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Abstract Over the past 100 years, human activity has greatly changed the rate of atmospheric N (nitrogen) deposition in terrestrial ecosystems, resulting in N saturation in some regions of the world. The contribution of N saturation to the global carbon budget remains uncertain due to the complicated nature of C-N (carbon-nitrogen) interactions and diverse geography. Although N deposition is included in most terrestrial ecosystem models, the effect of N saturation is frequently overlooked. In this study, the IBIS (Integrated BIosphere Simulator) was used to simulate the global-scale effects of N saturation during the period 1961–2009. The results of this model indicate that N saturation reduced global NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) by 0.26 and 0.03 Pg C yr −1 , respectively. The negative effects of N saturation on carbon sequestration occurred primarily in temperate forests and grasslands. In response to elevated CO 2 levels, global N turnover slowed due to increased biomass growth, resulting in a decline in soil mineral N. These changes in N cycling reduced the impact of N saturation on the global carbon budget. However, elevated N deposition in certain regions may further alter N saturation and C-N coupling.

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 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 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.