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Significance Understanding the location of carbon sources and sinks is essential for accurately predicting future changes in atmospheric carbon dioxide and climate. Mid- to high-latitude terrestrial ecosystems are well known to be the principal carbon sink regions, yet less attention has been paid to the mid- to low-latitude ecosystems. In this study, long-term eddy covariance observations demonstrate that there is a high carbon dioxide uptake (net ecosystem productivity) by the mid- to low-latitude East Asian monsoon subtropical forests that were shaped by the uplift of the Tibetan Plateau. Increasing nitrogen deposition, a young forest age structure, and sufficient water and heat availability combined to contribute to this large carbon dioxide uptake. , Temperate- and high-latitude forests have been shown to contribute a carbon sink in the Northern Hemisphere, but fewer studies have addressed the carbon balance of the subtropical forests. In the present study, we integrated eddy covariance observations established in the 1990s and 2000s to show that East Asian monsoon subtropical forests between 20°N and 40°N represent an average net ecosystem productivity (NEP) of 362 ± 39 g C m −2 yr −1 (mean ± 1 SE). This average forest NEP value is higher than that of Asian tropical and temperate forests and is also higher than that of forests at the same latitudes in Europe–Africa and North America. East Asian monsoon subtropical forests have comparable NEP to that of subtropical forests of the southeastern United States and intensively managed Western European forests. The total NEP of East Asian monsoon subtropical forests was estimated to be 0.72 ± 0.08 Pg C yr −1 , which accounts for 8% of the global forest NEP. This result indicates that the role of subtropical forests in the current global carbon cycle cannot be ignored and that the regional distributions of the Northern Hemisphere's terrestrial carbon sinks are needed to be reevaluated. The young stand ages and high nitrogen deposition, coupled with sufficient and synchronous water and heat availability, may be the primary reasons for the high NEP of this region, and further studies are needed to quantify the contribution of each underlying factor.

Moso bamboo forests have greater net carbon uptake benefits with increasing nitrogen deposition in the coming decades. , Atmospheric nitrogen (N) deposition affects the greenhouse gas (GHG) balance of ecosystems through the net atmospheric CO 2 exchange and the emission of non-CO 2 GHGs (CH 4 and N 2 O). We quantified the effects of N deposition on biomass increment, soil organic carbon (SOC), and N 2 O and CH 4 fluxes and, ultimately, the net GHG budget at ecosystem level of a Moso bamboo forest in China. Nitrogen addition significantly increased woody biomass increment and SOC decomposition, increased N 2 O emission, and reduced soil CH 4 uptake. Despite higher N 2 O and CH 4 fluxes, the ecosystem remained a net GHG sink of 26.8 to 29.4 megagrams of CO 2 equivalent hectare −1 year −1 after 4 years of N addition against 22.7 hectare −1 year −1 without N addition. The total net carbon benefits induced by atmospheric N deposition at current rates of 30 kilograms of N hectare −1 year −1 over Moso bamboo forests across China were estimated to be of 23.8 teragrams of CO 2 equivalent year −1 .

Abstract Interannual air temperature variability has changed over some regions in Northern Hemisphere (NH), accompanying with climate warming. However, whether and to what extent it regulates the interannual sensitivity of vegetation growth to temperature variability (i.e., interannual temperature sensitivity)—one central issue in understanding and predicting the responses of vegetation growth to changing climate—still remains poorly quantified and understood. Here we quantify the relationships between the interannual temperature sensitivity of mean growing‐season (April–October) normalized difference vegetation index (NDVI) and ecosystem model simulations of gross primary productivity (GPP), and variability in mean growing‐season temperature for forest, shrub, and grass over NH. We find that higher interannual variability in mean growing‐season temperature leads to consistent decrease in interannual temperature sensitivity of mean growing‐season NDVI among all vegetation types but not in model simulations of GPP. Drier condition associates with ~130 ± 150% further decrease in interannual temperature sensitivity of mean growing‐season NDVI by temperature variability in forest and shrub. These results illustrate that varying temperature variability can significantly regulate the interannual temperature sensitivity of vegetation growth over NH, interacted with drought variability and nonlinear responses of photosynthesis to temperature. Our findings call for an improved characterization of the nonlinear effects of temperature variability on vegetation growth within global ecosystem models. , Key Points It shows consistent decrease in temperature sensitivity of vegetation growth by temperature variability for all vegetation types Larger decrease in temperature sensitivity of vegetation growth by temperature variability is found in forest and shrub in dry regions Drier condition adds further decrease in temperature sensitivity of vegetation growth by temperature variability for forest and shrub in dry regions

Abstract Methane (CH 4 ) emissions from tropical wetlands contribute 60%–80% of global natural wetland CH 4 emissions. Decreased wetland CH 4 emissions can act as a negative feedback mechanism for future climate warming and vice versa. The impact of the El Niño–Southern Oscillation (ENSO) on CH 4 emissions from wetlands remains poorly quantified at both regional and global scales, and El Niño events are expected to become more severe based on climate models’ projections. We use a process‐based model of global wetland CH 4 emissions to investigate the impacts of the ENSO on CH 4 emissions in tropical wetlands for the period from 1950 to 2012. The results show that CH 4 emissions from tropical wetlands respond strongly to repeated ENSO events, with negative anomalies occurring during El Niño periods and with positive anomalies occurring during La Niña periods. An approximately 8‐month time lag was detected between tropical wetland CH 4 emissions and ENSO events, which was caused by the combined time lag effects of ENSO events on precipitation and temperature over tropical wetlands. The ENSO can explain 49% of interannual variations for tropical wetland CH 4 emissions. Furthermore, relative to neutral years, changes in temperature have much stronger effects on tropical wetland CH 4 emissions than the changes in precipitation during ENSO periods. The occurrence of several El Niño events contributed to a lower decadal mean growth rate in atmospheric CH 4 concentrations throughout the 1980s and 1990s and to stable atmospheric CH 4 concentrations from 1999 to 2006, resulting in negative feedback to global warming.

Abstract Our understanding and quantification of global soil nitrous oxide (N 2 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 CO 2 concentration, on global soil N 2 O emissions for the period 1861–2016 using a standard simulation protocol with seven process‐based terrestrial biosphere models. Results suggest global soil N 2 O emissions have increased from 6.3 ± 1.1 Tg N 2 O‐N/year in the preindustrial period (the 1860s) to 10.0 ± 2.0 Tg N 2 O‐N/year in the recent decade (2007–2016). Cropland soil emissions increased from 0.3 Tg N 2 O‐N/year to 3.3 Tg N 2 O‐N/year over the same period, accounting for 82% of the total increase. Regionally, China, South Asia, and Southeast Asia underwent rapid increases in cropland N 2 O emissions since the 1970s. However, US cropland N 2 O emissions had been relatively flat in magnitude since the 1980s, and EU cropland N 2 O emissions appear to have decreased by 14%. Soil N 2 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 N 2 O‐N/year (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 CO 2 concentration reduced soil N 2 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 N 2 O emissions, this study recommends several critical strategies for improving the process‐based simulations.

Abstract Increasing atmospheric methane (CH 4 ) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH 4 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 CH 4 emissions from wetlands, the largest natural global CH 4 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 CH 4 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 CH 4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH 4 emissions have not contributed significantly to the period of renewed atmospheric CH 4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH 4 emissions, and a decrease in the atmospheric oxidative sink.

Abstract Nitrous oxide (N 2 O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and spatiotemporal patterns of N 2 O fluxes and the key drivers of N 2 O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N 2 O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N 2 O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N 2 O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N 2 O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N 2 O dynamics; and 3) provide a benchmarking estimate of N 2 O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N 2 O estimation derived from the NMIP is a key component of the global N 2 O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.

Abstract 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).

Abstract 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 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales 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.

Abstract. 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.

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.