Building soil to reduce climate change impacts on global crop yield

Improving soil health and resilience is fundamental for sustainable food production, however the role of soil in maintaining or improving global crop productivity under climate warming is not well identified and quantified. Here, we examined the impact of soil on yield response to climate warming for four major crops (i.e., maize, wheat, rice and soybean), using global-scale datasets and random forest method. We found that each °C of warming reduced global yields of maize by 3.4%, wheat by 2.4%, rice by 0.3% and soybean by 5.0%, which were spatially heterogeneous with possible positive impacts. The random forest modeling analyses further showed that soil organic carbon (SOC), as an indicator of soil quality, dominantly explained the spatial heterogeneity of yield responses to warming and would regulate the negative warming responses. Improving SOC under the medium SOC sequestration scenario would reduce the warming-induced yield loss of maize, wheat, rice and soybean to 0.1% °C-1, 2.7% °C-1, 3.4% °C-1 and - 0.6% °C-1, respectively, avoiding an average of 3%-5% °C-1 of global yield loss. These yield benefits would occur on 53.2%, 67.8%, 51.8% and 71.6% of maize, wheat, rice and soybean planting areas, respectively, with particularly pronounced benefits in the regions with negative warming responses. With improved soil carbon, food systems are predicted to provide additional 20 to over 130 million tonnes of food that would otherwise lose due to future warming. Our findings highlight the critical role of soil in alleviating negative warming impacts on food security, especially for developing regions, given that sustainable actions on soil improvement could be taken broadly.

How climate change may shift power demand in Japan: Insights from data-driven analysis

The impact of climate change on power demand in Japan and its related CO2 emissions is a matter of concern for the Japanese authorities and power companies as it may have consequences on the power grid, but is also of global importance as Japan is a significant contributor to global greenhouse gas emissions. In this study, we trained random forest models against daily power data in ten Japanese regions and for different types of power generation to project changes in future power production and its carbon intensity. We used climate variables, heat stress indices, and one variable for the level of human activities. We then used the models trained from the present-day period to estimate the future power demand, carbon intensity, and pertaining CO2 emissions over the period 2020-2100 under three Shared Socioeconomic Pathways (SSPs) scenarios (SSP126, SSP370, and SSP585). The impact of climate change on CO2 emissions via power generation shows seasonal and regional disparities. In cold regions, a decrease in power demand during winter under future warming leads to an overall decrease in power demand over the year. In contrast, the decrease in winter power demand in hot regions can be overcompensated by an increase in summer power demand due to more frequent hot days, resulting in an overall annual increase. From our regional models, power demand is projected to increase the most in most Japanese regions in May, June, September, and October rather than in the middle of summer, as found in previous studies. This increase could result in regular power outages during those months as the power grid could become particularly tense. Overall, we observed that power demand in regions with extreme climates is more sensitive to global warming than in temperate regions. The impact of climate change on power demand induces a net annual decrease in CO2 emissions in all regions except for Okinawa, in which power demand strongly increases during the summer, resulting in a net annual increase in CO2 emissions. However, climate change's impact on carbon intensity may reverse the trend in some regions (Shikoku, Tohoku). Additionally, we assessed the relative impacts of socioeconomic factors such as population, GDP, and environmental policies on CO2 emissions. When combined with these factors, we found that the climate change effect is more important than when considered individually and significantly impacts total CO2 emissions under SSP585. The contrasting results observed in the warm and cold regions of Japan can offer valuable insight into the potential future variations in energy demand and resulting CO2 emissions on a global scale.

Temperature extremes of 2022 reduced carbon uptake by forests in Europe

The year 2022 saw record breaking temperatures in Europe during both summer and fall. Similar to the recent 2018 drought, close to 30% (3.0 million km2) of the European continent was under severe summer drought. In 2022, the drought was located in central and southeastern Europe, contrasting the Northern-centered 2018 drought. We show, using multiple sets of observations, a reduction of net biospheric carbon uptake in summer (56-62 TgC) over the drought area. Specific sites in France even showed a widespread summertime carbon release by forests, additional to wildfires. Partial compensation (32%) for the decreased carbon uptake due to drought was offered by a warm autumn with prolonged biospheric carbon uptake. The severity of this second drought event in 5 years suggests drought-induced reduced carbon uptake to no longer be exceptional, and important to factor into Europe's developing plans for net-zero greenhouse gas emissions that rely on carbon uptake by forests.

The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe

Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.

From political pledges to quantitative mapping of climate mitigation plans: Comparison of two European cities

BACKGROUND

Urban agglomerates play a crucial role in reaching global climate objectives. Many cities have committed to reducing their greenhouse gas emissions, but current emission trends remain unverifiable. Atmospheric monitoring of greenhouse gases offers an independent and transparent strategy to measure urban emissions. However, careful design of the monitoring network is crucial to be able to monitor the most important sectors as well as adjust to rapidly changing urban landscapes.

RESULTS

Our study of Paris and Munich demonstrates how climate action plans, carbon emission inventories, and urban development plans can help design optimal atmospheric monitoring networks. We show that these two European cities display widely different trajectories in space and time, reflecting different emission reduction strategies and constraints due to administrative boundaries. The projected carbon emissions rely on future actions, hence uncertain, and we demonstrate how emission reductions vary significantly at the sub-city level.

CONCLUSIONS

We conclude that quantified individual cities' climate actions are essential to construct more robust emissions trajectories at the city scale. Also, harmonization and compatibility of plans from various cities are necessary to make inter-comparisons of city climate targets possible. Furthermore, dense atmospheric networks extending beyond the city limits are needed to track emission trends over the coming decades.

Non-symmetric responses of leaf onset date to natural warming and cooling in northern ecosystems

The northern hemisphere has experienced regional cooling, especially during the global warming hiatus (1998-2012) due to ocean energy redistribution. However, the lack of studies about the natural cooling effects hampers our understanding of vegetation responses to climate change. Using 15,125 ground phenological time series at 3,620 sites since the 1950s and 31-year satellite greenness observations (1982-2012) covering the warming hiatus period, we show a stronger response of leaf onset date (LOD) to natural cooling than to warming, i.e. the delay of LOD caused by 1°C cooling is larger than the advance of LOD with 1°C warming. This might be because cooling leads to larger chilling accumulation and heating requirements for leaf onset, but this non-symmetric LOD response is partially offset by warming-related drying. Moreover, spring greening magnitude, in terms of satellite-based greenness and productivity, is more sensitive to LOD changes in the warming area than in the cooling. These results highlight the importance of considering non-symmetric responses of spring greening to warming and cooling when predicting vegetation-climate feedbacks.

Increasing riverine export of dissolved organic carbon from China

River transport of dissolved organic carbon (DOC) to the ocean is a crucial but poorly quantified regional carbon cycle component. Large uncertainties remaining on the riverine DOC export from China, as well as its trend and drivers of change, have challenged the reconciliation between atmosphere-based and land-based estimates of China's land carbon sink. Here, we harmonized a large database of riverine in-situ measurements and applied a random forest model, to quantify riverine DOC fluxes (FDOC ) and DOC concentrations (CDOC ) in rivers across China. This study proposes the first DOC modeling effort capable of reproducing well the magnitude of riverine CDOC and FDOC , as well as its trends, on a monthly scale and with a much wider spatial distribution over China compared to previous studies that mainly focused on annual-scale estimates and large rivers. Results show that over the period 2001-2015, the average CDOC was 2.25 ± 0.45 mg/L and average FDOC was 4.04 ± 1.02 Tg/year. Simultaneously, we found a significant increase in FDOC (+0.044 Tg/year2 , p = .01), but little change in CDOC (-0.001 mg/L/year, p > .10). Although the trend in CDOC is not significant at the country scale, it is significantly increasing in the Yangtze River Basin and Huaihe River Basin (0.005 and 0.013 mg/L/year, p < .05) while significantly decreasing in the Yellow River Basin and Southwest Rivers Basin (-0.043 and -0.014 mg/L/year, p = .01). Changes in hydrology, play a stronger role than direct impacts of anthropogenic activities in determining the spatio-temporal variability of FDOC and CDOC across China. However, and in contrast with other basins, the significant increase in CDOC in the Yangtze River Basin and Huaihe River Basin is attributable to direct anthropogenic activities. Given the dominance of hydrology in driving FDOC , the increase in FDOC is likely to continue under the projected increase in river discharge over China resulting from a future wetter climate.

Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects

Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.

Satellite reveals a steep decline in China's CO2 emissions in early 2022

Response actions to the coronavirus disease 2019 perturbed economies and carbon dioxide (CO2) emissions. The Omicron variant that emerged in 2022 caused more substantial infections than in 2020 and 2021 but it has not yet been ascertained whether Omicron interrupted the temporary post-2021 rebound of CO2 emissions. Here, using satellite nitrogen dioxide observations combined with atmospheric inversion, we show a larger decline in China's CO2 emissions between January and April 2022 than in those months during the first wave of 2020. China's CO2 emissions are estimated to have decreased by 15% (equivalent to -244.3 million metric tons of CO2) during the 2022 lockdown, greater than the 9% reduction during the 2020 lockdown. Omicron affected most of the populated and industrial provinces in 2022, hindering China's CO2 emissions rebound starting from 2021. China's emission variations agreed with downstream CO2 concentration changes, indicating a potential to monitor CO2 emissions by integrating satellite and ground measurements.

Increased precipitation over land due to climate feedback of large-scale bioenergy cultivation

Bioenergy with carbon capture and storage (BECCS) is considered to be a key technology for removing carbon dioxide from the atmosphere. However, large-scale bioenergy crop cultivation results in land cover changes and activates biophysical effects on climate, with earth's water recycling altered and energy budget re-adjusted. Here, we use a coupled atmosphere-land model with explicit representations of high-transpiration woody (i.e., eucalypt) and low-transpiration herbaceous (i.e., switchgrass) bioenergy crops to investigate the range of impact of large-scale rainfed bioenergy crop cultivation on the global water cycle and atmospheric water recycling. We find that global land precipitation increases under BECCS scenarios, due to enhanced evapotranspiration and inland moisture advection. Despite enhanced evapotranspiration, soil moisture decreases only slightly, due to increased precipitation and reduced runoff. Our results indicate that, at the global scale, the water consumption by bioenergy crop growth would be partially compensated by atmospheric feedbacks. Thus, to support more effective climate mitigation policies, a more comprehensive assessment, including the biophysical effects of bioenergy cultivation, is highly recommended.

Accelerating the energy transition towards photovoltaic and wind in China

China's goal to achieve carbon (C) neutrality by 2060 requires scaling up photovoltaic (PV) and wind power from 1 to 10-15 PWh year^-1 (refs. ^1-5). Following the historical rates of renewable installation¹, a recent high-resolution energy-system model⁶ and forecasts based on China's 14th Five-year Energy Development (CFED)⁷, however, only indicate that the capacity will reach 5-9.5 PWh year^-1 by 2060. Here we show that, by individually optimizing the deployment of 3,844 new utility-scale PV and wind power plants coordinated with ultra-high-voltage (UHV) transmission and energy storage and accounting for power-load flexibility and learning dynamics, the capacity of PV and wind power can be increased from 9 PWh year^-1 (corresponding to the CFED path) to 15 PWh year^-1, accompanied by a reduction in the average abatement cost from US$97 to US$6 per tonne of carbon dioxide (tCO₂). To achieve this, annualized investment in PV and wind power should ramp up from US$77 billion in 2020 (current level) to US$127 billion in the 2020s and further to US$426 billion year^-1 in the 2050s. The large-scale deployment of PV and wind power increases income for residents in the poorest regions as co-benefits. Our results highlight the importance of upgrading power systems by building energy storage, expanding transmission capacity and adjusting power load at the demand side to reduce the economic cost of deploying PV and wind power to achieve carbon neutrality in China.

Carbon Monitor Europe near-real-time daily CO2 emissions for 27 EU countries and the United Kingdom

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO₂ emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO₂ emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

Microbial carbon use efficiency promotes global soil carbon storage

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Machine learning for accelerating process-based computation of land biogeochemical cycles

Global change ecology nowadays embraces ever-growing large observational datasets (big-data) and complex mathematical models that track hundreds of ecological processes (big-model). The rapid advancement of the big-data-big-model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time-scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine-learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource-consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin-up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin-up, we show that an unoptimized MLA reduced the computation demand by 77%-80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA-derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one-order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade-off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin-up acceleration procedures, and opens the door to a wide variety of future applications, with complex non-linear models benefit most from the computational efficiency.

Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century

The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

Increasingly negative tropical water-interannual CO2 growth rate coupling

Terrestrial ecosystems have taken up about 32% of the total anthropogenic CO2 emissions in the past six decades1. Large uncertainties in terrestrial carbon-climate feedbacks, however, make it difficult to predict how the land carbon sink will respond to future climate change2. Interannual variations in the atmospheric CO2 growth rate (CGR) are dominated by land-atmosphere carbon fluxes in the tropics, providing an opportunity to explore land carbon-climate interactions3-6. It is thought that variations in CGR are largely controlled by temperature7-10 but there is also evidence for a tight coupling between water availability and CGR11. Here, we use a record of global atmospheric CO2, terrestrial water storage and precipitation data to investigate changes in the interannual relationship between tropical land climate conditions and CGR under a changing climate. We find that the interannual relationship between tropical water availability and CGR became increasingly negative during 1989-2018 compared to 1960-1989. This could be related to spatiotemporal changes in tropical water availability anomalies driven by shifts in El Niño/Southern Oscillation teleconnections, including declining spatial compensatory water effects9. We also demonstrate that most state-of-the-art coupled Earth System and Land Surface models do not reproduce the intensifying water-carbon coupling. Our results indicate that tropical water availability is increasingly controlling the interannual variability of the terrestrial carbon cycle and modulating tropical terrestrial carbon-climate feedbacks.

Compound droughts slow down the greening of the Earth

Vegetation response to soil and atmospheric drought has raised extensively controversy, however, the relative contributions of soil drought, atmospheric drought, and their compound droughts on global vegetation growth remain unclear. Combining the changes in soil moisture (SM), vapor pressure deficit (VPD), and vegetation growth (normalized difference vegetation index [NDVI]) during 1982-2015, here we evaluated the trends of these three drought types and quantified their impacts on global NDVI. We found that global VPD has increased 0.22 ± 0.05 kPa·decade^-1 during 1982-2015, and this trend was doubled after 1996 (0.32 ± 0.16 kPa·decade^-1 ) than before 1996 (0.16 ± 0.15 kPa·decade^-1 ). Regions with large increase in VPD trend generally accompanied with decreasing trend in SM, leading to a widespread increasing trend in compound droughts across 37.62% land areas. We further found compound droughts dominated the vegetation browning since late 1990s, contributing to a declined NDVI of 64.56%. Earth system models agree with the dominant role of compound droughts on vegetation growth, but their negative magnitudes are considerably underestimated, with half of the observed results (34.48%). Our results provided the evidence of compound droughts-induced global vegetation browning, highlighting the importance of correctly simulating the ecosystem-scale response to the under-appreciated exposure to compound droughts as it will increase with climate change.

Overestimated nitrogen loss from denitrification for natural terrestrial ecosystems in CMIP6 Earth System Models

Denitrification and leaching nitrogen (N) losses are poorly constrained in Earth System Models (ESMs). Here, we produce a global map of natural soil ¹⁵N abundance and quantify soil denitrification N loss for global natural ecosystems using an isotope-benchmarking method. We show an overestimation of denitrification by almost two times in the 13 ESMs of the Sixth Phase Coupled Model Intercomparison Project (CMIP6, 73 ± 31 Tg N yr^-1), compared with our estimate of 38 ± 11 Tg N yr^-1, which is rooted in isotope mass balance. Moreover, we find a negative correlation between the sensitivity of plant production to rising carbon dioxide (CO₂) concentration and denitrification in boreal regions, revealing that overestimated denitrification in ESMs would translate to an exaggeration of N limitation on the responses of plant growth to elevated CO₂. Our study highlights the need of improving the representation of the denitrification in ESMs and better assessing the effects of terrestrial ecosystems on CO₂ mitigation.

From regional to parcel scale: A high-resolution map of cover crops across Europe combining satellite data with statistical surveys

The reformed Common Agricultural Policy of 2023-2027 aims to promote a more sustainable and fair agricultural system in the European Union. Among the proposed measures, the incentivized adoption of cover crops to cover the soil during winter provides numerous benefits such as improved soil structure and reduced nutrient leaching and erosion. Despite this recognized importance, the availability of spatial data on cover crops is scarce. The increasing availability of field parcel declarations in the European Union has not yet filled this data gap due to its insufficient information content, limited public availability and a lack of standardization at continental scale. At present, the best information available is regionally aggregated survey data, which although indicative, hinders the development of spatially accurate studies. In this work, we propose a statistical model relating Sentinel-1 data to the existence of cover crops at the 100-m spatial resolution over the entirety of the European Union and United Kingdom and estimate its parameters using the spatially aggregated survey data. To validate the method in a spatially-explicit way, predictions were compared against farmers' registered declarations in France, where the adoption of cover crops is widespread. The results indicate a good agreement between predictions and parcel-level data. When interpreted as a binary classifier, the model yielded an Area Under the Curve (AUC) of 0.74 for the whole country. When the country was divided into five regions for the evaluation of regional biases, the AUC values were 0.77, 0.75, 0.74, 0.70, and 0.65 for the North, Center, West, East, and South regions respectively. Despite limitations such as the lack of data for validation outside France, and the non-standardized nomenclature for cover crops among Member States, this work constitutes the first effort to obtain a relevant cover crop map at a European scale for researchers and practitioners.

Extreme rainfall reduces one-twelfth of China's rice yield over the last two decades

Extreme climate events constitute a major risk to global food production. Among these, extreme rainfall is often dismissed from historical analyses and future projections, the impacts and mechanisms of which remain poorly understood. Here we used long-term nationwide observations and multi-level rainfall manipulative experiments to explore the magnitude and mechanisms of extreme rainfall impacts on rice yield in China. We find that rice yield reductions due to extreme rainfall were comparable to those induced by extreme heat over the last two decades, reaching 7.6 ± 0.9% (one standard error) according to nationwide observations and 8.1 ± 1.1% according to the crop model incorporating the mechanisms revealed from manipulative experiments. Extreme rainfall reduces rice yield mainly by limiting nitrogen availability for tillering that lowers per-area effective panicles and by exerting physical disturbance on pollination that declines per-panicle filled grains. Considering these mechanisms, we projected ~8% additional yield reduction due to extreme rainfall under warmer climate by the end of the century. These findings demonstrate that it is critical to account for extreme rainfall in food security assessments.

More than one quarter of Africa's tree cover is found outside areas previously classified as forest

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = -6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

The role of rice cultivation in changes in atmospheric methane concentration and the Global Methane Pledge

Resumption of the increase in atmospheric methane (CH₄ ) concentrations since 2007 is of global concern and may partly have resulted from emissions from rice cultivation. Estimates of CH₄ emissions from rice fields and abatement potential are essential to assess the contribution of improved rice management in achieving the targets of the Global Methane Pledge agreed upon by over 100 countries at COP26. However, the contribution of CH₄ emissions from rice fields to the resumed CH₄ growth and the global abatement potential remains unclear. In this study, we estimated the global CH₄ emissions from rice fields to be 27 ± 6 Tg CH₄ year^-1 in the recent decade (2008-2017) based on the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The trend of CH₄ emissions from rice cultivation showed an increase followed by no significant change and then, a stabilization over 1990-2020. Consequently, the contribution of CH₄ emissions from rice fields to the renewed increase in atmospheric CH₄ concentrations since 2007 was minor. We summarized the existing low-cost measures and showed that improved water and straw management could reduce one-third of global CH₄ emissions from rice fields. Straw returned as biochar could reduce CH₄ emissions by 12 Tg CH₄ year^-1 , equivalent to 10% of the total reduction of all anthropogenic emissions. We conclude that other sectors than rice cultivation must have contributed to the renewed increase in atmospheric CH₄ concentrations, and that optimizing multiple mitigation measures in rice fields could contribute significantly to the abatement goal outlined in the Global Methane Pledge.

Satellites reveal Earth's seasonally shifting dust emission sources

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m^-2 y^-1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y^-1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation.

The carbon footprint response to projected base stations of China's 5G mobile network

While the rapid expansion of China's 5G mobile network helps to speed up the nation's economic and social development, it tends to release more CO₂ due to the 5G's significant energy demand, hampering sustainable development of the 5G network. Previous assessments of CO₂ emissions from China's 5G development were based on a projected 5G network ranging from six to fifteen million base stations with the absent of a convincing business model in 5G's application. Under the scenario of business-estimated six million base stations in 2030, the share of electricity consumed by China's 5G networks in 2030 could reach 8.4 % of the national total power generation, causing 0.44 GtCO₂/yr CO₂ emissions. We collected 5G base station numbers in 2020 and 2021 in 31 provinces and province-level municipalities (PLM), the period with the rapid growth of the 5G base stations in China. We linked these provincial base stations with provincial Gross Domestic Product (GDP), population (POP), and big data development level (BDDL) and established a statistical model to predict 5G base stations by 2030. The model predicted 2-5 million 5G base stations by 2030, considerably lower than the business-projected base station number. Under the model predicted 5G base stations, China's 5G network could yield 0.15-0.29 GtCO₂/yr emissions subject to the nation's BDDL from 40 to 80 % by 2030. Both 5G base stations and CO₂ emissions are significantly lower than the previous estimates. We decomposed the CO₂ footprint of China's 5G networks and assessed the contribution of the number of 5G base stations and mobile data traffic to 5G-induced CO₂ emissions. We find that increasing the application of clean energy and promoting energy efficiency can reduce CO₂ emissions in the 5G network. To more accurately estimate 5G's climate effect, we propose that it urgently needs to improve vivid 5G business models.