Soils represent a significant natural source of nitric oxide (NO), a precursor of nitrogen oxides (NO x) that play an essential role in atmospheric chemistry and radiative forcing. However, historical estimates of global soil NO x emissions have been constrained by limited long-term nitrogen input datasets and methodological gaps in process representation. In this study, we present the first century-scale simulation of global soil NO x emissions (1850-2020) using the biogeochemical model VISIT (The Vegetation Integrative SImulator for Trace gases), coupled with the newly developed History of anthropogenic Nitrogen inputs dataset of harmonized anthropogenic nitrogen inputs. Our results reveal that total global soil NO x emissions increased gradually from an average of 22.1 Tg N yr-1 prior to 1900 to 25.3 Tg N yr-1 in the 2010s, with emissions above the canopy ranging from 12.2 to 13.6 Tg N yr-1. Notably, while soil NO x emissions from natural ecosystems showed a marginal decline, the contribution of agricultural soils experienced a more than fourfold increase to global NO x emissions, driven primarily by fertilizer and manure application since the mid-20th century. This led to a more than fourfold increase in the share of agricultural land in global soil NO x emissions, rising from 7.8% to 31.3%. Regional analysis indicates substantial increases in Europe, Asia, and North America, with soil NO x emissions becoming a dominant source in sparsely populated and arid regions. These findings underscore the growing importance of soil-derived NO x in the global nitrogen budget and its implications for future air quality and climate mitigation strategies. This work provides a robust basis for refining emission inventories and supports the development of region-specific nitrogen management to mitigate unintended NO x emissions from soils. On the other hand, benchmarking against the results in Nitrogen Model Inter-comparison Project (NMIP2) revealed significant divergence in global soil NO x emissions between models, with the VISIT estimates on the higher end, highlighting the urgent need for improved process representation and observational constraints.

Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by years, preventing a timely understanding of CH4 sources, sinks, and trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates using a machine-learning emulator to reconstruct spatially explicit monthly emission fields at 1 degrees & times; 1 degrees resolution. We apply this framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record that covers the 2000-2020 emissions through 2025. In the test data (similar to 30 % of the total dataset), the emulator achieved a global R-2 of 0.65 +/- 0.003 (mean +/- 95 % CI, hereafter) and an RMSE of 5.49 +/- 0.12 & times; 10(-3) Tg CH4 yr(-1). The emulator is trained on 35 GMB model estimates, including 22 process-based models and 13 atmospheric inversions, paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. Our results show that the global mean predicted wetland CH4 emissions for 2021-2025 (157.8 +/- 2.4 Tg CH4 yr(-1)) are not significantly higher (similar to 0.05 Tg CH4 yr(-1)) than the 2000-2020 baseline. However, this stability masks a significant hemispheric redistribution of emissions. We detect an increase in Northern Hemisphere (NH) emissions in 2021-2025, with mid- and high-latitudes increasing by 0.76 +/- 0.07 and 0.35 +/- 0.03 Tg CH4 yr(-1), respectively, while the tropics and Southern Hemisphere (SH) extratropics show offsetting negative trends (-0.95 +/- 0.19 and -0.11 +/- 0.02 Tg CH4 yr(-1), respectively). The predicted emissions are able to capture the low emissions in 2023 in South America linked to El Ni & ntilde;o-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Furthermore, we identify a distinct seasonal amplification of global emission trends that peaks in late boreal summer. This new modeled dataset and operational framework bridge the gap between the latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Extreme precipitation events, intensified by climate change, are increasingly disrupting nutrient dynamics in large river systems worldwide. Beyond altering the magnitude of individual nutrient fluxes, extreme precipitation may fundamentally reshape nutrient composition and stoichiometric balances, with critical implications for aquatic ecosystem health. Here, we apply an integrated data-model framework to assess how these events have altered nitrogen (N) and phosphorus (P) fluxes across the Mississippi River Basin from 1980 to 2018. We find that extreme rainfall disproportionately increases P export relative to N, driven primarily by enhanced soil erosion and mobilization of particulate-bound nutrients. Concurrent temporal and spatial changes in extreme precipitation regimes have induced declining N:P ratios in headwater streams and cumulative nutrient loads, shifting export stoichiometry toward the Redfield ratio. Therefore, extreme precipitation can increase nutrient fluxes that fuel harmful algal blooms, yet at the same time reduce N:P ratios that may favor less toxic communities. This trade-off calls for watershed management strategies that go beyond managing nutrient quantity alone.

Urban parks are vital for the well-being of Earth’s 4.6 billion urban residents, yet their global distribution and impact on public welfare remain poorly understood. Here we analyzed 440,000 urban parks across 1860 cities worldwide, introducing the new Comprehensive Benefit Index (CBI) to assess their richness, greenness, and accessibility, while identifying gaps in urban park construction. Our findings reveal significant global disparities: developed countries contain approximately 80% of urban parks, with high-income countries achieving an average CBI 1.64 times higher than lower-middle-income countries and 1.76 times higher than low-income countries. While upper-middle-income countries have a sufficient number of parks, they often lack greenness and accessibility. In contrast, low- and lower-middle-income countries struggle to meet the basic park availability needs of urban residents. These inequalities hinder inclusive urban development, underscoring the urgent need for targeted strategies to improve urban parks in underserved countries. Aligning with Sustainable Development Goal 11, this study offers critical insights to support sustainable urban planning and foster equitable urban park systems worldwide.

Abstract. Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by several years, preventing a timely understanding of CH4 sources, sinks, and their trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates and applying the framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record through 2025 at monthly 1°x1° resolution. We developed a machine-learning emulator to reconstruct spatially explicit monthly emission fields (global R2 =0.65 ± 0.003 (mean ± 95 % CI, hereafter) and RMSE=5.49 ± 0.12 ×10-3 Tg CH4/year in test data which is ~30 % of the total data). The emulator is trained on 35 GMB model estimates (22 process-based model estimates and 13 atmospheric inversion estimates) paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. While the global mean predicted wetland CH4 emissions for 2021–2025 (157.83 ± 2.38 Tg CH4/year) are only marginally higher (~0.05 Tg CH4/year) than the 2000–2020 baseline, this stability masks a significant hemispheric redistribution of emissions. We detect a surge in Northern Hemisphere emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 (z-score: 2.21) and 0.35 ± 0.03 Tg/year (z-score:1.01), respectively, while the tropics and Southern Hemisphere extratropics show offsetting negative trends (-0.95 ± 0.19 and -0.11 ± 0.02 Tg/year with z-scores of -2.81 and -0.34, respectively). The predicted emissions capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Post-2020 growth rates of emission anomalies are a magnitude higher than that in 2000–2025, suggesting an intensification of emission variability. Furthermore, we identify a distinct seasonal amplification of global emission growth peaking in late boreal summer. This new dataset and operational framework bridge the gap between latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget. We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024).

South America is a global hotspot for land use and land cover (LULC) change, marked by dramatic agricultural land expansion and deforestation. While previous studies have documented land use and land cover changes in South America over recent decades, there is still a lack of spatially explicit and time-series maps of crop types that capture shifts in crop distribution. Therefore, developing high-resolution, long-term, and crop-specific datasets is crucial for advancing our understanding of human–environment interactions and for assessing the impacts of agricultural activities on carbon and biogeochemical cycles, biodiversity, and climate. In this study, we integrated multi-source data, including high-resolution remote sensing data, model-based data, and historical agricultural census data, to reconstruct the historical dynamics of four major commodity crops (i.e., soybean, maize, wheat, and rice) in South America at an annual timescale and 1 km × 1 km spatial resolution from 1950 to 2020. The results showed that soybean and maize cultivation expanded rapidly in South America by encroaching on other vegetation (i.e., forest, pasture/rangeland, and unmanaged grass/shrubland) over the past 70 years, whereas wheat and rice areas remained relatively stable. Specifically, soybean is one of the most dramatically expanded crops, increasing from essentially zero in 1950 to 48.8 Mha in 2020, resulting in a total loss of 23.92 Mha of other vegetation. In addition, the area of maize increased by a factor of 2.1 from 12.7 Mha in 1950 to 26.9 Mha in 2020. The newly developed crop type dataset provides important insights for assessing the impacts of cropland expansion on crop production, biodiversity, greenhouse gas emissions, and carbon and nitrogen cycles in South America. Moreover, these data are instrumental for developing national policies, sustainable trade, investment, and development strategies aimed at securing food supply and other human and environmental objectives in South America. The datasets are available at https://doi.org/10.5281/zenodo.14002960 (Xu et al., 2024).

This study provides the first comprehensive quantification of three major greenhouse gases (GHGs, including CO 2 , CH 4 , and N 2 O) budgets for Central and West Asia (CWA) from 2000 to 2020, including contributions from fossil fuels, industry, and managed and unmanaged terrestrial ecosystems. We use bottom‐up (BU: inventories and process‐based models) and top‐down approaches (TD: atmospheric inversions) to elucidate CWA's GHG budget and its changes. BU and TD budgets consistently show that CWA was a significant and growing GHG source during the 2010s: average net emissions were 4,175 (range: 4,055–4,301) Tg CO 2 eq yr −1 based on BU and using global warming potentials over a 100‐year period (GWP100), and slightly higher net emissions of 4,293 (3,760–4,826) Tg CO 2 eq yr −1 based on TD. BU estimates show that CO 2 emissions from fossil fuel combustion and fugitive releases were the dominant source, accounting for 61% of the total budget in the 2010s, with 2,554 (2,526–2,582) Tg CO 2 eq yr −1 . Terrestrial natural ecosystems were a weak CO 2 sink and sources of CH 4 and N 2 O, which together resulted in a decadal mean net GHG emission of 220.5 (114.5–332.8) Tg CO 2 eq yr −1 . Non‐CO 2 gases, primarily CH 4 , contributed significantly to the region's GHG emissions, accounting for 32% (BU) and 24% (TD) of CWA's total GHG budget under GWP100, and increasing to 57% (BU) and 49% (TD) with GWP20, highlighting CH 4 stronger warming impact over shorter timescales. Overall, CWA contributed about 8% of global net GHG emissions in the 2010s, with about 10% of global CO 2 , 7% of CH 4 , and 3% of N 2 O.

Extreme dry-heat (EDH) climate poses significant challenges to global food production and exacerbates greenhouse gas (GHG) emissions, impeding efforts to mitigate agricultural climate impacts. However, the concurrent effects of long-term EDH climate and mitigation strategies on cropland productivity and GHG emissions remain poorly understood. Here, we integrated field observations, agroecosystem model outputs, and nursery data to examine how environmental factors and management practices influence wheat GHG emission intensity across the U.S. over the past six decades. Our findings indicate an overall increase in U.S. wheat production over the past 60 years, despite fluctuations in planted areas that have led to declines in production after 1990. The decline in GHG emissions from winter wheat after 1990 corresponds to fluctuations in planting areas, whereas emissions from spring wheat have continued to rise. Climate change and nitrogen fertilizer application have emerged as the primary drivers of these trends. EDH climates have intensified emissions intensity in over 80% of wheat-growing regions under current agricultural management practices. Specifically, the dry-heat sensitivity of emission intensity for spring wheat increased by 130% from 1960 to 2018, while for winter wheat, it surged several-fold after 2008. To address these challenges, we propose environment-specific tillage strategies to significantly reduce the dry-heat sensitivity of GHG emission intensity under local conditions. These strategies identify regionally optimal tillage schemes (including no-tillage and conventional tillage) to mitigate the adverse impacts of EDH climates. The implementation of these strategies in selected wheat-producing regions reduced dry-heat sensitivity by 9.8% (5.8%-17.7%) for spring wheat and 13.3% (8.0%-20.9%) for winter wheat emissions intensity. These findings underscore the critical need for targeted management approaches to alleviate the escalating indirect impacts of EDH climates. Such strategies are crucial for shaping agricultural and environmental policies aimed at achieving high-yield and low-emission targets in a warming world.

Nitrous oxide (N2O) is the most important stratospheric ozone-depleting agent based on current emissions and the third largest contributor to increased net radiative forcing. Increases in atmospheric N2O have been attributed primarily to enhanced soil N2O emissions. Critically, contributions from soils in the Northern High Latitudes (NHL, >50°N) remain poorly quantified despite their exposure to rapid rates of regional warming and changing hydrology due to climate change. In this study, we used an ensemble of six process-based terrestrial biosphere models (TBMs) from the Global Nitrogen/Nitrous Oxide Model Intercomparison Project (NMIP) to quantify soil N2​O emissions across the NHL during 1861-2016. Factorial simulations were conducted to disentangle the contributions of key driving factors, including climate change, nitrogen inputs, land use change, and rising atmospheric CO2 concentration​, to the trends in emissions. The NMIP models suggests NHL soil N2O emissions doubled from 1861 to 2016, increasing on average by 2.0 ± 1.0 Gg N/yr (p < 0.01). Over the entire study period, while N fertilizer application (42 ± 20 %) contributed the largest share to the increase in NHL soil emissions, climate change effect was comparable (37 ± 25 %), underscoring its significant role. In the recent decade (2007-2016), anthropogenic sources contributed 47 ± 17 % (279 ± 156 Gg N/yr) of the total N2O emissions from the NHL, while unmanaged soils contributed a comparable amount (290 ± 142 Gg N/yr). The trend of increasing emissions from nitrogen fertilizer reversed after the 1980 s because of reduced applications in non-permafrost regions. In addition, increased plant growth due to CO2 fertilization suppressed simulated emissions. However, permafrost soil N2O emissions continued increasing attributable to climate warming; the interaction of climate warming and increasing CO2 concentrations on nitrogen and carbon cycling will determine future trends in NHL soil N2O emissions. The rigorous interplay between process modeling and field experimentation will be essential for improving model representations of the mechanisms controlling N2O fluxes in the Northern High Latitudes and for reducing associated uncertainties.

Surface ozone (O3) pollution showed a continuous increasing trend during the recent decades in China, posing an increasing threat to food security. A wide range of yield reductions have been reported and thus more studies are needed to narrow down the uncertainty resulting from spatiotemporal accuracy of O3 metrics and extrapolation methods. Based on a high spatial resolution (0.1°) hourly surface O3 data, here we analyzed the spatiotemporal O3 pollution patterns and impacts on yield, production and economic losses for wheat, rice, and maize in China during 2005-2020. The accumulated O3 exposure over a threshold of 40 ppb (AOT40) increased by 10 % during 2005-2019, and a decrease of 5.56 % was observed in 2020 due to the COVID-19 lockdowns. Rising O3 pollution reduced national level wheat, rice and maize yields by 14.51 % ± 0.43 %, 11.10 % ± 0.6 %, and 3.99 % ± 0.11 %, respectively. A Business-As-Usual projection suggested that the relative yield loss (RYL) would potentially reach 8 %-18 % at the national scale by 2050 if no emission control is implemented. COVID-19 lockdowns in 2020 led to significantly reduced RYL for maize (0.52 %) and rice (2.17 %) but not for wheat (0.11 %), with the largest reduction (1.88 %-9.4 %) in North China Plain, highlighting the potential benefits of emission control. Our findings provided robust evidence that rising O3 pollution has significantly affected China's crop yields, production and economic losses, underscoring the urgent need to curb O3 pollution to safeguard food security, particularly in densely populated and industrialized regions.

The Pacific white shrimp (Litopenaeus vannamei) plays a crucial role in global aquaculture, contributing significantly to farmed shrimp production, with China being a major contributor. In the last two decades, China's production of farmed L. vannamei experienced substantial growth due to expanded aquaculture areas and intensification. These processes can lead to increases in greenhouse gas emissions. However, the magnitude of greenhouse gas emissions from L. vannamei aquaculture systems remains unclear. Therefore, in this study, we systematically quantified greenhouse gas (carbon dioxide, methane, and nitrous oxide) emissions in L. vannamei farming systems with different levels of intensification. Various emission sources were assessed, including infrastructure, energy use, feed production, and pond aquatic emissions. The estimates indicate that greenhouse gas emissions increased from 6159.35 +/- 475.24 kg CO2e/t of L. vannamei in semi-intensive systems to 24,059.81 +/- 3846.31 kg CO2e/t of L. vannamei in super-intensive systems. The increase in greenhouse gas emissions was primarily due to energy use (2395% increase) and infrastructure (15,939% increase). As a result, the greenhouse gas emissions from China's shrimp farming industry increased three-fold (from 5633.37 +/- 177.40 to 19,730.68 +/- 635.52 million kg CO2e) from 2003 to 2022, mainly contributed by coastal provinces. As the demand for highquality aquatic products continues to increase, coupled with the urgent necessity to reduce greenhouse gas emissions, there is a crucial requirement to lower the emissions associated with each unit of L. vannamei production. The results of this study suggest that reducing fossil fuel use, improving feed efficiency, promoting biofloc technology, and integrated multi-trophic aquaculture systems may help to build climate-resilient sustainable aquaculture.

Anthropogenic activities have substantially enhanced the loadings of reactive nitrogen (Nr) in the Earth system since pre-industrial times1,2, contributing to widespread eutrophication and air pollution3-6. Increased Nr can also influence global climate through a variety of effects on atmospheric and land processes but the cumulative net climate effect is yet to be unravelled. Here we show that anthropogenic Nr causes a net negative direct radiative forcing of -0.34 [-0.20, -0.50] W m-2 in the year 2019 relative to the year 1850. This net cooling effect is the result of increased aerosol loading, reduced methane lifetime and increased terrestrial carbon sequestration associated with increases in anthropogenic Nr, which are not offset by the warming effects of enhanced atmospheric nitrous oxide and ozone. Future predictions using three representative scenarios show that this cooling effect may be weakened primarily as a result of reduced aerosol loading and increased lifetime of methane, whereas in particular N2O-induced warming will probably continue to increase under all scenarios. Our results indicate that future reductions in anthropogenic Nr to achieve environmental protection goals need to be accompanied by enhanced efforts to reduce anthropogenic greenhouse gas emissions to achieve climate change mitigation in line with the Paris Agreement.

The use of observation-dependent methods for crop productivity and food security assessment is challenging in data-sparse regions. This study presents a transferable framework and applies it to North Korea (NK) to assess rice productivity based on climate similarity, transferable machine-learning techniques, and extendable multi-source data. We initially divided the primary phenological stages of rice in the study region and extracted dynamic rice distributions based on Moderate Resolution Imaging Spectroradiometer products and phenological observations. We compared the performances of four representative environmentally driven models (Linear Regression, back-propagation Neural Network, Support Vector Machine, and Random Forest) in simulating rice productivity using an extensive dataset that included multi-angle vegetation monitoring, climate variables, and planting distribution information. The framework integrated an optimal environmentally driven model with agricultural management practices for transferability to predict rice productivity in NK over multiple years. Additionally, two crop growth scenarios (whole growth period (WGP) and seeding-heading period (SHP)) were compared to assess pre-harvest forecasting capabilities and identify dominant factors. Finally, independent datasets from the Food and Agriculture Organization, World Food Program, and Global Gridded Crop Models were used to validate the magnitude and spatial distribution of the predicted results. The results showed that phenological identification based on remote sensing can accurately capture rice growth characteristics and map rice distribution. Random Forest outperformed other models in simulating rice productivity variation, with r-squares of 0.87 and 0.83 in the WGP and SHP, respectively. The solar-induced chlorophyll fluorescence, maximum temperature, and evapotranspiration collectively determined approximately 40 % of the variation in yield simulated using Random Forest. Conversely, planting areas contributed over 42 % of the variation in rice production. Compared to Food and Agriculture Organization statistics, the environmentally driven framework explained 78.72 % and 76.89 % of the production variation and 69.42 % and 71.15 % of the yield variation in NK under the WGP and SHP, respectively. Moreover, the environmental management-driven framework captured over 90 % of the yield variation. The predicted spatial pattern of rice productivity exhibited significant concordance with the World Food Program and Global Gridded Crop Model reports. In summary, the proposed transferable framework for crop productivity assessment contributes to early warnings of production reduction and has the potential for scalability across various crops and data-sparse regions.

Ecological restoration projects implemented over the past 20 years have substantially increased forest coverage in China, but the high tree mortality of new afforestation forest remains a challenging but unsolved problem. It is still not clear how much vegetation can be sustained by the forest lands with given water, energy and soil conditions, i.e., the carrying capacity for vegetation (CCV) of forest lands, which is the prerequisite for planning and implementing forest restoration projects. Here, we used a simplified method to evaluate the CCV across forest lands nationwide. Specifically, based on leaf area index (LAI) dataset, we use boosted regression tree and multiple linear regression model to analyze the CCV during 2001–2020 and 2021–2030 and explore the contribution of environmental factors. We found that there are three typical regions with lower CCV which located in the Loess Plateau and the southern region of the Inner Mongolia Plateau, the Hengduan Mountain region, and the Tianshan Mountains. More importantly, the vegetation in the regions near the dry-wet climate transition zone excess local carrying capacity for vegetation over the past two decades and they are more susceptible to potential climatic stress. In comparison, in the Greater Khingan Mountains and Hengduan Mountains, there are high potentials to improve the forest growth. Temperature, precipitation and soil affects the CCV by shaping the vegetation in the optimal range. This indicates that more consideration should be given to restrictions of regional environmental constraints when planning afforestation and forest management. This study has important implications for guiding future forest scheme in China.

Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N 2 O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N 2 O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N 2 O-N yr −1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N 2 O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N 2 O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr −1 . This suggests a global emission factor of 0.051% for inland water N 2 O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.

Abstract Many agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world.