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Abstract Forests are invaluable natural resources that provide essential services to humanity. However, the effects of global warming on forest carbon and nitrogen cycling remain uncertain. Here we project a decrease in total nitrogen input and accumulation by 7 ± 2 and 28 ± 9 million tonnes (Tg), respectively, and an increase in reactive nitrogen losses to the environment by 9 ± 3 Tg for 2100 due to warming in a fossil-fueled society. This would compromise the global carbon sink capacity by 0.45 ± 0.14 billion tonnes annually. Furthermore, warming-induced inequality in forest carbon and nitrogen cycles could widen the economic gap between the Global South and Global North. High-income countries are estimated to gain US$179 billion in benefits from forest assets under warming, while other regions could face net damages of US$31 billion. Implementing climate-smart forest management, such as comprehensive restoration and optimizing tree species composition, is imperative in the face of future climate change.

Abstract Climate-induced changes in hypoxia are among the most serious threats facing estuaries, which are among the most productive ecosystems on Earth. Future projections of estuarine hypoxia typically involve long-term multi-decadal continuous simulations or more computationally efficient time slice and delta methods that are restricted to short historical and future periods. We make a first comparison of these three methods by applying a linked terrestrial–estuarine model to the Chesapeake Bay, a large coastal-plain estuary in the eastern United States. Results show that the time slice approach accurately captures the behavior of the continuous approach, indicating a minimal impact of model memory. However, increases in mean annual hypoxic volume by the mid-twenty-first century simulated by the delta approach (+ 19%) are approximately twice as large as the time slice and continuous experiments (+ 9% and + 11%, respectively), indicating an important impact of changes in climate variability. Our findings suggest that system memory and projected changes in climate variability, as well as simulation length and natural variability of system hypoxia, should be considered when deciding to apply the more computationally efficient delta and time slice methods.

Abstract Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015–2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the 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). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

Abstract Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Abstract Forest carbon sequestration capacity in China remains uncertain due to underrepresented tree demographic dynamics and overlooked of harvest impacts. In this study, we employ a process-based biogeochemical model to make projections by using national forest inventories, covering approximately 415,000 permanent plots, revealing an expansion in biomass carbon stock by 13.6 ± 1.5 Pg C from 2020 to 2100, with additional sink through augmentation of wood product pool (0.6-2.0 Pg C) and spatiotemporal optimization of forest management (2.3 ± 0.03 Pg C). We find that statistical model might cause large bias in long-term projection due to underrepresentation or neglect of wood harvest and forest demographic changes. Remarkably, disregarding the repercussions of harvesting on forest age can result in a premature shift in the timing of the carbon sink peak by 1–3 decades. Our findings emphasize the pressing necessity for the swift implementation of optimal forest management strategies for carbon sequestration enhancement.

Abstract 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 synthesis of existing state-of-the-art estimates of the contemporary land carbon fluxes in the Amazon using a set of bottom-up methods (i.e., dynamic vegetation models and bookkeeping models) and a top-down inversion (atmospheric inversion model) over the Brazilian Amazon and the whole Biogeographical Amazon domain. Over the whole biogeographical Amazon region bottom-up methodologies suggest a small average carbon sink over 2010-2020, in contrast to a small carbon source simulated by top-down inversion (2010-2018). However, these estimates are not significantly different from one another when accounting for their large individual uncertainties, highlighting remaining knowledge gaps, and the urgent need to reduce such uncertainties. Nevertheless, both methodologies agreed that the Brazilian Amazon has been a net carbon source during recent climate extremes and that the 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.

Abstract Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N2O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N2O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N2O-N yr−1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N2O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N2O 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 N2O 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 U.S. rice paddies, critical for food security, are increasingly contributing to non‐CO2 greenhouse gas (GHG) emissions like methane (CH4) and nitrous oxide (N2O). Yet, the full assessment of GHG balance, considering trade‐offs between soil organic carbon (SOC) change and non‐CO2 GHG emissions, is lacking. Integrating an improved agroecosystem model with a meta‐analysis of multiple field studies, we found that U.S. rice paddies were the rapidly growing net GHG emission sources, increased 138% from 3.7 ± 1.2 Tg CO2eq yr−1 in the 1960s to 8.9 ± 2.7 Tg CO2eq yr−1 in the 2010s. CH4, as the primary contributor, accounted for 10.1 ± 2.3 Tg CO2eq yr−1 in the 2010s, alongside a notable rise in N2O emissions by 0.21 ± 0.03 Tg CO2eq yr−1. SOC change could offset 14.0% (1.45 ± 0.46 Tg CO2eq yr−1) of the climate‐warming effects of soil non‐CO2 GHG emissions in the 2010s. This escalation in net GHG emissions is linked to intensified land use, increased atmospheric CO2, higher synthetic nitrogen fertilizer and manure application, and climate change. However, no/reduced tillage and non‐continuous irrigation could reduce net soil GHG emissions by approximately 10% and non‐CO2 GHG emissions by about 39%, respectively. Despite the rise in net GHG emissions, the cost of achieving higher rice yields has decreased over time, with an average of 0.84 ± 0.18 kg CO2eq ha−1 emitted per kilogram of rice produced in the 2010s. The study suggests the potential for significant GHG emission reductions to achieve climate‐friendly rice production in the U.S. through optimizing the ratio of synthetic N to manure fertilizer, reducing tillage, and implementing intermittent irrigation.

Abstract Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Abstract Gross primary production is the basis of global carbon uptake. Gross primary production losses are often related to hydroclimatic extremes such as droughts and heatwaves, but the trend of such losses driven by hydroclimatic extremes remains unclear. Using observationally-constrained and process-based model data from 1982-2016, we show that drought-heat events, drought-cold events, droughts and heatwaves are the dominant drivers of gross primary production loss. Losses associated with these drivers increase in northern midlatitude ecosystem but decrease in pantropical ecosystems, thereby contributing to around 70% of the variability in total gross primary production losses. These asymmetric trends are caused by an increase in the magnitude of gross primary production losses in northern midlatitudes and by a decrease in the frequency of gross primary production loss events in pantropical ecosystems. Our results suggest that the pantropics may have become less vulnerable to hydroclimatic variability over recent decades whereas gross primary production losses and hydroclimatic extremes in northern midlatitudes have become more closely entangled.

Tropical forests store more than half of the world's terrestrial carbon (C) pool and account for one-third of global net primary productivity (NPP). Many terrestrial biosphere models (TBMs) estimate increased productivity in tropical forests throughout the 21st century due to CO2 fertilization. However, phosphorus (P) limitations on vegetation photosynthesis and productivity could significantly reduce the CO2 fertilization effect. Here, we used a carbon-nitrogen-phosphorus coupled model (Dynamic Land Ecosystem Model; DLEM-CNP) with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests during 1860–2018. Our model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem productivity. We further compared the simulations from DLEM-CNP, DLEM-CN, and DLEM-C and the results showed that the inclusion of P processes reduced the CO2 fertilization effect on gross primary production (GPP) by 25% and 45%, and net ecosystem production (NEP) by 28% and 41%, respectively, relative to CN-only and C-only models. From the 1860s to the 2010s, the DLEM-CNP estimated that in tropical forests GPP increased by 17%, plant respiration (Ra) increased by 18%, ecosystem respiration (Rh) increased by 13%, NEP increased by 121% per unit area, respectively. Additionally, factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO2 fertilization effect had been offset by 135% due to deforestation from the 1860s to the 2010s. Our study highlights the importance of P limitation on the C cycle and the weakened CO2 fertilization effect resulting from P limitation in tropical forests.

Abstract Projections of future carbon sinks and stocks are important because they show how the world's ecosystems will respond to elevated CO2 and changes in climate. Moreover, they are crucial to inform policy decisions around emissions reductions to stay within the global warming levels identified by the Paris Agreement. However, Earth System Models from the 6th Coupled Model Intercomparison Project (CMIP6) show substantial spread in future projections—especially of the terrestrial carbon cycle, leading to a large uncertainty in our knowledge of any remaining carbon budget (RCB). Here we evaluate the global terrestrial carbon cycle projections on a region‐by‐region basis and compare the global models with regional assessments made by the REgional Carbon Cycle Assessment and Processes, Phase 2 activity. Results show that for each region, the CMIP6 multi‐model mean is generally consistent with the regional assessment, but substantial cross‐model spread exists. Nonetheless, all models perform well in some regions and no region is without some well performing models. This gives confidence that the CMIP6 models can be used to look at future changes in carbon stocks on a regional basis with appropriate model assessment and benchmarking. We find that most regions of the world remain cumulative net sources of CO2 between now and 2100 when considering the balance of fossil‐fuels and natural sinks, even under aggressive mitigation scenarios. This paper identifies strengths and weaknesses for each model in terms of its performance over a particular region including how process representation might impact those results and sets the agenda for applying stricter constraints at regional scales to reduce the uncertainty in global projections.

Abstract 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 impact of land-use and cover-change (LUCC) on ecosystem carbon stock in China is poorly known due to large biases in existing databases. Here the authors develop a new LUCC database with corrected false signals and reveal that forest expansion is the dominant driver of China’s recent carbon sink.

The global net land sink is relatively well constrained. However, the responsible drivers and above/below-ground partitioning are highly uncertain. Model issues regarding turnover of individual plant and soil components are responsible.

Global wetlands are the largest and most uncertain natural source of atmospheric methane (CH _4 ). The FLUXNET-CH _4 synthesis initiative has established a global network of flux tower infrastructure, offering valuable data products and fostering a dedicated community for the measurement and analysis of methane flux data. Existing studies using the FLUXNET-CH _4 Community Product v1.0 have provided invaluable insights into the drivers of ecosystem-to-regional spatial patterns and daily-to-decadal temporal dynamics in temperate, boreal, and Arctic climate regions. However, as the wetland CH _4 monitoring network grows, there is a critical knowledge gap about where new monitoring infrastructure ought to be located to improve understanding of the global wetland CH _4 budget. Here we address this gap with a spatial representativeness analysis at existing and hypothetical observation sites, using 16 process-based wetland biogeochemistry models and machine learning. We find that, in addition to eddy covariance monitoring sites, existing chamber sites are important complements, especially over high latitudes and the tropics. Furthermore, expanding the current monitoring network for wetland CH _4 emissions should prioritize, first, tropical and second, sub-tropical semi-arid wetland regions. Considering those new hypothetical wetland sites from tropical and semi-arid climate zones could significantly improve global estimates of wetland CH _4 emissions and reduce bias by 79% (from 76 to 16 TgCH _4 y ^−1 ), compared with using solely existing monitoring networks. Our study thus demonstrates an approach for long-term strategic expansion of flux observations.

Conservation tillage has been promoted as an effective practice to preserve soil health and enhance agroecosystem services. Changes in tillage intensity have a profound impact on soil nitrogen cycling, yet their influence on nitrate losses at large spatiotemporal scales remains uncertain. This study examined the effects of tillage intensity on soil nitrate losses in the US Midwest from 1979–2018 using field data synthesis and process-based agroecosystem modeling approaches. Our results revealed that no-tillage (NT) or reduced tillage intensity (RTI) decreased nitrate runoff but increased nitrate leaching compared to conventional tillage. These trade-offs were largely caused by altered water fluxes, which elevated total nitrate losses. The structural equation model suggested that precipitation had more pronounced effects on nitrate leaching and runoff than soil properties (i.e. texture, pH, and bulk density). Reduction in nitrate runoff under NT or RTI was negatively correlated with precipitation, and the increased nitrate leaching was positively associated with soil bulk density. We further explored the combined effects of NT or RTI and winter cover crops and found that incorporating winter cover crops into NT systems effectively reduced nitrate runoff but did not significantly affect nitrate leaching. Our findings underscore the precautions of implementing NT or RTI to promote sustainable agriculture under changing climate conditions. This study provides valuable insights into the complex relationship between tillage intensity and nitrate loss pathways, contributing to informed decision-making in climate-smart agriculture.

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.

Abstract The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one‐third of anthropogenic CO2 emissions during the 1959–2019 period. This sink‐estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation‐based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter‐model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.

Grasslands, and the livestock that live there, are dynamic sources and sinks of greenhouse gases, but what controls these fluxes remains poorly characterized. Here the authors show that on the global level, grasslands are climate neutral owing to the cancelling effects of managed vs. natural systems.

Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson’s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson’s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO2 enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle.

Abstract While spatial heterogeneity of riverine nitrogen (N) loading is predominantly driven by the magnitude of basin‐wide anthropogenic N input, the temporal dynamics of N loading are closely related to the amount and timing of precipitation. However, existing studies do not disentangle the contributions of heavy precipitation versus non‐heavy precipitation predicted by future climate scenarios. Here, we explore the potential responses of N loading from the Mississippi Atchafalaya River Basin to precipitation changes using a well‐calibrated hydro‐ecological model and Coupled Model Intercomparison Project Phase 5 climate projections under two representative concentration pathway (RCP) scenarios. With present agricultural production and management practices, N loading could increase up to 30% by the end of the 21st century under future climate scenarios, half of which would be driven by heavy precipitation. Particularly, the RCP8.5 scenario, in which heavy precipitation and drought events become more frequent, would increase N loading disproportionately to projected increases in river discharge. N loading in spring would contribute 41% and 51% of annual N loading increase under the RCP4.5 and RCP8.5 scenarios, respectively, most of which is related to higher N yield due to increases in heavy precipitation. Anthropogenic N inputs would be increasingly susceptible to leaching loss in the Midwest and the Mississippi Alluvial Plain regions. Our results imply that future climate change alone, including more frequent and intense precipitation extremes, would increase N loading and intensify the eutrophication of the Gulf of Mexico over this coming century. More effective nutrient management interventions are needed to reverse this trend.

Over the past 40 years, precipitation extremes have become more important for delivering N to the Gulf of Mexico, according to simulations with a hydro-ecological model. This is likely to diminish the effectiveness of alternative N use practices

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.

Increasing food and biofuel demands have led to the cascading effects from cropland expansions, raised fertilizer use, to increased riverine nitrogen (N) loads. However, little is known about the current trade-off between riverine N pollution and crop production due to the lack of predictive understanding of ecological processes across the land-aquatic continuum. Here, we propose a riverine N footprint (RNF) concept to quantify how N loads change along with per unit crop production gain. Using data synthesis and a well-calibrated hydro-ecological model, we find that the RNF within the Mississippi–Atchafalaya River Basin peaked at 1.95 g N kg ^−1 grain during the 1990s, and then shifted from an increasing to a decreasing trend, reaching 0.65 g N kg ^−1 grain in the 2010s. This implies decoupled responses of crop production and N loads to key agricultural activities approximately after 2000, but this pattern varies considerably among sub-basins. Our study highlights the importance of developing a food–energy–water nexus indicator to examine the region-specific trade-offs between crop production and land-to-aquatic N loads for achieving nutrient mitigation goals while sustaining economic gains.

There was extensive degradation during the warm middle Holocene and permafrost area was reduced substantially. Here the authors synthesize data across the Tibetan permafrost region and find that paleoclimate is more important than modern climate in shaping current permafrost carbon distribution, and its importance increases with soil depth.

Global average, geographical distribution and temporal variations of the 13C isotopic signature of enteric fermentation emissions are not well understood. Here the authors established a global dataset and show a larger emission increase between the two periods (2002–2006 and 2008–2012) than previous studies.

Impact models projections are used in integrated assessments of climate change. Here the authors test systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions.

Abstract There is an urgent need for enhancing climate change resilience in rice production. Here, we report a reflective algal mulching‐based culture practice to attain a simple and low‐cost biological method for enhancement of climate change resilience. The dense mulching of Rhizoclonium sp. in field water highly reflected sunlight in the visible and infrared regions, thus significantly decreasing the root zone temperature, and significantly and simultaneously mitigated CO2, CH4, and N2O emissions through gas retention and removal in the field water layer, as well as reducing gas production. The algae could also enhance rice growth and soil fertility.

Non-technical summary We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding about the remaining options to achieve the Paris Agreement goals, through overcoming political barriers to carbon pricing, taking into account non-CO2 factors, a well-designed implementation of demand-side and nature-based solutions, resilience building of ecosystems and the recognition that climate change mitigation costs can be justified by benefits to the health of humans and nature alone. We consider new insights about what to expect if we fail to include a new dimension of fire extremes and the prospect of cascading climate tipping elements. Technical summary A synthesis is made of 10 topics within climate research, where there have been significant advances since January 2020. The insights are based on input from an international open call with broad disciplinary scope. Findings include: (1) the options to still keep global warming below 1.5 °C; (2) the impact of non-CO2 factors in global warming; (3) a new dimension of fire extremes forced by climate change; (4) the increasing pressure on interconnected climate tipping elements; (5) the dimensions of climate justice; (6) political challenges impeding the effectiveness of carbon pricing; (7) demand-side solutions as vehicles of climate mitigation; (8) the potentials and caveats of nature-based solutions; (9) how building resilience of marine ecosystems is possible; and (10) that the costs of climate change mitigation policies can be more than justified by the benefits to the health of humans and nature. Social media summary How do we limit global warming to 1.5 °C and why is it crucial? See highlights of latest climate science.

East Asia is a crucial region in the global methane (CH4) budget, with significant contributions from the livestock sector. However, the long-term trend and spatial pattern of CH4 emissions from livestock in this region have not been fully assessed. Here, we estimate CH4 emissions from 10 categories of livestock in East Asia during 1961−2019 following the Tier 2 approaches suggested by IPCC (2019). Our results show that livestock-sourced CH4 emission in 2019 was 13.22 [11.42−15.01] (mean [minimum−maximum of 95% confidence interval] Tg CH4 yr-1, accounting for an increase of 231% since 1961. From 1961 to 2019, the emissions increased first and then stabilized after 2000. The contribution of slaughtered livestock to total emissions increased from 3% in 1961 to 24% in 2019 as a result of a significant increase in the slaughtered population. Spatially, the emission hotspots were mostly distributed in eastern China, South Korea, and parts of Japan, but they tend to shift northward after 2000. This latest long-term inventory can help to understand CH4 budget and to assess CH4 mitigation potential at national and regional levels.

Abstract The biogeochemical processes of carbon (C), nitrogen (N), and phosphorous (P) are fully coupled in the Earth system, which shape the structure, functioning, and dynamics of terrestrial ecosystems. However, the representation of P cycle in terrestrial biosphere models (TBMs) is still in an early stage. Here we incorporated P processes and C‐N‐P interactions into the C‐N coupled Dynamic Land Ecosystem Model (DLEM‐CNP), which had a major feature of the ability in simulating the N and P colimitation on vegetation C assimilation. DLEM‐CNP was intensively calibrated and validated against daily or annual observations from four eddy covariance flux sites, two Hawaiian sites along a chronosequence of soils, and other 13 tropical forest sites. The results indicate that DLEM‐CNP significantly improved simulations of forest gross and net primary production (R2: 0.36–0.97, RMSE:1.1–1.49 g C m−2 year−1 for daily GPP at eddy covariance flux sites; R2 = 0.92, RMSE = 176.7 g C m−2 year−1 for annual NPP across 13 tropical forest sites). The simulations were also consistent with field observations in terms of biomass, leaf N:P ratio and plant response to fertilizer addition. A sensitivity analysis suggests that simulated results are reasonably robust against uncertainties in model parameter estimates and the model was very sensitive to parameters of P uptake. These results suggest that incorporating P processes and N‐P interaction into terrestrial biosphere models is of critical importance for accurately estimating C dynamics in tropical forests, particularly those P‐limited ones.

Underlying policy efforts to address global climate change is the scientific need to develop the methods to accurately measure and model carbon stocks and fluxes across the wide range of spatial and temporal scales in the Earth system. Initiated in 2010, the NASA Carbon Monitoring System is one of the most ambitious relevant science initiatives to date, exploiting the satellite remote sensing resources, computational capabilities, scientific knowledge, airborne science capabilities, and end-to-end system expertise that are major strengths of the NASA Earth Science program. Here we provide a synthesis of ‘Phase 2’ activities (2011–2019), encompassing 79 projects, 482 publications, and 136 data products. Our synthesis addresses four key questions: What has been attempted? What major results have been obtained? What major gaps and uncertainties remain? and What are the recommended next steps? Through this review, we take stock of what has been accomplished and identify future priorities toward meeting the nation’s needs for carbon monitoring reporting and verification.

Pollution and climate change are among the most challenging issues for countries with developing economies, but we know little about the ecological risks that result when these pressures occur together. We explored direct effects of, and interactions between, environmental pollution and climate change on ecosystem health in the Bohai Sea region of Northern China. We developed an integrated approach to assess ecological risks to this region under four scenarios of climate change. Although ecological risks to the system from pollution alone have been declining, interactions between pollution and climate change have enhanced ecological risks to this coastal/marine ecosystem. Our results suggest that current policies focused strictly on pollution control alone should be changed to take into account the interactive effects of climate change so as to better forecast and manage potential ecological risks.

Abstract To accurately assess the roles of agriculture in securing food security and maintaining environmental sustainability, it is essential to improve the representation of crop growth, development, and yield formation in global land models that traditionally focus on energy, water, carbon, and nitrogen exchanges between land and the atmosphere. In this study, a process‐based agricultural module has been coupled with the Dynamic Land Ecosystem Model (DLEM‐AG2.0) for assessing how multiple environmental factors (climate change, atmospheric CO2 concentration, tropospheric O3, and nitrogen deposition) and human activities (land use/cover change, nitrogen fertilizer use, and irrigation) have affected the crop growth, development, yield, carbon (C), nitrogen (N), and water cycles in agroecosystems. Here we describe the model structure for simulating crop growth, development, and yield formation in the DLEM‐AG2.0, and then we validate the model using field observations and a national yield survey for three major crops (wheat, maize, and rice) in China during 1980–2012. Results show that the DLEM‐AG2.0 is capable of simulating the dynamic processes of phenological development, leaf growth expansion, biomass accumulation, biomass allocation, and yield formation for wheat, maize, and rice with normalized root mean square errors of the simulations of less than 20%. Our model‐based yield estimation for the three major crops at the national scale for the period 1980–2012 is generally consistent with the national yield survey in China. The crop representation in the DLEM‐AG2.0 is flexible for extrapolating to a global scale after rigorous testing with both site‐specific and regional observations. Further advancement of agricultural modeling within the global land modeling framework will require consideration of human perception and behavior for adapting and mitigating global change.

Terrestrial ecosystems play a significant role in global carbon and water cycles because of the substantial amount of carbon assimilated through net primary production and large amount of water loss through evapotranspiration (ET). Using a process-based ecosystem model, we investigate the potential effects of climate change and rising atmospheric CO2 concentration on global terrestrial ecosystem water use efficiency (WUE) during the twenty-first century. Future climate change would reduce global WUE by 16.3% under high-emission climate change scenario (A2) and 2.2% under low-emission climate scenario (B1) during 2010–2099. However, the combination of rising atmospheric CO2 concentration and climate change would increase global WUE by 7.9% and 9.4% under A2 and B1 climate scenarios, respectively. This suggests that rising atmospheric CO2 concentration could ameliorate climate change-induced WUE decline. Future WUE would increase significantly at the high-latitude regions but decrease at the low-latitude regions under combined changes in climate and atmospheric CO2. The largest increase of WUE would occur in tundra and boreal needleleaf deciduous forest under the combined A2 climate and atmospheric CO2 scenario. More accurate prediction of WUE requires deeper understanding on the responses of ET to rising atmospheric CO2 concentrations and its interactions with climate.

China's economic growth has significantly increased emissions of tropospheric ozone (O3) precursors, resulting in increased regional O3 pollution. We analyzed data from >1400 monitoring stations and estimated the exposure of population and vegetation (crops and forests) to O3 pollution across China in 2015. Based on WHO metrics for human health protection, the current O3 level leads to +0.9% premature mortality (59,844 additional cases a year) with 96% of populated areas showing O3–induced premature death. For vegetation, O3 reduces annual forest tree biomass growth by 11–13% and yield of rice and wheat by 8% and 6%, respectively, relative to conditions below the respective AOT40 critical levels (CL). These CLs are exceeded over 98%, 75% and 83% of the areas of forests, rice and wheat, respectively. Using O3 exposure–response functions, we evaluated the costs of O3-induced losses in rice (7.5 billion US$), wheat (11.1 billion US$) and forest production (52.2 billion US$) and SOMO35–based morbidity for respiratory diseases (690.9 billion US$) and non–accidental mortality (7.5 billion US$), i.e. a total O3-related cost representing 7% of the China Gross Domestic Product in 2015. Keywords: Surface ozone, Human health, Wheat, Rice, Forests, Crops, Risk assessment, Impacts, Economic valuation

Year-to-year variability in CO _2 fluxes can yield insight into climate-carbon cycle relationships, a fundamental yet uncertain aspect of the terrestrial carbon cycle. In this study, we use global observations from NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite for years 2015–2019 and a geostatistical inverse model to evaluate 5 years of interannual variability (IAV) in CO _2 fluxes and its relationships with environmental drivers. OCO-2 launched in late 2014, and we specifically evaluate IAV during the time period when OCO-2 observations are available. We then compare inferences from OCO-2 with state-of-the-art process-based models (terrestrial biosphere model, TBMs). Results from OCO-2 suggest that the tropical grasslands biome (including grasslands, savanna, and agricultural lands within the tropics) makes contributions to global IAV during the 5 year study period that are comparable to tropical forests, a result that differs from a majority of TBMs. Furthermore, existing studies disagree on the environmental variables that drive IAV during this time period, and the analysis using OCO-2 suggests that both temperature and precipitation make comparable contributions. TBMs, by contrast, tend to estimate larger IAV during this time and usually estimate larger relative contributions from the extra-tropics. With that said, TBMs show little consensus on both the magnitude and the contributions of different regions to IAV. We further find that TBMs show a wide range of responses on the relationships of CO _2 fluxes with annual anomalies in temperature and precipitation, and these relationships across most of the TBMs have a larger magnitude than inferred from OCO-2. Overall, the findings of this study highlight large uncertainties in process-based estimates of IAV during recent years and provide an avenue for evaluating these processes against inferences from OCO-2.

Global terrestrial vegetation is greening, particularly in mountain areas, providing strong feedbacks to a series of ecosystem processes. This greening has been primarily attributed to climate change. However, the spatial variability and magnitude of such greening do not synchronize with those of climate change in mountain areas. By integrating two data sets of satellite-derived normalized difference vegetation index (NDVI) values, which are indicators of vegetation greenness, in the period 1982–2015 across the Tibetan Plateau (TP), we test the hypothesis that climate-change-induced greening is regulated by terrain, baseline climate and soil properties. We find a widespread greening trend over 91% of the TP vegetated areas, with an average greening rate (i.e. increase in NDVI) of 0.011 per decade. The linear mixed-effects model suggests that climate change alone can explain only 26% of the variation in the observed greening. Additionally, 58% of the variability can be explained by the combination of the mountainous characteristics of terrain, baseline climate and soil properties, and 32% of this variability was explained by terrain. Path analysis identified the interconnections of climate change, terrain, baseline climate and soil in determining greening. Our results demonstrate the important role of mountainous effects in greening in response to climate change.

As the fastest growing food production sector in the world, aquaculture may become an important source of nitrous oxide (N _2 O)—a potent greenhouse gas and the dominant source of ozone-depleting substances in the stratosphere. China is the largest aquaculture producer globally; however, the magnitude of N _2 O emission from Chinese aquaculture systems (CASs) has not yet been extensively investigated. Here, we quantified N _2 O emission from the CASs since the Reform and Opening-up (1979–2019) at the species-, provincial-, and national-levels using annual aquaculture production data, based on nitrogen (N) levels in feed type, feed amount, feed conversion ratio, and emission factor (EF). Our estimate indicates that over the past 41 years, N _2 O emission from CASs has increased approximately 25 times from 0.67 ± 0.04 GgN in 1979 to 16.69 ± 0.31 GgN in 2019. Freshwater fish farming, primarily in two provinces, namely, Guangdong and Hubei, where intensive freshwater fish farming has been adopted in the past decades, accounted for approximately 89% of this emission increase. We also calculated the EF for each species, ranging from 0.79 ± 0.23 g N _2 O kg ^−1 animal to 2.41 ± 0.14 g N _2 O kg ^−1 animal. The results of this study suggest that selecting low-EF species and improving feed use efficiency can help reduce aquaculture N _2 O emission for building a climate-resilient sustainable aquaculture.

The seasonal-cycle amplitude (SCA) of the atmosphere–ecosystem carbon dioxide (CO2) exchange rate is a useful metric of the responsiveness of the terrestrial biosphere to environmental variations. It is unclear, however, what underlying mechanisms are responsible for the observed increasing trend of SCA in atmospheric CO2 concentration. Using output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), we investigated how well the SCA of atmosphere–ecosystem CO2 exchange was simulated with 15 contemporary terrestrial ecosystem models during the period 1901–2010. Also, we made attempt to evaluate the contributions of potential mechanisms such as atmospheric CO2, climate, land-use, and nitrogen deposition, through factorial experiments using different combinations of forcing data. Under contemporary conditions, the simulated global-scale SCA of the cumulative net ecosystem carbon flux of most models was comparable in magnitude with the SCA of atmospheric CO2 concentrations. Results from factorial simulation experiments showed that elevated atmospheric CO2 exerted a strong influence on the seasonality amplification. When the model considered not only climate change but also land-use and atmospheric CO2 changes, the majority of the models showed amplification trends of the SCAs of photosynthesis, respiration, and net ecosystem production (+0.19 % to +0.50 % yr−1). In the case of land-use change, it was difficult to separate the contribution of agricultural management to SCA because of inadequacies in both the data and models. The simulated amplification of SCA was approximately consistent with the observational evidence of the SCA in atmospheric CO2 concentrations. Large inter-model differences remained, however, in the simulated global tendencies and spatial patterns of CO2 exchanges. Further studies are required to identify a consistent explanation for the simulated and observed amplification trends, including their underlying mechanisms. Nevertheless, this study implied that monitoring of ecosystem seasonality would provide useful insights concerning ecosystem dynamics.

Grassland ecosystems play an essential role in climate regulation through carbon (C) storage in plant and soil. But, anthropogenic practices such as livestock grazing, grazing related excreta nitrogen (N) deposition, and manure/fertilizer N application have the potential to reduce the effectiveness of grassland C sink through increased nitrous oxide (N _2 O) and methane (CH _4 ) emissions. Although the effect of anthropogenic activities on net greenhouse gas (GHG) fluxes in grassland ecosystems have been investigated at local to regional scales, estimates of net GHG balance at the global scale remains uncertain. With the data-model framework integrating empirical estimates of livestock CH _4 emissions with process-based modeling estimates of land CO _2 , N _2 O and CH _4 fluxes, we examined the overall global warming potential (GWP) of grassland ecosystems during 1961–2010. We then quantified the grassland-specific and regional variations to identify hotspots of GHG fluxes. Our results show that, over a 100-year time horizon, grassland ecosystems sequestered a cumulative total of 113.9 Pg CO _2 -eq in plant and soil, but then released 91.9 Pg CO _2 -eq to the atmosphere, offsetting 81% of the net CO _2 sink. We also found large grassland-specific variations in net GHG fluxes, with pasturelands acting as a small GHG source of 1.52 ± 143 Tg CO _2 -eq yr ^−1 (mean ± 1.0 s.d.) and rangelands a strong GHG sink (−442 ± 266 Tg CO _2 -eq yr ^−1 ) during 1961–2010. Regionally, Europe acted as a GHG source of 23 ± 10 Tg CO _2 -eq yr ^−1 , while other regions (i.e. Africa, Southern Asia) were strong GHG sinks during 2001–2010. Our study highlights the importance of considering regional and grassland-specific differences in GHG fluxes for guiding future management and climate mitigation strategies in global grasslands.

Tillage alters the thermal and wetness conditions in soil, which facilitates soil organic matter oxidation and water transportation, leading to rapid depletion of soil carbon (C). Little is known about tillage intensity change (TIC) and its impacts in the US corn-soybean rotation system over the past two decades. Using time-series tillage maps developed from a private survey and a process-based land ecosystem model, here we examined how tillage intensity has changed across the nation and affected soil organic carbon (SOC) storage from 1998 to 2016. Results derived from the combination of tillage survey data and cropland distribution maps show that total corn-soybean area consistently increased from 62.3 Mha in 1998 to 66.8 Mha in 2008 and to 73.1 Mha in 2016, among which the acreage under no-till system increased from 16.9 Mha in 1998 to 28.9 Mha in 2008, while conservation and conventional tillage decreased by 3.8 and 3.9 Mha, respectively. The extent of no-till practice in corn-soybean lands shrank by 2.6 Mha from 2008 to 2016, while conservation and conventional tillage increased by 2.8 and 6.1 Mha in the same period. Modeling simulations reveal that historical tillage practices led to a soil C loss of 10.3 (spring till only) to 15.2 (tilled in both spring and fall) Tg C yr ^−1 in the study area from 1998 to 2016. In addition, reduced tillage intensity in corn-soybean cropland contributed to SOC accumulation at 1.0 Tg C yr ^−1 (1.6 g C m ^−2 yr ^−1 ) from 1998 to 2008, but the SOC gain was offset by SOC reduction at 2.4 Tg C yr ^−1 (3.4 g C m ^−2 yr ^−1 ) from increased tillage intensity during the period 2008–2016. For both periods, TIC-induced hydrological C loss accounted for 15% of the SOC change, while the rest was attributed to gaseous-C loss.

Arctic ecosystems are particularly vulnerable to climate change because of Arctic amplification. Here, we assessed the climatic impacts of low-end, 1.5 °C, and 2.0 °C global temperature increases above pre-industrial levels, on the warming of terrestrial ecosystems in northern high latitudes (NHL, above 60 °N including pan-Arctic tundra and boreal forests) under the framework of the Inter-Sectoral Impact Model Intercomparison Project phase 2b protocol. We analyzed the simulated changes of net primary productivity, vegetation biomass, and soil carbon stocks of eight ecosystem models that were forced by the projections of four global climate models and two atmospheric greenhouse gas pathways (RCP2.6 and RCP6.0). Our results showed that considerable impacts on ecosystem carbon budgets, particularly primary productivity and vegetation biomass, are very likely to occur in the NHL areas. The models agreed on increases in primary productivity and biomass accumulation, despite considerable inter-model and inter-scenario differences in the magnitudes of the responses. The inter-model variability highlighted the inadequacies of the present models, which fail to consider important components such as permafrost and wildfire. The simulated impacts were attributable primarily to the rapid temperature increases in the NHL and the greater sensitivity of northern vegetation to warming, which contrasted with the less pronounced responses of soil carbon stocks. The simulated increases of vegetation biomass by 30–60 Pg C in this century have implications for climate policy such as the Paris Agreement. Comparison between the results at two warming levels showed the effectiveness of emission reductions in ameliorating the impacts and revealed unavoidable impacts for which adaptation options are urgently needed in the NHL ecosystems.

Abstract In arid and semi‐arid regions, grassland degradation has become a major environmental and economic problem, but little information is available on the response of grassland productivity to both climate change and grazing intensity. By developing a grazing module in a process‐based ecosystem model, the dynamic land ecosystem model (DLEM), we explore the roles of climate change, elevated CO2, and varying grazing intensities in affecting aboveground net primary productivity (ANPP) across different grassland sites in Mongolia. Our results show that both growing season precipitation totals and average temperature exert important controls on annual ANPP across six sites over a precipitation gradient, explaining 65% and 45% of the interannual variations, respectively. Interannual variation in ANPP, measured as the ratio of standard deviation among years to long‐term mean, increased from 9.5 to 18.9% to 23.9–32.5% along a gradient of high to low precipitation. Historical grazing resulted in a net reduction in ANPP across all sites ranging from 2% to 15.4%. Our results further show that grassland ANPP can be maintained at a grazing intensity of 1.0 and 0.5 sheep/ha at wet and dry sites, respectively, indicating that dry sites are more vulnerable to grazing compared to wet sites. In addition, precipitation use efficiency (PUE) decreased while nitrogen use efficiency (NUE) increased across a gradient of low to high precipitation. However, grazing resulted in a net reduction in both PUE and NUE by 47% and 67% across all sites. Our results indicate that seasonal precipitation totals, average temperatures and grazing are important regulators of grassland ANPP in Mongolia. These results have important implications for grassland productivity in semi‐arid regions in Central Asia and beyond.

Runoff in the United States is changing, and this study finds that the measured change is dependent on the geographic region and varies seasonally. Specifically, observed annual total runoff had an insignificant increasing trend in the US between 1950 and 2010, but this insignificance was due to regional heterogeneity with both significant and insignificant increases in the eastern, northern, and southern US, and a greater significant decrease in the western US. Trends for seasonal mean runoff also differed across regions. By region, the season with the largest observed trend was autumn for the east (positive), spring for the north (positive), winter for the south (positive), winter for the west (negative), and autumn for the US as a whole (positive). Based on the detection and attribution analysis using gridded WaterWatch runoff observations along with semi-factorial land surface model simulations from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we found that while the roles of CO _2 concentration, nitrogen deposition, and land use and land cover were inconsistent regionally and seasonally, the effect of climatic variations was detected for all regions and seasons, and the change in runoff could be attributed to climate change in summer and autumn in the south and in autumn in the west. We also found that the climate-only and historical transient simulations consistently underestimated the runoff trends, possibly due to precipitation bias in the MsTMIP driver or within the models themselves.

This paper evaluates the ability of eight global vegetation models to reproduce recent trends and inter-annual variability of biomass in natural terrestrial ecosystems. For the purpose of this evaluation, the simulated trajectories of biomass are expressed in terms of the relative rate of change in biomass (RRB), defined as the deviation of the actual rate of biomass turnover from its equilibrium counterpart. Cumulative changes in RRB explain long-term changes in biomass pools. RRB simulated by the global vegetation models is compared with its observational equivalent, derived from vegetation optical depth reconstructions of above-ground biomass (AGB) over the period 1993–2010. According to the RRB analysis, the rate of global biomass growth described by the ensemble of simulations substantially exceeds the observation. The observed fluctuations of global RRB are significantly correlated with El Niño Southern Oscillation events (ENSO), but only some of the simulations reproduce this correlation. However, the ENSO sensitivity of RRB in the tropics is not significant in the observation, while it is in some of the simulations. This mismatch points to an important limitation of the observed AGB reconstruction to capture biomass variations in tropical forests. Important discrepancies in RRB were also identified at the regional scale, in the tropical forests of Amazonia and Central Africa, as well as in the boreal forests of north-western America, western and central Siberia. In each of these regions, the RRBs derived from the simulations were analyzed in connection with underlying differences in net primary productivity and biomass turnover rate ̶as a basis for exploring in how far differences in simulated changes in biomass are attributed to the response of the carbon uptake to CO _2 increments, as well as to the model representation of factors affecting the rates of mortality and turnover of foliage and roots. Overall, our findings stress the usefulness of using RRB to evaluate complex vegetation models and highlight the importance of conducting further evaluations of both the actual rate of biomass turnover and its equilibrium counterpart, with special focus on their background values and sources of variation. In turn, this task would require the availability of more accurate multi-year observational data of biomass and net primary productivity for natural ecosystems, as well as detailed and updated information on land-cover classification.

Global agriculture is challenged to increase soil carbon sequestration and reduce greenhouse gas emissions while providing products for an increasing population. Growing crop production could be achieved through higher yield per hectare (i.e. intensive farming) or more hectares (extensive farming), which however, have different ecological and environmental consequences. Multiple lines of evidence indicate that expanding cropland for additional production may lead to loss of vegetation and soil carbon, and threaten the survival of wildlife. New concerns about the impacts of extensive farming have been raised for the US Corn Belt, one of the world’s most productive regions, as cropland has rapidly expanded northwestward unto grasslands and wetlands in recent years. Here we used a process-based ecosystem model to distinguish and quantify how natural drivers as well as intensive and extensive farming practices have altered grain production, soil carbon storage, and agricultural carbon footprint in the US Western Corn Belt since 1980. Compared to the period 1980–2005, we found that cropland expansion more than tripled in the most recent decade (2006–2016), becoming a significant factor contributing to growing grain production. Land use change in this period led to a soil carbon loss of 90.8 ± 14.7 Tg (1 Tg = 10 ^12 g). As a result, grain production in this region shifted from carbon neutral to a carbon loss of 2.3 kg C kg ^−1 grain produced. The enlarging negative carbon footprint (Δ C /Δ P ) indicates the major role that cropland expansion has had on the carbon cost of grain production in this region. Therefore, we should be more cautious to pursue high crop production through agricultural cropland conversion, particularly in those carbon-rich soils.