Delayed use of bioenergy crops might threaten climate and food security

The potential of mitigation actions to limit global warming within 2 °C (ref. ¹) might rely on the abundant supply of biomass for large-scale bioenergy with carbon capture and storage (BECCS) that is assumed to scale up markedly in the future^2-5. However, the detrimental effects of climate change on crop yields may reduce the capacity of BECCS and threaten food security^6-8, thus creating an unrecognized positive feedback loop on global warming. We quantified the strength of this feedback by implementing the responses of crop yields to increases in growing-season temperature, atmospheric CO₂ concentration and intensity of nitrogen (N) fertilization in a compact Earth system model⁹. Exceeding a threshold of climate change would cause transformative changes in social-ecological systems by jeopardizing climate stability and threatening food security. If global mitigation alongside large-scale BECCS is delayed to 2060 when global warming exceeds about 2.5 °C, then the yields of agricultural residues for BECCS would be too low to meet the Paris goal of 2 °C by 2200. This risk of failure is amplified by the sustained demand for food, leading to an expansion of cropland or intensification of N fertilization to compensate for climate-induced yield losses. Our findings thereby reinforce the urgency of early mitigation, preferably by 2040, to avoid irreversible climate change and serious food crises unless other negative-emission technologies become available in the near future to compensate for the reduced capacity of BECCS.

Convergence in phosphorus constraints to photosynthesis in forests around the world

Tropical forests take up more carbon (C) from the atmosphere per annum by photosynthesis than any other type of vegetation. Phosphorus (P) limitations to C uptake are paramount for tropical and subtropical forests around the globe. Yet the generality of photosynthesis-P relationships underlying these limitations are in question, and hence are not represented well in terrestrial biosphere models. Here we demonstrate the dependence of photosynthesis and underlying processes on both leaf N and P concentrations. The regulation of photosynthetic capacity by P was similar across four continents. Implementing P constraints in the ORCHIDEE-CNP model, gross photosynthesis was reduced by 36% across the tropics and subtropics relative to traditional N constraints and unlimiting leaf P. Our results provide a quantitative relationship for the P dependence for photosynthesis for the front-end of global terrestrial C models that is consistent with canopy leaf measurements.

Phosphorus (P) limitation is pervasive in tropical forests. Here the authors analyse the dependence of photosynthesis on leaf N and P in tropical forests, and show that incorporating leaf P constraints in a terrestrial biosphere model enhances its predictive power.

Stringent Emission Controls Are Needed to Reach Clean Air Targets for Cities in China under a Warming Climate

Quantifying the threat that climate change poses to fine particle (PM_2.5) pollution is hampered by large uncertainties in the relationship between PM_2.5 and meteorology. To constrain the impact of climate change on PM_2.5, statistical models are often employed in a different manner than physical-chemical models to reduce the requirement of input data. A majority of statistical models predict PM_2.5 concentration (often log-transformed) as a simple function of meteorology, which could be biased due to the conversion of precursor gases to PM_2.5. We reduced this bias by developing a unique statistic model where the sum of PM_2.5 and the weighted precursor gases, rather than the PM_2.5 alone, was predicted as a function of meteorology and a proxy of primary emissions, where the input data of PM₁₀, CO, O₃, NO_x, and SO₂ were obtained from routine measurements. This modification, without losing the simplicity of statistical models, reduced the mean-square error from 27 to 17% and increased the coefficient of determination from 47 to 67% in the model cross-validation using daily PM_2.5 observations during 2013-2018 for 74 cities over China. We found a previously unrecognized mechanism that synoptic climate change in the past half-century might have increased low quantiles of PM_2.5 more strenuously than the upper quantiles in large cities over China. Climate change during 1971-2018 was projected to increase the annual mean concentration of PM_2.5 at a degree that could be comparable with the toughest-ever clean air policy during 2013-2018 had counteracted it, as inferred from the decline in the daily concentration of carbon monoxide as an inert gas. Our estimate of the impact of climate change on PM_2.5 is higher than previous statistical models, suggesting that aerosol chemistry might play a more important role than previously thought in the interaction between climate change and air pollution. Our result indicated that air quality might degrade if the future synoptic climate change could continue interacting with aerosol chemistry as it had occurred in the past half-century.

Direct observations of CO2 emission reductions due to COVID-19 lockdown across European urban districts

The measures taken to contain the spread of COVID-19 in 2020 included restrictions of people's mobility and reductions in economic activities. These drastic changes in daily life, enforced through national lockdowns, led to abrupt reductions of anthropogenic CO₂ emissions in urbanized areas all over the world. To examine the effect of social restrictions on local emissions of CO₂, we analysed district level CO₂ fluxes measured by the eddy-covariance technique from 13 stations in 11 European cities. The data span several years before the pandemic until October 2020 (six months after the pandemic began in Europe). All sites showed a reduction in CO₂ emissions during the national lockdowns. The magnitude of these reductions varies in time and space, from city to city as well as between different areas of the same city. We found that, during the first lockdowns, urban CO₂ emissions were cut with respect to the same period in previous years by 5% to 87% across the analysed districts, mainly as a result of limitations on mobility. However, as the restrictions were lifted in the following months, emissions quickly rebounded to their pre-COVID levels in the majority of sites.

Exploring complex water stress-gross primary production relationships: Impact of climatic drivers, main effects, and interactive effects

The dominance of vapor pressure deficit (VPD) and soil water content (SWC) for plant water stress is still under debate. These two variables are strongly coupled and influenced by climatic drivers. The impacts of climatic drivers on the relationships between gross primary production (GPP) and water stress from VPD/SWC and the interaction between VPD and SWC are not fully understood. Here, applying statistical methods and extreme gradient boosting models-Shapley additive explanations framework to eddy-covariance observations from the global FLUXNET2015 data set, we found that the VPD-GPP relationship was strongly influenced by climatic interactions and that VPD was more important for plant water stress than SWC across most plant functional types when we removed the effect of main climatic drivers, e.g. air temperature, incoming shortwave radiation and wind speed. However, we found no evidence for a significant influence of elevated CO₂ on stress alleviation, possibly because of the short duration of the records (approximately one decade). Additionally, the interactive effect between VPD and SWC differed from their individual effect. When SWC was high, the SHAP interaction value of SWC and VPD on GPP was decreased with increasing VPD, but when SWC was low, the trend was the opposite. Additionally, we revealed a threshold effect for VPD stress on GPP loss; above the threshold value, the stress on GPP was flattened off. Our results have important implications for independently identifying VPD and SWC limitations on plant productivity, which is meaningful for capturing the magnitude of ecosystem responses to water stress in dynamic global vegetation models.

Gridded maps of wetlands dynamics over mid-low latitudes for 1980–2020 based on TOPMODEL

Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a diagnostic model based on the TOPography-based hydrological MODEL (TOPMODEL), this study produced an ensemble of 28 gridded maps of monthly global/regional wetland extents (with more reliable estimates at mid-low latitudes) for 1980–2020 at 0.25° × 0.25° spatial resolution, calibrated with a combination of four observation-based wetland data and seven gridded soil moisture reanalysis datasets. The gridded dynamic maps of wetlands capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with independent satellite-based terrestrial water storage estimates over wetland areas. The long temporal coverage extending beyond the era of satellite datasets, the global coverage, and the opportunity to provide real-time updates from ongoing soil moisture data make these products helpful for various applications such as analyzing the wetland-related methane emission.

Measurement(s)	wetland area
Technology Type(s)	computational modeling technique
Factor Type(s)	geographic location • temporal interval
Sample Characteristic - Environment	land
Sample Characteristic - Location	global

Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 public stimulus packages

It has been claimed that COVID-19 public stimulus packages could be sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 °C target of the Paris Agreement. Here, we provide complementary perspectives to reiterate that substantial, broad, and sustained policy efforts beyond stimulus packages will be needed for achieving the Paris Agreement long-term targets. Low-carbon investments will need to scale up and persist over the next several decades following short-term stimulus packages. The required total energy investments in the real world can be larger than the currently available estimates from integrated assessment models (IAMs). Existing databases from IAMs are not sufficient for analyzing the effect of public spending on emission reduction. To inform what role COVID-19 stimulus packages and public investments may play for reaching the Paris Agreement targets, explicit modelling of such policies is required.

Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas

Vegetation is a key component in the global carbon cycle as it stores ~450 GtC as biomass, and removes about a third of anthropogenic CO₂ emissions. However, in some regions, the rate of plant carbon uptake is beginning to slow, largely because of water stress. Here, we develop a new observation-based methodology to diagnose vegetation water stress and link it to environmental drivers. We used the ratio of remotely sensed land surface to near surface atmospheric temperatures (LST/T_air ) to represent vegetation water stress, and built regression tree models (random forests) to assess the relationship between LST/T_air and the main environmental drivers of surface energy fluxes in the tropical Americas. We further determined ecosystem traits associated with water stress and surface energy partitioning, pinpointed critical thresholds for water stress, and quantified changes in ecosystem carbon uptake associated with crossing these critical thresholds. We found that the top drivers of LST/T_air , explaining over a quarter of its local variability in the study region, are (1) radiation, in 58% of the study region; (2) water supply from precipitation, in 30% of the study region; and (3) atmospheric water demand from vapor pressure deficits (VPD), in 22% of the study region. Regions in which LST/T_air variation is driven by radiation are located in regions of high aboveground biomass or at high elevations, while regions in which LST/T_air is driven by water supply from precipitation or atmospheric demand tend to have low species richness. Carbon uptake by photosynthesis can be reduced by up to 80% in water-limited regions when critical thresholds for precipitation and air dryness are exceeded simultaneously, that is, as compound events. Our results demonstrate that vegetation structure and diversity can be important for regulating surface energy and carbon fluxes over tropical regions.

Field-based tree mortality constraint reduces estimates of model-projected forest carbon sinks

Considerable uncertainty and debate exist in projecting the future capacity of forests to sequester atmospheric CO₂. Here we estimate spatially explicit patterns of biomass loss by tree mortality (LOSS) from largely unmanaged forest plots to constrain projected (2015–2099) net primary productivity (NPP), heterotrophic respiration (HR) and net carbon sink in six dynamic global vegetation models (DGVMs) across continents. This approach relies on a strong relationship among LOSS, NPP, and HR at continental or biome scales. The DGVMs overestimated historical LOSS, particularly in tropical regions and eastern North America by as much as 5 Mg ha⁻¹ y⁻¹. The modeled spread of DGVM-projected NPP and HR uncertainties was substantially reduced in tropical regions after incorporating the field-based mortality constraint. The observation-constrained models show a decrease in the tropical forest carbon sink by the end of the century, particularly across South America (from 2 to 1.4 PgC y⁻¹), and an increase in the sink in North America (from 0.8 to 1.1 PgC y⁻¹). These results highlight the feasibility of using forest demographic data to empirically constrain forest carbon sink projections and the potential overestimation of projected tropical forest carbon sinks.

Here the authors use broad-scale tree mortality data to estimate biomass loss, constraining uncertainty of projected forest net primary productivity in 6 models, finding weaker tropical forest carbon sinks with climate change.

Trade-off between tree planting and wetland conservation in China

Trade-offs between tree planting programs and wetland conservation are unclear. Here, we employ satellite-derived inundation data and a process-based land surface model (ORCHIDEE-Hillslope) to investigate the impacts of tree planting on wetland dynamics in China for 2000-2016 and the potential impacts of near-term tree planting activities for 2017-2035. We find that 160,000-190,000 km2 (25.3-25.6%) of historical tree planting over wetland grid cells has resulted in 1,300-1,500 km2 (0.3-0.4%) net wetland loss. Compared to moist southern regions, the dry northern and western regions show a much higher sensitivity of wetland reduction to tree planting. With most protected wetlands in China located in the drier northern and western basins, continuing tree planting scenarios are projected to lead to a > 10% wetland loss relative to 2000 across 4-8 out of 38 national wetland nature reserves. Our work shows how spatial optimization can help the balance of tree planting and wetland conservation targets.

Extreme fire weather is the major driver of severe bushfires in southeast Australia

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019-2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires.

Large CO2 Emitters as Seen From Satellite: Comparison to a Gridded Global Emission Inventory

Using the multiyear archive of the two Orbiting Carbon Observatories (OCO) of NASA, we have retrieved large fossil fuel CO₂ emissions (larger than 1.0 ktCO₂ h^-1 per 10^-2 square degree grid cell) over the globe with a simple plume cross-sectional inversion approach. We have compared our results with a global gridded and hourly inventory. The corresponding OCO emission retrievals explain more than one third of the inventory variance at the corresponding cells and hours. We have binned the data at diverse time scales from the year (with OCO-2) to the average morning and afternoon (with OCO-3). We see consistent variations of the median emissions, indicating that the retrieval-inventory differences (with standard deviations of a few tens of percent) are mostly random and that trends can be calculated robustly in areas of favorable observing conditions, when the future satellite CO₂ imagers provide an order of magnitude more data.

Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems

Understanding the critical soil moisture (SM) threshold (θ_crit ) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θ_crit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θ_crit across Europe by evaluating evaporative fraction (EF)-SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry-down periods. We found that the θ_crit and soil matric potential threshold in Europe are 16.5% and -0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water-limited stage, and the lowest in forests. The sign of the covariance between daily VPD and GPP consistently changed from positive to negative during dry-down across all sites when EF shifted from relatively high to low values. This sign of the covariance changed after longer period of SM decline in forests than in grasslands and savannas. Estimated θ_crit from the VPD-GPP covariance method match well with the EF-SM method, showing this covariance method can be used to detect the θ_crit . We further found that soil texture dominates the spatial variability of θ_crit while shortwave radiation and VPD are the major drivers in determining the spatial pattern of EF sensitivities. Our results highlight for the first time that the sign change of the covariance between daily VPD and GPP can be used as an indicator of how ecosystems transition from energy to SM limitation. We also characterized the corresponding θ_crit and its drivers across diverse ecosystems in Europe, an essential variable to improve the representation of water stress in land surface models.

Atmospheric dryness reduces photosynthesis along a large range of soil water deficits

Both low soil water content (SWC) and high atmospheric dryness (vapor pressure deficit, VPD) can negatively affect terrestrial gross primary production (GPP). The sensitivity of GPP to soil versus atmospheric dryness is difficult to disentangle, however, because of their covariation. Using global eddy-covariance observations, here we show that a decrease in SWC is not universally associated with GPP reduction. GPP increases in response to decreasing SWC when SWC is high and decreases only when SWC is below a threshold. By contrast, the sensitivity of GPP to an increase of VPD is always negative across the full SWC range. We further find canopy conductance decreases with increasing VPD (irrespective of SWC), and with decreasing SWC on drier soils. Maximum photosynthetic assimilation rate has negative sensitivity to VPD, and a positive sensitivity to decreasing SWC when SWC is high. Earth System Models underestimate the negative effect of VPD and the positive effect of SWC on GPP such that they should underestimate the GPP reduction due to increasing VPD in future climates.

Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment

The Paris metropolitan area, the largest urban region in the European Union, has experienced two national COVID-19 confinements in 2020 with different levels of restrictions on mobility and economic activity, which caused reductions in CO₂ emissions. To quantify the timing and magnitude of daily emission reductions during the two lockdowns, we used continuous atmospheric CO₂ monitoring, a new high-resolution near-real-time emission inventory, and an atmospheric Bayesian inverse model. The atmospheric inversion estimated the changes in fossil fuel CO₂ emissions over the Greater Paris region during the two lockdowns, in comparison with the same periods in 2018 and 2019. It shows decreases by 42-53% during the first lockdown with stringent measures and by only 20% during the second lockdown when traffic reduction was weaker. Both lockdown emission reductions are mainly due to decreases in traffic. These results are consistent with independent estimates based on activity data made by the city environmental agency. We also show that unusual persistent anticyclonic weather patterns with north-easterly winds that prevailed at the start of the first lockdown period contributed a substantial drop in measured CO₂ concentration enhancements over Paris, superimposed on the reduction of urban CO₂ emissions. We conclude that atmospheric CO₂ monitoring makes it possible to identify significant emission changes (>20%) at subannual time scales over an urban region.

Global assessment of oil and gas methane ultra-emitters

Methane emissions from oil and gas (O&G) production and transmission represent a considerable contribution to climate change. These emissions comprise sporadic releases of large amounts of methane during maintenance operations or equipment failures not accounted for in current inventory estimates. We collected and analyzed hundreds of very large releases from atmospheric methane images sampled by the TROPOspheric Monitoring Instrument (TROPOMI) between 2019 and 2020. Ultra-emitters are primarily detected over the largest O&G basins throughout the world. With a total contribution equivalent to 8 to 12% (~8 million metric tons of methane per year) of the global O&G production methane emissions, mitigation of ultra-emitters is largely achievable at low costs and would lead to robust net benefits in billions of US dollars for the six major O&G-producing countries when considering societal costs of methane.

French crop yield, area and production data for ten staple crops from 1900 to 2018 at county resolution

Agricultural performance is influenced by environmental conditions, management decisions and economic circumstances. It is important to quantify their respective contribution to allow for detecting major hazards to production, projecting future yields under climate change and deriving adaptation options. For this purpose, time series of agricultural yields with high spatial and long-term temporal resolution are a primary requisite. Here we present a data set of crop performance in France, one of Europe's major crop producers. The data set comprises ten crops (barley, maize, oats, potatoes, rapeseed, sugarbeet, sunflower, durum wheat, soft wheat and wine) and covers the years 1900 to 2018. It contains harvested area, production and yield data for all 96 French départements (i.e. counties or NUTS3 level) with a total number of 375,264 data points. Entries until 1988 have been digitized manually from statistical yearbooks. The technical validation indicates a high consistency of the data set within itself and with external resources. The data set may contribute to an enhanced understanding of the manifold influences on agricultural performance.

Tropical tall forests are more sensitive and vulnerable to drought than short forests

Our limited understanding of the impacts of drought on tropical forests significantly impedes our ability in accurately predicting the impacts of climate change on this biome. Here, we investigated the impact of drought on the dynamics of forest canopies with different heights using time-series records of remotely sensed Ku-band vegetation optical depth (Ku-VOD), a proxy of top-canopy foliar mass and water content, and separated the signal of Ku-VOD changes into drought-induced reductions and subsequent non-drought gains. Both drought-induced reductions and non-drought increases in Ku-VOD varied significantly with canopy height. Taller tropical forests experienced greater relative Ku-VOD reductions during drought and larger non-drought increases than shorter forests, but the net effect of drought was more negative in the taller forests. Meta-analysis of in situ hydraulic traits supports the hypothesis that taller tropical forests are more vulnerable to drought stress due to smaller xylem-transport safety margins. Additionally, Ku-VOD of taller forests showed larger reductions due to increased atmospheric dryness, as assessed by vapor pressure deficit, and showed larger gains in response to enhanced water supply than shorter forests. Including the height-dependent variation of hydraulic transport in ecosystem models will improve the simulated response of tropical forests to drought.

Data-driven estimates of fertilizer-induced soil NH3 , NO and N2 O emissions from croplands in China and their climate change impacts

Gaseous reactive nitrogen (Nr) emissions from agricultural soils to the atmosphere constitute an integral part of global N cycle, directly or indirectly causing climate change impacts. The extensive use of N fertilizer in crop production will compromise our efforts to reduce agricultural Nr emissions in China. A national inventory of fertilizer N-induced gaseous Nr emissions from croplands in China remains to be developed to reveal its role in shaping climate change. Here we present a data-driven estimate of fertilizer N-induced soil Nr emissions based on regional and crop-specific emission factors (EFs) compiled from 379 manipulative studies. In China, agricultural soil Nr emissions from the use of synthetic N fertilizer and manure in 2018 are estimated to be 3.81 and 0.73 Tg N yr^-1 , with a combined contribution of 23%, 20% and 15% to the global agricultural emission total of ammonia (NH₃ ), nitrous oxide (N₂ O) and nitric oxide (NO), respectively. Over the past three decades, NH₃ volatilization from croplands has experienced a shift from a rapid increase to a decline trend, whereas N₂ O and NO emissions always maintain a strong growth momentum due to a robust and continuous rise of EFs. Regionally, croplands in Central south (1.51 Tg N yr^-1 ) and East (0.99 Tg N yr^-1 ) of China exhibit as hotspots of soil Nr emissions. In terms of crop-specific emissions, rice, maize and vegetable show as three leading Nr emitters, together accounting for 61% of synthetic N fertilizer-induced Nr emissions from croplands. The global warming effect derived from cropland N₂ O emissions in China was found to dominate over the local cooling effects of NH₃ and NO emissions. Our established regional and crop-specific EFs for gaseous Nr forms provide a new benchmark for constraining the IPCC Tier 1 default EF values. The spatio-temporal insight into soil Nr emission data from N fertilizer application in our estimate is expected to advance our efforts towards more accurate global or regional cropland Nr emission inventories and effective mitigation strategies.

Natural forests promote phosphorus retention in soil

Soil phosphorus (P) availability often limits plant productivity. Classical theories suggest that total P content declines at the temporal scale of pedogenesis, and ecosystems develop toward the efficient use of scarce P during succession. However, the trajectory of ecosystem P within shorter time scales of succession remains unclear. We analyzed changes to P pools at the early (I), middle (II), and late (III) stages of growth of plantation forests (PFs) and the successional stages of natural forests (NFs) at 1969 sites in China. We found significantly lower P contents at later growth stages compared to earlier ones in the PF (p < .05), but higher contents at late successional stages than in earlier stages in the NF (p < .05). Our results indicate that increasing P demand of natural vegetation during succession, may raise, retain, and accumulate P from deeper soil layers. In contrast, ecosystem P in PF was depleted by the more rapidly increasing demand outpacing the development of a P-efficient system. We advocate for more studies to illuminate the mechanisms for determining the divergent changes, which would improve forest management and avoid the vast degradation of PF ecosystems suffering from the ongoing depletion of P.

Three Gorges Dam: Friend or Foe of Riverine Greenhouse Gases?

Dams are often regarded as greenhouse gas (GHG) emitters. However, our study indicated that the world's largest dam, the Three Gorges Dam (TGD), has caused significant drops in annual average emissions of CO₂, CH₄ and N₂O over 4300 km along the Yangtze River, accompanied by remarkable reductions in the annual export of CO₂ (79%), CH₄ (50%) and N₂O (9%) to the sea. Since the commencement of its operation in 2003, the TGD has altered the carbonate equilibrium in the reservoir area, enhanced methanogenesis in the upstream, and restrained methanogenesis and denitrification via modifying anoxic habitats through long-distance scouring in the downstream. These findings suggest that ‘large-dam effects’ are far beyond our previous understanding spatiotemporally, which highlights the fundamental importance of whole-system budgeting of GHGs under the profound impacts of huge dams.

Near-real-time global gridded daily CO2 emissions

Precise and high-resolution carbon dioxide (CO₂) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO₂ Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO₂ emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO₂) retrievals. Our study on the global CO₂ emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO₂ emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO₂ emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

Decarbonising the iron and steel sector for a 2 °C target using inherent waste streams

The decarbonisation of the iron and steel industry, contributing approximately 8% of current global anthropogenic CO₂ emissions, is challenged by the persistently growing global steel demand and limitations of techno-economically feasible options for low-carbon steelmaking. Here we explore the inherent potential of recovering energy and re-using materials from waste streams, high-temperature slag, and re-investing the revenues for carbon capture and storage. In a pathway based on energy recovery and resource recycling of glassy blast furnace slag and crystalline steel slag, we show that a reduction of 28.5 ± 5.7% CO₂ emissions to the sectoral 2 °C target requirements in the iron and steel industry could be realized in 2050 under strong decarbonization policy consistent with low warming targets. The technological schemes applied to engineer this high-potential pathway could generate a revenue of US$35 ± 16 and US$40 ± 18 billion globally in 2035 and 2050, respectively. If this revenue is used for carbon capture and storage implementation, equivalent CO₂ emission to the 2 °C sectoral target requirements is expected to be reduced before 2050, without any external investments.

The iron and steel industry is emissions intensive. Here the authors explore its decarbonisation potential based on recovering energy and recycling materials from waste streams in 2020-2050. 28.5% of CO₂ emissions under sectoral 2 °C target requirements can be reduced in a high-potential pathway.

Regional trends and drivers of the global methane budget

The ongoing development of the Global Carbon Project (GCP) global methane (CH₄ ) budget shows a continuation of increasing CH₄ emissions and CH₄ accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH₄ emission estimates demonstrates robust temporal trends with CH₄ emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH₄  yr^-1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH₄ emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH₄ burden and the greatest opportunities for greenhouse gas abatement.