Table of contents

Fire is a complex Earth system phenomenon that fundamentally affects vegetation distributions, biogeochemical cycling, climate, and human society across most of Earth's land surface. Fire regimes are currently changing due to multiple interacting global change drivers, most notably climate change, land use, and direct human influences via ignition and suppression. It is therefore critical to better understand the drivers, patterns, and impacts of these changing fire regimes now and continuing into the future. Our review contributes to this focus issue by synthesizing results from 27 studies covering a broad range of topics. Studies are categorized into (i) Understanding contemporary fire patterns, drivers, and effects; (ii) Human influences on fire regimes; (iii) Changes in historical fire regimes; (iv) Future projections; (v) Novel techniques; and (vi) Reviews. We conclude with a discussion on progress made, major remaining research challenges, and recommended directions.

Nanoparticles (NPs) and antibiotic resistant genes (ARGs), as emerging environmental contaminants, have been reported to be accumulated in the soil environment. The use of NPs have raised increasing concerns about their environmental impacts, but the combined effect of NPs and antibiotics on ARGs remains less understood. Here, we established laboratory microcosms to explore the impacts of different concentrations of SiO₂ NPs on β-lactam and sulfonamide resistance genes in soils amended with β-lactam or sulfonamide. Illumina sequencing and quantitative PCR revealed that the addition of NPs increased the bacterial community diversity but had no significant effects on the bacterial abundance. Moreover, NPs and sulfonamide jointly increased the abundances of sulfonamide resistance genes, while the exposure of NPs and β-lactam decreased β-lactam resistance genes. The detected ARGs were associated closely with two mobile genetic elements (MGEs, the tnpA and intI1 genes), indicating that MGEs may contribute to the dissemination of ARGs. Correlation analysis indicated the shifts in potential bacterial hosts and the frequency of horizontal gene transfer were important factors explaining the patterns of ARGs. Furthermore, structural equation models indicated that NPs exposure decreased the abundances of β-lactam resistance genes by driving changes in bacterial community and MGEs, whereas the increased abundances of sulfonamide resistance genes were mainly associated with the bacterial community, diversity and MGEs mediated by NPs and antibiotics. These results suggested that the combined effects of NPs and antibiotics on soil bacterial resistance were different due to the types of antibiotics.

Anthropogenic land cover change (LCC) can have significant impacts at regional and seasonal scales but also for extreme weather events to which socio-economical systems are vulnerable. However, the effects of LCC on extreme events remain either largely unexplored and/or without consensus following modelling over the historical period (often based on a single model), regional or idealized studies. Here, using simulations performed with five earth system models under common future global LCC scenarios (the RCP8.5 and RCP2.6 Representative Concentration Pathways) and analyzing 20 extreme weather indices, we find future LCC substantially modulates projected weather extremes. On average by the end of the 21st century, under RCP8.5, future LCC robustly lessens global projections of high rainfall extremes by 22% for heavy precipitation days (>10 mm) and by 16% for total precipitation amount of wet days (PRCPTOT). Accounting for LCC diminishes their regional projections by >50% (70%) in southern Africa (northeastern Brazil) but intensifies projected dry days in eastern Africa by 29%. LCC does not substantially affect projections of global and regional temperature extremes (<5%), but it can impact global rainfall extremes 2.5 times more than global mean rainfall projections. Under an RCP2.6 scenario, global LCC impacts are similar but of lesser magnitude, while at regional scale in Amazon or Asia, LCC enhances drought projections. We stress here that multi-coupled modelling frameworks incorporating all aspects of land use are needed for reliable projections of extreme events.

This study investigates the relative contributions of components of flows on multiple time scales to westward and eastward-moving tropical cyclones (TCs) over the South China Sea (SCS) from a local and instantaneous perspective of TC position along the TC tracks during June to October from 1965 to 2015. The total steering flows obtained by vertical integration of winds from 850 to 300 hPa averaged along the entire TC tracks are separated into climatological mean flows, interannual, intraseasonal, and synoptic time scales. For westward-moving TCs, the zonal steering flows are contributed dominantly by climatological mean easterly, whereas the meridional steering flows are contributed dominantly by climatological mean and synoptic-scale southerly winds. In contrast, for eastward-moving TCs, the zonal steering flows are contributed equally by the intraseasonal and synoptic components, while the meridional steering flows are contributed dominantly by climatological mean southerly winds along with the secondary contribution of synoptic-scale southerly winds. This work provides a better understanding of relative contributions of different time scale components of steering flows to the TC motions over the SCS.

Beijing Haze has been phenomenal, especially for winter, and widely considered a result of the increasing anthropogenic emissions of atmospheric pollutants in the region. Since 2013, the pollutant emissions have been reduced with the help of a series of emission-control actions. However, severe haze events still occurred frequently in Beijing in recent winters, e.g., those of 2015 and 2016, implying that other factors such as meteorological conditions and interannual climate variability have also played an important role in forming the haze. Based on homogenized station observations, atmospheric circulation reanalysis and anthropogenic emissions data for the period 1980–2017, this paper attempts to quantify the relative importance of anthropogenic emissions and climatic conditions to the frequency and intensity of Beijing Haze in winter. It is found that the frequency (number) of hazy days exhibits large interannual variability and little trend, and its variations were mainly controlled by climate variability, with a correlation coefficient of 0.77. On the other hand, the intensity of haze displays strong interannual variability and a significant increasing trend during 1980–2012 and a notable decreasing trend during 2012–2017. The multiple linear regression model suggests that about half of the total variance of the haze intensity is explained by climate variability (mainly for interannual variations), and another half by the changing emissions (mainly for the trends).

Soil nitrogen (N) mineralization is crucial for the sustainability of available soil N and hence ecosystem productivity and functioning. Metabolic quotient of N mineralization (Q_min), which is defined as net soil N mineralization per unit of soil microbial biomass N, reflects the efficiency of soil N mineralization. However, it is far from clear how soil Q_min changes and what are the controlling factors at the global scale. We compiled 871 observations of soil Q_min from 79 published articles across terrestrial ecosystems (croplands, forests, grasslands, and wetlands) to elucidate the global variation of soil Q_min and its predictors. Soil Q_min decreased from the equator to two poles, which was significant in the North Hemisphere. Soil Q_min correlated negatively with soil pH, total soil N, the ratio of soil carbon (C) to N, and soil microbial biomass C, and positively with mean annual temperature and C:N ratio of soil microbial biomass at a global scale. Soil microbial biomass, climate, and soil physical and chemical properties in combination accounted for 41% of the total variations of global soil Q_min. Among those predictors, C:N ratio of soil microbial biomass was the most important factor contributing to the variations of soil Q_min (the standardized coefficient = 0.39) within or across ecosystem types. This study emphasizes the critical role of microbial stoichiometry in soil N cycling, and suggests the necessity of incorporating soil Q_min into Earth system models to better predict N cycling under environmental change.

The large interannual variability of global atmospheric CO₂ growth rate originates primarily from variation in carbon dioxide (CO₂) uptake of pantropical terrestrial ecosystems, which covaries with the El Niño–Southern Oscillation (ENSO) modulated climate fluctuations of water availability and temperature change. However, the role of ENSO in modulating the contributions of regional to overall water availability interannual variability, and the phase and strength of water availability-CO₂ coupling remain poorly constrained across functionally diverse pantropical terrestrial ecosystems. Using satellite microwave and ground water availability and well-mixed global atmospheric CO₂ concentration observations, the coupling in interannual variability of water availability-CO₂ and their relationship with ENSO was investigated from 1998 to 2016. The results demonstrated causal sequence of ENSO, water availability, and global atmospheric CO₂ growth rate, the phase and magnitude of water availability-CO₂ coupling was primarily determined by phase and strength of correlation between ENSO and water availability, revealing ENSO-driven robust and reverse coupling of water availability-CO₂. Moreover, tropical rainforests, savannas, and shrublands dominated the pantropical water availability variations and showed stronger coupling strength. Therefore, the strong interannual variability of atmospheric CO₂ growth rate originates from ENSO-driven frequent variations of water availability and the subsequently concurrent carbon uptake over pantropical rainforests, savannas, and shrublands. The findings provided new insights to understand and predict interannual variability of water availability and CO₂ growth rate based on ENSO and its predictability.

Uncertainties in the rate and magnitude of sea-level rise (SLR) complicate decision making on coastal adaptation. Large uncertainty arises from potential ice mass-loss from Antarctica that could rapidly increase SLR in the second half of this century. The implications of SLR may be existential for a low-lying country like the Netherlands and warrant exploration of high-impact low-likelihood scenarios. To deal with uncertain SLR, the Netherlands has adopted an adaptive pathways plan. This paper analyzes the implications of storylines leading to extreme SLR for the current adaptive plan in the Netherlands, focusing on flood risk, fresh water resources, and coastline management. It further discusses implications for coastal adaptation in low-lying coastal zones considering timescales of adaptation including the decisions lifetime and lead-in time for preparation and implementation. We find that as sea levels rise faster and higher, sand nourishment volumes to maintain the Dutch coast may need to be up to 20 times larger than to date in 2100, storm surge barriers will need to close at increasing frequency until closed permanently, and intensified saltwater intrusion will reduce freshwater availability while the demand is rising. The expected lifetime of investments will reduce drastically. Consequently, step-wise adaptation needs to occur at an increasing frequency or with larger increments while there is still large SLR uncertainty with the risk of under- or overinvesting. Anticipating deeply uncertain, high SLR scenarios helps to enable timely adaptation and to appreciate the value of emission reduction and monitoring of the Antarctica contribution to SLR.

Recent floods in America, Europe, Asia and Africa reminded societies across the world of the need to revisit their climate adaptation strategies. Rapid urbanization coinciding with a growing frequency and intensity of floods requires transformative actions in cities worldwide. While abandoning flood prone areas is sometimes discussed as a public climate adaptation option, little attention is paid to studying cumulative impacts of outmigration as an individual choice. To explore the aggregated consequences of households' outmigration decisions in response to increasing flood hazards, we employ a computational agent-based model grounded in empirical heuristics of buyers' and sellers' behaviour in a flood-prone housing market. Our results suggest that pure market-driven processes can cause shifts in demographics in climate-sensitive hotspots placing low-income households further at risk. They get trapped in hazard zones, even when individual risk perceptions and behavioural location preferences are independent of income, suggesting increasing climate gentrification as an outcome of market sorting.

Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIR_v,Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIR_v,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIR_v,Ref), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF₇₆₀). The strong linear relationship between NIR_v,Rad and absorbed photosynthetically active radiation by green leaves (APAR_green), and that between APAR_green and GPP, explain the good NIR_v,Rad-GPP relationship. The NIR_v,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIR_v,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.

Indian agriculture is globally well-documented to reflect the impacts of changing climate significantly. However, climate adaptation efforts are often hindered due to the inadequate assessment of coupled human-environment interactions. In this study, we propose a novel unified country-level framework to quantify the decadal agricultural risks derived from multiple hydro-meteorological exposures and adaptive consequences. We identify, for the first time, that rice and wheat risks have increased in the recent decade, with wheat at a twofold higher magnitude than rice. Increasing crops risk is found to be predominantly driven by the decreasing number of cultivators; in particular, the wheat risk is also attributed to increasing minimum temperatures during the crop growing season. We provide convincing evidence indicating that the hydro-climatic hazards related to precipitation extremes and droughts are specifically alarming the crops risk as compared to temperature extremes. These observation-based results highlight the sensitivity of India's agriculture and the risk associated with multiple agro-ecological and climatic components. We recommend these findings to facilitate the informed planning of adaptive measures and ensure sustainable food security of the nation.

Tree plantations and forest restoration are leading strategies for enhancing terrestrial carbon (C) sequestration and mitigating climate change. While it is well established that species-rich natural forests offer superior C sequestering benefits relative to short-rotation commercial monoculture plantations, differences in rates of C capture and storage between longer-lived plantations (commercial or non-commercial) and natural forests remain unclear. Using a natural experiment in the Western Ghats of India, where late-20th century conservation laws prohibited timber extraction from monodominant plantations and natural forests within nature reserves, we assessed forests and plantations for aboveground C storage and the magnitude and temporal stability of rates of photosynthetic C capture (gross primary production). Specifically, we tested the hypothesis that species-rich forests show greater temporal stability of C capture, and are more resistant to drought, than monodominant plantations. Carbon stocks in monodominant teak (Tectona grandis) and Eucalyptus (Eucalyptus spp.) plantations were 30%–50% lower than in natural evergreen forests, but differed little from moist-deciduous forests. Plantations had 4%–9% higher average C capture rates (estimated using the Enhanced Vegetation Index–EVI) than natural forests during wet seasons, but up to 29% lower C capture during dry seasons across the 2000–18 period. In both seasons, the rate of C capture by plantations was less stable across years, and decreased more during drought years (i.e. lower resistance to drought), compared to forests. Thus, even as certain monodominant plantations could match natural forests for C capture and storage potential, plantations are unlikely to match the stability–and hence reliability–of C capture exhibited by forests, particularly in the face of increasing droughts and other climatic perturbations. Promoting natural forest regeneration and/or multi-species native tree plantations instead of plantation monocultures could therefore benefit climate change mitigation efforts, while offering valuable co-benefits for biodiversity conservation and other ecosystem services.

The velocity and impact of climate change on forest appear to be site, environment, and tree species-specific. The primary objective of this research is to assess the changes in productivity of five major temperate tree species (Pinus densiflora, PD; Larix kaempferi, LK; Pinus koraiensis, PK; Quercus variabilis, QV; and Quercus mongolica, QM) in South Korea using terrestrial inventory and satellite remote sensing data. The area covered by each tree species was further categorized into either lowland forest (LLF) or high mountain forest (HMF) and investigated. We used the repeated Korean national forest inventory (NFI) data to calculate a stand-level annual increment (SAI). We then compared the SAI, a ground-based productivity measure, to MODerate resolution Imaging Spectroradiometer (MODIS) net primary productivity as a measure of productivity based on satellite imagery. In addition, the growth index of each increment core, which eliminated the effect of tree age on radial growth, was derived as an indicator of the variation in primary productivity by tree species over the past four decades. Based on our result from NFI plots and increment core data sets, the productivity of PD, QV, and QM in LLF was relatively higher than those in HMF, while LK and PK in HMF were more productive than lowland ones. Our analysis of the increment core data revealed a contrasting pattern of long-term productivity changes between coniferous and oak tree species. While the productivity of oak tree species tended to increase after the 1990s, the productivity in coniferous forests tended to decrease. These differences across forest types and their altitudinal classes are also noticeable from the MODIS product. The results of our study can be used to develop climate-smart forest management strategies to ensure that the forests continue to be resilient and continue to provide a wide range of ecosystem services in the Eastern Asian region.

This study assesses near-term future changes in temperature extremes over China and Europe in scenarios with two very different anthropogenic aerosol (AA) pathways from 2016 to 2049: a maximum technically feasible aerosol reduction (MTFR), and a current legislation aerosol scenario (CLE), both with greenhouses gas forcing following RCP 4.5. Simulations with a fully coupled atmosphere-ocean model HadGEM3-GC2 show that there is an increase in hot extremes and a decrease in cold extremes relative to the present day (1995–2014) over China and Europe in both scenarios. However, the magnitude of the changes in both hot and cold extremes depends strongly on the AA pathway. The AA reduction in MTFR amplifies the changes in temperature extremes relative to CLE, and accounts for 40% and 30% of the projected changes in temperature extremes relative to present day over China and Europe, respectively. Thus, this study suggests that future and current policy decisions about AA emissions have the potential for a large near-term impact on temperature extremes.

Droughts represent a severe and increasing risk for the livestock sector as they can reduce yields of hay and feed grain. Droughts are predicted to increase in frequency and magnitude under climate change. Here we estimate the so far unexplored effect of drought shocks on feed prices. We use an empirical example from Germany and focus on the prices of hay as well as feed wheat and barley. Our results show that regional and national droughts substantially increase hay prices by up to 15%, starting with a delay of about 3 months and lasting for about a year. In contrast, feed grain prices in our sample are not affected by regional or national droughts. These price responses can be linked to market integration, as the hay market is usually regionally organized while feed grains are traded transnationally. It is important to include this knowledge into farm management and policy actions, especially considering climate change.

Land use/cover change (LUCC) has taken place since the 1990s in central Taiwan; however, its impacts on the local and regional hydroclimatology are not understood thoroughly. This study is grounded in a numerical experiment using the Weather Research and Forecasting (WRF) model and statistical assessments of continuous land cover and gridded precipitation data derived for central Taiwan. We incorporate survey-based land use data in 1995 and 2007 in driving WRF to simulate selective non-rainy and rainy (dry and wet) cases under weak synoptic forcings in July and August (JA). The two land-use conditions reveal changes in simulation fields on account of increased urban and built-up lands. Results averaged over the dry cases show increased (diminished) sensible heat fluxes and 2 m temperatures (latent heat fluxes and 2 m specific humidity) in 2007 compared to that in 1995. The wet-case simulation further identifies intensified precipitation over the downwind areas of urban and built-up lands, strongly subject to local topography and prevailing winds. Statistical assessments of the Landsat land cover and gridded precipitation data verify significant increasing trends in urbanization and the JA rainfall. Regression-based analysis that scales the effect of the LUCC on the change in precipitation corroborates the WRF simulation: LUCC has induced eastward, downwind association with the JA rainfall.

Large-scale crop yield estimation is critical for understanding the dynamics of global food security. Understanding and quantifying the temporal cumulative effect of crop growth and spatial variances across different regions remains challenging for large-scale crop yield estimation. In this study, a deep spatial-temporal learning framework, named DeepCropNet (DCN), has been developed to hierarchically capture the features for county-level corn yield estimation. The temporal features are learned by an attention-based long short-term memory network and the spatial features are learned by the multi-task learning (MTL) output layers. The DCN model has been applied to quantify the relationship between meteorological factors and the county-level corn yield in the US Corn Belt from 1981 to 2016. Three meteorological factors, including growing degree days, killing degree days, and precipitation, are used as time-series inputs. The results show that DCN provides an improved estimation accuracy (RMSE = 0.82 Mg ha⁻¹) as compared to that of conventional methods such as LASSO (RMSE = 1.14 Mg ha⁻¹) and Random Forest (RMSE = 1.05 Mg ha⁻¹). Temporally, the attention values computed from the temporal learning module indicate that DCN captures the temporal cumulative effect and this temporal pattern is consistent across all states. Spatially, the spatial learning module improves the estimation accuracy based on the regional specific features captured by the MTL mechanism. The study highlights that the DCN model provides a promising spatial-temporal learning framework for corn yield estimation under changing meteorological conditions across large spatial regions.

Achieving sustainable development goals requires targeting and monitoring sustainable solutions tailored to different social and ecological contexts. A social-ecological systems (SESs) framework was developed to help diagnose problems, identify complex interactions, and solutions tailored to each SES. Here we develop a data-driven method for upscaling the SES framework and apply it to a context where data is scarce, but also where solutions towards sustainable development are needed. The purpose of upscaling the framework is to create a tool that facilitates decision-making in data-scarce contexts. We mapped SES by applying the framework to poverty alleviation and food security issues in the Volta River basin in Ghana and Burkina Faso. We found archetypical configurations of SES in space, and discuss where agricultural innovations such as water reservoirs might have a stronger impact at increasing food availability and therefore alleviating poverty and hunger. We conclude by outlining how the method can be used in other SES comparative studies.

To effectively manage economic transition and pursue sustainable development, the Chinese government has promulgated a series of policies in the 13th Five Year (2016–2020) Plan (FYP), covering social security, economic growth, energy transition, resource conservation, and environmental protection. To balance the various 13th FYP policy targets, we propose a multi-objective optimization model based on multi-regional input–output analysis. The model integrates the management of employment, energy consumption, water use, carbon emissions, and pollutant emissions by determining a policy-dominated industrial restructuring pathway that would best achieve consistency in sustainable development policies, adaptation to the national industrial development trend, and regional equity among China's provinces. Synergies and trade-offs among various policies are also discussed. Our optimization results show that an energy-consumption-dominated industrial restructuring pathway is the best solution, as it would satisfy various sustainable targets, facilitate (restrain) development of high-value-added (high-energy-consumption and high-emissions) sectors, as well as improve regional equity. Therefore, to realize sustainability, the energy policy should be prioritized when formulating an industrial restructuring pathway. Applying such a multi-objective optimization model provides policymakers with a comprehensive approach to support sustainable development policies.

Electrification is widely considered an attractive solution for reducing the oil dependency and environmental impact of road transportation. Many countries have been establishing increasingly stringent and ambitious targets in support of transport electrification. We conducted scenario simulations to depict the role of transport electrification in climate change mitigation and how the transport sector would interact with the energy-supply sector. The results showed that transport electrification without the replacement of fossil-fuel power plants leads to the unfortunate result of increasing emissions instead of achieving a low-carbon transition. While transport electrification alone would not contribute to climate change mitigation, it is interesting to note that switching to electrified road transport under the sustainable shared socioeconomic pathways permitted an optimistic outlook for a low-carbon transition, even in the absence of a decarbonized power sector. Another interesting finding was that the stringent penetration of electric vehicles can reduce the mitigation cost generated by the 2 °C climate stabilization target, implying a positive impact for transport policies on the economic system. With technological innovations such as electrified road transport, climate change mitigation does not have to occur at the expense of economic growth. Because a transport electrification policy closely interacts with energy and economic systems, transport planners, economists, and energy policymakers need to work together to propose policy schemes that consider a cross-sectoral balance for a green sustainable future.

Winter is often understudied in ecosystem sciences and viewed as a burden for human systems and infrastructure. However, the importance of winter in regulating ecological processes and shaping human communities has emerged as a topic of great interest, particularly in areas that experience seasonal snow cover. Traditional seasonal definitions may not fully represent below freezing winters and snow accumulation that have historically characterized these areas. Here we: (1) propose the concept of 'frigid winter' to address longstanding problems with traditional delineations of winter; and (2) define frigid winter as a period of sustained temperatures below freezing and snow accumulation that together regulate ecological processes and their services. We explore this definition and the changes occurring within it using 100 years of meteorological data from northeastern North America. Trend analysis demonstrates that frigid winters have shortened by ∼3 weeks over the last century, that cold, snowy conditions have become more intermittent, and that the choice of winter delineation (astronomical, meteorological, hibernal, or frigid) influences the apparent rate at which winter conditions disappear.

It is common to conceptualize the water table as a subdued replica of surface topography, where groundwater recharges at, and flows from, topographic highs and flows to, and discharges at, topographic lows, in humid (i.e. wetter) environments. This concept is also regularly applied to peatland hydrology, where hydraulic gradients are shown to be towards the peatland. However, this may not be a realistic representation of hydrology for low-relief and sub-humid regions. While it is widely accepted that peatlands maintain internal water tables in drought conditions through a system of autogenic negative feedback loops, there is a general lack of knowledge concerning the controls on, and patterns of, forestland hydrologic process that drive the hydraulic gradients between wetlands and their adjacent forestlands in water-limited conditions in low-relief areas. This study identifies the hydrologic function (i.e. source or sink of water) of forested uplands and peatlands in the Boreal Plains region of Canada and demonstrates that during a mesic (non-drought) year most peatlands are, in fact, potential sources of groundwater to adjacent forestlands. Sixteen forestland-peatland pairs were selected to represent a spectrum of forested hummock and peatland morphometries, topographic positions, and geologic settings. Hydraulic gradients determined for each well pair during the ice-off season demonstrate that the dominant gradient under mesic climatic conditions is from peatlands to adjacent forestlands, opposite of the topographic gradient, and that the sink-source function of each land unit does not change seasonally. Water table depressions under each forested hummock indicate that boreal forestlands are not reliable sources of groundwater recharge, spatially or temporally, which supports previous research showing that peatlands are the primary water source for runoff; illustrating the need for alternative conceptualizations of catchment hydrology in water limited regions of the boreal. Social Media Summary. Forests are poor sources of water to boreal peatlands and landscapes due to water table depressions.

The semi-arid ecosystems dominate the inter-annual variability of the global carbon sink and the driving role of semi-arid ecosystems is becoming increasingly important. However, the impacts of climate change on the dynamics of carbon and water fluxes in global semi-arid ecosystems are still not well understood. We used a data-driven (or machine learning) approach, along with observations from a number of FLUXNET sites and spatially continuous satellite and meteorological data, to generate gridded carbon and water flux estimates for semi-arid regions globally, and then examined the magnitude, spatial patterns, and trends of carbon and water fluxes and their responses to climate change during the period 1982–2015. The average annual gross primary productivity (GPP), net ecosystem productivity (NEP), evapotranspiration (ET), and water use efficiency (WUE) were 628.6 g C m⁻² yr⁻¹, 9.6 g C m⁻² yr⁻¹, 463.1 mm yr⁻¹, and 1.60 g C Kg⁻¹ H₂O, respectively. The climate conditions during the period 1982–2015 enhanced gross and net carbon uptake in global semi-arid regions. The spatially-averaged annual GPP, NEP, ET, and WUE in semi-arid regions showed significant increases both globally and regionally (Asia, Africa, and Australia). As with GPP and ET, WUE significantly increased in North America, Asia, Africa, and Australia. Australia was the most sensitive semi-arid region in terms of changes in carbon and water fluxes and their responses to climate. Semi-arid forests, shrublands, and savannas were net carbon sinks; croplands were minor carbon sources; grasslands were nearly carbon neutral. Overall, precipitation was the most important climate factor influencing the carbon and water fluxes; WUE in 40.9% of the semi-arid region was significantly influenced by precipitation. The global climate change is expected to influence global semi-arid ecosystems in many ways and our findings have implications for semi-arid ecosystem management and policy making.

Modeling of climate change impacts have mainly been focused on a small number of annual staple crops that provide most of the world's calories. Crop models typically do not represent perennial crops despite their high economic, nutritional, or cultural value. Here we assess climate change impacts on global tea production, chosen because of its high importance in culture and livelihoods of people around the world. We extended the dynamic global vegetation model with managed land, LPJmL4, global crop model to simulate the cultivation of tea plants. Simulated tea yields were validated and found in good agreement with historical observations as well as experiments on the effects of increasing CO₂ concentrations. We then projected yields into the future under a range of climate scenarios from the Inter-Sectoral Impact Model Intercomparison Project. Under current irrigation levels and lowest climate change scenarios, tea yields are expected to decrease in major producing countries. In most climate scenarios, we project that tea yields are set to increase in China, India, and Vietnam. However, yield losses are expected to affect Kenya, Indonesia, and Sri Lanka. If abundant water supply and full irrigation is assumed for all tea cultivation areas, yields are projected to increase in all regions.

Tidal marshes are valuable global carbon sinks, yet large uncertainties in coastal marsh carbon budgets and mediating mechanisms limit our ability to estimate fluxes and predict feedbacks with global change. To improve mechanistic understanding, we assess how net carbon storage is influenced by interactions between crab activity, water movement, and biogeochemistry. We show that crab burrows enhance carbon loss from tidal marsh sediments by physical and chemical feedback processes. Burrows increase near-creek sediment permeability in the summer by an order of magnitude compared to the winter crab dormancy period, promoting carbon-rich fluid exchange between the marsh and creek. Burrows also enhance vertical exchange by increasing the depth of the strongly carbon-oxidizing zone and reducing the capacity for carbon sequestration. Results reveal the mechanism through which crab burrows mediate the movement of carbon through tidal wetlands and highlight the importance of considering burrowing activity when making budget projections across temporal and spatial scales.

Nitrogen availability in Arctic ecosystems is a key driver for biological activity, including plant, growth and thereby directly linked to the greening of the Arctic. Here, we model the redistribution of meltwater following spring snowmelt as well as the accumulation of meltwater and dissolved nitrate at landscape scale. By combining snow mapping with unmanned aerial systems, snow chemistry, and hydrological modelling, we argue that the majority of nitrate in the snowpack is flushed out of the landscape due to the limited storage capacity of meltwater in the early growing season frozen soil. We illustrate how landscape micro-topography is a crucial parameter to quantify storage capacity of meltwater at landscape scale and thereby the associated pool of soluble compounds such as nitrate. This pool will be available for plants and may be important for plant diversity and growth rates in the wettest part of the landscape. This study illustrates that the evenly distributed nitrate input during the Arctic winter may be redistributed during the initial snowmelt and lead to marked differences in biologically available nitrate at the onset of the growing season, but also that the majority of deposited nitrate in snow is lost from the terrestrial to the aquatic environment during snowmelt.

Wildfires are a significant agent of disturbance in forests and highly sensitive to climate change. Short-interval fires and high severity (mortality-causing) fires in particular, may catalyze rapid and substantial ecosystem shifts by eliminating woody species and triggering conversions from forest to shrub or grassland ecosystems. Modeling and fine-scale observations suggest negative feedbacks between fire and fuels should limit reburn prevalence as overall fire frequency rises. However, while we have good information on reburning patterns for individual fires or small regions, the validity of scaling these conclusions to broad regions like the US West remains unknown. Both the prevalence of reburning and the strength of feedbacks on likelihood of reburning over differing timescales have not been documented at the regional scale. Here we show that while there is a strong negative feedback for very short reburning intervals throughout wildland forests of the Western US, that feedback weakens after 10–20 years. The relationship between reburning intervals and drought diverges depending on location, with coastal systems reburning quicker (e.g. shorter interval between fires) in wetter conditions and interior forests in drier. This supports the idea that vegetation productivity—primarily fine fuels that accumulate rapidly (<10 years)—is of primary importance in determining reburn intervals. Our results demonstrate that while over short time intervals increasing fires inhibits reburning at broad scales, that breaks down after a decade. This provides important insights about patterns at very broad scales and agrees with finer scale work, suggesting that lessons from those scales apply across the entire western US.

Rapid changes in climatic conditions threaten both socioeconomic and ecological systems, as these might not be able to adapt or to migrate at the same pace as that of global warming. In particular, an increase of weather and climate extremes can lead to increased stress on human and natural systems, and a tendency for serious adverse effects. We rely on the EURO-CORDEX simulations and focus on the the screen-level daily mean temperature (T2m). We compare the shifting velocities of the cold and hot extremes with these of the associated central trends, i.e. the arithmetical mean or median. We define the extremes relative to the T2m distribution as it evolves with time over the period of 1951–2100. We find that temperature extremes shift at a similar velocity compared to that of the central trends. Accordingly, the T2m probability distribution shifts mostly as a whole, as the tails of the distribution increase together with the central trends. Exceptions occur however in specific regions and for the clustering of warm days, which shifts slower than all other extremes investigated in this study.

Reducing deforestation can generate multiple economic, social and ecological benefits by safeguarding the climate and other ecosystem services provided by forests. Understanding the relative contribution of different drivers of deforestation is needed to guide policies seeking to maintain natural forest cover. We assessed 119 randomly selected plots from areas deforested between 2010 and 2017, in Tanzania. Through ground surveys and stakeholder interviews we assessed the proximate deforestation drivers at each point. Crop cultivation was the most commonly observed driver occurring in 89% of plots, compared to livestock grazing (69%) and charcoal (35%). There was evidence of fire in 77% of plots. Most deforestation events involved multiple drivers, with 83% of plots showing signs of two or more drivers. Stakeholder interviews identified agriculture as the primary deforestation driver in 81% of plots, substantially more than charcoal production (12%), timber harvesting (1%) and livestock (1%). Policy-makers in Tanzania have sought to reduce deforestation by reducing demand for charcoal. However, our work demonstrates that agriculture, not charcoal, is the main driver of deforestation in Tanzania. Beyond protected areas, there is no clear policy limiting the conversion of forests to agricultural land. Reducing deforestation in Tanzania requires greater inter-sectoral coordination between the agriculture, livestock, land, energy and forest sectors.

Reducing methane emissions from the oil and gas industry is a critical climate action policy tool in Canada and the US. Optical gas imaging-based leak detection and repair (LDAR) surveys are commonly used to address fugitive methane emissions or leaks. Despite widespread use, there is little empirical measurement of the effectiveness of LDAR programs at reducing long-term leakage, especially over the scale of months to years. In this study, we measure the effectiveness of LDAR surveys by quantifying emissions at 36 unconventional liquids-rich natural gas facilities in Alberta, Canada. A representative subset of these 36 facilities were visited twice by the same detection team: an initial survey and a post-repair re-survey occurring ∼0.5–2 years after the initial survey. Overall, total emissions reduced by 44% after one LDAR survey, combining a reduction in fugitive emissions of 22% and vented emissions by 47%. Furthermore, >90% of the leaks found in the initial survey were not emitting in the re-survey, suggesting high repair effectiveness. However, fugitive emissions reduced by only 22% because of new leaks that occurred between the surveys. This indicates a need for frequent, effective, and low-cost LDAR surveys to target new leaks. The large reduction in vent emissions is associated with potentially stochastic changes to tank-related emissions, which contributed ∼45% of all emissions. Our data suggest a key role for tank-specific abatement strategies as an effective way to reduce oil and gas methane emissions. Finally, mitigation policies will also benefit from more definitive classification of leaks and vents.

Marine reserves can be effective conservation and fishery management tools, particularly when their design accounts for spatial population connectivity. Yet climate change is expected to significantly alter larval connectivity of many marine species, questioning whether marine reserves designed today will still be effective in the future. Here we predict how alternative marine reserve designs will affect fishery yields. We apply a range of empirically-grounded scenarios for future larval dispersal to fishery models of seven species currently managed through marine reserves in the nearshore waters in Southern California, USA. We show that networks of reserves optimized for future climate conditions differ substantially from networks designed for today's conditions. However, the benefits of redesign are modest: a set of reserves designed for current conditions commonly produces outcomes within 10% of the best redesigned network, and far outperforms haphazardly designed networks. Thus, investing in the strategic design of marine reserves networks today may pay dividends even if the networks are not modified to keep up with environmental change.

Convection-permitting models (CPMs)—the newest generation of high-resolution climate models—have been shown to greatly improve the representation of subdaily and hourly precipitation, in particular for extreme rainfall. Intense precipitation events, however, often occur on subhourly timescales. The distribution of subhourly precipitation, extreme or otherwise, during a rain event can furthermore have important knock-on effects on hydrological processes. Little is known about how well CPMs represent precipitation at the subhourly timescale, compared to the hourly. Here we perform multi-decadal CPM simulations centred over Catalonia and, comparing with a high temporal-resolution gauge network, find that the CPM simulates subhourly precipitation at least as well as hourly precipitation is simulated. While the CPM inherits a dry bias found in its parent model, across a range of diagnostics and aggregation times (5, 15, 30 and 60 min) we find no consistent evidence that the CPM precipitation bias worsens with shortening temporal aggregation. We furthermore show that the CPM excels in its representation of subhourly extremes, extending previous findings at the hourly timescale. Our findings support the use of CPMs for modelling subhourly rainfall and add confidence to CPM-based climate projections of future changes in subhourly precipitation, particularly for extremes.

Large-scale agricultural expansion can influence near-surface climate by altering surface energy fluxes, water, and albedo. It is less clear whether such effects extend through the full troposphere and how such effects vary in time. Here we use a novel dataset documenting the massive land use and land cover change due to agricultural expansion in Northeast China from 1982 to 2010 to assess how such expansion has influenced climate over the full troposphere. Confronting our land classification and climate data with a number of statistical approaches (linear regression, correlation analysis, Granger-causality), we find that cropland significantly increased by ∼28% over the near 30 year period in Northeast China-an average rate of nearly a percentage per year. This massive 30 year agricultural expansion is tightly associated with near-surface cooling identified in station data during the late growing season (August to September). Assuming no cropland expansion over the 30 year period, surface temperature would have increased by 0.93 °C ± 0.4 °C. Furthermore, the fingerprint of cropland-associated cooling extends upward into the atmospheric column, influencing the vertical structure of the regional troposphere and potentially its circulation. For every 10 percentage points increase in cropland fraction over Northeast China, regional full-troposphere temperature and geopotential height significantly decrease by 0.2 °C–0.6 °C and 20 m–80 m, respectively. These observed relationships are remarkably coherent across datasets, methodological choices, atmospheric levels, and theory, suggesting that the observational effects we identify are robust and imply the possibility of detectable land use change effects on regional circulation, with potential consequences for the East Asian monsoon.

In November 2016, after 52 years of armed conflict, the Colombian government and the primary rebel group, the FARC (Fuerzas Armadas Revolucionarias de Colombia) reached a peace agreement. The agreement incorporated three changes to institutions governing forest land occupation and use: (a) the demobilization of FARC from forested places, (2) the future distribution of legal land titles and new road construction into forests, and (3) the eradication of illicit crops. However, we document unprecedented rates of forest disturbance in the months following the peace agreement in biodiversity hotspots across the country. Are the declaration of peace and the increased rates of forest disturbance related? Here, we present the first systematic assessment of the impact of the Colombian peace agreement on forest disturbance. Focusing on the Andes-Amazon Transition Belt (AATB), we used automated satellite image disturbance detection methods and ethnographic data to quantify and interpret forest cover change from 2010 to 2018 that span wartime, peace negotiation, and post-peace agreement stages. Our findings indicate that during the post-peace agreement period (2017–2018), the area of forest disturbance increased by 50% (about 238 000 ha) across the AATB in comparison with the four-year peace negotiation stage (2013–2016); these changes reflect the end of FARC-led gunpoint conservation in the region. Forest disturbance also spread deeper into the Amazon watershed and increased in area by 187% within the AATB's protected areas. We find that following the peace agreement and the withdrawal of FARC, key actors (viz. drug cartels, large landowners, campesinos and dissidents) with expectations of favorable land tenure policies swept into the region; this led to increases in large-scale cattle ranching, coca cultivation dispersal, and speculative illegal land markets each of which contributed to the widespread forest disturbance that we mapped. The rapid increase in forest disturbance occurred despite the interest of the international community in promoting forest conservation initiatives in the AATB and Colombia's existing conservation and land titling frameworks for public lands. Our findings underscore the need for conservation strategies sensitive to rapid institutional and demographic changes in the course of the peace agreement to prevent forests from becoming an unexpected casualty of premature and unstable peace.

Mangroves are one of the world's most threatened ecosystems, and Myanmar is regarded as the current mangrove deforestation hotspot globally. Here, we use multi-sensor satellite data and Intensity Analysis to quantify and explain patterns of net and gross mangrove cover change (loss, gain, persistence) for the 1996–2016 period across all of Myanmar. Net national mangrove cover declined by 52% over 20 years, with annual net loss rates of 3.60%–3.87%. Gross mangrove deforestation was more profound: 63% of the 1996 mangrove extent had been temporarily or permanently converted by 2016. Rice, oil palm, and rubber expansion accounted for most conversion; however, our analysis revealed targeted systematic transitions of mangroves to water (presumably aquaculture) and built-up areas indicated emerging threats for mangroves from those land uses. Restoration programmes facilitated mangrove gains and represent a critical area for investment alongside protection. This study demonstrates the importance of multi-sensor satellite data for national-level mangrove change assessments, along with gross land cover transition analyses to assess landscape dynamics as well as prioritise threats and interventions in an effort to develop holistic strategies that aim to conserve important habitats.

In recent decades, trends in photovoltaic (PV) technology deployment have shown an overall increase across the world. Comprehensive knowledge of the solar resource and its future evolution is demanded by the energy sector. Solar resource and PV potential have been estimated in several studies using both the global climate model (GCM) and regional climate model (RCM), revealing a GCM–RCM discrepancy in the projected change over Europe. An increase in surface solar radiation (SSR) (and therefore in PV potential production) is projected by GCMs, whereas most RCM simulations project a decrease in SSR over Europe. In this work, we investigate the role of aerosol forcing in RCMs as a key explaining factor of this inconsistency. The results show that RCM simulations including evolving aerosols agree with GCMs in the sign and amplitude of the SSR change over Europe for mid-21st century projections (2021–2050 compared to 1971–2000 for representative concentration pathway climate change scenario RCP8.5). The opposite signal is projected by the rest of the RCMs. The amplitude of the changes likely depends on the RCM and on its aerosol forcing choice. In terms of PV potential, RCMs including evolving aerosols simulate an increase, especially in summer for Central and Eastern Europe, with maximum values reaching +10% in some cases. This study illustrates the key role of the often-neglected aerosol forcing evolution in RCMs. It also suggests that it is important to be very careful when using the multi-model Coordinated Regional Climate Downscaling Experiment (CORDEX) projections for solar radiation and related variables, and argues for the inclusion of aerosol forcing evolution in the next generation of CORDEX simulations.

Most glaciers in South America and on the Antarctic Peninsula are retreating and thinning. They are considered strong contributors to global sea level rise. However, there is a lack of glacier mass balance studies in other areas of the Southern Hemisphere, such as the surrounding Antarctic Islands. Here, we present a detailed quantification of the 21st century glacier elevation and mass changes for the entire South Georgia Island using bi-static synthetic aperture radar interferometry between 2000 and 2013. The results suggest a significant mass loss since the beginning of the present century. We calculate an average glacier mass balance of −1.04 ± 0.09 m w.e.a⁻¹ and a mass loss rate of 2.28 ± 0.19 Gt a⁻¹ (2000–2013), contributing 0.006 ± 0.001 mm a⁻¹ to sea-level rise. Additionally, we calculate a subaqueous mass loss of 0.77 ± 0.04 Gt a⁻¹ (2003–2016), with an area change at the marine and lake-terminating glacier fronts of −6.58 ± 0.33 km² a⁻¹, corresponding to ∼4% of the total glacier area. Overall, we observe negative mass balance rates in South Georgia, with the highest thinning and retreat rates at the large outlet glaciers located at the north-east coast. Although the spaceborne remote sensing dataset analysed in this research is a key contribution to better understanding of the glacier changes in South Georgia, more detailed field measurements, glacier dynamics studies or further long-term analysis with high-resolution regional climate models are required to precisely identify the forcing factors.

This study examines the effects of 1.5 °C and 2 °C global warming levels (GWLs) on intra-seasonal rainfall characteristics over the Greater Horn of Africa. The impacts are analysed based on the outputs of a 25-member regional multi-model ensemble from the Coordinated Regional Climate Downscaling Experiment project. The regional climate models were driven by Coupled Model Intercomparison Project Phase 5 Global Climate Models for historical and future (RCP8.5) periods. We analyse the three major seasons over the region, namely March–May, June–September, and October–December. Results indicate widespread robust changes in the mean intra-seasonal rainfall characteristics at 1.5 °C and 2 °C GWLs especially for the June–September and October–December seasons. The March–May season is projected to shift for both GWL scenarios with the season starting and ending early. During the June–September season, there is a robust indication of delayed onset, reduction in consecutive wet days and shortening of the length of rainy season over parts of the northern sector under 2 °C GWL. During the October–December season, the region is projected to have late-onset, delayed cessation, reduced consecutive wet days and a longer season over most of the equatorial region under the 2 °C GWL. These results indicate that it is crucial to limit the GWL to below 1.5 °C as the differences between the 1.5 °C and 2 °C GWLs in some cases exacerbates changes in the intra-seasonal rainfall characteristics over the Greater Horn of Africa.

This study characterizes the degree to which current polar-orbiting satellites can evaluate the daytime change in NO₂ vertical column density (VCD) in urban, suburban, and rural areas. We examine these issues by considering the diurnal cycle of NO₂ over the United States, using the large NO₂ monitoring network supported by states, tribes, and the US Environmental Protection Agency (EPA). Through this analysis, we identify the potential opportunities and limitations of current space-based NO₂ data in capturing diurnal change. Ground-based monitoring data from the US EPA are compared with satellite retrievals of NO₂ from the KNMI Tropospheric Emissions Monitoring Internet Service (TEMIS) for two instruments: GOME-2 with a mid-morning overpass, and OMI with an early afternoon overpass. Satellite data show evidence of higher morning NO₂ in the vicinity of large urban areas. Both satellites and ground monitors show ∼1.5–2x greater NO₂ abundance between morning and afternoon in urban areas. Despite differences in horizontal resolution and overpass time, the two satellite retrievals show similar agreement with ground-based NO₂ measurements. When analyzed on a pixel-by-pixel basis, we find evidence for spatial structure in the diurnal change in NO₂ between city center and surrounding areas in Southern California. Wider analysis of urban-suburban structure in diurnal NO₂ change is hindered by resolution differences in the two satellite instruments, which have the potential to create data artefacts. This study highlights the value of future geostationary instruments to provide comparable satellite retrievals for NO₂ over the course of a day, and research needs related to the effective utilization of NO₂ satellite data for air quality applications.

Recent studies have revealed large and robust correlations between seasonal climate and violent crime rates at regional scales within the continental United States, begging the question of how future climate change will influence violent crime rates. Here, we combine empirical models from previous studies with 42 state-of-the-art global climate models to make such projections, while accounting for key factors like regionality and seasonality, and appropriately combining multiple of sources of uncertainty. Our results indicate that the United States should expect an additional 3.2 [2.1–4.5] or 2.3 [1.5–3.2] million violent crimes between 2020 and 2099, depending on greenhouse gas emissions scenario. We also reveal critical dependencies of these violent crime projections on various global warming targets, such as those associated with the Paris Agreement (1.5 °C and 2 °C). These results emphasize the often-overlooked socially-mediated impacts of climate change on human health, with an estimated economic cost of $5 billion annually.

Agriculture is one of the most impactful ways that we interact with the environment. Food demand is expected to increase 70% by 2050 as a result of population growth and the emergence of the global middle class. Meeting the expected demand in a sustainable manner will require an integrated systems-level approach to food production and supply. We present a conceptual framework for estimating the cradle-to-market life-cycle seasonal greenhouse gas emissions impact of fresh produce commodities, including the production, post-harvest processing, packaging, and transportation stages. Using oranges as a case study, we estimate the carbon footprint per kilogram of fruit delivered to wholesale market in New York City, Los Angeles, Chicago, and Atlanta and assess the relative importance of transportation mode, transportation distance (i.e. localness), and seasonality. We find that the cradle-to-market carbon footprint of oranges delivered to US cities can vary by more than a factor of two, depending on the production origin (e.g. 0.3 kgCO₂e/kg for Californian oranges delivered to New York City versus 0.7 kgCO₂e/kg for Mexican oranges delivered to New York City). The transportation mode was found to have a significant impact on the results; transportation-related greenhouse gas emissions associated with oranges trucked from Mexico to New York City were found to be six times higher than those transported by containership from Chile, in spite of traveling less than half the distance. Seasonality had a moderate impact on the results and varied depending on the destination city; based on our cradle-to-market analysis, the average carbon footprint of 'out-of-season' oranges relative to 'in-season' oranges increased by 51%, 46%, 14%, and 24% for Atlanta, Chicago, Los Angeles, and New York City, respectively. This study highlights the value of regionally-specific carbon footprinting for fresh produce and the need for a consistent and standardized data reporting framework for agricultural systems.

Urban land use land cover (LULC) change raises ambient temperature and modifies atmospheric moisture, which increases heat-related health risks in cities. Greenspace and bluespace commonly coexist in urban landscapes and are nature-based heat mitigation strategies. Yet, their interactive effects on urban thermal environments are rarely assessed and it remains unclear how extreme heat events (EHEs) affect their ability to regulate human thermal comfort. Using multi-year observations from a dense urban observational network in Madison, WI, we found that green and blue spaces jointly modify the intraurban spatiotemporal variability of temperature and humidity, and the resultant effects on thermal comfort show diurnal and seasonal asymmetry. Greenspace is more effective at cooling throughout the year, particularly at night. Accelerated cooling efficiency is found in areas with dominant greenspace coverage and little co-influence from bluespace. The thermal comfort benefit due to greenspaces can be offset by bluespaces because of intensified nighttime warming and humidifying effects during the warm months, although a weak daytime cooling of bluespace is observed. EHEs enhance bluespace cooling, but the overall joint thermal regulation remains the same due to the enhanced moisture effect. Our findings suggest that diverse outcomes of green and blue spaces cross multiple temporal scales should be holistically assessed in urban planning. The analysis framework based on generalized additive models is robust and transferable to other cities and applications to disentangle the nonlinear co-influences of different drivers of urban environmental phenomena.

Vegetation phenology in spring has received much attention for its importance to terrestrial ecosystem carbon exchange and climate–biosphere interactions studies. Through control on surface water and heat balance, snow cover largely impacts on spring vegetation phenology. However, under the background of global warming and rapid reduction of spring snow cover extent across the Northern Hemisphere (NH), the responses of spring vegetation phenology have not been well documented. Using two satellite-derived land cover dynamic datasets and 420 in situ vegetation phenology observations from five filed datasets, this study evaluated the accuracy of satellite-derived vegetation phenology datasets and explored the changes of start of the growing season (SOS) across the NH snow-covered landmass for the period 2001–2014. Compared with MEaSUREs VIPPHEN, the MODIS SOS maps displayed higher accuracy in capture the real SOS climatology by validating with in situ observations (R² = 0.67, bias = −3.99 d). Moreover, evidences from MODIS SOS maps pointed out that the SOS advanced by approximately 2.36 d in NH middle to high latitudes (43.5°N–70.0°N), but delayed by about 0.53 d in lower latitudes (33.0°N–43.5°N) from 2001 to 2014. The contrast SOS anomalies across the NH snow-covered landmass were further proved by changes in spring NDVI derived from GIMMS in the corresponding period. In addition, the observed changes in SOS were consistent with the spatiotemporal pattern of spring snow end date found in previous studies, indicating vegetation phenology changes should be taken into account in estimating the impacts of snow in climate–biosphere interactions studies.

The observed warming in the atmosphere and ocean can be used to estimate the climate sensitivity linked to present-day feedbacks, which is referred to as the effective climate sensitivity (S_hist). However, such an estimate is affected by uncertainty in the radiative forcing, particularly aerosols, over the historical period. Here, we make use of detection and attribution techniques to derive the surface air temperature and ocean warming that can be attributed directly to greenhouse gas increases. These serve as inputs to a simple energy budget to infer the likelihood of S_hist in response to observed greenhouse gases increases over two time periods (1862–2012 and 1955–2012). The benefit of using greenhouse gas attributable quantities is that they are not subject to uncertainties in the aerosol forcing (other than uncertainty in the attribution to greenhouse gas versus aerosol forcing not captured by the multi-model aerosol response pattern). The resulting effective climate sensitivity estimate, S_hist, ranges from 1.3 °C to 3.1 °C (5%–95% range) over the full instrumental period (1862–2012) for our best estimate, and gets slightly wider when considering further uncertainties. This estimate increases to 1.7 °C–4.6 °C if using the shorter period (1955–2012). We also evaluate the climate model simulated surface air temperature and ocean heat content increase in response to greenhouse gas forcing over the same periods, and compare them with the observationally-constrained values. We find that that the ocean warming simulated in greenhouse gas only simulations in models considered here is consistent with that attributed to greenhouse gas increases from observations, while one model simulates more greenhouse gas-induced surface air warming than observed. However, other models with sensitivity outside our range show greenhouse gas warming that is consistent with that attributed in observations, emphasising that feedbacks during the historical period may differ from the feedbacks at CO₂ doubling and from those at true equilibrium.

The Transient Climate Response to Cumulative CO₂ Emissions (TCRE) is the proportionality between global temperature change and cumulative CO₂ emissions. The TCRE implies a finite quantity of CO₂ emissions, or carbon budget, consistent with a given temperature change limit. The uncertainty of the TCRE is often assumed be normally distributed, but this assumption has yet to be validated. We calculated the TCRE using a zero-dimensional ocean diffusive model and a Monte-Carlo error propagation (n = 10 000 000) randomly drawing from probability density functions of the climate feedback parameter, the land-borne fraction of carbon, radiative forcing from an e-fold increase in CO₂ concentration, effective ocean diffusivity, and the ratio of sea to global surface temperature change. The calculated TCRE has a positively skewed distribution, ranging from 1.1 to 2.9 K EgC⁻¹ (5%–95% confidence), with a mean and median value of 1.9 and 1.8 K EgC⁻¹. The calculated distribution of the TCRE is well described by a log-normal distribution. The CO₂-only carbon budget compatible with 2 °C warming is 1100 PgC, ranging from 700 to 1800 PgC (5%–95% confidence) estimated using a simplified model of ocean dynamics. Climate sensitivity is the most influential Earth System parameter on the TCRE, followed by the land-borne fraction of carbon, radiative forcing from an e-fold increase in CO₂, effective ocean diffusivity, and the ratio of sea to global surface temperature change. While the uncertainty of the TCRE is considerable, the use of a log-normal distribution may improve estimations of the TCRE and associated carbon budgets.

Researchers and policy-makers often assume that public preferences for climate change adaptation are positive and stable compared to those of mitigation. However, public judgments about adaptation in natural resource sectors (like forestry) require that people make difficult, value-laden and uncertain trade-offs across complex social-ecological systems. The deliberative methods (e.g. focus groups and in-depth interviews) that are typically used to explore the malleability of these judgments may underestimate the level of preference malleability in broader publics by encouraging participants to rationalize their choices in relation to their own knowledge, values and beliefs, as well as those of others. Here, we use a public survey (N = 1926) from British Columbia, Canada—where forestry is economically, environmentally and culturally vital—to investigate the malleability of public preferences for genomics-based assisted migration (AM) for climate change adaptation in forests. Following an initial judgment, respondents are given new information about AM's potential implementation and impacts—simple messages similar to those that they might encounter through traditional and social media. The results show that respondents' initial judgments are surprisingly malleable, and prone to large bi-directional shifts across all message types. The magnitude of this malleability is related to the degree of the proposed intervention, the type of message, and individuals' demographic and psychographic characteristics. These results suggest that high levels of initial public support may be illusory, and that more attention should be paid to the potential for malleability, controversy and contradiction as adaptation policies are developed and implemented. Process-based arguments related to transparent, evidence-based and adaptive governance may be more influential than risk-based arguments related to climate change and economic impacts.

Using the Seoul National University Earth System Model Version 0 (SEM0) and Community Earth System Model version 1 (CESM1), we analyzed the impacts of El Niño-Southern oscillation (ENSO) and Madden–Julian oscillation (MJO) on the genesis of tropical cyclones (TG). SEM0 is known to simulate ENSO, MJO, and TG reasonably well, all of which are difficult to simulate with general circulation models (GCMs). Observational analysis revealed that both ENSO and MJO have substantial impacts on global TG and that the impact of ENSO (MJO) on regional TG varies in a complex manner depending on the phase of MJO (ENSO). Both GCMs underestimate the observed TG over the North Atlantic and have relatively poor performance for simulating the TG anomalies associated with ENSO compared to simulating those associated with MJO. Overall, SEM0 shows much better performance than CESM1 in terms of reproducing the observed impacts of MJO and combined impacts of ENSO and MJO on TG. Therefore, SEM0 can serve as a useful tool for studying the interactions among them to improve the forecasting of tropical cyclones.

Sea salt aerosols (SSA), one of the most abundant aerosol species over the global oceans, play important roles for Earth's climate. State-of-the-art SSA parameterizations in global climate models (GCMs) are typically modeled using near-surface wind speed, sea surface temperature (SST), and precipitation. However, these have non-trivial biases in CMIP3 and CMIP5 GCMs over the tropical Pacific Ocean that can contribute to biases in the simulated SSA. This study investigates the impacts of falling ice radiative effects on the biases of the aforementioned modeled parameters and the resulting modeled SSA biases. We compare the CMIP5 modeled SSA against satellite observations from MISR and MODIS using a pair of sensitivity experiments with falling ice radiative effects on and off in the CESM1-CAM5 model. The results show that when falling ice radiative effects are not taken into account, models have weaker surface wind speeds, warmer SSTs, excessive precipitation, and diluted sea surface salinity (SSS) over the Pacific trade-wind regions, leading to underestimated SSA. In the tropical Pacific Ocean, the inclusion of falling ice radiative effects leads to improvements in the modeled near-surface wind speeds, SSTs, and precipitation through cloud-precipitation-radiation-circulation coupling, which results in more representative patterns of SSA and reduces the SSA biases by ∼10% to 15% relative to the satellite observations. Models including falling ice radiative effects in CMIP5 produce smaller biases in SSA than those without falling ice radiative effects. We suggest that one of the causes of these biases is likely the failure to account for falling ice radiative effects, and these biases in turn affect the direct and indirect effects of SSA in the GCMs.

Precipitation extremes are among the most dangerous climate-related hazards, and these hazards often cause large socioeconomic losses and exert severe human health impacts each year. It is thus crucial to assess future exposure changes to precipitation extremes under different warming scenarios to improve the mitigation of climate change. Here, we project future exposure using a set of Coupled Earth System Model low-warming simulations and RCP8.5 large ensemble simulations. We find that the precipitation extremes are projected to significantly increase over the coming century under different future warming scenarios at both the global and regional levels. Compared to a 1.5 °C warmer climate, the 0.5 °C of additional warming under a 2.0 °C warmer future would increase the number of days of global aggregate precipitation extremes by approximately 3.6% by the end of this century. As a result, the global aggregate exposure is reported to increase by approximately 2.3% if the surface air temperature increases to 2.0 °C rather than 1.5 °C. An increase in exposure is also obvious for most regions across the world, and the largest increase in the future occurs over North Asia in response to the 0.5 °C of additional warming. Furthermore, exposure would increase more rapidly if the temperature increased following the RCP8.5 pathway. The exposure increase varies at the regional level, but in most cases, climate change shows more influential than that of the population; in addition, this influence does not depend on the population outcomes used here.

Significant progress has been made towards mitigating climate change and its impacts across countries. However, the transboundary effect of CO₂ emissions means that excluding the actions and inactions of certain countries and territories that escalate emissions is alarming. On this note, we examined the heterogeneous contribution of immediate and underlying drivers of emissions across 206 countries and territories for the period spanning 1960–2018. We deployed a dynamic panel estimation technique that accounts for cross-sectional dependence, heterogeneous parameters across countries, and dynamic correlated effects—a constraint for socio-economic, consumption- and pollution-based models. A global accounting of economic policy and debt, population structure, density and urbanization, and environmental-related aggregate indicators in a carbon emission function is presented. The empirical results demonstrate that the overarching effect of the instantaneous increase in economic development, population dynamics and energy utilization stimulate global emissions at national, urban and household levels across countries and territories. Industrialization and trade were found to escalate global pollution levels due to the impact of carbonized and energy-intensive economic structure in many developing and developed economies. Urbanization, urban income growth, and urban energy consumption are intertwined, hence, the institution of urban-related policy interventions is likely to negate the trio-impact on environmental sustainability. The triple effect (exploitation of natural resources, production and consumption) of economic development spurs environmental pollution, thus, calls for structural change from a carbonized to a decarbonized economy. The complex interaction highlights diversification of the energy mix by the inclusion of clean and renewable energy sources, fossil fuel-switching, and modern technologies like carbon capture and storage to improve energy efficiency and decline emission intensities.

The investigation of risk due to weather and climate events is an example of policy relevant science. Risk is the result of complex interactions between the physical environment (geophysical events or conditions, including but not limited to weather and climate events) and societal factors (vulnerability and exposure). The societal impact of two similar meteorological events at different times or different locations may therefore vary widely. Despite the complex relation between meteorological conditions and impacts, most meteorological research is focused on the occurrence or severity of extreme meteorological events, and climate impact research often undersamples climatological natural variability. Here we argue that an approach of ensemble climate-impact modelling is required to adequately investigate the relationship between meteorology and extreme impact events. We demonstrate that extreme weather conditions do not always lead to extreme impacts; in contrast, extreme impacts may result from (coinciding) moderate weather conditions. Explicit modelling of climate impacts, using the complete distribution of weather realisations, is thus necessary to ensure that the most extreme impact events are identified. The approach allows for the investigation of high-impact meteorological conditions and provides higher accuracy for consequent estimates of risk.

To understand whether high temperatures and temperature extremes are important for climate change adaptation in Scotland, we place the 2018 heatwave in the context of past, present, and future climate, and provide a rapid but comprehensive impact analysis. The observed hottest day (d), 5 d, and 30 d period of 2018 and the 5 d period with the warmest nights had return periods of 5–15 years for 1950–2018. The warmest night and the maximum 30 d average nighttime temperature were more unusual with return periods of >30 years. Anthropogenic climate change since 1850 has made all these high-temperature extremes more likely. Higher risk ratios are found for experiments from the CMIP6-generation global climate model HadGEM3-GA6 compared to those from the very-large ensemble system weather@home. Between them, the best estimates of the risk ratios for daytime extremes range between 1.2–2.4, 1.2–2.3, and 1.4–4.0 for the 1, 5, and 30 d averages. For the corresponding nighttime extremes, the values are higher and the ranges wider (1.5- >50, 1.5–5.5, and 1.6- >50). The short-period nighttime extremes were more likely in 2018 than in 2017, suggesting a contribution from year-to-year climate variability to the risk enhancement of extreme temperatures due to anthropogenic effects. Climate projections suggest further substantial increases in the likelihood of 2018 temperatures between now and 2050, and that towards the end of the century every summer might be as hot as 2018. Major negative impacts occurred, especially on rural sectors, while transport and water infrastructure alleviated most impacts by implementing costly special measures. Overall, Scotland could cope with the impacts of the 2018 heatwave. However, given the likelihood increase of high-temperature extremes, uncertainty about consequences of even higher temperatures and/or repeated heatwaves, and substantial costs of preventing negative impacts, we conclude that despite its cool climate, high-temperature extremes are important to consider for climate change adaptation in Scotland.

The Air Pollution Prevention and Control Action Plan (referred to as the Action Plan henceforth) provides a golden opportunity for China to evaluate whether the remarkable air quality improvements as a result of the plan have brought about health benefits to residents. Based on the ground-level particulate matter with an aerodynamic diameter of less than 2.5 μm (PM_2.5) concentrations and the daily respiratory–cardiovascular mortality, we aimed to assess changes in the mortality effect associated with short-term exposure to PM_2.5 due to the implementation of the Action Plan in Beijing. We analyzed the changes in PM_2.5 concentrations and air quality during the pre- and post-emission reduction periods. We then used the generalized additive model to estimate the changes in mortality risk associated with PM_2.5 exposure during both periods. We found that following the introduction of the Action Plan, the annual average PM_2.5 concentrations declined from 101.7 μg m⁻³ in 2013 to 58.6 μg m⁻³ in 2017, attaining the target of the plan (60 μg m⁻³). The remarkable reduction in PM_2.5 concentrations has led to a marked decrease in mortality risk. Compared with the pre-emission reduction level, total respiratory mortality decreased from 0.56% (95% CI: 0.40%–0.73%) to 0.43% (95% CI: 0.23%–0.63%), while the total cardiovascular mortality decreased from 0.44% (95% CI: 0.37%–0.52%) to 0.29% (95% CI: 0.19%–0.39%). Significant decreases were also observed in sex-specific subgroups. Our study implied that the significant efforts of the nation to clean China's air has yielded positive results of air quality improvement and human health protection in Beijing. However, the ambient air pollution in Beijing remains severe. The PM_2.5 concentrations still exceed the level (annual average of 10 μg m⁻³) recommended by the World Health Organization. Thus, consistent efforts are required to implement the emission abatement measures to continuously curb air pollution and protect public health.

Seasonally dry tropical forests (SDTFs) account for one-third of the interannual variability of global net primary productive (NPP). Large-scale shifts in dry tropical forest structure may thus significantly affect global CO₂ fluxes in ways that are not fully accounted for in current projections. This study quantifies how changing climate might reshape one of the largest SDTFs in the world, the Caatinga region of northeast Brazil. We combine historical data and future climate projections under different representative concentration pathways (RCPs), together with spatially explicit aboveground biomass estimates to establish relationships between climate and vegetation distribution. We find that physiognomies, aboveground biomass, and climate are closely related in the Caatinga—and that the region's bioclimatic envelope is shifting rapidly. From 2008–2017, more than 90% of the region has shifted to a dryer climate space compared to the reference period 1950–1979. An ensemble of global climate models (based on IPCC AR5) indicates that by the end of the 21st century the driest Caatinga physiognomies (thorn woodlands to non-vegetated areas) could expand from 55% to 78% (RCP 2.6) or as much as 87% (RCP8.5) of the region. Those changes would correspond to a decrease of 30%–50% of the equilibrium aboveground biomass by the end of the century (RCP 2.6 and RCP8.5, respectively). Our results are consistent with historic vegetation shifts reported for other SDTFs. Projected changes for the Caatinga would have large-scale impacts on the region's biomass and biodiversity, underscoring the importance of SDTFs for the global carbon budget. Understanding such changes as presented in this study will be useful for regional planning and could help mitigate their negative social impacts.

The height of the atmospheric mixed layer is a critical parameter controlling the vertical dispersion of pollutants. Here we calculate daily maximum mixed layer height (MMLH) using operational radiosonde and surface meteorological measurements made at 219 carefully selected WMO weather stations and analyze their long-term trends from 1973 to 2018. We found that 74 stations showed significant increases in MMLH, whereas 48 sites showed negative trends. Positive trends are mainly found in Central US, Europe, Africa, East and Southeast Asia and East Australia. Stations over the coastal US, India and West Australia generally exhibit negative trends. The trends can be attributed to changes in vertical temperature gradient ${\rm{\nabla }}{\theta }_{v}$ between 950 and 700 hPa and diurnal temperature range (DTR). ${\rm{\nabla }}{\theta }_{v}$ in general decreased and caused positive MMLH trends over the majority of the regions. DTR decreases globally, causing negative MMLH trends (corresponding to decreased DTR), and plays a more important role over India and Central Asia.

A number of studies have investigated the mechanisms that determine changes in precipitation, including how a wet region gets wetter. However, not all monsoon areas get wetter—there is a need to understand the major factors behind changes in regional monsoon precipitation, in terms of external forcing and internal variabilities in the last six decades by a combination of different observed datasets and model runs. We have found that time of emergence of anthropogenic signals is robustly detected in the northern African monsoon before the 1990s with the use of the CESM Large Ensemble Project. From CMIP5 model runs and three reanalysis datasets, the results found are that the change in rainfall over African monsoon (AFM) is mainly due to anthropogenic forcing and that over Asian-Australian monsoon (AAM) is affected by internal variability. Moreover, the cause of American monsoon (AMM) rainfall change cannot be known due to a discrepancy among observed datasets. Here we show that the asymmetry between Northern Hemisphere (NH) and Southern Hemisphere (SH) parts by green-house gas (GHG) is detected over the AFM and AAM regions. However, the land monsoon rainfall in the northern AMM is decreased by a combination of GHG and aerosol forcing. In general, the aerosol forcing causes a decreasing rainfall over the monsoon regions. In future projection, the land rainfall over the AAM and AMM are expected to increase and decrease in the future from most models' results. The asymmetry between an increase in NH and a decrease in SH is dominant in the future from most models' future simulation results, which is well shown over the AFM and AAM. This study suggests that the physical process of GHG and aerosol effects in rainfall should be explored in the context of regional aspects.

It is increasingly recognized that various chemical components of PM_2.5 might have differential toxicities to human health, although such studies are hindered by the sparse or non-existent coverage of ground PM_2.5 speciation monitors. The Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite has an innovative design to provide information about aerosol shape, size and extinction that are more related to PM_2.5 speciation concentrations. In this study, we developed random forest models that incorporated ground measurements of PM_2.5 species, MISR fractional AODs, simulated PM_2.5 speciation concentrations from a chemical transport model (CTM), land use variables and meteorological fields, to predict ground-level daily PM_2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) concentrations in California between 2005 and 2014. Our models had out-of-bag R² of 0.72, 0.70, 0.68 and 0.70 for sulfate, nitrate, OC and EC, respectively. We also conducted sensitivity tests to explore the influence of variable selection on model performance. Results show that if there are sufficient ground measurements and predictor data to support the most sophisticated model structure, fractional AODs and total AOD have similar predicting power in estimating PM_2.5 species. Otherwise, models using fractional AODs outperform those with total AOD. PM_2.5 speciation concentrations are more sensitive to land use variables than other supporting data (e.g., CTM simulations and meteorological information).

Secondary vegetation (SV) from land abandonment is a common transition phase between agricultural uses following tropical deforestation. The impact of SV on carbon sequestration and habitat fragmentation across tropical forest frontiers therefore depends on SV dynamics and demographics. Here, we used time series of annual MapBiomas land cover data to generate the first estimates of SV extent, age, and net carbon uptake in the Brazilian Amazon between 1985 and 2017. SV increased over time, totaling 12 Mha in 2017, 44% of which was ≤5 years old. Between 1988 and 2017, 19.6 Mha of SV was cleared, adding 45.5% to the area of primary deforestation detected by the Brazilian monitoring system (PRODES). Rates of SV loss have exceeded PRODES deforestation since 2011. Based on the age and extent of gains and losses, SV was a small net carbon sink during this period (8.9 Tg C yr⁻¹). As SV is not formally protected by national environmental legislation or monitored by PRODES, long-term benefits from SV in the Brazilian Amazon remain uncertain.

Vegetated coastal ecosystems along the Red Sea and Arabian Gulf coasts of Saudi Arabia thrive in an extremely arid and oligotrophic environment, with high seawater temperatures and salinity. Mangrove, seagrass and saltmarsh ecosystems have been shown to act as efficient sinks of sediment organic carbon, earning these vegetated ecosystems the moniker 'blue carbon' ecosystems. However, their role as nitrogen and phosphorus (N and P) sinks remains poorly understood. In this study, we examine the capacity of blue carbon ecosystems to trap and store nitrogen and phosphorous in their sediments in the central Red Sea and Arabian Gulf. We estimated the N and P stocks (in 0.2 m thick-sediments) and accumulation rates (for the last century based on ²¹⁰Pb and for the last millennia based on ¹⁴C) in mangrove, seagrass and saltmarsh sediments from eight locations along the coast of Saudi Arabia (81 cores in total). The N and P stocks contained in the top 20 cm sediments ranged from 61 g N m⁻² in Red Sea seagrass to 265 g N m⁻² in the Gulf saltmarshes and from 70 g P m⁻² in Red Sea seagrass meadows and mangroves to 58 g P m⁻² in the Gulf saltmarshes. The short-term N and P accumulation rates ranged from 0.09 mg N cm⁻² yr⁻¹ in Red Sea seagrass to 0.38 mg N cm⁻² yr⁻¹ in Gulf mangrove, and from 0.027 mg P cm⁻² yr⁻¹ in the Gulf seagrass to 0.092 mg P cm⁻² yr⁻¹ in Red Sea mangroves. Short-term N and P accumulation rates were up to 10-fold higher than long-term accumulation rates, highlighting increasing sequestration of N and P over the past century, likely due to anthropogenic activities such as coastal development and wastewater inputs.

Western United States snowpacks are generated by cold-season storms, yet the vast majority of snow trend studies utilize undifferentiated air temperature records. Previous studies do not distinguish between days with and without precipitation, which effectively dilutes temperature trends relevant for snowpack monitoring. We examined trends in cold-season precipitation and impacts on snow in nine mountain regions across the western United States. Using 33 years of daily meteorological data (1984–2016) from 567 Snow Telemetry (SNOTEL) sites and a homogenized daily temperature dataset (TopoWx), we investigated seasonal and regional trends in storm day temperatures, storm day frequency, and resulting snowpack fate. We found widespread warming of 0.4 °C–1.2 °C per decade, especially during the fall and in the Interior West using tests for statistically significant trends. Disaggregation showed that days with precipitation are warming nearly twice as fast as dry days in the fall and winter. We also observed increases in November dry days, increased melt on dry days throughout the accumulation season, spring cooling and declines in daily snowmelt during snow-depleting storm days. These findings demonstrate the importance of disaggregating temperature data to elucidate trends (in storm day frequency, accumulation and melt, and warming and cooling) and their impacts on snow in the western US.

Studies on the impact of climate change in lakes have mainly focused on the average response of lake surface temperature during three summer months (July, August, September, usually termed JAS). Focusing on the Laurentian Great Lakes, we challenge this common assumption by showing that the thermal behaviour is diversified in time both among different lakes and within a single one. Deep regions experience a stronger warming concentrated in early summer, mainly due to anticipated stratification, while shallow parts respond more uniformly throughout the year. To perform such analysis, we use the difference between the five warmest and coldest years in a series of 20 years as a proxy of possible effects of climate alterations, and compare the warming of lake surface temperature with that of air temperature. In this way, based on past observations obtained from satellite images, we show how the warming is heterogeneously distributed in time and in space, and that the quantification of lakes' thermal response to climate change is chiefly influenced by the time window used in the analysis. Should we be more careful when considering averaged indicators of lake thermal response to climate change?

Wildfires are an important factor in controlling forest ecosystem dynamics across the circumpolar boreal zone. An improved understanding of their direct and indirect, short- to long-term impacts on vegetation cover and permafrost–vegetation coupling is particularly important to predict changes in carbon, nutrient and water cycles under projected climate warming. Here, we apply dendrochronological techniques on a multi-parameter dataset to reconstruct the effect of wildfires on tree growth and seasonal permafrost thaw depth in Central Siberia. Based on annually-resolved and absolutely dated information from 19 Gmelin larch (Larix gmelinii (Rupr.) Rupr.) trees and active soil layer thickness measurements, we find substantial stand-level die-off, as well as the removal of ground vegetation and the organic layer following a major wildfire in 1896. Reduced stem growth coincides with increased δ¹³C in the cellulose of the surviving trees during the first decade after the wildfire, when stomatal conductance was reduced. The next six to seven decades are characterized by increased permafrost active soil layer thickness. During this period of post-wildfire ecosystem recovery, enhanced tree growth together with positive δ¹³C and negative δ¹⁸O trends are indicative of higher rates of photosynthesis and improved water supply. Afterwards, a thinner active soil layer leads to reduced growth because tree physiological processes become limited by summer temperature and water availability. Revealing long-term effects of forest fires on active soil layer thickness, ground vegetation composition and tree growth, this study demonstrates the importance of complex vegetation–permafrost interactions that modify the trajectory of post-fire forest recovery across much of the circumpolar boreal zone. To further quantify the influence of boreal wildfires on large-scale carbon cycle dynamics, future work should consider a wide range of tree species from different habitats in the high-northern latitudes.

Decreases in terrestrial near-surface wind speed (NSWS) were documented in many regions over the past decades. Various drivers have been proposed for this terrestrial stilling, such as weakening of ocean-land pressure gradients related to climate change and increased surface roughness linked to vegetation growth; but none have been robustly established as the cause. A plausible reason for this quandary is that the local impact of urbanization on NSWS has been overlooked. Here, we used homogenized NSWS records from in situ weather stations and a satellite-based dynamic urban–rural classification scheme to quantitatively assess the contribution of urbanization to observed terrestrial stilling during 1980–2016 over the Beijing–Tianjin–Hebei region of China. Results suggested that urbanization contributed approximately 8% to the observed decrease in the regional NSWS in urban areas, implying that urbanization played a minor role in terrestrial stilling, even in this rapidly developing region. The largest NSWS changes related to urbanization occurred in winter, followed by spring, autumn, and summer. Urbanization reduced the days with relatively strong winds but increased the days with light and gentle winds. We found that except for the Japanese 55 year reanalysis (JRA-55) dataset, none of the common reanalysis products reproduced the observed NSWS trends in urban or rural areas. However, this could be because of JRA-55's intrinsic negative bias in NSWS trends over land. Thus, regional terrestrial NSWS trends derived from reanalysis products deserve careful examination.

Terrestrial ecosystem gross primary productivity (GPP) is the largest land-atmosphere carbon flux and the primary mechanism of photosynthetic fixation of atmospheric CO₂ into plant biomass. Anomalous rainfall events have been shown to have a great impact on the global carbon cycle. However, less is known about the impact of these events on GPP, especially in Africa, where in situ observations are sparse. Here, we use a suite of satellite and other geospatial data to examine the responses of major ecosystems in Africa to anomalous rainfall events from 2003 to 2017. Our results reveal that higher-than-average groundwater storage in tropical ecosystems offsets the rainfall deficit during the dry years. While the inter-annual variations in GPP in semi-arid ecosystems are controlled by near surface soil water, deeper soil moisture and groundwater control the inter-annual variability of the GPP in dense tropical forests. Our study highlights the critical role of groundwater in buffering rainfall shortages and continued availability of near-surface water to plants through dry spells.

The effect of air temperature on photosynthesis is important for the terrestrial carbon cycle. The optimum air temperature for photosynthesis is one of the major parameters in data-driven and process-based photosynthesis models that estimate the gross primary production (GPP) of vegetation under a changing climate. To date, most models use the biome-specific optimum air temperature (${T}_{{\rm{o}}{\rm{p}}{\rm{t}}-{\rm{b}}}$) parameter. To what degree will the site-specific optimum air temperature (${T}_{{\rm{o}}{\rm{p}}{\rm{t}}-{\rm{s}}}$) affect GPP simulation results remains unclear. In this study, we estimated ${T}_{{\rm{o}}{\rm{p}}{\rm{t}}-{\rm{s}}}$ by using GPP data from 11 grassland eddy flux tower sites (GPP_EC) and satellite vegetation indices (NDVI and EVI). We found that T_opt-s parameter values estimated from EVI have good consistency with those from GPP_EC at individual sites. We also evaluated the effects of site-specific and biome-specific optimum air temperature parameters on grassland photosynthesis. The results showed that the use of ${T}_{{\rm{o}}{\rm{p}}{\rm{t}}-{\rm{s}}}$ in the Vegetation Photosynthesis Model improved to various degrees in both daily and annual GPP estimates in those grassland flux tower sites. Our results highlight the necessity and potential for the use of ${T}_{{\rm{o}}{\rm{p}}{\rm{t}}-{\rm{s}}}$ in terrestrial GPP models, especially in those situations with large temperature variation (heatwave and cold spill events).

Coal mining directly employs over 7 million workers and benefits millions more through indirect jobs. However, to meet the 1.5 °C global climate target, coal's share in global energy supply should decline between 73% and 97% by 2050. But what will happen to coal miners as coal jobs disappear ?Answering this question is necessary to ensure a just transition and to ensure that politically powerful coal mining interests do not impede energy transitions. Some suggest that coal miners can transition to renewable jobs. However, prior research has not investigated the potential for renewable jobs to replace 'local' coal mining jobs. Historic analyses of coal industry declines show that coal miners do not migrate when they lose their jobs. By focusing on China, India, the US, and Australia, which represent 70% of global coal production, we investigate: (1) the local solar and wind capacity required in each coal mining area to enable all coal miners to transition to solar/wind jobs; (2) whether there are suitable solar and wind power resources in coal mining areas in order to install solar/wind plants and create those jobs; and (3) the scale of renewables deployment required to transition coal miners in areas suitable for solar/wind power. We find that with the exception of the US, several GWs of solar or wind capacity would be required in each coal mining area to transition all coal miners to solar/wind jobs. Moreover, while solar has more resource suitability than wind in coal mining areas, these resources are not available everywhere. In China, the country with the largest coal mining workforce, only 29% of coal mining areas are suitable for solar power. In all four countries, less than 7% of coal mining areas have suitable wind resources. Further, countries would have to scale-up their current solar capacity significantly to transition coal miners who work in areas suitable for solar development.

Global bioenergy potentials have been the subject of extensive research and continued controversy. Due to vast uncertainties regarding future yields, diets and other influencing parameters, estimates of future agricultural biomass potentials vary widely. Most scenarios compatible with ambitious climate targets foresee a large expansion of bioenergy, mainly from energy crops that needs to be kept consistent with projections of agriculture and food production. Using the global biomass balance model BioBaM, we here present an assessment of agricultural bioenergy potentials compatible with the Food and Agriculture Organization's (2018) 'Alternative pathways to 2050' projections. Mobilizing biomass at larger scales may be associated with systemic feedbacks causing greenhouse gas (GHG) emissions, e.g. crop residue removal resulting in loss of soil carbon stocks and increased emissions from fertilization. To assess these effects, we derive 'GHG cost supply-curves', i.e. integrated representations of biomass potentials and their systemic GHG costs. Livestock manure is most favourable in terms of GHG costs, as anaerobic digestion yields reductions of GHG emissions from manure management. Global potentials from intensive livestock systems are about 5 EJ/yr. Crop residues can provide up to 20 EJ/yr at moderate GHG costs. For energy crops, we find that the medium range of literature estimates (∼40 to 90 EJ/yr) is only compatible with FAO yield and human diet projections if energy plantations expand into grazing areas (∼4–5 million km²) and grazing land is intensified globally. Direct carbon stock changes associated with perennial energy crops are beneficial for climate mitigation, yet there are—sometimes considerable—'opportunity GHG costs' if one accounts the foregone opportunity of afforestation. Our results indicate that the large potentials of energy crops foreseen in many energy scenarios are not freely and unconditionally available. Disregarding systemic effects in agriculture can result in misjudgement of GHG saving potentials and flawed climate mitigation strategies.

This paper reviews recent important advances in our understanding of the response of precipitation extremes to warming from theory and from idealized cloud-resolving simulations. A theoretical scaling for precipitation extremes has been proposed and refined in the past decades, allowing to address separately the contributions from the thermodynamics, the dynamics and the microphysics. Theoretical constraints, as well as remaining uncertainties, associated with each of these three contributions to precipitation extremes, are discussed. Notably, although to leading order precipitation extremes seem to follow the thermodynamic theoretical expectation in idealized simulations, considerable uncertainty remains regarding the response of the dynamics and of the microphysics to warming, and considerable departure from this theoretical expectation is found in observations and in more realistic simulations. We also emphasize key outstanding questions, in particular the response of mesoscale convective organization to warming. Observations suggest that extreme rainfall often comes from an organized system in very moist environments. Improved understanding of the physical processes behind convective organization is needed in order to achieve accurate extreme rainfall prediction in our current, and in a warming climate.

The Proto-Shang, the Shang and the Zhou dynasties (∼2000–221 BCE: Before Common Era) are key periods in the origin and evolution of ancient civilizations in China since the periods include the processes and mechanisms of social development in the Central Plains of China during the Bronze Age. However, human-environment interactions in the context of trans-Eurasia cultural exchange during that time are not well-understood. In this study, isotopic analysis and radiocarbon dating of human and animal bones from Xinancheng cemetery in southeast Shanxi Province are reported. It was deduced that, for the period ∼1000–800 BCE, humans buried in Xinancheng cemetery relied primarily on C₄-based foods and upper-status individuals consumed more animal protein and probably C₃ crops. Also, considering the paleoclimate and other archaeological data of the Central Plains, the human diet and subsistence strategies changed significantly with more C₃ staples such as wheat being consumed during the Eastern Zhou (770–221 BCE), as evidenced by an increased intake of wheat by lower-status individuals and the development of a mixed wheat and millet agricultural system. It is argued that the socio-economic change around the late western Zhou-early eastern Zhou Dynasty occurred as a result of the necessity to adapt to the aggravation caused by climate deterioration and population pressures, factors which profoundly influenced the economic and lifestyle patterns in ancient China. The socio-economic system of the Eastern Zhou Dynasty displayed more resilience to climate change than that of earlier periods.

Zero deforestation commitments (ZDCs) are voluntary initiatives where companies or countries pledge to eliminate deforestation from their supply chains. These commitments offer much promise for sustainable commodity production, but are undermined by a lack of transparency about their coverage and impacts. Here, using state-of-the-art supply chain data, we introduce an approach to evaluate the impact of ZDCs, linking traders and international markets to commodity-associated deforestation in the sub-national jurisdictions from which they source. We focus on the Brazilian soy sector, where we find that ZDC coverage is increasing, but under-represents the Cerrado biome where most soy-associated deforestation currently takes place. Though soy-associated deforestation declined in the Amazon after the introduction of the Soy Moratorium, we observe no change in the exposure of companies or countries adopting ZDCs to soy-associated deforestation in the Cerrado. We further assess the formulation and implementation of these ZDCs and identify several systematic weaknesses that must be addressed to increase the likelihood that they achieve meaningful reductions in deforestation in future. As the 2020 deadline for several of these commitments approaches, our approach can provide independent monitoring of progress toward the goal of ending commodity-associated deforestation.

Urban areas are currently responsible for ∼70% of the global energy-related carbon dioxide (CO₂) emissions, and rapid ongoing global urbanization is increasing the number and size of cities. Thus, understanding city-scale CO₂ emissions and how they vary between cities with different urban densities is a critical task. While the relationship between CO₂ emissions and population density has been explored widely in prior studies, their conclusions were sensitive to inconsistent definitions of urban boundaries and the reliance upon CO₂ emission inventories that implicitly assumed population relationships. Here we provide the first independent estimates of direct per capita CO₂ emissions (E_pc) from spaceborne atmospheric CO₂ measurements from the Orbiting Carbon Observatory-2 (OCO-2) for a total 20 cities across multiple continents. The analysis accounts for the influence of meteorology on the satellite observations with an atmospheric model. The resultant upwind source region sampled by the satellite serves as an objective urban extent for aggregating emissions and population densities. Thus, we are able to detect emission 'hotspots' on a per capita basis from a few cities, subject to sampling restrictions from OCO-2. Our results suggest that E_pc declines as population densities increase, albeit the decrease in E_pc is partially limited by the positive correlation between E_pc and per capita gross domestic product. As additional CO₂-observing satellites are launched in the coming years, our space-based approach to understanding CO₂ emissions from cities has significant potential in tracking and evaluating the future trajectory of urban growth and informing the effects of carbon reduction plans.

Observational evidence of precipitation extremes is vital to better understand how these events might change in a future warmer climate. Over the terrestrial regions of a quasi-global domain, we assess the representation of annual maxima of daily precipitation (Rx1day) in 22 observational products gridded at 1° × 1° resolution and clustered into four categories: station-based in situ, satellite observations with or without a correction to rain gauges, and reanalyses (5, 8, 4 and 5 datasets, respectively). We also evaluate the interproduct spread across the ensemble and within the four clusters, as a measure of observational uncertainty. We find that reanalyses present a heterogeneous representation of Rx1day in particular over the tropics, and their interproduct spread is the highest compared to any other cluster. Extreme precipitation in satellite data broadly compares well with in situ-based data. We find a general better agreement with in situ-based observations and less interproduct spread for the satellite products with a correction to rain gauges compared to the uncorrected products. Given the level of uncertainties associated with the estimation of Rx1day in the observations, none of the datasets can be thought of as the best estimate. Our recommendation is to avoid using reanalyses as observational evidence and to consider in situ and satellite data (the corrected version preferably) in an ensemble of products for a better estimation of precipitation extremes and their observational uncertainties. Based on this we choose a subsample of 10 datasets to reduce the interproduct spread in both the representation of Rx1day and its timing throughout the year, compared to all 22 datasets. We emphasize that the recommendations and selection of datasets given here may not be relevant for different precipitation indices, and other grid resolutions and time scales.

The agro-food system satisfying human food demand releases heavy nitrogen (N) loads into the environment. The N footprint is an indicator of N loads from individual consumption of food as well as energy. A bottom-up approach called the 'N-calculator method' calculates the food N footprint using the N content in consumed foods, such that the N footprint of protein-free foods is treated as zero. This method underestimates the N footprint of protein-free foods, such as oil and sugar, when the source crops require N input in production. In this study, we propose a substitution factor, the virtual nitrogen factor for protein-free foods (VNFree), defined as the potential N load per unit weight of consumed food, to explicitly calculate the production N footprint. Oil palm and its products, palm oil (PO) and palm kernel oil (PKO), were chosen for this case study of protein-free foods. Global mean VNFree values of PO and PKO obtained by averaging national-scale data of the three countries with the largest production (Indonesia, Malaysia, and Thailand) were 0.0241 and 0.0037 kg N kg^–1 oil, respectively. The 6.5-times difference in VNFree values was attributed to the difference in oil yield. The food N footprint of PO and PKO calculated here represented less than 2% of the previously reported total food N footprints of several countries. However, oil palm products are also used for industry, and the chemical fertilizer consumption for oil palm accounted for only 8%–12% of that of all oil and sugar crops. The protein-free N footprint of all these products will be much larger. We expect that the current N-calculator method as a bottom-up approach will be improved by expanding the VNFree concept, which enables the calculation of the concealed N footprint in protein-free products, including all uses of oil and sugar crops.

The security, resilience, and sustainability of urban water supply systems (UWSS) are challenged by global change pressures, including climate and land use changes, rapid urbanization, and population growth. Building on prior work on UWSS security and resilience, we quantify the sustainability of UWSS based on the performance of local sustainable governance and the size of global water and ecological footprints. We develop a new framework that integrates security, resilience, and sustainability to investigate trade-offs between these three distinct and inter-related dimensions. Security refers to the level of services, resilience is the system's ability to respond to and recover from shocks, and sustainability refers to local and global impacts, and to the long-term viability of system services. Security and resilience are both relevant at local scale (city and surroundings), while for sustainability cross-scale and -sectoral feedbacks are important. We apply the new framework to seven cities selected from diverse hydro-climatic and socio-economic settings on four continents. We find that UWSS security, resilience, and local sustainability coevolve, while global sustainability correlates negatively with security. Approaching these interdependent goals requires governance strategies that balance the three dimensions within desirable and viable operating spaces. Cities outside these boundaries risk system failure in the short-term, due to lack of security and resilience, or face long-term consequences of unsustainable governance strategies. We discuss these risks in the context of poverty and rigidity traps. Our findings have strong implications for policy-making, strategic management, and for designing systems to operate sustainably at local and global scales.

The exchange of carbon between the Earth's atmosphere and biosphere influences the atmospheric abundances of carbon dioxide (CO₂) and methane (CH₄). Airborne eddy covariance (EC) can quantify surface-atmosphere exchange from landscape-to-regional scales, offering a unique perspective on carbon cycle dynamics. We use extensive airborne measurements to quantify fluxes of sensible heat, latent heat, CO₂, and CH₄ across multiple ecosystems in the Mid-Atlantic region during September 2016 and May 2017. In conjunction with footprint analysis and land cover information, we use the airborne dataset to explore the effects of landscape heterogeneity on measured fluxes. Our results demonstrate large variability in CO₂ uptake over mixed agricultural and forested sites, with fluxes ranging from −3.4 ± 0.7 to −11.5 ± 1.6 μmol m⁻² s⁻¹ for croplands and −9.1 ± 1.5 to −22.7 ± 3.2 μmol m⁻² s⁻¹ for forests. We also report substantial CH₄ emissions of 32.3 ± 17.0 to 76.1 ± 29.4 nmol m⁻² s⁻¹ from a brackish herbaceous wetland and 58.4 ± 12.0 to 181.2 ± 36.8 nmol m⁻² s⁻¹ from a freshwater forested wetland. Comparison of ecosystem-specific aircraft observations with measurements from EC flux towers along the flight path demonstrate that towers capture ∼30%–75% of the regional variability in ecosystem fluxes. Diel patterns measured at the tower sites suggest that peak, midday flux measurements from aircraft accurately predict net daily CO₂ exchange. We discuss next steps in applying airborne observations to evaluate bottom-up flux models and improve understanding of the biophysical processes that drive carbon exchange from landscape-to-regional scales.

This paper presents an original approach to characterizing historical fire regimes for regions with limited fire data. Fire variables were derived from satellite datasets and regional fire occurrence statistics. They defined the integral elements of a fire regime such as historical trends, spatiotemporal evolution, fire seasonality and causes. Temporal evolution was investigated based on a regime shift detection method developed by Rodionov while changes in the fire regime were analyzed for statistical significance using the Mann–Kendall trend test and Sen's slope estimator. A descriptive analysis was performed to assess fire seasonality, causes, and together formed the basis for this methodology. We validated the proposed approach by assessing historical fire activity in the Sakha Republic (Yakutia), which is one of the most fire-prone regions of Russia. The assessment was conducted with data from the period of 1996–2018. We detected increases in historical fire activity as well as thresholds of change in the fire regime. Changes during the analysis period included lengthening of fire season, increased burned area extent, and extension of peak fire period. Overall, significant changes in the fire regime were detected in the regions strongly affected by warming and increasing anthropogenic alteration.

Using data collected from a specially designed experiment at the Dunhuang Station (40°10'N, 94°31'E, 1150 m) from September 2017 to September 2018, we have characterized the influences of soil moisture and solar altitude on surface spectral albedo in an arid area. The specific settings of our experiment allowed us to minimize the influences of underlying surface, cloud cover, aerosol and weather conditions, and thus highlight the influence of soil moisture and solar altitude. During the timespan of the experiment, we observed the annual mean surface albedo of global radiation (GR), ultraviolet radiation (UV), visible radiation (VIS) and near-infrared radiation (NIR) to be 0.24, 0.11, 0.24 and 0.25. A significantly negative linear correlation between surface albedo and soil moisture was identified, with the correlation coefficients between GR, UV, VIS, NIR and soil moisture being −0.68, −0.75, −0.70 and −0.61. In addition, we identified an exponential relationship between surface albedo and solar altitude. The exponential regression coefficients are −0.21, −0.077, −0.53 and −0.21, respectively. From these analyses, we derived a new two-factor parametric formula for depicting the influence of soil moisture and solar altitude on surface spectral albedo. Using observation data, we demonstrate that the formula recapitulates the real-world relationship between soil moisture, solar altitude and surface spectral albedo with little deviation. These findings may help us gain a deeper understanding of improving land surface parameterizations and have potential implications for solar energy research and applications.