Votre recherche

Abstract Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-N mass -LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.

Tropical rainforest ecosystems are important when considering the global methane (CH4) budget and in climate change mitigation. However, there is a lack of direct and year-round observations of ecosystem-scale CH4 fluxes from tropical rainforest ecosystems. In this study, we examined the temporal variations in CH4 flux at the ecosystem scale and its annual budget and environmental controlling factors in a tropical rainforest of Hainan Island, China, using 3 years of continuous eddy covariance measurements from 2016 to 2018. Our results show that CH4 uptake generally occurred in this tropical rainforest, where strong CH4 uptake occurred in the daytime, and a weak CH4 uptake occurred at night with a mean daily CH4 flux of −4.5 nmol m−2 s−1. In this rainforest, the mean annual budget of CH4 for the 3 years was −1260 mg CH4 m−2 year−1. Furthermore, the daily averaged CH4 flux was not distinctly different between the dry season and wet season. Sixty-nine percent of the total variance in the daily CH4 flux could be explained by the artificial neural network (ANN) model, with a combination of air temperature (Tair), latent heat flux (LE), soil volumetric water content (VWC), atmospheric pressure (Pa), and soil temperature at −10 cm (Tsoil), although the linear correlation between the daily CH4 flux and any of these individual variables was relatively low. This indicates that CH4 uptake in tropical rainforests is controlled by multiple environmental factors and that their relationships are nonlinear. Our findings also suggest that tropical rainforests in China acted as a CH4 sink during 2016–2018, helping to counteract global warming.

Abstract Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO 2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.

Abstract With a pace of about twice the observed rate of global warming, the temperature on the Qinghai‐Tibetan Plateau (Earth's ‘third pole’) has increased by 0.2 °C per decade over the past 50 years, which results in significant permafrost thawing and glacier retreat. Our review suggested that warming enhanced net primary production and soil respiration, decreased methane ( CH 4 ) emissions from wetlands and increased CH 4 consumption of meadows, but might increase CH 4 emissions from lakes. Warming‐induced permafrost thawing and glaciers melting would also result in substantial emission of old carbon dioxide ( CO 2 ) and CH 4 . Nitrous oxide ( N 2 O ) emission was not stimulated by warming itself, but might be slightly enhanced by wetting. However, there are many uncertainties in such biogeochemical cycles under climate change. Human activities (e.g. grazing, land cover changes) further modified the biogeochemical cycles and amplified such uncertainties on the plateau. If the projected warming and wetting continues, the future biogeochemical cycles will be more complicated. So facing research in this field is an ongoing challenge of integrating field observations with process‐based ecosystem models to predict the impacts of future climate change and human activities at various temporal and spatial scales. To reduce the uncertainties and to improve the precision of the predictions of the impacts of climate change and human activities on biogeochemical cycles, efforts should focus on conducting more field observation studies, integrating data within improved models, and developing new knowledge about coupling among carbon, nitrogen, and phosphorus biogeochemical cycles as well as about the role of microbes in these cycles.

Abstract Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH 4 ) emissions in China is important for improving our knowledge on CH 4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH 4 model to quantify the human and climate change induced contributions to natural wetland CH 4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH 4 emissions reduction (0.92 TgCH 4 ), and climate change contributed 20.4% to the CH 4 emissions increase (0.31 TgCH 4 ), suggesting that decreasing CH 4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH 4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH 4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH 4 emissions estimation.

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 Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature‐based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr −1 , respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time‐varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr −1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models. , Key Points Large uncertainty exists in estimates of terrestrial NPP and NBP in China Methodological differences greatly contribute to the uncertainty in NPP and NBP Uncertainty in the interannual pattern of NBP is greater than that of NPP

Abstract Process‐based land surface models are important tools for estimating global wetland methane (CH 4 ) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site‐level patterns of freshwater wetland CH 4 fluxes (FCH 4 ) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model‐observation disagreements are mainly at multi‐day time scales (<15 days); (b) most of the models can capture the CH 4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH 4 production). Our evaluation suggests the need to accurately replicate FCH 4 variability, especially at short time scales, in future wetland CH 4 model developments. , Plain Language Summary Land surface models are useful tools to estimate and predict wetland methane (CH 4 ) flux but there is no evaluation of modeled CH 4 flux error at different time scales. Here we use a statistical approach and observations from eddy covariance sites to evaluate the performance of seven wetland models for different wetland types. The results suggest models have captured CH 4 flux variability at monthly or seasonal time scales for boreal and Arctic tundra wetlands but failed to capture the observed seasonal variability for temperate and tropical/subtropical wetlands. The analysis suggests that improving modeled flux at short time scale is important for future model development. , Key Points Significant model‐observation disagreements were found at multi‐day and weekly time scales (<15 days) Models captured variability at monthly and seasonal time (42–142 days) scales for boreal and Arctic tundra sites but not for temperate and tropical sites The model errors show that biases at multi‐day time scales may contribute to persistent systematic biases on longer time scales

Abstract The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.

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.