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Multiple climate-driven stressors, including warming and increased nutrient delivery, are exacerbating hypoxia in coastal marine environments. Within coastal watersheds, environmental managers are particularly interested in climate impacts on terrestrial processes, which may undermine the efficacy of management actions designed to reduce eutrophication and consequent low-oxygen conditions in receiving coastal waters. However, substantial uncertainty accompanies the application of Earth System Model (ESM) projections to a regional modeling framework when quantifying future changes to estuarine hypoxia due to climate change. In this study, two downscaling methods are applied to multiple ESMs and used to force two independent watershed models for Chesapeake Bay, a large coastal-plain estuary of the eastern United States. The projected watershed changes are then used to force a coupled 3-D hydrodynamic-biogeochemical estuarine model to project climate impacts on hypoxia, with particular emphasis on projection uncertainties. Results indicate that all three factors (ESM, downscaling method, and watershed model) are found to contribute significantly to the uncertainty associated with future hypoxia, with the choice of ESM being the largest contributor. Overall, in the absence of management actions, there is a high likelihood that climate change impacts on the watershed will expand low-oxygen conditions by 2050, relative to a 1990s baseline period; however, the projected increase in hypoxia is quite small (4 %) because only climate-induced changes in watershed inputs are considered and not those on the estuary itself. Results also demonstrate that the attainment of established nutrient reduction targets will reduce annual hypoxia by about 50 % compared to the 1990s. Given these estimates, it is virtually certain that fully implemented management actions reducing excess nutrient loadings will outweigh hypoxia increases driven by climate-induced changes in terrestrial runoff.

The Australian continent contributes substantially to the year-to-year variability of the global terrestrial carbon dioxide (CO2) sink. However, the scarcity of in-situ observations in remote areas prevents deciphering the processes that force the CO2 flux variability. Here, examining atmospheric CO2 measurements from satellites in the period 2009-2018, we find recurrent end-of-dry-season CO2 pulses over the Australian continent. These pulses largely control the year-to-year variability of Australia's CO2 balance, due to 2-3 times higher seasonal variations compared to previous top-down inversions and bottom-up estimates. The CO2 pulses occur shortly after the onset of rainfall and are driven by enhanced soil respiration preceding photosynthetic uptake in Australia's semi-arid regions. The suggested continental-scale relevance of soil rewetting processes has large implications for our understanding and modelling of global climate-carbon cycle feedbacks., Comment: 28 pages (including supplementary materials), 3 main figures, 7 supplementary figures; v2 changes: Last name of first author changed

Excessive anthropogenic nitrogen (N) inputs to the biosphere have disrupted the global nitrogen cycle. To better quantify the spatial and temporal patterns of anthropogenic N inputs, assess their impacts on the biogeochemical cycles of the planet and the living organisms, and improve nitrogen use efficiency (NUE) for sustainable development, we have developed a comprehensive and synthetic dataset for reconstructing the History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere. The HaNi dataset takes advantage of different data sources in a spatiotemporally consistent way to generate a set of high-resolution gridded N input products from the preindustrial period to the present (1860–2019). The HaNi dataset includes annual rates of synthetic N fertilizer, manure application/deposition, and atmospheric N deposition on cropland, pasture, and rangeland at a spatial resolution of 5 arcmin × 5 arcmin. Specifically, the N inputs are categorized, according to the N forms and land uses, into 10 types: (1) NH4+-N fertilizer applied to cropland, (2) NO3--N fertilizer applied to cropland, (3) NH4+-N fertilizer applied to pasture, (4) NO3--N fertilizer applied to pasture, (5) manure N application on cropland, (6) manure N application on pasture, (7) manure N deposition on pasture, (8) manure N deposition on rangeland, (9) NHx-N deposition, and (10) NOy-N deposition. The total anthropogenic N (TN) inputs to global terrestrial ecosystems increased from 29.05 Tg N yr−1 in the 1860s to 267.23 Tg N yr−1 in the 2010s, with the dominant N source changing from atmospheric N deposition (before the 1900s) to manure N (in the 1910s–2000s) and then to synthetic fertilizer in the 2010s. The proportion of synthetic NH4+-N in fertilizer input increased from 64 % in the 1960s to 90 % in the 2010s, while synthetic NO3--N fertilizer decreased from 36 % in the 1960s to 10 % in the 2010s. Hotspots of TN inputs shifted from Europe and North America to East and South Asia during the 1960s–2010s. Such spatial and temporal dynamics captured by the HaNi dataset are expected to facilitate a comprehensive assessment of the coupled human–Earth system and address a variety of social welfare issues, such as the climate–biosphere feedback, air pollution, water quality, and biodiversity. The data are available at https://doi.org/10.1594/PANGAEA.942069 (Tian et al., 2022).

International audience; Natural climate solutions (NCS) are strategies for climate mitigation in the land sector that increase carbon storage or avoid GHG emissions. Here we estimate China's historic NCS mitigation at 0.6 (0.5-0.7) PgCO2e yr−1 (95% CI) during 2000-2020 (8% of fossil CO2 emissions in the contemporary period). Through new NCS activities, the future maximum potential for NCS is projected at 0.6 (0.3-1.0) PgCO2e yr−1 (6% of fossil CO2 emissions) during 2020-2030 and 1.0 (0.6-1.4) PgCO2e yr−1 during 2020-2060. Of the future NCS potentials, 26-31%, 62-65% and 90-91% can be achieved at mitigation costs of US$10, US$50 and US$100 (MgCO2e)−1, respectively. Thus, NCS can contribute substantially to China's Nationally Determined Contributions over the next 10 to 40 years but require a national strategy to reach climate goals and ensure co-benefits for people and nature.

This dataset is the sediment yield resultssimulated by theDynamic Land Ecosystem Model (DLEM).The spreadsheet file includes the model-simulated annual total land sediment yield and runoff in the Mississippi River Basin during 1901-2018. The spatial maps of sediment yield (unit: t km-2 yr-1) during 1980-2018 arein ASCII format with a resolution of 5 arc-min.&nbsp

Inland waters are important sources of the greenhouse gasses (GHGs) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) to the atmosphere. While a growing number of global estimates of inland water GHG emissions exists, the integration of inland waters into regional GHG budgets is often hampered by the lack of adequate geo-spatial datasets. Moreover, existing estimates diverge substantially, in part due to persisting uncertainties related to the size and distribution of effective inland water surface areas. In the framework of the 2nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we synthesize existing estimates of GHG emissions from streams, rivers, lakes and reservoirs, and homogenize them with regard to underlying global maps of inland water surface areas and the effects of seasonal ice cover. We then produce estimates of inland water GHG emissions for 10 extensive land regions that are used for the regional land budgets of RECCAP2. According to our synthesis, global inland waters emit 5.6 (3.5-9.1) Pg CO2 yr-1, 101 (83-135) Tg CH4 yr-1 and 326 (254-592) Gg N2O yr-1. South American rivers contribute about one third of global inland water CO2 emissions. North-American and Russian lakes contribute together one third of global inland water CH4 emissions. Finally, North America alone contributes one fourth of global inland water N2O emissions.The global inland water emissions sum up to a global warming potential (GWP) of an equivalent emission of 13.6 (10.0-20.3) and 8.3 (5.8-12.7) Pg CO2 yr-1 at a 20- and 100-year horizon, respectively. At 100-year horizon, the contribution of CO2 dominates the GWP of global inland water GHG emissions, with rivers being the largest emitters. At the 20-year horizon, on the contrary, lakes and rivers are equally important emitters, and the contributions of CH4 to the GWP of inland water GHG emissions even exceed those of CO2. Contributions of N2O to the GWP appear to be less significant at both time horizons. Normalized to the area of the RECCAP-2 land regions, South America and South East Asia show the highest inland water emission rates in terms of GWP, dominated by riverine CO2 emissions.

Background Precipitation plays an important role in crop production and soil greenhouse gas emissions. However, how crop yield and soil nitrous oxide (N2O) emission respond to precipitation change, particularly with different background precipitations (dry, normal, and wet years), has not been well investigated. In this study, we examined the impacts of precipitation changes on corn yield and soil N2O emission using a long-term (1981–2020, 40 years) climate dataset as well as seven manipulated precipitation treatments with different background precipitations using the DeNitrification-DeComposition (DNDC) model. Results Results showed large variations of corn yield and precipitation but small variation of soil N2O emission among 40 years. Both corn yield and soil N2O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N2O emission to precipitation manipulations. Corn yield showed a positive linear response to precipitation manipulations in the dry year, but no response to increases in precipitation in the normal year, and a trend of decrease in the wet year. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N2O emission mostly responded linearly to precipitation manipulations. Decreases in precipitation in the dry year reduced more soil N2O emission than those in the normal and wet years, while increases in precipitation increased more soil N2O emission in the normal and wet years than in the dry year. Conclusions This study revealed different response patterns of corn yield and soil N2O emission to precipitation and highlights that mitigation strategy for soil N2O emission reduction should consider different background climate conditions.

The Amazon is the largest continuous tropical forest in the world and plays a key role in the global carbon cycle. Human-induced disturbances and climate change have impacted the Amazon carbon balance. Here we conduct a comprehensive analysis of state-of-the-art estimates of the contemporary land carbon fluxes in the Amazon. Over the whole Amazon region bottom-up methodologies suggest a small average carbon sink over 2010-2020, in contrast with a carbon small source simulated by top-down inversions (2010-2018). However, these estimates are not significantly different from one another when accounting for their large individual uncertainties, highlighting remaining knowledge gaps, and urgent need to reduce such uncertainties. Nevertheless, both methodologies agreed on an Amazon net carbon source during recent climate extremes and that south-eastern Amazon was a net land carbon source over the whole study period (2010-2020). Overall, our results point to increasing human-induced disturbances (deforestation and forest degradation by wildfires) and reduction in the old-growth forest sink during drought. If the current trends in deforestation and forest degradation and regional drying continue, it will have negative implications for reducing carbon emissions and maintaining globally important natural carbon stocks, as part of the requirements for achieving the Paris Agreement goals.

Terrestrial carbon (C) sequestration is limited by nitrogen (N), a constraint that could intensify under CO2 fertilisation and future global change. The terrestrial C sink is estimated to currently sequester approximately a third of annual anthropogenic CO2 emissions based on an ensemble of terrestrial biosphere models, which have been evaluated in their ability to reproduce observations of the C, water, and energy cycles. However, their ability to reproduce observations of N cycling and thus the regulation of terrestrial C sequestration by N has been largely unexplored. Here, we evaluate an ensemble of terrestrial biosphere models with coupled C-N cycling and their performance at simulating N cycling, outlining a framework for evaluating N cycling that can be applied across terrestrial biosphere models. We find that models exhibit significant variability across N pools and fluxes, simulating different magnitudes and trends over the historical period, despite their ability to generally reproduce the historical terrestrial C sink. This suggests that the underlying N processes that regulate terrestrial C sequestration operate differently across models and may not be fully captured. Furthermore, models tended to overestimate tropical biological N fixation, vegetation C:N ratio, and soil C:N ratio but underestimate temperate biological N fixation relative to observations. However, there is significant uncertainty associated with measurements of N cycling processes given their scarcity (especially relative to those of C cycling processes) and their high spatiotemporal variability. Overall, our results suggest that terrestrial biosphere models that represent coupled C-N cycling (let alone those without a representation of N cycling) could be overestimating C storage per unit N, which could lead to biases in projections of the future terrestrial C sink under CO2 fertilisation and future global change. More extensive observations of N cycling processes are crucial to evaluate N cycling and its impact on C cycling as well as guide its development in terrestrial biosphere models.

In many regions of the world, frequent and continual dry spells are exacerbating drought conditions, which have severe impacts on vegetation biomes. Vegetation in southern Africa is among the most affected by drought. Here, we assessed the spatiotemporal characteristics of meteorological drought in southern Africa using the standardized precipitation evapotranspiration index (SPEI) over a 30-year period (1982–2011). The severity and the effects of droughts on vegetation productiveness were examined at different drought timescales (1- to 24-month timescales). In this study, we characterized vegetation using the leaf area index (LAI) after evaluating its relationship with the normalized difference vegetation index (NDVI). Correlating the LAI with the SPEI, we found that the LAI responds strongly (r=0.6) to drought over the central and southeastern parts of the region, with weaker impacts (r

Regional land carbon budgets provide insights into the spatial distribution of the land uptake of atmospheric carbon dioxide and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields, or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions due to different definitions and component fluxes being reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers that connect CO2 uptake in one area with its release in another also requires better definitions and protocols to reach harmonized regional budgets that can be summed up to a globe scale and compared with the atmospheric CO2 growth rate and inversion results. In this study, using the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims to be an update to regional carbon budgets over the last 2 decades based on observations for 10 regions covering the globe with a better harmonization than the precursor project, we provide recommendations for using atmospheric inversion results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes, and land use fluxes.

Contains fulltext : 293580.pdf (Publisher’s version ) (Closed access) Accession PRJNA812708 Data Type Raw sequence reads Scope Multispecies Submission Registration date: 4-Mar-2022 majorbiio 19 april 2023

Numerous devastating air pollution events from wildfire smoke occurred in this century in the western USA, leading to severe environmental consequences. This study projects future fire emissions in this region under climate change with a focus on comparing the relative contributions from future changes in burned area, fuel loading and fuel moisture. The three properties were projected using an empirical fire model, a dynamical global vegetation model and meteorological conditions respectively. The regional climate change scenarios for the western USA were obtained by dynamical downscaling of global climate projections. The results show overall increasing wildfires and fuel loading and decreasing fuel moisture. As a result, fire emissions are projected to increase by ~50% from 2001–2010 to 2050–2059. The changes in wildfires and fuel loading contribute nearly 75% and 25% of the total fire emission increase, respectively, but the contribution from fuel moisture change is minimal. The findings suggest that the air pollution events caused by wildfire smoke could become much more serious in the western USA by the middle of this century, and that it would be essential to take the future changes in fuel conditions into account to improve the accuracy of fire emission projections.

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity. Regardless of its short atmospheric lifetime, surface ozone has significantly increased since the 1970s across the Northern Hemisphere, particularly in China. However, high temporal resolution surface ozone concentration data is still lacking in China, largely hindering accurate assessment of associated environmental and human health impacts. Here, we collected hourly ground ozone observations (over 6 million records), meteorological data, remote sensing products, and social-economic information, and applied the Long Short-Term Memory (LSTM) recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2005–2020. Benefiting from its advantage in time-series prediction, the LSTM model well captured the spatiotemporal dynamics of observed ozone concentrations, with the sample-based, site-based, and by-year cross-validation coefficient of determination (R2) values being 0.72, 0.65 and 0.71, and root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb), respectively. Air temperature, atmospheric pressure, and relative humidity were found to be the primary influencing factors. Spatially, surface ozone concentrations were high in northwestern China and low in the Sichuan Basin and northeastern China. Among the four megacity clusters in China, namely the Beijing-Tianjin-Hebei region, the Pearl River Delta, the Yangtze River Delta, and the Sichuan Basin, surface ozone concentration kept decreasing before 2016. However, it tended to increase thereafter in the former three regions, though an abrupt decrease in surface ozone concentrations occurred in 2020. Overall, the HrSOD provides critical information for surface ozone pollution dynamics in China and can support fine-resolution environmental impact and human health risk assessment. The data set is available at https://doi.org/10.5281/zenodo.7415326 (Zhang et al., 2022).

Satellite data reveal widespread changes in Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesise data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data-products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr-1 (9.9 ± 0.5 GtC yr-1 when the cement carbonation sink is included), ELUC was 1.1 ± 0.7 GtC yr-1, for a total anthropogenic CO2 emission of 11.1 ± 0.8 GtC yr-1 (40.8 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr-1 (2.5 ± 0.1 ppm yr-1), SOCEAN was 2.9 ± 0.4 GtC yr-1 and SLAND was 3.5 ± 0.9 GtC yr-1, with a BIM of -0.6 GtC yr-1 (i.e. total estimated sources too low or sinks too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022, suggest an increase in EFOS relative to 2021 of +1.1 % (0 % to 1.7 %) globally, and atmospheric CO2 concentration reaching 417.3 ppm, more than 50 % above pre-industrial level. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr-1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2022a; Friedlingstein et al., 2020; Friedlingstein et al., 2019; Le Quéré et al., 2018b, 2018a, 2016, 2015b, 2015a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Carbon budget accounting relies heavily on Food and Agriculture Organization land-use data reported by governments. Here we develop a new land-use and cover-change database for China, finding that differing historical survey methods biased China’s reported data causing large errors in Food and Agriculture Organization databases. Land ecosystem model simulations driven with the new data reveal a strong carbon sink of 8.9 ± 0.8 Pg carbon from 1980 to 2019 in China, which was not captured in Food and Agriculture Organization data-based estimations due to biased land-use and cover-change signals. The land-use and cover-change in China, characterized by a rapid forest expansion from 1980 to 2019, contributed to nearly 44% of the national terrestrial carbon sink. In contrast, climate changes (22.3%), increasing nitrogen deposition (12.9%), and rising carbon dioxide (8.1%) are less important contributors. This indicates that previous studies have greatly underestimated the impact of land-use and cover-change on the terrestrial carbon balance of China. This study underlines the importance of reliable land-use and cover-change databases in global carbon budget accounting.

Knowledge of the spatial distribution of the fluxes of greenhouse gases and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its Global Stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27+UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results, inverse modelling estimates, and extends the previous period 1990–2017 to 2020. BU and TD products are compared with European National GHG Inventories (NGHGI) reported by Parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. The uncertainties of NGHGIs were evaluated using the standard deviation obtained by varying parameters of inventory calculations, reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates e.g. anthropogenic and natural fluxes, which, in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGAR v5v6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, 2015), close to the NGHGI estimates of 17.5 ± 2.1 Tg CH4 yr−1. TD inversions estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 Tg CH4 yr−1 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soils emissions from the JSBACH-HIMMELI model, natural rivers, lakes and reservoirs emissions, geological sources and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGAR v5v6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8 ± 55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT which reported no data). The TD and BU comparison method defined in this study can be "operationalized" for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27+UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, of great importance for CH4 and N2O, which may help identify sector contributions to divergence between prior and posterior estimates at the annual/inter-annual scale. Even if currently comparison between CH4 and N2O inversions estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modelling and observations, as well as modelling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emissions inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.6992472 (Petrescu et al., 2022).

Continental North America has been found to be a carbon (C) sink over recent decades by multiple studies employing a variety of estimation approaches. However, several key questions and uncertainties remain with these assessments. Here we used results from an ensemble of 19 state-of-the-art dynamic global vegetation models from the TRENDYv9 project to improve these estimates and study the drivers of its interannual variability. Our results show that North America has been a C sink with a magnitude of 0.37 ± 0.38 (mean and one standard deviation) PgC year$^{-1}$ for the period 2000–2019 (0.31 and 0.44 PgC year$^{-1}$ in each decade); split into 0.18 ± 0.12 PgC year$^{-1}$ in Canada (0.15 and 0.20), 0.16 ± 0.17 in the United States (0.14 and 0.17), 0.02 ± 0.05 PgC year$^{-1}$ in Mexico (0.02 and 0.02) and 0.01 ± 0.02 in Central America and the Caribbean (0.01 and 0.01). About 57% of the new C assimilated by terrestrial ecosystems is allocated into vegetation, 30% into soils, and 13% into litter. Losses of C due to fire account for 41% of the interannual variability of the mean net biome productivity for all North America in the model ensemble. Finally, we show that drought years (e.g., 2002) have the potential to shift the region to a small net C source in the simulations (−0.02 ± 0.46 PgC year$^{-1}$). Our results highlight the importance of identifying the major drivers of the interannual variability of the continental-scale land C cycle along with the spatial distribution of local sink-source dynamics.

International audience

Input–output estimates of nitrogen on cropland are essential for improving nitrogen management and better understanding the global nitrogen cycle. Here, we compare 13 nitrogen budget datasets covering 115 countries and regions over 1961–2015. Although most datasets showed similar spatiotemporal patterns, some annual estimates varied widely among them, resulting in large ranges and uncertainty. In 2010, global medians (in TgN yr−1) and associated minimum–maximum ranges were 73 (64–84) for global harvested crop nitrogen; 161 (139–192) for total nitrogen inputs; 86 (68–97) for nitrogen surplus; and 46% (40–53%) for nitrogen use efficiency. Some of the most uncertain nitrogen budget terms by country showed ranges as large as their medians, revealing areas for improvement. A benchmark nitrogen budget dataset, derived from central tendencies of the original datasets, can be used in model comparisons and inform sustainable nitrogen management in food systems. Existing datasets of nitrogen (N) balance in agriculture are often discrepant. Comparing 13 of them regarding five metrics (fertilizer application, manure application, biological N fixation, atmospheric deposition, and N harvested as crop products) over 1961–2015 reveals why. Recommendations for improving N quantification and an N budget benchmark dataset are also proposed.

Global vegetation photosynthesis and productivity have increased substantially since the 1980s, but this trend is heterogeneous in both time and space. Here, we categorize the secular trend in global vegetation greenness into sustained greening, sustained browning and greening-to-browning. We found that by 2016, increased global vegetation greenness had begun to level off, with the area of browning increasing in the last decade, reaching 39.0 million km2 (35.9% of the world’s vegetated area). This area is larger than the area with sustained increasing growth (27.8 million km2, 26.4%); thus, 12.0% ± 3.1% (0.019 ± 0.004 NDVI a−1) of the previous earlier increase has been offset since 2010 (2010–2016, P < 0.05). Global gross primary production also leveled off, following the trend in vegetation greenness in time and space. This leveling off was caused by increasing soil water limitations due to the spatial expansion of drought, whose impact dominated over the impacts of temperature and solar radiation. This response of global gross primary production to soil water limitation was not identified by land submodels within Earth system models. Our results provide empirical evidence that global vegetation greenness and primary production are offset by water stress and suggest that as global warming continues, land submodels may overestimate the world’s capacity to take up carbon with global vegetation greening. This work was supported by the National Key Research and Development Program of China (2017YFA0604700), the National Natural Science Foundation of China (41722104) and the Key Research Project of Chinese Academy of Sciences (QYZDY-SSWDQC025 and 2019DC0027). Philippe Ciais, Marcos Fernández-Martínez, Jordi Sardans, and Josep Peñuelas were supported by the European Research Council Synergy (ERC-2013-SyG-610028 IMBALANCE-P), the Spanish Government (CGL2016-79835), and the Catalan Government (SGR 2017-1005).

Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC yr−1 for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC yr−1). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).

Observations from the Orbiting Carbon Observatory 2 (OCO-2) satellite have been used to estimate CO2 fluxes in many regions of the globe and provide new insight into the global carbon cycle. The objective of this study is to infer the relationships between patterns in OCO-2 observations and environmental drivers (e.g., temperature, precipitation) and therefore inform a process understanding of carbon fluxes using OCO-2. We use a multiple regression and inverse model, and the regression coefficients quantify the relationships between observations from OCO-2 and environmental driver datasets within individual years for 2015–2018 and within seven global biomes. We subsequently compare these inferences to the relationships estimated from 15 terrestrial biosphere models (TBMs) that participated in the TRENDY model inter-comparison. Using OCO-2, we are able to quantify only a limited number of relationships between patterns in atmospheric CO2 observations and patterns in environmental driver datasets (i.e., 10 out of the 42 relationships examined). We further find that the ensemble of TBMs exhibits a large spread in the relationships with these key environmental driver datasets. The largest uncertainty in the models is in the relationship with precipitation, particularly in the tropics, with smaller uncertainties for temperature and photosynthetically active radiation (PAR). Using observations from OCO-2, we find that precipitation is associated with increased CO2 uptake in all tropical biomes, a result that agrees with half of the TBMs. By contrast, the relationships that we infer from OCO-2 for temperature and PAR are similar to the ensemble mean of the TBMs, though the results differ from many individual TBMs. These results point to the limitations of current space-based observations for inferring environmental relationships but also indicate the potential to help inform key relationships that are very uncertain in state-of-the-art TBMs.

Eurasian drylands are the regions that are most vulnerable to climate change. Climate extremes have caused enormous or even devastating impacts on ecosystems and the social economy in this region, and the compound climate extremes (com_CEs, two or more extreme events occurring simultaneously) and cascading climate extremes (cas_CEs, two or more extreme events occurring successively) have exacerbated these problems. However, little is known about the occurrence patterns of com_CEs and cas_CEs in the Eurasian drylands. Based on the ERA5 reanalysis data range from 1979 to 2020, we improved the methodology for the extraction of co-occurrence events and identified high-frequency types, their hotspots, and occurrence rhythms (seasonally and annually) in Eurasian drylands. Our results showed that com_CEs and cas_CEs have high similarities in the types and spatial hotspots of extreme events; however, the former has a wider geographical and spatial distribution, and the latter has a longer duration. Specifically, co-occurring drought and heatwave events (DH) frequently appear in South Asia and western mid-latitude regions during summer, while in the winter, high latitude regions should be alert to the co-occurrence of drought and low-temperature events (DT). Central Asia and the Mongolian Plateau regions are prone to frequent drought and wind events (DW), and wind and high precipitation events (WP) in the spring and autumn. We have noticed that mid-latitude may suffer from extreme events that have never occurred before, such as com_DH being scattered sporadically in the first two decades and suddenly surging in West Asia and East Asia after the year 2000, and com_DT migrating from high-latitude areas such as the Arctic Ocean coast to mid-latitudes. Our results contribute to understanding hotspots of co-occurring CEs in Eurasian drylands, where more efforts will be needed in the future, especially in mid-latitudes which may suffer extreme climate events that have never occurred before.

Inland waters (rivers, lakes, and reservoirs) have been recognized as hotspots of methane (CH

To stave off world hunger, humanity manipulated and unbalanced the nitrogen cycle. Today’s excess fixed nitrogen is a global pollutant, of which our food system is a primary driver. Yet, food insecurity remains a global concern. In this Voices, food-system experts offer insights into the challenge of the sustainable management of nitrogen.

Livestock contributes approximately one-third of global anthropogenic methane (CH

A new special collection in AGU journals will present findings from the Second REgional Carbon Cycle Assessment and Processes (RECCAP2) study with a decade of data on greenhouse gas growth.

Balancing crop production and greenhouse gas (GHG) emissions from agriculture soil requires a better understanding and quantification of crop GHG emissions intensity, a measure of GHG emissions per unit crop production. Here we conduct a state-of-the-art estimate of the spatial-temporal variability of GHG emissions intensities for wheat, maize, and rice in China from 1949 to 2012 using an improved agricultural ecosystem model (Dynamic Land Ecosystem Model-Agriculture Version 2.0) and meta-analysis covering 172 field-GHG emissions experiments. The results show that the GHG emissions intensities of these croplands from 1949 to 2012, on average, were 0.10-1.31 kg CO2 -eq/kg, with a significant increase rate of 1.84-3.58 × 10-3 kg CO2 -eq kg-1 year-1 . Nitrogen fertilizer was the dominant factor contributing to the increase in GHG emissions intensity in northern China and increased its impact in southern China in the 2000s. Increasing GHG emissions intensity implies that excessive fertilizer failed to markedly stimulate crop yield increase in China but still exacerbated soil GHG emissions. This study found that overfertilization of more than 60% was mainly located in the winter wheat-summer maize rotation systems in the North China Plain, the winter wheat-rice rotation systems in the middle and lower reaches of the Yangtze River and southwest China, and most of the double rice systems in the South. Our simulations suggest that roughly a one-third reduction in the current N fertilizer application level over these "overfertilization" regions would not significantly influence crop yield but decrease soil GHG emissions by 29.60%-32.50% and GHG emissions intensity by 0.13-0.25 kg CO2 -eq/kg. This reduction is about 29% and 5% of total agricultural soil GHG emissions in China and the world, respectively. This study suggests that improving nitrogen use efficiency would be an effective strategy to mitigate GHG emissions and sustain China's food security.

The Chesapeake Bay is a large coastal-plain estuary that has experienced considerable anthropogenic change over the past century. At the regional scale, land-use change has doubled the nutrient input from rivers and led to an increase in riverine carbon and alkalinity. The bay has also experienced global changes, including the rise of atmospheric temperature and CO2. Here we seek to understand the relative impact of these changes on the inorganic carbon balance of the bay between the early 1900s and the early 2000s. We use a linked land–estuarine–ocean modeling system that includes both inorganic and organic carbon and nitrogen cycling. Sensitivity experiments are performed to isolate the effect of changes in (1) atmospheric CO2, (2) temperature, (3) riverine nitrogen loading and (4) riverine carbon and alkalinity loading. Specifically, we find that over the past century global changes have increased ingassing by roughly the same amount (∼30 Gg-C yr−1) as has the increased riverine loadings. While the former is due primarily to increases in atmospheric CO2, the latter results from increased net ecosystem production that enhances ingassing. Interestingly, these increases in ingassing are partially mitigated by increased temperatures and increased riverine carbon and alkalinity inputs, both of which enhance outgassing. Overall, the bay has evolved over the century to take up more atmospheric CO2 and produce more organic carbon. These results suggest that over the past century, changes in riverine nutrient loads have played an important role in altering coastal carbon budgets, but that ongoing global changes have also substantially affected coastal carbonate chemistry.

Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1（454 mm yr−1）to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p 0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are uncertain. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP between 1981 and 2010, driven by common climate forcing, LULCC and CO2 data, was found to be 7.4±1.8 Pg C yr−1. In general, climate was the dominant control factor of the annual trends, IAV and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China, where vegetation is dense. LULCC decreased the IAV of China's total GPP by ∼7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation, and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.

Soil organic carbon (SOC) in croplands is a key property of soil quality for ensuring food security and agricultural sustainability, and also plays a central role in the global carbon (C) budget. When managed sustainably, soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere. However, the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity, complicated mechanisms, and multiple influencing factors. Here, we use a process-based agroecosystem model (DLEM-Ag) in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change, elevated CO2, nitrogen deposition, land cover change, and land management practices such as nitrogen fertilizer use and irrigation. Model results show that the total SOC and SOC density in the 2000s increased by 125% and 48.8%, respectively, compared to the early 20th century. This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use. Factorial analysis suggests that climate change reduced approximately 3.2% (or 2,166 Tg C) of the total SOC over the past 110 years. Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices, which may partially offset SOC loss caused by climate change.

The spatial and temporal variations of the vegetation carbon flux in China from 1901 to 2005 were studied using the vegetation net primary production (NPP) values from seven Earth-system models. In addition, the temporal and spatial changes in the nitrogen deposition flux in China were studied using the NH x and NO y fluxes from 1901 to 2005. The relationship between changes in the carbon flux, nitrogen flux and climate was analyzed. The results show that (1) over the past 100 years, NPP in China has shown an upward trend. The average trend coefficient is 0.88 and the NPP distribution trend is generally low in the north and high in the south, with a gradual increase from the northwest to the southeast. Temperature, precipitation and radiation are all conducive to plant growth in the direction of the gradient. The correlation coefficients between the ensemble model mean NPP and temperature, precipitation, longwave radiation and shortwave radiation are 0.88, 0.73, 0.91 and 0.67, respectively. (2) In the past 100 years, the NH x and NO y fluxes in China have shown an upward trend, with trend coefficients of 0.98 and 0.98, respectively, which pass the 99.9% confidence level of the t -test. NH x and NO y fluxes are also generally low in the north and high in the south, with a gradual increase from the northwest to the southeast in a step-like pattern. (3) The spatial distribution of the correlation coefficients between the NH x and NO y fluxes and air temperature is similar, with only slight differences in values. The spatial distribution of the correlation coefficients between the NH x and NO y fluxes and precipitation is similar in overall pattern, but the pattern is relatively complicated, with a positive-negative-positive-negative-positive pattern occurring across the monsoon region from north to south, and a negative-positive-negative-positive pattern occurring beyond the monsoon region from east to west. (4) The spatial distribution of the correlation coefficients between the NH x and NO y fluxes and NPP shows a generally consistent pattern, but the pattern is relatively uneven. The average distribution of the ensemble model mean is positive correlation in northeast China and southwest China, and alternating positive and negative correlation in other regions.

Large wildfires exert strong disturbance on regional and global climate systems and ecosystems by perturbing radiative forcing as well as the carbon and water balance between the atmosphere and land surface, while short- and long-term variations in fire weather, terrestrial ecosystems, and human activity modulate fire intensity and reshape fire regimes. The complex climate–fire–ecosystem interactions were not fully integrated in previous climate model studies, and the resulting effects on the projections of future climate change are not well understood. Here we use the fully interactive REgion-Specific ecosystem feedback Fire model (RESFire) that was developed in the Community Earth System Model (CESM) to investigate these interactions and their impacts on climate systems and fire activity. We designed two sets of decadal simulations using CESM-RESFire for present-day (2001–2010) and future (2051–2060) scenarios, respectively, and conducted a series of sensitivity experiments to assess the effects of individual feedback pathways among climate, fire, and ecosystems. Our implementation of RESFire, which includes online land–atmosphere coupling of fire emissions and fire-induced land cover change (LCC), reproduces the observed aerosol optical depth (AOD) from space-based Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products and ground-based AErosol RObotic NETwork (AERONET) data; it agrees well with carbon budget benchmarks from previous studies. We estimate the global averaged net radiative effect of both fire aerosols and fire-induced LCC at -0.59±0.52 W m−2, which is dominated by fire aerosol–cloud interactions (-0.82±0.19 W m−2), in the present-day scenario under climatological conditions of the 2000s. The fire-related net cooling effect increases by ∼170 % to -1.60±0.27 W m−2 in the 2050s under the conditions of the Representative Concentration Pathway 4.5 (RCP4.5) scenario. Such considerably enhanced radiative effect is attributed to the largely increased global burned area (+19 %) and fire carbon emissions (+100 %) from the 2000s to the 2050s driven by climate change. The net ecosystem exchange (NEE) of carbon between the land and atmosphere components in the simulations increases by 33 % accordingly, implying that biomass burning is an increasing carbon source at short-term timescales in the future. High-latitude regions with prevalent peatlands would be more vulnerable to increased fire threats due to climate change, and the increase in fire aerosols could counter the projected decrease in anthropogenic aerosols due to air pollution control policies in many regions. We also evaluate two distinct feedback mechanisms that are associated with fire aerosols and fire-induced LCC, respectively. On a global scale, the first mechanism imposes positive feedbacks to fire activity through enhanced droughts with suppressed precipitation by fire aerosol–cloud interactions, while the second one manifests as negative feedbacks due to reduced fuel loads by fire consumption and post-fire tree mortality and recovery processes. These two feedback pathways with opposite effects compete at regional to global scales and increase the complexity of climate–fire–ecosystem interactions and their climatic impacts.