7.9° by 2100.

Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in growing cities. Yet, effective air quality management is hindered by limited data. We deployed robust, low-cost and low-power devices in a large-scale measurement campaign and characterized within-city variations in fine particulate matter (PM_2.5) and black carbon (BC) pollution in Accra, Ghana. Between April 2019 and June 2020, we measured weekly gravimetric (filter-based) and minute-by-minute PM_2.5 concentrations at 146 unique locations, comprising of 10 fixed (∼1 year) and 136 rotating (7 day) sites covering a range of land-use and source influences. Filters were weighed for mass, and light absorbance (10⁻⁵m⁻¹) of the filters was used as proxy for BC concentration. Year-long data at four fixed sites that were monitored in a previous study (2006–2007) were compared to assess changes in PM_2.5 concentrations. The mean annual PM_2.5 across the fixed sites ranged from 26 μg m⁻³ at a peri-urban site to 43 μg m⁻³ at a commercial, business, and industrial (CBI) site. CBI areas had the highest PM_2.5 levels (mean: 37 μg m⁻³), followed by high-density residential neighborhoods (mean: 36 μg m⁻³), while peri-urban areas recorded the lowest (mean: 26 μg m⁻³). Both PM_2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM_2.5: 89 μg m⁻³) compared to non-Harmattan season (mean PM_2.5: 23 μg m⁻³). PM_2.5 at all sites peaked at dawn and dusk, coinciding with morning and evening heavy traffic. We found about a 50% reduction (71 vs 37 μg m⁻³) in mean annual PM_2.5 concentrations when compared to measurements in 2006–2007 in Accra. Ambient PM_2.5 concentrations in Accra may have plateaued at levels lower than those seen in large Asian megacities. However, levels are still 2- to 4-fold higher than the WHO guideline. Effective and equitable policies are needed to reduce pollution levels and protect public health.

Understanding plant responses to hydrological extremes is critical for projections of the future terrestrial carbon uptake, but much more is known about the impacts of drought than of extreme wet conditions. However, the latter may control ecosystem-scale photosynthesis more strongly than the former in certain regions. Here we take a data-driven, location-based approach to evaluate where wet and dry extremes most affect photosynthesis. By comparing the sensitivity of vegetation greenness during extreme wetness to that during extreme dryness over a 34 year record, we find that regions where the impact of wet extremes dominates are nearly as common as regions where drought impacts dominate. We also demonstrate that the responses of wet-sensitive regions are not uniform and are instead controlled by multiple, often interacting, mechanisms. Given predicted increases in the frequency and intensity of extreme hydrological events with climate change, the consequences of extreme wet conditions on local and global carbon cycling will likely be amplified in future decades.

Cloud feedback remains the largest source of uncertainty in equilibrium climate sensitivity (ECS). Many studies have attempted to narrow uncertainties in cloud feedback and ECS by proposing observable metrics with high skill at predicting future climate, referred to as emergent constraints. These constraints are often associated with clouds, convection, and circulation, and are interrelated. However, physical explanations for these connections remain unclear. Here, we propose a new mechanism relating convection and clouds across multiple climate models. Some models show overly active deep convection on daily timescales in the subtropical low cloud regions, which contributes to weaker subsidence inversion and smaller amounts of low-level clouds. Such models predict smaller shortwave (SW) cloud feedback. Using precipitation frequency in these regions as an emergent constraint, encapsulating this mechanism, models with lower SW cloud feedback (<0.50 W m⁻² °C⁻¹) are found to exhibit erroneously frequent convection. Our results suggest that further improvements in understanding and better modeling of cloud and convective systems are necessary for accurate climate predictions.

The rising temperature, altering precipitation, and increasing extreme events under climate warming affect the stability and sustainability of grassland ecosystems. The dynamics of above-ground biomass (AGB), below-ground biomass (BGB), and biomass partitioning (BGB:AGB ratio) of grasslands are of fundamental importance to understand their feedback to climate change. In this study, we used grassland productivity data extracted from the Oak Ridge National Laboratory Distributed Active Archive Center, Tennessee, USA, in which the AGB was collected within a 1.0 m × 0.25 m quadrat and the BGB was sampled within the center of the quadrat. Using multiple pairwise tests and Pearson's correlation analysis, we assessed the variations of grassland productivity and examined the response of single-harvest and annual biomass partitioning of C₃- and C₄-dominated grasslands to the growing-season and annual climatic variability and climate extremes in seven sites belonging to four ecoregions (i.e. cold steppe, humid temperate, humid savanna, and savanna). The results show that the annual and single-harvest BGB:AGB ratio varied significantly across the plant types and ecoregions. Overall, the C₃-dominated grasslands exhibited a higher BGB:AGB ratio than that of C₄-dominated grasslands. Growing-season temperatures (GSTs) were found to be the key determinants in explaining the single-harvest BGB:AGB ratio rather than growing-season precipitation. For instance, the single-harvest BGB:AGB ratio of C₄-dominated grasslands increased, while that of C₃-dominated grasslands decreased with elevated GSTs. The growing-season extreme dry climates significantly increased the single-harvest BGB:AGB ratio of C₄ plants by a large reduction of AGB, potentially affecting the ecosystem functioning and stability. The C₃-dominated grasslands in the cold steppe ecoregion are at great threat of drought-induced stress, as we observed that growing-season extreme dry climates reduced, albeit insignificantly, both the single-harvest AGB and BGB. This study provides key insights into factors influencing the biomass partitioning of C₃- and C₄-dominated grasslands and has important implications for assessing the grassland functioning and stability under increasing climate extremes.

More than 15% of global terrestrial area is under some form of protection and there is a growing impetus to increase this coverage to 30% by 2030. But not all protection is effective and the reasons some countries' protected areas (PAs) are more effective than others' are poorly understood. We evaluate the effectiveness of national PA networks established between 2000 and 2012 globally in avoiding forest loss, taking into account underlying deforestation threats using a combination of matching methods and cross-sectional regressions. We then assess which demographic, agricultural, economic, and governance factors are most strongly associated with national PA effectiveness using machine learning methods. We estimate that national PAs established between 2000 and 2012 reduced deforestation in those areas by 72%, avoiding 86 062 km² of forest loss. The effectiveness of national PAs varied by strictness of protection based on International Union for Conservation of Nature category. Strictly PAs reduced forest loss by 81% compared to what would have occurred without protection, while less strictly PAs reduced forest loss by 67%. Thus, the 26% of new PAs that were strictly protected contributed 39% of the total forest loss avoided within PAs between 2000 and 2012. If every country's PAs were as effective as the country with the most effective PAs within the same region, they would have increased the area of deforestation avoided by 38%, saving a further 119 082 km² of forest. Part of the variation in PA effectiveness across countries is explained by the placement of PA in areas facing higher deforestation threat. Countries with lower agricultural activity, higher economic growth and better governance are most strongly associated with greater country-level PA effectiveness.

espite a strong scientific consensus about the existence of anthropogenic climate change, widespread scepticism in the general population continues to exist. Past research has largely relied on self-reported behaviours or behavioural intentions when investigating downstream 'behavioural' consequences of climate change denial. As a consequence, there remains a large gap in the literature about how belief in climate change interacts with the pursuit of self-interested, environmentally harmful behaviours. To fill that gap, the present research uses a novel, experimental economic paradigm that allows to attach true environmental consequences to laboratory decisions. Based on ∼56 000 pollution decisions from 2273 participants in more than 30 countries, we find that belief in climate change meaningfully affects decision-making. Our results show that climate change scepticism predicts self-interested choices and showcases that sceptics have an insensitive acceptance of emissions, reaping benefits no matter how large the climate cost are or how small the personal benefits become. Therefore, our results critically augment meta-analytic evidence arguing that downstream behavioural consequences are small to medium in their effect size. We discuss the use of experimental economic paradigms as a crucial innovation tool for psychological research addressing people's willingness to engage in climate action.

Extreme precipitation and winds can have a severe impact on society, particularly when they occur at the same place and time. In this study the Met Office's Global Seasonal forecast system version 5 (GloSea5) model ensembles are evaluated against the reanalysis dataset ERA5, to find out how well they represent three hourly extreme precipitation, extreme wind and extreme co-occurring events over Europe. Although substantial differences in magnitude are found between precipitation and wind extremes between the datasets, the conditional probability of exceedance above the 99th percentile, which measures the co-occurrence between the two extremes, compares well spatially over Europe. However, significant differences in frequency are found around and over some areas of high topography. Generally GloSea5 underestimates this co-occurrence over sea. The model's co-occurring events at individual locations investigated occur with very similar synoptic patterns to ERA5, indicating that the compound extremes are produced for the correct reasons.

The Paris Agreement does not only stipulate to limit the global average temperature increase to well below 2 °C, it also calls for 'making finance flows consistent with a pathway towards low greenhouse gas emissions'. Consequently, there is an urgent need to understand the implications of climate targets for energy systems and quantify the associated investment requirements in the coming decade. A meaningful analysis must however consider the near-term mitigation requirements to avoid the overshoot of a temperature goal. It must also include the recently observed fast technological progress in key mitigation options. Here, we use a new and unique scenario ensemble that limit peak warming by construction and that stems from seven up-to-date integrated assessment models. This allows us to study the near-term implications of different limits to peak temperature increase under a consistent and up-to-date set of assumptions. We find that ambitious immediate action allows for limiting median warming outcomes to well below 2 °C in all models. By contrast, current nationally determined contributions for 2030 would add around 0.2 °C of peak warming, leading to an unavoidable transgression of 1.5 °C in all models, and 2 °C in some. In contrast to the incremental changes as foreseen by current plans, ambitious peak warming targets require decisive emission cuts until 2030, with the most substantial contribution to decarbonization coming from the power sector. Therefore, investments into low-carbon power generation need to increase beyond current levels to meet the Paris goals, especially for solar and wind technologies and related system enhancements for electricity transmission, distribution and storage. Estimates on absolute investment levels, up-scaling of other low-carbon power generation technologies and investment shares in less ambitious scenarios vary considerably across models. In scenarios limiting peak warming to below 2 °C, while coal is phased out quickly, oil and gas are still being used significantly until 2030, albeit at lower than current levels. This requires continued investments into existing oil and gas infrastructure, but investments into new fields in such scenarios might not be needed. The results show that credible and effective policy action is essential for ensuring efficient allocation of investments aligned with medium-term climate targets.

Technologies for carbon dioxide removal (CDR) from the atmosphere have been recognized as an important part of limiting warming to well below 2 °C called for in the Paris Agreement. However, many scenarios so far rely on bioenergy in combination with carbon capture and storage as the only CDR technology. Various other options have been proposed, but have scarcely been taken up in an integrated assessment of mitigation pathways. In this study we analyze a comprehensive portfolio of CDR options in terms of their regional and temporal deployment patterns in climate change mitigation pathways and the resulting challenges. We show that any CDR option with sufficient potential can reduce the economic costs of achieving the 1.5 °C target substantially without increasing the temperature overshoot. CDR helps to reduce net CO₂ emissions faster and achieve carbon neutrality earlier. The regional distribution of CDR deployment in cost-effective mitigation pathways depends on which options are available. If only enhanced weathering of rocks on croplands or re- and afforestation are available, Latin America and Asia cover nearly all of global CDR deployment. Besides fairness and sustainability concerns, such a regional concentration would require large international transfers and thus strong international institutions. In our study, the full portfolio scenario is the most balanced from a regional perspective. This indicates that different CDR options should be developed such that all regions can contribute according to their regional potentials.

The Nile River is a unique environmental system and essential water resource for its basin riparian nations. Population growth, changes in precipitation patterns, damming and usage rights disputes present extreme challenges in utilizing and managing the basin's primary water resource. These stress factors are of particular concern for highly populated Egypt, the furthest downstream recipient of the Nile's water flow. Previously, colonial agreements had granted Egypt and Sudan the majority of water use rights on the Nile without neighboring Ethiopia receiving any specific allocation. Today, Ethiopia plans to increase its energy production through its Nile-powered Grand Ethiopian Renaissance Dam (GERD). While the 74-billion cubic meter (BCM) dam presents promising development opportunities for Ethiopia, the Nile's altered flow will increase the existing water deficit for Egypt—the quantification and mitigation of which are still largely unconstrained and under intense debate. To address this deficiency, we estimate that the median total annual water budget deficit for Egypt during the filling period, considering seepage into the fractured rocks below and around the GERD reservoir, as well as the intrinsic water deficit and assuming no possible mitigation efforts by Egyptian authorities, will be ∼31 BCM yr⁻¹, which would surpass one third of Egypt's current total water budget. Additionally, we provide a feasibility index for the different proposed solutions to mitigate the above deficit and assess their economic impact on the GDP per capita. Our results suggest that the unmet annual deficit during the filling period can be partially addressed by adjusting the Aswan High Dam (AHD) operation, expanding groundwater extraction and by adopting new policies for cultivation of crops. If no prompt mitigation is performed, the short-term three-year filling scenario would generate a deficit that is equivalent to losses to the present cultivated area by up to 72% resulting in a total loss of the agricultural GDP by $51 billion during the above-mentioned filling period. Such figures are equivalent to a decrease in the total national GDP per capita by ∼8%, augmenting existing unemployment rates by 11%, potentially leading to severe socioeconomic instability.

How relative humidity is changing is important for our understanding of future changes in precipitation and evaporation. For example, decreases in relative humidity have the potential to increase evaporation and evapotranspiration increasing water scarcity. Since projected precipitation changes are highly uncertain, there is significant research relating precipitation changes to more certain local temperature increases, but such research often assumes relative humidity will remain constant. Here, we investigate how absolute and relative humidity across Australia have changed over 1955–2020. Absolute humidity, measured by dew point temperature, has remained relatively constant, while relative humidity has decreased on average over land by approximately −1%/decade. This suggests that assuming constant relative humidity when predicting future extreme precipitation using temperature or absolute humidity associations may result in over-estimation of future extreme precipitation intensities. As absolute humidity, measured by dew point temperature, was found to be relatively constant, we conclude the decrease in relative humidity is not due to a lack of water available for evaporation but may instead be the result of evaporation not increasing in line with temperature increases.

Although the prediction of the Indian Summer Monsoon (ISM) onset is of crucial importance for water-resource management and agricultural planning on the Indian sub-continent, the long-term predictability—especially at seasonal time scales—is little explored and remains challenging. We propose a method based on artificial neural networks that provides skilful long-term forecasts (beyond 3 months) of the ISM onset, although only trained on short and noisy data. It is shown that the meridional tropospheric temperature gradient in the boreal winter season already contains the signals needed for predicting the ISM onset in the subsequent summer season. Our study demonstrates that machine-learning-based approaches can be simultaneously helpful for both data-driven prediction and enhancing the process understanding of climate phenomena.

Gridded population projections constitute an essential input for climate change impacts, adaptation, and vulnerability (IAV) assessments as they allow for exploring how future changes in the spatial distribution of population drive climate change impacts. We develop such spatial population projections, using a gravity-based modeling approach that accounts for rural-urban and inland-coastal migration as well as for spatial development patterns (i.e. urban sprawl). We calibrate the model (called CONCLUDE) to the socioeconomically diverse Mediterranean region, additionally considering differences in socioeconomic development in two geographical regions: the northern Mediterranean and the southern and eastern Mediterranean. We produce high-resolution population projections (approximately 1 km) for 2020–2100 that are consistent with the Shared Socioeconomic Pathways (SSPs), both in terms of qualitative narrative assumptions as well as national-level projections. We find that future spatial population patterns differ considerably under all SSPs, with four to eight times higher urban population densities and three to 16 times higher coastal populations in southern and eastern Mediterranean countries compared to northern Mediterranean countries in 2100. In the South and East, the highest urban density (8000 people km⁻²) and coastal population (107 million) are projected under SSP3, while in the North, the highest urban density (1500 people km⁻²) is projected under SSP1 and the highest coastal population (15.2 million) under SSP5. As these projections account for internal migration processes and spatial development patterns, they can provide new insights in a wide range of IAV assessments. Furthermore, CONCLUDE can be extended to other continental or global scales due to its modest data requirements based on freely available global datasets.

In the European Alps, air temperature has increased almost twice as much as the global average over the last century and, as a corollary, snow cover duration has decreased substantially. In the Arctic, dendroecological studies have evidenced that shrub growth is highly sensitive to temperature—this phenomenon has often been linked to shrub expansion and ecosystem greening. Yet, the impacts of climate change on mountain shrub radial growth have not been studied with a comparable level of detail so far. Moreover, dendroecological studies performed in mountain environments did not account for the potential modulation and/or buffering of global warming impacts by topography, despite its possible crucial role in complex alpine environments. To fill this gap, we analyzed a network of eight sites dominated by the dwarf shrub Rhododendron ferrugineum. The sites selected for analysis represent the diversity of continentality, elevation and slope aspect that can be found across the French Alps. We quantified annual radial increment growth for 119 individuals, assembled meteorological reanalyzes specifically accounting for topographic effects (elevation, slope and aspect) and assessed climate-growth relations using a mixed modeling approach. In agreement with a vast majority of dendroecological work conducted in alpine and arctic environments, we find that the number of growing degree days during the snow-free period snow-free growing degree days (SFGDDs) is a strong and consistent driver of R. ferrugineum growth across all sites since 1960 until the late 1980s. We also document a marked loss of sensitivity of radial growth to increasing SFGDD since the 1990s, with this decoupling being more pronounced at the driest sites. Our observations of the spatial and temporal variability of shrub sensitivity to limiting factors can be compared to the 'divergence' problem observed in tree-ring series from circumpolar and alpine regions and, accordingly, sheds light on possible future trajectories of alpine shrub growth in response to ongoing climate change.

Forecasting tropical cyclone (TC) activities has been a topic of great interest and research. Many studies and existing seasonal forecasting models have examined and predicted the number of TCs (including geneses and landfalls) mainly based on the environmental factors in the peak TC season. However, these predictions can be time-consuming, computationally expensive and uncertain, depending on the efficiency and predictability of the dynamical models. Therefore, here we propose an effective statistical seasonal forecasting model, namely the Sun Yat-sen University (SYSU) Model, for predicting the number of TCs (intensity at tropical storm or above) over the western North Pacific based on the environmental factors in the preseason. The nine categories comprising 103 candidate predictors in 1980–2015 (36 years) are systematically investigated. The best subset selection regression shows that the sea surface temperatures at the tropical North Atlantic and eastern North Pacific in April, the 500 hPa geopotential height difference between April and January at the open ocean southwest of Australia and the 700 hPa geopotential height at the North Pacific in April are the most significant predictors. The correlation coefficient between the modeled results and observations reaches 0.89. The model is successfully validated by leave-one-out, nine-fold cross-validations, and later 5 year (2016–2020) observations. The prediction of the SYSU Model exhibits a 95% hit rate in 1980–2020 (39 out of 41), suggesting an operational potential in the seasonal forecasting of TCs over the western North Pacific.

A better understanding of the dominant climatic drivers that control vegetation trends across regions and biomes is essential for assessing ecosystem dynamics and land-climate interactions in a warming world. Temperature (TMP) has long been considered as dominant control in global vegetation trends, and growing evidence suggests that water availability plays an increasingly important role in determining trends in vegetation growth over many biomes. However, a detailed spatial-temporal evolution of the vegetation trends and the climatic drivers that effect vegetation trends are not well known. In this study, using a time-varying trend (extracted by the ensemble empirical mode decomposition) of climate and satellite-derived normalized difference vegetation index (as a proxy for vegetation productivity) from 1981 to 2015, we find that the trends in vegetation greening and terrestrial carbon uptake reversed, beginning in the early 2000s, largely driven by the recent drying trend. The relative importance of climatic controls on vegetation productivity trend is estimated using a principal component analysis procedure, and the results demonstrate a global shift in the dominant driver of vegetation trends from TMP to precipitation, and point to intensified water limitation to vegetation growth as warming continues. The findings provide empirical evidence of the spatial-temporal evolution of different climatic drivers behind trends in vegetation productivity.

Several studies investigated the possible impacts of the restriction measures related to the containment of the spread of the COrona VIrus Disease (COVID-19) to atmospheric ozone (O₃) at global, regional, and local scales during 2020. O₃ is a secondary pollutant with adverse effects on population health and ecosystems and with negative impacts on climate, acting as greenhouse gas. Most of these studies focused on spring 2020 (i.e. March–May) and on observations in the planetary boundary layer (PBL), mostly in the vicinity of urban agglomerates. Here, we analyzed the variability of O₃ above the PBL of northern Italy in 2020 by using continuous observations carried out at a high mountain WMO/GAW global station in Italy (Mt. Cimone–CMN; 44°12' N, 10°42' E, 2165 m a.s.l.). Low O₃ monthly anomalies were observed during spring (MAM) and summer (JJA), when periods of low O₃ intertwined with periods with higher O₃, within climatological ranges. A similar variability was observed for O₃ precursors like NO₂ and 15 anthropogenic non-methane volatile organic carbons, but the systematic O₃ anomalies were not reflected in these variables. The analysis of meteorological variables and diel O₃ cycles did not suggest major changes in the vertical transport related to the thermal circulation system in the mountain area. The analysis of five days back-trajectories suggested that the observed O₃ anomalies cannot be explained by differences in the synoptic-scale circulation with respect to the previous years alone. On the other hand, the characterization of two transport patterns (i.e. air masses from the regional PBL or from the free troposphere) and the analysis of back-trajectories suggested an important contribution of transport from the continental PBL during the periods with the lowest O₃ at CMN. When proxies of air mass transport from the regional PBL are considered, a lower NO_x content was pointed out with respect to the previous years, suggesting a lower O₃ production in a NO_x-limited atmosphere. Our study suggested for the first time that, during MAM and JJA 2020, the reduced anthropogenic emissions related to the COVID-19 restrictions lowered the amount of this short-lived climate forcer/pollutant at remote locations above the PBL over northern Italy. This work suggests the importance of limiting anthropogenic precursor emissions for decreasing the O₃ amount at remote locations and in upper atmospheric layers.

Land surface models are used to provide global estimates of soil organic carbon (SOC) changes after past and future change land use change (LUC), in particular re-/deforestation. To evaluate how well the models capture decadal-scale changes in SOC after LUC, we provide the first consistent comparison of simulated time series of LUC by six land models all of which participated in the coupled model intercomparison project phase 6 (CMIP6) with soil carbon chronosequences (SCCs). For this comparison we use SOC measurements of adjacent plots at four high-quality data sites in temperate and tropical regions. We find that initial SOC stocks differ among models due to different approaches to represent SOC. Models generally meet the direction of SOC change after reforestation of cropland but the amplitude and rate of changes vary strongly among them. The normalized root mean square errors of the multi model mean range from 0.5 to 0.8 across sites and 0.1–0.7 when excluding outliers. Further, models simulate SOC losses after deforestation for crop or grassland too slow due to the lack of crop harvest impacts in the models or an overestimation of the SOC recovery on grassland. The representation of management, especially nitrogen levels is important to capture drops in SOC after land abandonment for forest regrowth. Crop harvest and fire management are important to match SOC dynamics but more difficult to quantify as SCC rarely report on these events. Based on our findings, we identify strengths and propose potential improvements of the applied models in simulating SOC changes after LUC.

The mitigation of dangerous climate change requires massive investments in low-carbon technologies. Accordingly, the redirection of finance flows is a key objective of the Paris Agreement, and countries have started to enact policies to influence financial actors to this end. However, transparency on such policies is low, and it is hardly possible to compare policy activity internationally. To fill this gap, here we present a comparative analysis of green financial policy output in OECD countries from 2001 to 2019, based on a newly compiled inventory of 136 policies from 29 countries and the European Union. We show that policy output accelerated rapidly since the Paris Agreement, with countries implementing 3.3 policies on average using various governing resources. Key instruments include carbon disclosure requirements, low-carbon investment policies for public funds, and green state investment banks. However, there are huge differences in policy output between countries, and some countries that host important financial centers have implemented few policies to date. On the basis of our results, we develop a research agenda to deepen the understanding of this important but little-studied area of climate policies.

As the largest emitter in the world, China recently pledged to reach a carbon peak before 2030 and carbon neutrality before 2060, which could accelerate the progress of mitigating negative climate change effects. In this study, we used the Minimum Complexity Earth Simulator and a semi-empirical statistical model to quantify the global mean temperature and sea-level rise (SLR) response under a suite of emission pathways that are constructed to cover various carbon peak and carbon neutrality years in China. The results show that China will require a carbon emission reduction rate of no less than 6%/year and a growth rate of more than 10%/year for carbon capture capacity to achieve carbon neutrality by 2060. Carbon peak years and peak emissions contribute significantly to mitigating climate change in the near term, while carbon neutrality years are more influential in the long term. Mitigation due to recent China's pledge alone will contribute a 0.16 °C–0.21 °C avoided warming at 2100 and also lessen the cumulative warming above 1.5 °C level. When accompanied by coordinated international efforts to reach global carbon neutrality before 2070, the 2 °C target can be achieved. However, the 1.5 °C target requires additional efforts, such as global scale adoption of negative emission technology for CO₂, as well as a deep cut in non-CO₂ GHG emissions. Collectively, the efforts of adopting negative emission technolgy and curbing all greenhouse gas emissions will reduce global warming by 0.9 °C −1.2 °C at 2100, and also reduce SLR by 49–59 cm in 2200, compared to a baseline mitigation pathway already aiming at 2 °C. Our findings suggest that while China's ambitious carbon-neutral pledge contributes to Paris Agreement's targets, additional major efforts will be needed, such as reaching an earlier and lower CO₂ emission peak, developing negative emission technology for CO₂, and cutting other non-CO₂ GHGs such as N₂O, CH₄, O₃, and HFCs.

Between 1970 and 2015 urban population almost doubled worldwide with the fastest growth taking place in developing regions. To aid the understanding of how urbanisation has influenced anthropogenic CO₂ and air pollutant emissions across all world regions, we make use of the latest developments of the Emissions Database for Global Atmospheric Research. In this study, we systematically analyse over 5 decades of emissions from different types of human settlements (from urban centres to rural areas) for different sectors in all countries. Our analysis shows that by 2015, urban centres were the source of a third of global anthropogenic greenhouse gases and most of the air pollutant emissions. The high levels of both population and emissions in urban centres therefore call for focused urban mitigation efforts. Moreover, despite the overall increase in urban emissions, megacities with more than 10 million inhabitants in high-income countries have been reducing their emissions, while emissions in developing regions are still growing. We further discuss per capita emissions to compare different types of urban centres at the global level.

As storm-driven coastal flooding increases under climate change, wetlands such as saltmarshes are held as a nature-based solution. Yet evidence supporting wetlands' storm protection role in estuaries—where both waves and upstream surge drive coastal flooding—remains scarce. Here we address this gap using numerical hydrodynamic models within eight contextually diverse estuaries, simulating storms of varying intensity and coupling flood predictions to damage valuation. Saltmarshes reduced flooding across all studied estuaries and particularly for the largest—100 year—storms, for which they mitigated average flood extents by 35% and damages by 37% ($8.4 M). Across all storm scenarios, wetlands delivered mean annual damage savings of $2.7 M per estuary, exceeding annualised values of better studied wetland services such as carbon storage. Spatial decomposition of processes revealed flood mitigation arose from both localised wave attenuation and estuary-scale surge attenuation, with the latter process dominating: mean flood reductions were 17% in the sheltered top third of estuaries, compared to 8% near wave-exposed estuary mouths. Saltmarshes therefore play a generalised role in mitigating storm flooding and associated costs in estuaries via multi-scale processes. Ecosystem service modelling must integrate processes operating across scales or risk grossly underestimating the value of nature-based solutions to the growing threat of storm-driven coastal flooding.

Changes in heavy precipitation frequency can be viewed as a change in the event return period, a common metric of risk. Compared with intensity, frequency changes are less well-studied and past work has largely been constrained to analysis of well-instrumented regions. We exploit the latest ERA5 reanalysis and its global hourly accumulations at 1/4° spatial resolution and apply a metric that captures frequency changes across both wet and dry regions. According to ERA5 and in a global average sense, during 1989–2018, hourly events that occurred once per year in 1979–1988 increased in frequency by 71 (53–93, 95% range) %, while the one day per year heavy event frequency increased by 44 (37–54) %. Thus, hourly events that occurred once per year in the baseline decade are on track to double in frequency by 2021–2030, and the daily events by 2047–2056. Furthermore, our results replicate prior findings that relative frequency increases are larger for increasingly rare events, and for the first time we quantify that mean frequency increases have been greater over ocean than land. Ocean increases are larger by factors of 3.0 and 2.1 for the hourly and daily events that occurred once per year in 1979–1988, respectively.

Marine heatwaves (MHWs), which are characterized by extremely warm water, can harm the marine ecosystem and fishing industry; improving the prediction of such events could reduce their harmful impact. In this study, we examined MHWs occurring in the North Pacific in winter/early spring, and their relationship with North Pacific subtropical mode water (STMW), based on the data analysis and numerical experiments. The time-lagged correlation between the cumulative intensity of MHWs and volume of STMW in March of each year suggests that STMW can modulate MHWs for up to three years after its formation. A patch of statistically significant negative correlation initially appeared in the formation region of the STMW but was found to the east of it near the Transition Zone Chlorophyll Front (TZCF) after one year. This patch stagnated near this remote site in the second winter and early spring. Passive tracer experiments using a numerical model indicate that the STMW, formed near the Kuroshio Extension in March, moves to the east underneath the mixed layer and is entrained to the surface in the following winter while altering the properties of STMW. The STMW reemerges in the second winter, after stagnating under the mixed layer near the TZCF. This suggests that the reemergence of STMW can suppress MHWs in the North Pacific during winter and early spring by reducing the sea surface temperature; if the volume of STMW is anomalously low, there is a greater likelihood of the occurrence of MHWs near the TZCF in the following two winters and early springs. Our results indicate that understanding STMW formation is crucial for predicting MHWs in the North Pacific Ocean during winter and early spring.

Earth's lakes vary greatly in size and morphometry, from small circular lakes of hundreds of m² to large and deep fractal systems of several thousands of km². Previous research has demonstrated a link between the size of lakes and their carbon dynamics. However, the influence of lake morphometry on lake carbon biogeochemistry remains largely unexplored. Here, we analyze the morphometry and carbon concentrations of more than 250 lakes across boreal Quebec, encompassing a wide range in lake size from 0.002 to 4300 km². We show that, in addition to lake size, the biogeochemistry of lake carbon is influenced by the circularity, shoreline complexity and vertical profile of the lake. Yet the type and degree of influence vary among the different carbon species. A comparative exercise shows that taking into account the morphometry of lakes moderately increases the predictive power of empirical models of carbon concentration across lakes. Therefore, future studies might benefit from adding lake morphometry metrics to the empirical rules used for prediction and upscaling.

Globally about 800 million people live without electricity at home, over two thirds of which are in sub-Saharan Africa. Planning electricity access infrastructure and allocating resources efficiently requires a careful assessment of the diverse energy needs across space, time, and sectors. Because of data scarcity, most country or regional-scale electrification planning studies have however assumed a spatio-temporally homogeneous (top-down) potential electricity demand. Poorly representing the heterogeneity in the potential electricity demand across space, time, and energy sectors can lead to inappropriate energy planning, inaccurate energy system sizing, and misleading cost assessments. Here we introduce M-LED, a Multi-sectoral Latent Electricity Demand geospatial data processing platform to estimate electricity demand in communities that live in energy poverty. The platform shows how big data and bottom-up energy modelling can be leveraged together to represent the potential electricity demand with high spatio-temporal and sectoral granularity. We apply the methodology to Kenya as a country-study and devote specific attention to the implications for water-energy-agriculture-development interlinkages. A more detailed representation of the demand-side in large-scale electrification planning tools bears a potential for improving energy planning and policy.

We introduce the concept of time of emergence of economic impacts (ToEI), which identifies the initial moment when the climate change impact signal exceeds a previously defined threshold of past economic output shocks in a given geographic area. We compute the ToEI using probabilistic climate change projections and impact functions from three integrated assessment models of climate change: DICE, RICE and CLIMRISK. Our results demonstrate that, in terms of the business-as-usual carbon emissions scenario, the global economy could reach its ToEI by 2095. Regional results highlight areas that are likely to reach the ToEI sooner, namely Western Europe by 2075, India by 2083, and Africa by 2085. We also explore local-scale variations in the ToEI demonstrating that, for example, Paris already reached the ToEI around 2020, while Shanghai will reach it around 2080. We conclude that the ToEI methodology can be applied to impact models of varying scales when sufficient historical impact data are available. Moreover, unprecedented impacts of climate change in the 21st century may be experienced even in economically developed regions in the US and Europe. Finally, moderate to stringent climate change mitigation policies could delay the extreme economic impacts of climate change by three decades in Latin America, the Middle East, and Japan, by two decades in India, Western Europe, and the US, and by one decade in Africa. Our results can be used by policymakers interested in implementing timely climate policies to prevent potentially large economic shocks due to climate change.

Support for climate policy depends on the public's perception of climate change costs. Assessing the determinants of climate change attitudes contributes to explaining cross-country differences in climate policy implementation. In this paper, I examine the influence of experience with a political system on individuals' concern for the consequences of climate change. To address biases introduced by the endogeneity of the political system, I use the natural experiment created by the division and reunification of Germany. I find evidence suggesting that experience with the political system of East Germany has a lasting negative effect on climate change concern that is discernible more than 20 years after reunification. Results suggest that the influence of political institutions on climate change attitudes and policy adoption can persist long after they have been replaced.

Soil moisture performs a key function in the hydrologic process and understanding the global-scale water cycle. However, estimations of soil moisture taken from current sun-synchronous orbit satellites are limited in that they are neither spatially nor temporally continuous. This limitation creates discontinuous soil moisture observation from space and hampers our understanding of the fundamental processes that control the surface hydrologic cycle across both time and space domains. Here, we propose to use frequent soil moisture observations from NASA's constellation of eight micro-satellites called the Cyclone Global Navigation Satellite System (CYGNSS) together with the Soil Moisture Active Passive (SMAP) to assimilate subdaily scale soil moisture into a land surface model (LSM). Our results, which are based on triple collocation analysis (TCA), show how current scientific advances in satellite systems can fill previous gaps in soil moisture observations in subdaily scale by past observations, and eventually adds value to improvements in global scale soil moisture estimates in LSMs. Overall, TCA-based fractional mean square errors of LSM soil moisture are improved by 61.3% with the synergetic assimilation of CYGNSS data with SMAP soil moisture observations. However, assimilating satellite-based soil moisture over dense vegetation areas can degrade the performance of LSMs as these areas propagate erroneous soil moisture information to LSMs. To our knowledge, this study is the first global assimilation of GNSS-based soil moisture observations in LSMs.

Recent observations indicate that two cryospheric components, namely the Antarctic sea ice and ice shelf over the Southern Ocean, have been changing over the decades. Here we analyze results from an ocean–sea ice–ice shelf model to examine variability in the Antarctic sea-ice extent and ice-shelf basal melting. The model reproduces seasonal and interannual variability in the Antarctic sea-ice extent and demonstrates that summertime ice-shelf basal melting is closely anti-correlated with the sea-ice extent anomaly. For example, the unprecedented minimum of the Antarctic sea-ice extent in the 2016 spring was accompanied by a substantial increase in the Antarctic ice-shelf melting in the model. Detailed analysis of Antarctic coastal water masses flowing into the ice-shelf cavities illustrates the physical linkage in the strong anti-correlation. This study suggests that the Antarctic summer sea-ice extent in the regions where the sea-ice edge approaches the Antarctic coastline can be a proxy for Antarctic coastal water masses and subsequent ice-shelf basal melting.

We quantify the impact of nutrient (nitrogen and phosphorus) limitation on land carbon uptake and the sensitivity of this uptake to atmospheric carbon dioxide (CO₂) concentrations (land carbon-concentration feedback) and surface air temperature (land-carbon climate feedback). We analyse simulations of the Australian Earth System Model (ACCESS-ESM1.5) with a prescribed 1% yr⁻¹ CO₂ increase in three different configurations: (1) without nutrient limitation, (2) with nitrogen limitation only and (3) with nitrogen and phosphorus limitation combined. Our results suggest a reduction in land carbon uptake and feedback strength of about 30% by including nitrogen limitation only. This reduction agrees well with the ensemble mean of models with and without nitrogen limitation from the sixth Coupled Model Intercomparison Project (CMIP6). By adding phosphorus limitation to our model, the response is even stronger with a further 50% reduction for the carbon-concentration feedback and a further 75% reduction for the carbon-climate feedback. We find that the feedback strength in all three nutrient configurations is mostly determined by vegetation productivity (i.e. Net Primary Production) with little contribution from soil heterotrophic respiration. Our simulations show that nutrient limitation has the largest impact in the northern mid latitudes (around 50^∘), reducing land carbon uptake by about 50% when accounting for nitrogen and phosphorus limitation combined. The strong response of land carbon uptake and feedback strength to nutrients in our model simulations underlines the importance of including both nitrogen and phosphorus limitation in other Earth System Models in order to derive better estimates of future land carbon uptake and to assess the effectiveness of land-based carbon mitigation.

Wetting in the south while drying in the north during the last few decades constitutes the well-known 'southern flood–northern drought' (SFND) precipitation pattern over eastern China. The fingerprint of anthropogenic influence on this dipole pattern of regional precipitation trends has not been confirmed, especially for forced changes in relevant dynamics at the synoptic scale. Using a process-based approach involving model experiments both with and without anthropogenic inputs, it is demonstrated that the occurrences of daily circulation patterns (CPs) governing precipitation over eastern China during 1961–2013 have been altered by human influence. Due to anthropogenic forcing, CPs favoring SFND have become more likely to occur at the expense of CPs unfavorable to SFND. Regression analysis shows that changes recorded in the occurrence of CPs from the factual simulations could explain a large part of the precipitation trends over eastern China. CP frequencies driven by purely natural forcing do not reproduce this dipole pattern nor the inferred magnitude of precipitation trends over eastern China. These results suggest that human influence has played a critical role in shaping the contrasting north–south precipitation trends.

Global crop production and population distributions have undergone great changes under climate change and socioeconomic development, and have drawn considerable public attention. How to explain the similarity of the migration patterns of crop yield and population density for different countries/regions is still uncertain and worth studying. Here, we estimated the similarity between migrations of main crop caloric yield (i.e. maize, rice, wheat, and soybean) and population density using Fréchet distance, and investigated the regression relationship between Fréchet distance and related climatic and socioeconomic variables for countries/regions with different economic development stages. The results indicated that different countries/regions showed different Fréchet distances during 2000–2015, with a maximum value of 24.44 for Russia and a minimum value of 0.11 for Georgia. For countries/regions with different economic development stages, the built regression models can explain 39%–93% of the variability in the Fréchet distance. Log(land area), log(GDP), and log(land area under cereal production) were always included in regression models and had higher importance in explaining the variability of Fréchet distance. For the model for all countries/regions, both the log(land area) and log(GDP per capita) may positively link to the Fréchet distance. Possible reasons for these results are that countries/regions with high GDP (or GDP per capita) may ease the conflict of land resources between humans and crops to achieve agricultural industrialization, which causes the far connection of the migrations for crop caloric yield and population density. The complicated interactions of crop production, population dynamic, and socioeconomic development should be given greater attention in the future.

The intensification of crop production has been identified as one of the major drivers of environmental degradation. While significant advances could still be made with more widespread adoption of sustainable intensification technologies that address the agronomic efficiency of nitrogen fertilizers, the dynamic use of agricultural land across seasons and associated crop-specific responses to fertilizer applications have so far been largely overlooked. This paper explores the potential for improving the economic-environmental performance of crop production through spatially integrated modeling and optimization, as applied to Bangladesh. Results show that per-billion-Taka nitrogen loss from soil would decline by 83% from the baseline level through factoring in crop-specific, seasonal and spatial variations in crop nitrogen-use efficiency and nitrogen transport. The approach should complement other policy analysis and decision-support tools to assess alternative options for maximizing the positive outcomes of nitrogen fertilizers with regard to farm income and food security, while maintaining environmental sustainability.

Plant growth and distribution in high-latitude tundra ecosystems is strongly limited by nutrient availability and is critical for quantifying centennial-scale carbon-climate interactions. However, land model representations of plant–nutrient interactions are uncertain, leading to poor comparisons with high-latitude observations. Although it has been recognized for decades in the observational community that plants continue to acquire nutrients well past when aboveground activity has ceased, most large-scale land models ignore this process. Here we address the role tundra plant nutrient acquisition during the non-growing season (NGS) has on centennial-scale vegetation growth and dynamics, with a focus on shrub expansion. We apply a well-tested mechanistic model of coupled plant, microbial, hydrological, and thermal dynamics that explicitly represents nutrient acquisition based on plant and microbial traits, thereby allowing a prognostic assessment of NGS nutrient uptake. We first show that the model accurately represents observed seasonality of NGS plant nutrient uptake in a northern Alaskan tundra site. Applying the model across the North America tundra indicates that NGS nutrient uptake is consistent with observations and ranges between ∼5% and 50% of annual uptake, with large spatial variability and dependence on plant functional type. We show that NGS plant nutrient acquisition strongly enhances modeled 21^st century tundra shrub growth and expansion rates. Our results suggest that without NGS nutrient uptake, total shrub aboveground dominance would be ∼50% lower, limited primarily by their inability to grow tall enough to maximize their inherent capacity for light competition. Evergreen shrubs would be more strongly affected because of their relatively lower capacity for nutrient remobilization and acquisition compared to deciduous shrubs. Our results highlight the importance of NGS plant and soil processes on high-latitude biogeochemistry and vegetation dynamics and motivate new observations and model structures to represent these dynamics.

Peatland pole forest is the most carbon-dense ecosystem in Amazonia, but its spatial distribution and species composition are poorly known. To address this knowledge gap, we quantified variation in the floristic composition, peat thickness, and the amount of carbon stored above and below ground of 102 forest plots and 53 transects in northern Peruvian Amazonia. This large dataset includes 571 ground reference points of peat thickness measurements across six ecosystem types. These field data were also used to generate a new land-cover classification based on multiple satellite products using a random forest classification. Peatland pole forests are floristically distinctive and dominated by thin-stemmed woody species such as Pachira nitida (Malvaceae), Platycarpum loretense (Rubiaceae), and Hevea guianensis (Euphorbiaceae). In contrast, palm swamps and open peatlands are dominated by Mauritia flexuosa (Arecaceae). Peatland pole forests have high peat thickness (274 ± 22 cm, mean ± 95% CI, n = 184) similar to open peatlands (282 ± 46 cm, n = 46), but greater than palm swamps (161 ± 17 cm, n = 220) and seasonally-flooded forest, terra firme, and white-sand forest where peat is rare or absent. As a result, peatland pole forest has exceptional carbon density (1,133 ± 93 Mg C ha⁻¹). The new sites expand the known distribution of peatland pole forest by 61% within the Pastaza-Marañón Foreland basin, mainly alongside the Tigre river, to cover a total of 7540 km² in northern Peruvian Amazonia. However, only 15% of the pole forest area is within a protected area, whilst an additional 26% lies within indigenous territories. The current low levels of protection and forest degradation but high threat from road paving projects makes the Tigre river basin a priority for conservation. The long-term conservation of peatland pole forests has the potential to make a large contribution towards international commitments to mitigate climate change.

Crop productivity is potentially affected by several air pollutants, although these are usually studied in isolation. A significant challenge to understanding the effects of multiple pollutants in many regions is the dearth of air quality data near agricultural fields. Here we empirically estimate the effect of four key pollutants (ozone (O₃), particulate matter (PM), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂)) on maize and soybean yields in the United States using a combination of administrative data and satellite-derived yield estimates. We identify clear negative effects of exposure to O₃, PM, and SO₂ in both crops, using yields measured in the vicinity of monitoring stations. We also show that while stations measuring NO₂ are too sparse to reliably estimate a yield effect, the strong gradient of NO₂ concentrations near power plants allows us to more precisely estimate NO₂ effects using satellite measured yield gradients. The presence of some powerplants that turn on and others that shut down during the study period are particularly useful for attributing yield gradients to pollution. We estimate that total yield losses from these pollutants averaged roughly 5% for both maize and soybean over the past two decades. While all four pollutants have statistically significant effects, PM and NO₂ appear more damaging to crops at current levels than O₃ and SO₂. Finally, we find that the significant improvement in air quality since 1999 has halved the impact of poor air quality on major crops and contributed to yield increases that represent roughly 20% of overall yield gains over that period.

Energy reform that required amendments to the Mexican Constitution in 2013 and implementing legislation aimed to increase the efficiency, economic competitiveness, and decarbonization of Mexico's electricity sector. Emissions inventories were developed for the 2016 base year and a capacity development pathway established by Mexico over a 15-year planning horizon to 2031. Between 2016 and 2031, steep declines in generation from fuel oil-fired thermoelectric, turbogas, and coal plants in favor of a buildout of natural gas combined cycle and clean energy technologies were predicted to drive reductions in emissions of sulfur dioxide (SO₂), fine particulate matter (PM_2.5), carbon dioxide (CO₂) and nitrogen oxides (NO_x) of 68%, 61%, 13% and 7%, respectively, with an increase in carbon monoxide (CO) of 4%. Retirement of fuel oil-fired thermoelectric and coal generation contributed to substantial reductions in 24 h average PM_2.5 concentrations in Mexican and U.S. border states even with rising demand. In contrast, little change in maximum daily average eight-hour ozone concentrations was predicted with expansion of natural gas combined cycle generation, which is a source of NO_x and CO. Mexico's electricity sector planning process has been highly dynamic since the reform. Insights indicate how changes in national strategies could affect emissions and air quality outcomes.

Green Roofs (GRs) are one of the measures considered for Urban Heat Island (UHI) mitigation. The cooling effects of GRs are well studied in the literature. However, previous work has not addressed the impacts of GRs on heavy rainfall in cities. This study develops and tests the hypothesis that incorporating green roofs in urban areas enhances the magnitude of rain for heavy rainfall events. To test this, examples of heavy rainfall events over three different years are examined over Mumbai, India, one of the megapoleis that continues to witness heavy rains and urban flooding. The heavy rain events are simulated using Weather Research and Forecasting (WRF) model for different green roof fraction (GF) scenarios (10%, 25%, 50%, 75%, and 100%) over the urban area. The GF simulations are compared to the 'no GF' simulation (control run). The results indicate a consistent increase (1%–72%) in the total accumulated precipitation in all GF scenarios. Additional moisture and increased equivalent potential temperature aided the formation and sustenance of localized pockets of enhanced rain occurrences, contributing to the total amount of rainfall for the rain events for the domain. The increase in rainfall amounts leads to higher runoff and can increase the risk of flash floods. Thus, it is necessary to account for this rainfall-based feedback of GR before adopting it as a mitigation option. The results of this work may be helpful in effective urban planning and managing the urban climate extremes.

Coronavirus disease 2019 (COVID-19) pandemic has led to a rare reduction in human activities. In such a background, data from ground-based environmental stations, satellites, and reanalysis materials are utilized to conduct a comprehensive analysis of the global air quality changes during the COVID-19 outbreak. The results showed that under the impact of the COVID-19 outbreak, a significant decrease in particulate matter (PM_x) and nitrogen dioxide (NO₂) occurred in more than 40% of the world's land area, with NO₂ (PM_x) decreasing by ∼30% (∼20%). The mobility, meteorological factors, and the response speed to COVID-19 outbreaks were examined. It was further found that in quick-response cities, lockdowns produced a sharp decline in mobility and had a dominant impact on air quality. In contrast, in slow-response cities, mobility dropped gradually since the confirmation of the first COVID-19 case (FCC) and he impact of the FCC, lockdowns, and meteorological factors were comparable.

Ozonesonde launches were routinely performed in Beijing from March 2001 to February 2019 to generate a unique long-term (18 years) vertical ozone profile dataset over mainland China. This study elucidates the vertical ozone structure on various temporal scales during this 18 years period by using the entire ozonesonde data product for the first time. Moreover, the long-term variability in the integrated ozone column over the North China Plain (NCP) is also explored by comparing the retrievals from ozonesonde at the Beijing urban site and a Dobson ozone spectrometer at the Xianghe suburban site. Our results indicate that vertical ozone exhibited clear monthly variability characterized by high values of tropospheric ozone during warm seasons and high values of stratospheric ozone during cold seasons. Stratospheric intrusions frequently occurred during spring and effectively transported cold air masses with high ozone from the lower stratosphere downward into the upper troposphere. Evident interannual variability in the lower troposphere and in ozone-rich areas of the stratosphere was revealed by vertical ozone distributions. The integrated total ozone columns retrieved from ozonesonde and Dobson bear close resemblance and exhibit strong sinusoidal monthly variations. In the troposphere and boundary layer, the integrated ozone column presented a significant positive trend during 2001–2012 in Beijing; a sudden decline occurred between 2011 and 2013, which was followed by a slow and insignificant increase after the implementation of the Clean Air Action plan in 2013 on the NCP.

Aerosol concentrations over Asia play a key role in modulating the Indian summer monsoon (ISM) rainfall. Lockdown measures imposed to prevent the spread of the COVID-19 pandemic led to substantial reductions in observed Asian aerosol loadings. Here, we use bottom-up estimates of anthropogenic emissions based on national mobility data from Google and Apple, along with simulations from the ECHAM6-HAMMOZ state-of-the-art aerosol-chemistry-climate model to investigate the impact of the reduced aerosol and gases pollution loadings on the ISM. We show that the decrease in anthropogenic emissions led to a 4 W m⁻² increase in surface solar radiation over parts of South Asia, which resulted in a strengthening of the ISM. Simultaneously, while natural emission parameterizations are kept the same in all our simulations, the anthropogenic emission reduction led to changes in the atmospheric circulation, causing accumulation of dust over the Tibetan plateau (TP) during the pre-monsoon and monsoon seasons. This accumulated dust has intensified the warm core over the TP that reinforced the intensification of the Hadley circulation. The associated cross-equatorial moisture influx over the Indian landmass led to an enhanced amount of rainfall by 4% (0.2 mm d⁻¹) over the Indian landmass and 5%–15% (0.8–3 mm d⁻¹) over central India. These estimates may vary under the influence of large-scale coupled atmosphere–ocean oscillations (e.g. El Nino Southern Oscillation, Indian Ocean Dipole). Our study indicates that the reduced anthropogenic emissions caused by the unprecedented COVID-19 restrictions had a favourable effect on the hydrological cycle over South Asia, which has been facing water scarcity during the past decades. This emphasizes the need for stringent measures to limit future anthropogenic emissions in South Asia for protecting one of the world's most densely populated regions.

The response of vegetation to climate extremes, including droughts and hot extremes, has been evaluated extensively in recent decades. However, quantitative assessments of individual and combined impacts of dry and hot conditions on vegetation are rather limited. In this study, we developed a multivariate approach for analyzing vegetation responses to dry, hot, and compound dry-hot conditions from a probabilistic perspective using precipitation, temperature, and the Normalized Difference Vegetation Index (NDVI) for the period from 1982 to 2015. The Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) were used to define individual and compound dry and hot conditions. Based on the diagnosis of the correlation between SPI/STI and NDVI during growing seasons, we investigated the conditional probability of vegetation decline under different climate conditions. The results showed that vegetation was affected by compound dry and hot conditions (defined as SPI ⩽ −1.3 and STI > 1.3) in arid and semi-arid regions. In these regions, the conditional probabilities of vegetation decline under compound dry and hot conditions increased by 7% and 28% compared with those under individual dry and hot conditions, respectively. The impact of compound dry and hot events on vegetation for different biomes was also assessed. Temperate grassland was found to be particularly vulnerable to compound dry and hot conditions. This study highlights the necessity of considering compound dry and hot extremes when assessing vegetation responses to climate extremes under global warming.

Extreme surface ocean waves are often primary drivers of coastal flooding and erosion over various time scales. Hence, understanding future changes in extreme wave events owing to global warming is of socio-economic and environmental significance. However, our current knowledge of potential changes in high-frequency (defined here as having return periods of less than 1 year) extreme wave events are largely unknown, despite being strongly linked to coastal hazards across time scales relevant to coastal management. Here, we present global climate-modeling evidence, based on the most comprehensive multi-method, multi-model wave ensemble, of projected changes in a core set of extreme wave indices describing high-frequency, extra-tropical storm-driven waves. We find changes in high-frequency extreme wave events of up to ∼50%–100% under RCP8.5 high-emission scenario; which is nearly double the expected changes for RCP4.5 scenario, when globally integrated. The projected changes exhibit strong inter-hemispheric asymmetry, with strong increases in extreme wave activity across the tropics and high latitudes of the Southern Hemisphere region, and a widespread decrease across most of the Northern Hemisphere. We find that the patterns of projected increase across these extreme wave events over the Southern Hemisphere region resemble their historical response to the positive anomaly of the Southern Annular Mode. Our findings highlight that many countries with low-adaptive capacity are likely to face increasing exposure to much more frequent extreme wave events in the future.

As global warming makes the Arctic Ocean more accessible, concerns have been raised about the environmental consequences of a possible expansion of commercial fisheries into pristine marine ecosystems. Using a recently released global dataset, we quantify for the first time how fishing activities are responding to diminishing sea ice and a warmer Arctic Ocean. We show that trawling dominates Arctic fisheries and that this activity penetrates rapidly into Arctic shelf areas previously protected by extensive ice-cover as a response to interannual sea ice loss. We model the development of trawling activity under a climate change scenario and use the model to identify areas with high risk of increased trawling activity and estimate the amount of trawling avoided in recently established fishery protection zones. Stronger responsibility must be undertaken by Arctic coastal states to regulate increased fishing pressure and protect vulnerable Arctic shelf ecosystems.

Commercial microwave links (CMLs) from cellular telecommunication networks can provide a valuable 'opportunistic' source of high-resolution space-time rainfall information, complementing traditional in-situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Their greatest potential lies in areas with low gauge densities and lack of weather radars, often in developing countries with a subtropical or tropical climate and generally large spatial rainfall variability. Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka for a 3.5 month period based on CML data from on average 1140 link paths. These are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the dual-frequency precipitation radar on board the global precipitation measurement core observatory satellite. The potential of CMLs for real-time tropical rainfall monitoring is demonstrated.

This study investigated simultaneous flood risk among all the 109 class-A river basins over Japan using the big data of (over 1000 years) annual maximum hourly flow simulated from a large ensemble climate simulation database for policy decision making for future climate change, and proposed a novel approach in its geospatial analysis by applying two informatics techniques: the association rule analysis and graph theory. Frequency analysis of the number of rivers with the annual maximum flow over the flow capacity in the same year (defined as simultaneous flooding here) indicated that simultaneous flood risk will increase in the future climate under 4-degree rise scenarios in Japan, whose increment is larger than the variation of sea surface temperature projections. As the result, the return period of simultaneous flooding in eight river basins (the same number as in a severe storm in western Japan, 2018, causing the second worst economic damage since 1962) is estimated at 400 years in the historical experiment, 25 years in the 4-degree rise experiment. The association rule and graph theory analyses for the big data of annual maximum flows in the future climate scenarios indicated that simultaneous flood occurrence is dominated by spatial distance at a national scale as well as by the spatial relation between mountainous ridges and typhoon courses at a regional scale. Large ensemble climate simulation data combined with the informatics technology is a powerful approach to simultaneous flood risk analysis.

The livestock sector is a major contributor to agricultural greenhouse gas (GHG) and nitrogen (N) emissions and efforts are being made to reduce these emissions. National emission inventories are the main tool for reporting emissions. They have to be consistent, comparable, complete, accurate and transparent. The quality of emission inventories is affected by the reporting methodology, emission factors and knowledge of individual sources. In this paper, we investigate the effects of moving from the 1996 IPCC Guidelines for National Greenhouse Gas Inventories to the 2006 IPCC Guidelines on the emission estimates from the livestock sector. With Austria as a case study, we estimated the emissions according to the two guidelines, revealing marked changes in emission estimates from different source categories resulting from changes in the applied methodology. Overall estimated GHG emissions from the livestock sector decreased when applying the IPCC 2006 methodology, except for emissions from enteric fermentation. Our study revealed shifts in the relative importance of main emission sources. While the share of CH₄ emissions from enteric fermentation and manure management increased, the share of N₂O emissions from manure management and soils decreased. The most marked decrease was observed for the share of indirect N₂O emissions. Our study reveals a strong relationship between the emission inventory methodology and mitigation options as mitigation measures will only be effective for meeting emission reduction targets if their effectiveness can be demonstrated in the national emission inventories. We include an outlook on the 2019 IPCC Refinement and its potential effects on livestock emissions estimates. Emission inventory reports are a potent tool to show the effect of mitigation measures and the methodology prescribed in inventory guidelines will have a distinct effect on the selection of mitigation measures.

Large amounts of phosphorus resources, such as mineral fertilizers and manure, are mobilized globally to produce the food consumed in cities. Accounting for the use of these resources can allow cities to plan for interventions that reduce related pressures in their hinterlands, conserve resources, and lead to more circular food systems. In this study we calculate a resource-based phosphorus footprint for the food consumption in Brussels Capital Region and use it to compare different strategies towards increased circularity: waste reuse, waste reduction, dietary changes and shifts to locally produced food. The P footprint of an average inhabitant in Brussels is 7.7 kgP cap yr⁻¹, 10 times higher than the physical P consumption of 0.7 kgP cap yr⁻¹. About 60% of the total P inputs into food production are through manure, and the rest through mineral fertilizers; almost 80% of the inputs occur outside Belgium. Most of these inputs are related to the cultivation of feed for livestock, which is why a shift to vegetarian and vegan diets can reduce the footprint down to 4.8 kgP cap yr⁻¹ and 0.9 kgP cap yr⁻¹. To the contrary, consuming only food produced in Belgium would increase the footprint to 12 kgP cap yr⁻¹, mostly as a result of the high manure use in the north of the country. A reduction in the P footprint signifies an absolute reduce in total resource use that can alleviate pressures in the hinterland and promote a city's transition towards circularity.

Apart from nitrogen (N) rates, N use efficiency (NUE) (yield N/total input N) is affected by seasons, crop developmental stages, and varieties. Knowledge of how these factors affect NUE in rice production in Kenya is limited. Therefore, field experiments were conducted with 'low rates' of N (simulating farmers' practices) of 0, 26, 52 and 78 kg N ha⁻¹ with five varieties (MWUR1, MWUR4, IRAT109, NERICA4 and NERICA10) and higher rates of N (125, 175, and 225 kg N ha⁻¹) simulating researchers' doses with two lowland varieties (Basmati 370 and BW 196) and IR 72. Another experiment on NUE responses to sites, N rates and dose (split or full dose) was undertaken with the IR97 variety. With the 'low rate', yields increased with incremental N rates up to 52 Kg N ha⁻¹ and declined (during cold periods, for some varieties). In this scenario, the N agronomic efficiencies (AE_N) declined with increasing N but depended on sites and seasons. However, most AE_N values were above 100, implying nutrient mining. In most cases (except at the Mwea site), the N utilization efficiency (NUtE) ranged from 16 to 22kg kg⁻¹ and were not significantly affected by sources and methods of N application. In all cases, an increase in N elicited declining trends in NUtE. Moreover, N uptake efficiency ranged between 22 and 90kg kg⁻¹ without significant variation among varieties. For the 'high N rates', high biomass yield resulted in higher grain yields in BW 196 and IR 72 but yield declined beyond 75 kg ha⁻¹ N rates due to poor grain filling, particularly when a cold period coincided with booting and grain filling. We conclude that N rates, doses and rice varieties are key determinants of AE_N and NUtE in contrasting rice growing seasons in Kenya. Cropping seasons and rice varieties are therefore potential key determinants of sustainable rice productivity and improved NUE in rice-based systems in the studied regions of Kenya.

Mountaintop mining, like all forms of surface mining, fundamentally alters the landscape to extract resources that lie 10–100 ms below the land surface. Despite these deep, critical zone alterations, post-mining landscapes are required by United States law to be restored to ecosystems of equal or greater value than the ones they replace. Yet, remote sensing of vegetation across more than 1000 km² of reclaimed surface mines in WV, USA reveals little evidence that these habitats are returning to the diverse Appalachian forests that were removed by mining. Instead, even decades after reclamation, mined landscapes are dominated by shorter and sparser trees. Based on detailed field studies and literature synthesis, we suggest that part of these widespread failures in re-establishing native forest result from the fundamental changes in critical zone processes on the post-mining landscape. Former surface mines have substantially altered topography, hydrology and chemistry. In these post-mining, synthetic landscapes, water moves more slowly through piles of exploded bedrock, changing the system from one dominated by stormflow in unmined catchments, to one dominated by baseflow after mining. This slow-moving water, travelling through high surface-area debris and pyrite-rich bedrock, creates ideal conditions for highly elevated weathering in mines both old and new. These foundational changes to the critical zone set ecosystem recovery along a novel trajectory, in which the legacy of past disturbance is likely to constrain the establishment of native forest for many decades.

Reactive nitrogen (N_r) that is released to the environment has several negative implications for the atmosphere, hydrosphere, biodiversity and human health. A nitrogen (N) footprint is a measure that can help to assess and communicate the impact of personal lifestyle and consumption choices regarding their influences on N_r losses. The N-Calculator tool was developed to estimate this footprint. However, underlying loss factors for the food sector in the N-Calculator rely on data from the US, for which the calculator was originally established. Since the conditions in agriculture and the food industry differ significantly between the US and other countries, and the fact that the food sector is considered the main source of N_r losses in the N-Calculator, a revision of the N-Calculator is required if applied to other countries. Here we present a revised N-Calculator for Germany that is based on German food production data. In this study, virtual nitrogen factors describe the losses of nitrogen in a supply chain. Losses were calculated for 20 plant-based products, 17 feed materials, 18 compound feeds and 14 animal-based products. The N footprint varies considerably between products. While plant-based products amount to a weighted average of 3.4 g N loss per kg product, animal-based products cause significantly higher losses with 40.5 g N loss per kg. Overall, the average N footprint for the German consumer is calculated to be at 9.94 kg per capita and year. To validate the results, the individual categories were scaled up to the national level and then compared with statistical data on N flows in Germany. In general, the results showed good agreement with key production figures and the overall N budget for Germany. Furthermore, some improvements are proposed to increase the informative value and user acceptance of an N-Calculator.

Northern herbivore ranges are expanding in response to a warming climate. Forage quality also influences herbivore distributions, but less is known about the effects of climate change on plant biochemical properties. Remote sensing could enable landscape-scale estimations of forage quality, which is of interest to wildlife managers. Despite the importance of integrated forage quality metrics like digestible protein (DP) and digestible dry matter (DDM), few studies investigate remote sensing approaches to estimate these characteristics. We evaluated how well DP and DDM could be estimated using hyperspectral remote sensing and assessed whether incorporating shrub structural metrics affected by browsing would improve our ability to predict DP and DDM. We collected canopy-level spectra, destructive-vegetation samples, and flew unoccupied aerial vehicles (UAVs) in willow (Salix spp.) dominated areas in north central Alaska in July 2019. We derived vegetation canopy structural metrics from 3D point cloud data obtained from UAV imagery using structure-from-motion photogrammetry. The best performing model for DP included a spectral vegetation index (SVI) that used a red-edge and shortwave infrared band, and shrub height variability (hvar; Nagelkerke R² = 0.81, root mean square error RMSE = 1.42%, cross validation ρ = 0.88). DDM's best model included a SVI with a blue and a red band, the normalized difference red-edge index, and hvar (adjusted R² = 0.73, RMSE = 4.16%, cross validation ρ = 0.80). Results from our study demonstrate that integrated forage quality metrics may be successfully quantified using hyperspectral remote sensing data, and that models based on those data may be improved by incorporating additional shrub structural metrics such as height variability. Modern airborne sensor platforms such as Goddard's LiDAR, Hyperspectral & Thermal Imager provide opportunities to fuse data streams from both structural and optical data, which may enhance our ability to estimate and scale important foliar properties.

To examine the associations between ambient temperature and hospitalizations for acute kidney injury (AKI) in Queensland, Australia, 1995–2016. Data were collected on a total of 34 379 hospitalizations for AKI from Queensland between 1 January 1995 and 31 December 2016. Meteorological data were downloaded from the Queensland Government's Department of Environment and Science. We assessed the temperature-AKI relationship using a time-stratified case-crossover design fitted with conditional quasi-Poisson regression model and time-varying distributed lag non-linear model. Stratified analyses were performed by age, sex, climate zone and socioeconomic group. Both cold and hot temperatures were associated with hospitalizations for AKI. There were stronger temperature-AKI associations among women than men. Cold effects were only positive in the ⩾70 years age group. Hot effects were stronger in the ⩽59 years age group than in the >60 years age group. In different climate zone areas, cold effects decreased with increasing local mean temperatures, while hot effects increased. In different socio-economic status groups, hot effects were stronger in the poor areas than the affluent areas. From 1995 to 2016, the magnitude of associations between cold temperature and hospitalizations for AKI decreased, while the hot effect increased. The associations between hot temperature and hospitalizations for AKI become stronger, while the magnitude of cold effect decreased from 1995 to 2016. This trend may accelerate over the coming decades, which warrants further research. More attention is needed toward susceptible population including women, people ⩾70 years, and the people living in hot climate zones and in low socioeconomic status areas.

The high productivity in the US Corn Belt is largely enabled by the consumption of millions of tons of manufactured fertilizer. Excessive application of nitrogen (N) fertilizer has been pervasive in this region, and the unrecovered N eventually escaped from croplands in forms of nitrous oxide (N₂O) emission and N leaching. Mitigating these negative impacts is hindered by a lack of practical information on where to focus and how much mitigation potential to expect. At a large scale, process-based crop models are the primary tools for predicting variables required by decision making, but their applications are prohibited by expensive computational and data storage costs. To overcome these challenges, we built a series of metamodels to learn the key mechanisms regarding the carbon (C) and N cycle from a well-validated process-based biogeochemical model, ecosys. The trained metamodel captures over 98% of the variability of the ecosys simulated outputs for 99 randomly selected counties in Iowa, Illinois, and Indiana. To identify hotspots with high mitigation potential, we introduce net societal benefit (NSB) as an indicator for synthesizing the loss in yield and social benefits through emissions and pollutants avoided. Our results show that reducing N fertilizer by 10% leads to 9.8% less N₂O emissions and 9.6% less N leaching at the cost of 4.9% more SOC depletion and 0.6% yield reduction over the study region. The estimated total annual NSB is $395 M (uncertainty ranges from $114 M to $1271 M), including $334 from social benefits (uncertainty ranges from $46 M to $1076 M), $100 M from saving fertilizer (uncertainty ranges from $13 M to $455 M), and −$40 M due to yield changes (uncertainty ranges from −$261 M to $69 M). For the median scenario, we noted that 20% of the study area accounts for nearly 50% of the NSB, and thus represent hotspot locations for targeted mitigation. Although the uncertainty range suggests that developing such a high-resolution framework is not yet settled and the scenario based estimations are not appropriate to inform the management practices for individual farmers, our efforts shed light on the new generation of analytical tools for life cycle assessment.

Urban landscape combines built-up areas with strongly altered natural (green and blue) and other open spaces. Voluminous literature examines urban socio-environmental interactions in tropical and temperate cities, whereas high-latitude cities are rarely considered. Here, we create a historical perspective on urban green (vegetation) and blue (water) spaces in a sub-Arctic city of Nadym in Russia. Our study explores a novel way to combine quantitative information from satellite imagery and biometric studies with qualitative information from interviews with stakeholders and residents. Such a joint analysis helps to understand dynamics of the urban green and blue space as well as its value for society. Furthermore, we propose objective indicators reflecting societal values of spaces in connection with recreational and ecological services. By contrast to temperate city studies, we found that green space is less used in summer, but still highly valued, deep lakes are used and valued more than warmer shallow lakes, and winter white space do not shrink but enhance the urban public space. Satellite images reveal inevitable loss of green space to urban construction and its remediation by artificial plantings (almost by 30% at present), whereas less valued blue space decreased almost three-fold. Interviews reveal that shallow lakes have reduced recreational values due to ice bottom and algae bloom. High values are attributed to deep artificial lakes, which are more than ten times deeper than natural lakes and do not freeze throughout in winter. Our biometric studies show that trees in urban environment are significantly taller than in the corresponding undisturbed areas. Since majority of the Arctic cities are built using very similar planning ideas and technologies, our findings shall help objective appreciation of green and blue spaces in other settlements.