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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.

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.

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 synthesize 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 first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gases inventories, supporting the assessment of collective countries’ climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1 and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021, suggest a rebound in EFOS relative to 2020 of +4.9 % (4.1 % to 5.7 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, 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., 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-2021 (Friedlingstein et al., 2021).

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.

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.

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r>0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r=0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop+SH) and extratropical Northern Hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO2 fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO2 fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products.

Year-to-year variability in CO2 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 CO2 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 CO2 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.

Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961–2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990–2014, but give a ∼15% lower EF in 1961–1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr–1) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways.

International audience; The application of manure and mineral nitrogen (N) fertilizer, and livestock excreta deposition are the main drivers of nitrous oxide (N2O) emissions in agricultural systems. However, the magnitude and spatiotemporal variations of N2O emissions due to different management practices (excreta deposition and manure/fertilizer application) from grassland ecosystems remain unclear. In this study, we used the Dynamic Land Ecosystem Model to simulate the spatiotemporal variation in global N2O emissions and their attribution to different sources from both intensively managed (pasturelands) and extensively managed (rangelands) grasslands during 1961–2014. Over the study period, pasturelands and rangelands experienced a significant increase in N2O emissions from 1.74 Tg N2O‐N in 1961 to 3.11 Tg N2O‐N in 2014 (p < 0.05). Globally, pasturelands and rangelands were responsible for 54% (2.2 Tg N2O‐N) of the total agricultural N2O emissions (4.1 Tg N2O‐N) in 2006. Natural and anthropogenic sources contributed 26% (0.64 Tg N2O‐N/year) and 74% (1.78 Tg N2O‐N/year) of the net emissions, respectively. Across different biomes, pasturelands (i.e., C3 and C4) were the single largest contributor to N2O fluxes, accounting for 86% of the net global emissions from grasslands. Among different sources, livestock excreta deposition contributed 54% of the net emissions, followed by manure N (13%) and mineral N (7%) application. Regionally, southern Asia contributed 38% of the total emissions, followed by Europe (29%) and North America (16%). Our modeling study demonstrates that livestock excreta deposition and manure/fertilizer application have dramatically altered the N cycle in pasturelands, with a substantial impact on the climate system.

International audience; We synthesized N2O emissions over North America using 17 bottom-up (BU) estimates from 1980–2016 and five top-down (TD) estimates from 1998 to 2016. The BU-based total emission shows a slight increase owing to U.S. agriculture, while no consistent trend is shown in TD estimates. During 2007–2016, North American N2O emissions are estimated at 1.7 (1.0–3.0) Tg N yr−1 (BU) and 1.3 (0.9–1.5) Tg N yr−1 (TD). Anthropogenic emissions were twice as large as natural fluxes from soil and water. Direct agricultural and industrial activities accounted for 68% of total anthropogenic emissions, 71% of which was contributed by the U.S. Our estimates of U.S. agricultural emissions are comparable to the EPA greenhouse gas (GHG) inventory, which includes estimates from IPCC tier 1 (emission factor) and tier 3 (process-based modeling) approaches. Conversely, our estimated agricultural emissions for Canada and Mexico are twice as large as the respective national GHG inventories.Nitrous oxide (N2O) is the third most important greenhouse gase (GHG) after CO2 and CH4 causing global warming. Among world regions, North America (defined herein as U.S., Canada, and Mexico) is the second largest source of N2O emissions globally, and previous source estimates for this region vary widely. This study aims to provide a comprehensive N2O assessment over North America including all available estimates based on a number of approaches. We report total emissions, and emissions from four anthropogenic source sectors, over the past four decades. Agriculture and industry are two major N2O sources in North America. Our results show a minor increase in the total N2O emission due to agricultural trends in the U.S. Our bottom-up estimate of U.S. agricultural N2O emissions are close to those in the EPA national GHG inventory that includes both empirical and model results. The high consistency suggests the need to take process-based modeling results into account for future national GHG inventories.

Mitigating soil nitrous oxide (N2O) emissions is essential for staying below a 2 °C warming threshold. However, accurate assessments of mitigation potential are limited by uncertainty and variability in direct emission factors (EFs). To assess where and why EFs differ, we created high-resolution maps of crop-specific EFs based on 1,507 georeferenced field observations. Here, using a data-driven approach, we show that EFs vary by two orders of magnitude over space. At global and regional scales, such variation is primarily driven by climatic and edaphic factors rather than the well-recognized management practices. Combining spatially explicit EFs with N surplus information, we conclude that global mitigation potential without compromising crop production is 30% (95% confidence interval, 17–53%) of direct soil emissions of N2O, equivalent to the entire direct soil emissions of China and the United States combined. Two-thirds (65%) of the mitigation potential could be achieved on one-fifth of the global harvested area, mainly located in humid subtropical climates and across gleysols and acrisols. These findings highlight the value of a targeted policy approach on global hotspots that could deliver large N2O mitigation as well as environmental and food co-benefits. Estimating the global cropland N2O mitigation potential is limited by the uncertainty and variability of direct emission factors (EFs). Here, using a data-driven approach with 1,507 chamber-based field observations of EFs, the study shows that EF variation is primarily driven by climatic and edaphic factors. Two-thirds of the mitigation potential could be achieved on one-fifth of the global harvested area, mainly located in humid subtropical climates and across gleysols and acrisols.

International audience; The terrestrial carbon sink provides a critical negative feedback to climate warming, yet large uncertainty exists on its long-term dynamics. Here we combined terrestrial biosphere models (TBMs) and climate projections, together with climate-specific land use change, to investigate both the trend and interannual variability (IAV) of the terrestrial carbon sink from 1986 to 2099 under two representative concentration pathways RCP2.6 and RCP6.0. The results reveal a saturation of the terrestrial carbon sink by the end of this century under RCP6.0 due to warming and declined CO2 effects. Compared to 1986–2005 (0.96 ± 0.44 Pg C yr−1), during 2080–2099 the terrestrial carbon sink would decrease to 0.60 ± 0.71 Pg C yr−1 but increase to 3.36 ± 0.77 Pg C yr−1, respectively, under RCP2.6 and RCP6.0. The carbon sink caused by CO2, land use change and climate change during 2080–2099 is −0.08 ± 0.11 Pg C yr−1, 0.44 ± 0.05 Pg C yr−1, and 0.24 ± 0.70 Pg C yr−1 under RCP2.6, and 4.61 ± 0.17 Pg C yr−1, 0.22 ± 0.07 Pg C yr−1, and -1.47 ± 0.72 Pg C yr−1 under RCP6.0. In addition, the carbon sink IAV shows stronger variance under RCP6.0 than RCP2.6. Under RCP2.6, temperature shows higher correlation with the carbon sink IAV than precipitation in most time, which however is the opposite under RCP6.0. These results suggest that the role of terrestrial carbon sink in curbing climate warming would be weakened in a no-mitigation world in future, and active mitigation efforts are required as assumed under RCP2.6.

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and surface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at. (Petrescu et al. 2020b)., SCOPUS: re.j, info:eu-repo/semantics/published

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.

Changes in vegetation structure are expected to influence the redistribution of heat and moisture; however, how variations in the leaf area index (LAI) affect this global energy partitioning is not yet quantified. Here, we estimate that a unit change in LAI leads to 3.66 ± 0.45 and −3.26 ± 0.41 W m−2 in latent (LE) and sensible (H) fluxes, respectively, over the 1982–2016 period. Analysis of an ensemble of data-driven products shows that these sensitivities increase by about 20% over the observational period, prominently in regions with a limited water supply, probably because of an increased transpiration/evaporation ratio. Global greening has caused a decrease in the Bowen ratio (B = H/LE) of −0.010 ± 0.002 per decade, which is attributable to the increased evaporative surface. Such a direct LAI effect on energy fluxes is largely modulated by plant functional types (PFTs) and background climate conditions. Land surface models (LSMs) misrepresent this vegetation control, possibly due to underestimation of the biophysical responses to changes in the water availability and poor representation of LAI dynamics. Changes in the leaf area index alter the distribution of heat and moisture. The change in energy partitioning related to leaf area, increasing latent and decreasing sensible fluxes over the observational period 1982–2016, is moderated by plant functional type and background climate.

International audience; Carbon fluxes at the land-atmosphere interface are strongly influenced by weather and climate conditions. Yet what is usually known as “climate extremes” does not always translate into very high or low carbon fluxes or so-called “carbon extremes.” To reveal the patterns of how climate extremes influence terrestrial carbon fluxes, we analyzed the interannual variations in ecosystem carbon fluxes simulated by the Terrestrial Biosphere Models (TBMs) in the Inter-Sectoral Impact Model Intercomparison Project. At the global level, TBMs simulated reduced ecosystem net primary productivity (NPP; 18.5 ± 9.3 g C m−2 yr−1), but enhanced heterotrophic respiration (Rh; 7 ± 4.6 g C m−2 yr−1) during extremely hot events. TBMs also simulated reduced NPP (60.9 ± 24.4 g C m−2 yr−1) and reduced Rh (16.5 ± 11.4 g C m−2 yr−1) during extreme dry events. Influences of precipitation extremes on terrestrial carbon uptake were larger in the arid/semiarid zones than other regions. During hot extremes, ecosystems in the low latitudes experienced a larger reduction in carbon uptake. However, a large fraction of carbon extremes did not occur in concert with either temperature or precipitation extremes. Rather these carbon extremes are likely to be caused by the interactive effects of the concurrent temperature and precipitation anomalies. The interactive effects showed considerable spatial variations with the largest effects on NPP in South America and Africa. Additionally, TBMs simulated a stronger sensitivity of ecosystem productivity to precipitation than satellite estimates. This study provides new insights into the complex ecosystem responses to climate extremes, especially the emergent properties of carbon dynamics resulting from compound climate extremes.

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of ten terrestrial biosphere models to reproduce observed sensitivity of ecosystem productivity to rainfall changes at ten sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed.The key results are:(a) Inter‐model variation is generally large and model agreement varies with time scales. In severely water limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent gross primary productivity. In more mesic sites model agreement for both water and carbon fluxes is typically higher on fine (daily‐monthly) time scales and reduces on longer (seasonal‐annual) scales.(b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability.(c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition.(d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

A decline in the carbon fertilization effect One source of uncertainty in climate science is how the carbon fertilization effect (CFE) will contribute to mitigation of anthropogenic climate change. Wang et al. explored the temporal dynamics of CFE on vegetation photosynthesis at the global scale. There has been a decline over recent decades in the contribution of CFE to vegetation photosynthesis, perhaps owing to the limiting effects of plant nutrients such as nitrogen and phosphorus. This declining trend has not been adequately accounted for in carbon cycle models. CFE thus has limitations for long-term mitigation of climate change, and future warming might currently be underestimated. Science , this issue p. 1295

International audience; Understanding changes in terrestrial carbon balance is important to improve our knowledge of the regional carbon cycle and climate change. However, evaluating regional changes in the terrestrial carbon balance is challenging due to the lack of surface flux measurements. This study reveals that the terrestrial carbon uptake over the Republic of Korea has been enhanced from 1999 to 2017 by analyzing long‐term atmospheric CO2 concentration measurements at the Anmyeondo Station (36.53°N, 126.32°E) located in the western coast. The influence of terrestrial carbon flux on atmospheric CO2 concentrations (ΔCO2) is estimated from the difference of CO2 concentrations that were influenced by the land sector (through easterly winds) and the Yellow Sea sector (through westerly winds). We find a significant trend in ΔCO2 of −4.75 ppm per decade (p < .05) during the vegetation growing season (May through October), suggesting that the regional terrestrial carbon uptake has increased relative to the surrounding ocean areas. Combined analysis with satellite measured normalized difference vegetation index and gross primary production shows that the enhanced carbon uptake is associated with significant nationwide increases in vegetation and its production. Process‐based terrestrial model and inverse model simulations estimate that regional terrestrial carbon uptake increases by up to 18.9 and 8.0 Tg C for the study period, accounting for 13.4% and 5.7% of the average annual domestic carbon emissions, respectively. Atmospheric chemical transport model simulations indicate that the enhanced terrestrial carbon sink is the primary reason for the observed ΔCO2 trend rather than anthropogenic emissions and atmospheric circulation changes. Our results highlight the fact that atmospheric CO2 measurements could open up the possibility of detecting regional changes in the terrestrial carbon cycle even where anthropogenic emissions are not negligible.

Ecosystems responded asymmetrically to the 2018 European drought., In summer 2018, central and northern Europe were stricken by extreme drought and heat (DH2018). The DH2018 differed from previous events in being preceded by extreme spring warming and brightening, but moderate rainfall deficits, yet registering the fastest transition between wet winter conditions and extreme summer drought. Using 11 vegetation models, we show that spring conditions promoted increased vegetation growth, which, in turn, contributed to fast soil moisture depletion, amplifying the summer drought. We find regional asymmetries in summer ecosystem carbon fluxes: increased (reduced) sink in the northern (southern) areas affected by drought. These asymmetries can be explained by distinct legacy effects of spring growth and of water-use efficiency dynamics mediated by vegetation composition, rather than by distinct ecosystem responses to summer heat/drought. The asymmetries in carbon and water exchanges during spring and summer 2018 suggest that future land-management strategies could influence patterns of summer heat waves and droughts under long-term warming.

Accurate knowledge of 13C isotopic signature (δ13C) of methane from each source is crucial for separating biogenic, fossil fuel and pyrogenic emissions in bottom-up and top-down methane budget. Livestock production is the largest anthropogenic source in the global methane budget, mostly from enteric fermentation of domestic ruminants. However, the global average, geographical distribution and temporal variations of the δ13C of enteric emissions are not well understood yet. Here, we provide a new estimation of C3-C4 diet composition of domestic ruminants (cattle, buffaloes, goats and sheep), a revised estimation of yearly enteric CH4 emissions, and a new estimation for the evolution of its δ13C during the period 1961–2012. Compared to previous estimates, our results suggest a larger contribution of ruminants’ enteric emissions to the increasing trend in global methane emissions between 2000 and 2012, and also a larger contribution to the observed decrease in the δ13C of atmospheric methane., 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.

Nitrous oxide (N2O) is the third most important long-lived GHG and an important stratospheric ozone depleting substance. Agricultural practices and the use of N-fertilizers have greatly enhanced emissions of N2O. Here, we present estimates of N2O emissions determined from three global atmospheric inversion frameworks during the period 1998–2016. We find that global N2O emissions increased substantially from 2009 and at a faster rate than estimated by the IPCC emission factor approach. The regions of East Asia and South America made the largest contributions to the global increase. From the inversion-based emissions, we estimate a global emission factor of 2.3 ± 0.6%, which is significantly larger than the IPCC Tier-1 default for combined direct and indirect emissions of 1.375%. The larger emission factor and accelerating emission increase found from the inversions suggest that N2O emission may have a nonlinear response at global and regional scales with high levels of N-input.

Satellite approaches for estimation of the partial pressure of CO2 (pCO2) and air-sea flux of CO2 in coastal regions offer the potential to reduce uncertainties in coastal carbon budgets and improve understanding of spatial and temporal patterns and the factors influencing them. We used satellite-derived products in combination with an extensive data set of ship-based observations to develop an unprecedented multi-year time-series of pCO2 and air-sea flux of CO2 in the northern Gulf of Mexico for the period 2006–2010. A regression tree algorithm was used to relate satellite-derived products for chlorophyll, sea surface temperature, and dissolved and detrital organic matter to ship observations of pCO2. The resulting relationship had an r2 of 0.827 and a prediction error of 31.7 μatm pCO2 (root mean-squared error of the relationship was 28.8 μatm). Using a wind speed and gas exchange relationship along with satellite winds, estimates of air-sea flux of CO2 were derived yielding an average annual flux over the period 2006–2010 of − 0.8 to − 1.5 (annual mean = − 1.1 ± 0.3) mol C m− 2 y− 1, where the negative value indicates net ocean uptake. The estimated total annual CO2 flux for the study region was − 4.3 + 1.1 Tg C y− 1. Relationships of satellite-derived pCO2 with salinity were consistent with shipboard observations and exhibited a concave relationship with low values at mid- and low salinities attributed to strong biological drawdown of CO2 in the high productivity river-mixing zone. The time-series of satellite-derived pCO2 was characterized by a seasonal pattern with values lower during winter and spring, low to intermediate values during fall, and higher and more variable values during summer. These findings were similar to simulations from a coupled physical-biogeochemical model. A seasonal pattern was also evident in the air-sea flux of CO2 with generally more negative fluxes (i.e., ocean uptake) during winter and spring, and positive fluxes during summer months with fall being a period of transition. Interannual variations in annual means of both air-sea flux of CO2 and DIN loading were significant, with higher DIN loading coinciding in some cases with more negative air-sea flux of CO2 (i.e., net ocean uptake). Spatial patterns of pCO2 reflected regional environmental forcing including effects of river discharge, wind forcing, and shelf-slope circulation. Our study also illustrates the utility of satellite extrapolation for highlighting areas that may contribute significantly to regional signals and for guiding prioritization of locations for acquiring further observations. The approach should be readily applicable to other regions given adequate availability of in situ observations for algorithm development.

Amazon droughts have impacted regional ecosystem functioning as well as global carbon cycling. The severe dry-season droughts in 2005 and 2010, driven by Atlantic sea surface temperature (SST) anomaly, have been widely investigated in terms of drought severity and impacts on ecosystems. Although the influence of Pacific SST anomaly on wet-season precipitation has been well recognized, it remains uncertain to what extent the droughts driven by Pacific SST anomaly could affect forest greenness and photosynthesis in the Amazon. Here, we examined the monthly and annual dynamics of forest greenness and photosynthetic capacity when Amazon ecosystems experienced an extreme drought in 2015/2016 driven by a strong El Niño event. We found that the drought during August 2015-July 2016 was one of the two most severe meteorological droughts since 1901. Due to the enhanced solar radiation during this drought, overall forest greenness showed a small increase, and 21.6% of forests even greened up (greenness index anomaly ≥1 standard deviation). In contrast, solar-induced chlorophyll fluorescence (SIF), an indicator of vegetation photosynthetic capacity, showed a significant decrease. Responses of forest greenness and photosynthesis decoupled during this drought, indicating that forest photosynthesis could still be suppressed regardless of the variation in canopy greenness. If future El Niño frequency increases as projected by earth system models, droughts would result in persistent reduction in Amazon forest productivity, substantial changes in tree composition, and considerable carbon emissions from Amazon.

The accurate estimation of stream water temperature is essential for understanding environmental controls on the structure and functioning of aquatic ecosystems. Few studies have coupled soil and stream water temperatures to capture the synergy of thermal balances between terrestrial and riverine systems. As a result, little is known about how multiple environmental stresses have affected water temperature, particularly for different orders of streams. Here we incorporated a new water transport scheme into the Dynamic Land Ecosystem Model (DLEM) to predict water temperature in 1st order and higher-order streams (>1st order). Driven by a 4-km geo-referenced dataset of multiple environmental factors, our new water temperature model was utilized to predict the spatiotemporal variations of water temperature in the U.S. Mid-Atlantic Region during 1900–2015. Results revealed that water temperature during 1970–2015 increased significantly (p

It is well documented that warming can accelerate greenhouse gas (GHG) emissions, further inducing a positive feedback and reinforcing future climate warming. However, how different kinds of GHGs respond to various warming magnitudes remains largely unclear, especially in the cold regions that are more sensitive to climate warming. Here, we concurrently measured carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes and their total balance in an alpine meadow in response to three levels of warming (ambient, +1.5 °C, +3.0 °C). We found warming-induced increases in CH4 uptake, decreases in N2O emissions and increases in CO2 emissions at the annual basis. Expressed as CO2-equivalents with a global warming potential of 100 years (GWP100), the enhancement of CH4 uptake and reduction of N2O emissions offset only 9% of the warming-induced increase in CO2 emissions for 1.5 °C warming, and only 7% for 3.0 °C warming. CO2 emissions were strongly stimulated, leading to a significantly positive feedback to climate system, for 3.0 °C warming but less for 1.5 °C warming. The warming with 3.0 °C altered the total GHG balance mainly by stimulating CO2 emissions in the non-growing season due to warmer soil temperatures, longer unfrozen period, and increased soil water content. The findings provide an empirical evidence that warming beyond global 2 °C target can trigger a positive GHG-climate feedback and highlight the contribution from non-growing season to this positive feedback loop in cold ecosystems.

Given the important role of nitrogen input from livestock systems in terrestrial nutrient cycles and the atmospheric chemical composition, it is vital to have a robust estimation of the magnitude and spatiotemporal variation in manure nitrogen production and its application to cropland across the globe. In this study, we used the dataset from the Global Livestock Impact Mapping System (GLIMS) in conjunction with country-specific annual livestock populations to reconstruct the manure nitrogen production during 1860–2014. The estimated manure nitrogen production increased from 21.4 Tg N yr−1 in 1860 to 131.0 Tg N yr−1 in 2014 with a significant annual increasing trend (0.7 Tg N yr−1, p https://doi.org/10.1594/PANGAEA.871980 (Zhang et al., 2017).

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 los...

Clarifying how increased atmospheric CO2 concentration (eCO2) contributes to accelerated land carbon sequestration remains important since this process is the largest negative feedback in the coupled carbon–climate system. Here, we constrain the sensitivity of the terrestrial carbon sink to eCO2 over the temperate Northern Hemisphere for the past five decades, using 12 terrestrial ecosystem models and data from seven CO2 enrichment experiments. This constraint uses the heuristic finding that the northern temperate carbon sink sensitivity to eCO2 is linearly related to the site-scale sensitivity across the models. The emerging data-constrained eCO2 sensitivity is 0.64 ± 0.28 PgC yr−1 per hundred ppm of eCO2. Extrapolating worldwide, this northern temperate sensitivity projects the global terrestrial carbon sink to increase by 3.5 ± 1.9 PgC yr−1 for an increase in CO2 of 100 ppm. This value suggests that CO2 fertilization alone explains most of the observed increase in global land carbon sink since the 1960s. More CO2 enrichment experiments, particularly in boreal, arctic and tropical ecosystems, are required to explain further the responsible processes. The northern temperate carbon sink is estimated to increase by 0.64 PgC each year for each increase in atmospheric CO2 concentrations by 100 ppm, suggests an analysis of data from field experiments at 7 sites constraints.

Large wildfires exert strong disturbance to regional and global climate systems and ecosystems by perturbing radiative forcing as well as 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 included in previous climate model studies, and the resulting effects on the projections of future climate change are not well understood. Here we used a 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), reproduced the observed Aerosol Optical Depth (AOD) from space-based Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products and ground-based AErosol RObotic NETwork (AERONET) data and agreed well with carbon budget benchmarks from previous studies. We estimated the global averaged net radiative effect of both fire aerosols and fire-induced LCC at −0.59 ± 0.52 W m−2, which was 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 increased 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 greatly enhanced radiative effect was 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 increased 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 of fire aerosols could counter the climate effects of the projected decrease of anthropogenic aerosols due to air pollution control policies in many regions. We also evaluated two distinct feedback mechanisms that were associated with fire aerosols and fire-induced LCC. On a global scale, the first mechanism imposed positive feedback to fire activity through enhanced droughts with suppressed precipitation by fire aerosol-cloud interactions, while the second one manifested negative feedback due to reduced fuel loads by fire consumption and post-fire tree mortality and recovery processes. These two feedback pathways with opposite effects competed at regional to global scales and increased the complexity of climate-fire-ecosystem interactions and their climatic impacts.

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 +/- 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 +/- 0.06) and the lowest in tropical forests (0.40 +/- 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 +/- 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 +/- 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

Our understanding and quantification of global soil nitrous oxide (N2 O) emissions and the underlying processes remain largely uncertain. Here we assessed the effects of multiple anthropogenic and natural factors, including nitrogen fertilizer (N) application, atmospheric N deposition, manure N application, land cover change, climate change and rising atmospheric CO2 concentration, on global soil N2 O emissions for the period 1861-2016 using a standard simulation protocol with seven process-based terrestrial biosphere models. Results suggest global soil N2 O emissions have increased from 6.3 ± 1.1 Tg N2 O-N yr-1 in the pre-industrial period (the 1860s) to 10.0 ± 2.0 Tg N2 O-N yr-1 in the recent decade (2007-2016). Cropland soil emissions increased from 0.3 Tg N2 O-N yr-1 to 3.3 Tg N2 O-N yr-1 over the same period, accounting for 82% of the total increase. Regionally, China, South Asia and Southeast Asia underwent rapid increases in cropland N2 O emissions since the 1970s. However, US cropland N2 O emissions had been relatively flat in magnitude since the 1980s, and EU cropland N2 O emissions appear to have decreased by 14%. Soil N2 O emissions from predominantly natural ecosystems accounted for 67% of the global soil emissions in the recent decade but showed only a relatively small increase of 0.7 ± 0.5 Tg N2 O-N yr-1 (11%) since the 1860s. In the recent decade, N fertilizer application, N deposition, manure N application and climate change contributed 54%, 26%, 15% and 24%, respectively, to the total increase. Rising atmospheric CO2 concentration reduced soil N2 O emissions by 10% through the enhanced plant N uptake, while land cover change played a minor role. Our estimation here does not account for indirect emissions from soils and the directed emissions from excreta of grazing livestock. To address uncertainties in estimating regional and global soil N2 O emissions, this study recommends several critical strategies for improving the process-based simulations.

Given the importance of the potential positive feedback between methane (CH4) emissions and climate change, it is critical to accurately estimate the magnitude and spatio-temporal patterns of CH4 emissions from global rice fields and better understand the underlying determinants governing the emissions. Here, we used a coupled biogeochemical model in combination with satellite-derived contemporary inundation area to quantify the magnitude and spatio-temporal variation of CH4 emissions from global rice fields and attribute the environmental controls of CH4 emissions during 1901-2010. Our study estimated that CH4 emissions from global rice fields varied from 18.3 ± 0.1 Tg CH4/yr (Avg. ± 1 std. dev.) under intermittent irrigation to 38.8 ± 1.0 Tg CH4/yr under continuous flooding in the 2000s, indicating that the magnitude of CH4 emissions from global rice fields was largely dependent on different water schemes. Over the past 110 years, our simulated results showed that global CH4 emissions from rice cultivation increased 85%. The expansion of rice fields was the dominant factor for the increasing trends of CH4 emissions, followed by elevated CO2 concentration, and nitrogen fertilizer use. On the contrary, climate had the negative effect on the cumulative CH4 emissions for most of the years over the study period. Our results imply that CH4 emissions from global rice fields could be reduced through implementing optimized irrigation practices. Therefore, the future magnitude of CH4 emissions from rice fields will be determined by the human demand for rice production as well as the implementation of optimized water management practices.

© 2016, Springer International Publishing Switzerland. Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO2seasonal uptake over agricultural regions.

Fire frequency, extent, and size exhibit a strong linkage with climate conditions and play a vital role in the climate system. Previous studies have shown that the frequency of large fires in the western United States increased significantly since the mid-1980s due to climate warming and frequent droughts. However, less work has been conducted to examine burned area and fire emissions of large fires at a national scale, and the underlying mechanisms accounting for the increases in the frequency of large fires are far from clear. In this study, we integrated remote-sensed fire perimeter and burn severity data sets into the Dynamic Land Ecosystem Model to estimate carbon emissions from large fires (i.e., fires with size larger than 1000 acres or 4.05 km2) in conterminous United States from 1984 to 2012. The results show that average area burned by large fires was 1.44 × 104 km2 yr−1 and carbon emissions from large fires were 17.65 Tg C yr−1 during the study period. According to the Mann-Kendall trend test, annual burned area and pyrogenic carbon emissions presented significant upward trends at the rates of 810 km2 yr−1 and 0.87 Tg C yr−1, respectively. Characteristic fire size (fire size with the largest contribution to the total burned area) in the period of 2004–2012 increased by 176.1% compared to the period of 1984–1993. We further found that the larger fires were associated with higher burn severity and occurred more frequently in the warmer and drier conditions. This finding implies that the continued warming and drying trends in the 21st century would enhance the total burned area and fire emissions due to the contributions of larger and more severe wildfires.

Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA’s Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model’s soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.

Excessive ammonia (NH3 ) emitted from nitrogen (N) fertilizer applications in global croplands plays an important role in atmospheric aerosol production, resulting in visibility reduction and regional haze. However, large uncertainty exists in the estimates of NH3 emissions from global and regional croplands, which utilize different data and methods. In this study, we have coupled a process-based Dynamic Land Ecosystem Model (DLEM) with the bidirectional NH3 exchange module in the Community Multiscale Air-Quality (CMAQ) model (DLEM-Bi-NH3 ) to quantify NH3 emissions at the global and regional scale, and crop-specific NH3 emissions globally at a spatial resolution of 0.5° × 0.5° during 1961-2010. Results indicate that global NH3 emissions from N fertilizer use have increased from 1.9 ± 0.03 to 16.7 ± 0.5 Tg N/year between 1961 and 2010. The annual increase of NH3 emissions shows large spatial variations across the global land surface. Southern Asia, including China and India, has accounted for more than 50% of total global NH3 emissions since the 1980s, followed by North America and Europe. Rice cultivation has been the largest contributor to total global NH3 emissions since the 1990s, followed by corn and wheat. In addition, results show that empirical methods without considering environmental factors (constant emission factor in the IPCC Tier 1 guideline) could underestimate NH3 emissions in context of climate change, with the highest difference (i.e., 6.9 Tg N/year) occurring in 2010. This study provides a robust estimate on global and regional NH3 emissions over the past 50 years, which offers a reference for assessing air quality consequences of future nitrogen enrichment as well as nitrogen use efficiency improvement.

Field measurements of aboveground net primary productivity (ANPP) in temperate grasslands suggest that both positive and negative asymmetric responses to changes in precipitation (P) may occur. Under normal range of precipitation variability, wet years typically result in ANPP gains being larger than ANPP declines in dry years (positive asymmetry), whereas increases in ANPP are lower in magnitude in extreme wet years compared to reductions during extreme drought (negative asymmetry). Whether the current generation of ecosystem models with a coupled carbon–water system in grasslands are capable of simulating these asymmetric ANPP responses is an unresolved question. In this study, we evaluated the simulated responses of temperate grassland primary productivity to scenarios of altered precipitation with 14 ecosystem models at three sites: Shortgrass steppe (SGS), Konza Prairie (KNZ) and Stubai Valley meadow (STU), spanning a rainfall gradient from dry to moist. We found that (1) the spatial slopes derived from modeled primary productivity and precipitation across sites were steeper than the temporal slopes obtained from inter-annual variations, which was consistent with empirical data; (2) the asymmetry of the responses of modeled primary productivity under normal inter-annual precipitation variability differed among models, and the mean of the model ensemble suggested a negative asymmetry across the three sites, which was contrary to empirical evidence based on filed observations; (3) the mean sensitivity of modeled productivity to rainfall suggested greater negative response with reduced precipitation than positive response to an increased precipitation under extreme conditions at the three sites; and (4) gross primary productivity (GPP), net primary productivity (NPP), aboveground NPP (ANPP) and belowground NPP (BNPP) all showed concave-down nonlinear responses to altered precipitation in all the models, but with different curvatures and mean values. Our results indicated that most models overestimate the negative drought effects and/or underestimate the positive effects of increased precipitation on primary productivity under normal climate conditions, highlighting the need for improving eco-hydrological processes in those models in the future.

It is highly uncertain on how human and natural environmental factors have altered methane (CH 4 ) emissions from terrestrial ecosystems in India. Using a process-based, Dynamic Land Ecosystem Model (DLEM) driven by climate, land cover and land use change (LCLUC), atmospheric nitrogen deposition (NDEP), atmospheric carbon dioxide (CO 2 ) concentration, and tropospheric ozone (O 3 ) pollution, we examined CH 4 flux from terrestrial in India during 1901–2010. The DLEM simulations have shown that total CH 4 flux over the country ranged from 2.9 Tg C year −1 to 6.5 Tg C year −1 with significant inter-annual variations driven by climate during 1901–2010. Contemporary CH 4 emissions have primarily occurred from rice fields (3.9 ± 0.9 Tg C year −1 ) while wetlands contributed to 2.1 ± 0.6 Tg C year −1 in the 2000s. During 1901–2010, total CH 4 emission from the terrestrial biosphere has increased by ∼2.1 Tg C year −1 . LCLUC has increased CH 4 emissions by 2.3 Tg C year −1 primarily due to increase in the rice-based cropping systems as well as irrigation expansion during the study period. Elevated CO 2 concentration stimulated plant biomass production in both rice fields and wetlands that increased CH 4 emissions by 0.7 Tg C year −1 . On the contrary, climate change decreased net CH 4 emissions by ∼1.2 Tg C year −1 due to negative effects of extreme high temperature as well as occurrences of extreme drought events on plant growth. Our study suggests that LCLUC and elevated CO 2 concentration have significantly increased CH 4 emissions from terrestrial ecosystems in India.

India is very important but relatively unexplored region in terms of carbon studies, where significant environmental changes have occurred in the 20th century that can alter terrestrial net primary productivity (NPP). Here, we used a process-based, Dynamic Land Ecosystem Model (DLEM), driven by land cover and land use change (LCLUC), climate change, elevated atmospheric CO2 concentration, atmospheric nitrogen deposition (NDEP), and tropospheric ozone (O3) pollution to estimate terrestrial NPP in India during 1901–2010. Over the country, terrestrial NPP showed significant inter-annual variations ranging 1.2 Pg C year−1 to 1.7 Pg C year−1 during the 1901–2010. Overall, multiple environmental changes have increased terrestrial NPP by 0.23 Pg C year−1. Elevated atmospheric CO2 concentration has increased NPP by 0.29 Pg C; however climate change has offset a portion of terrestrial NPP (0.11 Pg C) during this study period. On an average, terrestrial NPP reduced by 0.12 Pg C year−1 in drought years; when precipitation was 100 mm year−1 lower than long term average, suggesting that terrestrial carbon cycle in India is strongly linked to climate change. LCLUC, including land conversions and cropland management practices, increased terrestrial NPP by 0.043 Pg C year−1 over the country. Tropospheric O3 pollution reduced terrestrial NPP by 0.06 Pg C year−1 and the decrease was comparatively higher in croplands than other biomes after the 1980s. Our results have shown that climate change and tropospheric O3 pollution may partially offset terrestrial NPP increase caused by elevated CO2 concentration, LCLUC, and NDEP over India.

Terrestrial net primary production (NPP) is of fundamental importance to food security and ecosystem sustainability. However, little is known about how terrestrial NPP in African ecosystems has responded to recent changes in climate and other environmental factors. Here, we used an integrated ecosystem model (the dynamic land ecosystem model; DLEM) to simulate the dynamic variations in terrestrial NPP of African ecosystems driven by climate and other environmental factors during 1980–2009. We estimate a terrestrial NPP of 10.22 (minimum–maximum range of 8.9–11.3) Pg C/yr during the study period. Our results show that precipitation variability had a significant effect on terrestrial NPP, explaining 74% of interannual variations in NPP. Over the 30‐yr period, African ecosystems experienced an increase in NPP of 0.03 Pg C/yr, resulting from the combined effects of climate variability, elevated atmospheric CO 2 concentration, and nitrogen deposition. Our further analyses show that there is a difference in NPP of 1.6 Pg C/yr between wet and dry years, indicating that interannual climatic variations play an important role in determining the magnitude of terrestrial NPP. Central Africa, dominated by tropical forests, was the most productive region and accounted for 50% of the carbon sequestered as NPP in Africa. Our results indicate that warmer and wetter climatic conditions, together with elevated atmospheric CO 2 concentration and nitrogen deposition, have resulted in a significant increase in African terrestrial NPP during 1980–2009, with the largest contribution from tropical forests.

a b s t r a c t Significant changes in the water cycle are expected under current global environmental change. Robust assessment of present-day water cycle dynamics at continental to global scales is confounded by short- comings in the observed record. Modeled assessments also yield conflicting results which are linked to differences in model structure and simulation protocol. Here we compare simulated gridded (1 ◦ spatial resolution) runoff from six terrestrial biosphere models (TBMs), seven reanalysis products, and one grid- ded surface station product in the contiguous United States (CONUS) from 2001 to 2005. We evaluate the consistency of these 14 estimates with stream gauge data, both as depleted flow and corrected for net withdrawals (2005 only), at the CONUS and water resource region scale, as well as examining similarity across TBMs and reanalysis products at the grid cell scale. Mean runoff across all simulated products and regions varies widely (range: 71 to 356 mm yr −1 ) relative to observed continental-scale runoff (209 or 280 mm yr −1

Simulating the magnitude and variability of terrestrial methane sources and sinks poses a challenge to ecosystem models because the biophysical and biogeochemical processes that lead to methane emissions from terrestrial and freshwater ecosystems are, by their nature, episodic and spatially disjunct. As a consequence, model predictions of regional methane emissions based on field campaigns from short eddy covariance towers or static chambers have large uncertainties, because measurements focused on a particular known source of methane emission will be biased compared to regional estimates with regards to magnitude, spatial scale, or frequency of these emissions. Given the relatively large importance of predicting future terrestrial methane fluxes for constraining future atmospheric methane growth rates, a clear need exists to reduce spatiotemporal uncertainties. In 2010, an Ameriflux tower (US-PFa) near Park Falls, WI, USA, was instrumented with closed-path methane flux measurements at 122 m above groundin a mixed wetland-upland landscape representative of the Great Lakes region. Two years of flux observations revealed an average annual methane (CH4) efflux of 785 ± 75 mg C-CH4m-2yr-1, compared to a mean CO2 sink of -80 g C-CO2m-2yr-1, a ratio of 1% in magnitude on a mole basis. Interannual variability in methane flux was 30% of the mean flux and driven by suppression of methane emissions during dry conditions in late summer 2012. Though relatively small, the magnitude of the methane source from the very tall tower measurements was mostly within the range previously measured using static chambers at nearby wetlands, but larger than a simple scaling of those fluxes to the tower footprint. Seasonal patterns in methane fluxes were similar to those simulated in the Dynamic Land Ecosystem Model (DLEM), but magnitude depends on model parameterization and input data, especially regarding wetland extent. The model was unable to simulate short-term (sub-weekly) variability. Temperature was found to be a stronger driver of regional CH4 flux than moisture availability or net ecosystem production at the daily to monthly scale. Taken together, these results emphasize the multi-timescale dependence of drivers of regional methane flux and the importance of long, continuous time series for their characterization.

A wide variety of studies have revealed a substantial increase in nitrogen (N) deposition in China, but the lack of spatially-explicit time-series N deposition data set has long hindered us from assessing the impacts of atmospheric N input on ecosystem services. In this study, we combined site-level monitoring, gridded precipitation data and atmospheric transport modeling results to generate annual N bulk deposition data in China with a spatial resolution of 10 km × 10 km and a time span from 1961 to 2008. It shows that national average N deposition rate had large interannual variation, and it increased by 59%, from 12.64 kg N ha−1 yr−1 in the 1960s to 20.07 kg N ha−1 yr−1 in the recent decade, with the most rapid increase centered in the southeastern China that is already N-enriched. Large spatial variation as well as dry deposition input has to be taken into account when estimating the amount of N deposited onto land surface of China. The spatial and temporal information on N deposition derived from this study could be used by ecosystem, hydrological, and climate modeling as well as by policy makers for assessing the impacts of nitrogen enrichment on regional climate, water resources, and biogeochemical cycles.

Given the important role of nitrogen input from livestock system in the terrestrial nutrient cycles and the atmospheric chemical composition, it is vital to have a robust estimation of the magnitude, spatiotemporal variation of manure nitrogen production and the application to cropland and rangeland across the globe. In this study, we used the dataset from Global Livestock Impact Mapping System (GLIMS) in conjunction with country-specific annual livestock population to reconstruct the manure nitrogen production from 1860 to 2014. The estimated manure nitrogen production increased from 21.4 Tg N yr−1 in 1860 to 131.0 Tg N yr−1 in 2014, with a significant increasing trend during 1860–2014 (0.7 Tg N yr−1, p < 0.01). Changes in manure nitrogen production exhibited highly spatial variability and concentrated in several hotspots (e.g., Western Europe, India, Northeast China and Southeast Australia) across the globe over the study period. In the 1860s, northern mid-latitude accounted for ~ 52 % of the global total manure production, while tropical region became the largest share (~ 48 %) in the recent five years (2010–2014). Among all the continents, Asia accounted for over one-fourth of the global manure production during 1860–2014. Cattle dominated the manure nitrogen production and contributed ~ 44 % of the total manure nitrogen production in 2014, followed by goat, sheep, chicken and swine. The manure nitrogen production applied to cropland and rangeland accounts for less than one-fifth of the total manure nitrogen production over the study period. The 5-arc minute gridded global data set of manure nitrogen production generated from this study could be used as an input for global or regional land surface/ecosystem models to evaluate the impacts of manure nitrogen on key biogeochemical processes and water quality, and the best management practices of manure nitrogen applications to cropland and rangeland across the globe could be important for food security and environmental sustainability. Datasets available at: doi:10.1594/PANGAEA.871980.

Increasing atmospheric methane (CH4) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999–2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH4 emissions from wetlands, the largest natural global CH4 source, for 2000–2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000–2012, boreal wetland CH4 emissions increased by 1.2 Tg yr−1 (−0.2–3.5 Tg yr−1), tropical emissions decreased by 0.9 Tg yr−1 (−3.2−1.1 Tg yr−1), yet globally, emissions remained unchanged at 184 ± 22 Tg yr−1. Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH4 emissions have not contributed significantly to the period of renewed atmospheric CH4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH4 emissions, and a decrease in the atmospheric oxidative sink.