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Abstract Cold‐season methane (CH 4 ) emissions may be poorly constrained in wetland models. We examined cold‐season CH 4 emissions simulated by 16 models participating in the Global Carbon Project model intercomparison and analyzed temporal and spatial patterns in simulation results using prescribed inundation data for 2000–2020. Estimated annual CH 4 emissions from northern (>60°N) wetlands averaged 10.0 ± 5.5 Tg CH 4  yr −1 . While summer CH 4 emissions were well simulated compared to in‐situ flux measurement observations, the models underestimated CH 4 during September to May relative to annual total (27 ± 9%, compared to 45% in observations) and substantially in the months with subzero air temperatures (5 ± 5%, compared to 27% in observations). Because of winter warming, nevertheless, the contribution of cold‐season emissions was simulated to increase at 0.4 ± 0.8% decade −1 . Different parameterizations of processes, for example, freezing–thawing and snow insulation, caused conspicuous variability among models, implying the necessity of model refinement. , Plain Language Summary Wetlands in the northern high latitudes are a major source of methane (CH 4 ) to the atmosphere, mainly during the warm season. Previously, models have assumed that cold‐season CH 4 emissions are low, but recent observations suggest high‐latitude wetlands can be substantial sources even in winter. We compared CH 4 emissions simulated by 16 state‐of‐the‐art wetland models, participating in a model intercomparison project with a focus on the cold‐season in northern wetlands. The model simulations indicated that nearly one third of annual emissions were simulated to occur from September to May, and CH 4 emissions to the atmosphere were not negligible even under freezing air temperatures, although the results differed greatly among the models. However, field studies suggest cold‐season emissions account for an even larger fraction of annual emissions. These results highlight the contribution of cold‐season emissions to the annual CH 4 budget, which future climatic warming is expected to affect severely, and they also show that simulations of cold‐season CH 4 emissions from wetlands need to be improved. , Key Points Cold‐season methane (CH 4 ) emissions simulated by 16 Global Carbon Project‐CH 4 wetland models were analyzed Most models underestimate the cold‐season emissions in comparison with observational data Further model improvement by including cold‐season processes is required to reduce the model bias and uncertainty

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 Precipitation changes modify C, N, and P cycles, which regulate the functions and structure of terrestrial ecosystems. Although altered precipitation affects above‐ and belowground C:N:P stoichiometry, considerable uncertainties remain regarding plant–microbial nutrient allocation strategies under increased (IPPT) and decreased (DPPT) precipitation. We meta‐analyzed 827 observations from 235 field studies to investigate the effects of IPPT and DPPT on the C:N:P stoichiometry of plants, soils, and microorganisms. DPPT reduced leaf C:N ratio, but increased the leaf and root N:P ratios reflecting stronger decrease of P compared with N mobility in soil under drought. IPPT increased microbial biomass C (+13%), N (+15%), P (26%), and the C:N ratio, whereas DPPT decreased microbial biomass N (−12%) and the N:P ratio. The C:N and N:P ratios of plant leaves were more sensitive to medium DPPT than to IPPT because drought increased plant N content, particularly in humid areas. The responses of plant and soil C:N:P stoichiometry to altered precipitation did not fit the double asymmetry model with a positive asymmetry under IPPT and a negative asymmetry under extreme DPPT. Soil microorganisms were more sensitive to IPPT than to DPPT, but they were more sensitive to extreme DPPT than extreme IPPT, consistent with the double asymmetry model. Soil microorganisms maintained stoichiometric homeostasis, whereas N:P ratios of plants follow that of the soils under altered precipitation. In conclusion, specific N allocation strategies of plants and microbial communities as well as N and P availability in soil critically mediate C:N:P stoichiometry by altered precipitation that need to be considered by prediction of ecosystem functions and C cycling under future climate change scenarios.

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

Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM 2.5 ) due to their low cost, wide coverage, and all-weather capability. Estimation of PM 2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM 2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons ( p < 0.01) and different spatial resolutions ( p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM 2.5 monitoring stations and historical PM 2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.

Climate change scenarios established by the Intergovernmental Panel on Climate Change have developed a significant tool for analyzing, modeling, and predicting future climate change impacts in different research fields after more than 30 years of development and refinement. In the wake of future climate change, the changes in forest structure and functions have become a frontier and focal area of global change research. This study mainly reviews and synthesizes climate change scenarios and their applications in forest ecosystem research over the past decade. These applications include changes in (1) forest structure and spatial vegetation distribution, (2) ecosystem structure, (3) ecosystem services, and (4) ecosystem stability. Although climate change scenarios are useful for predicting future climate change impacts on forest ecosystems, the accuracy of model simulations needs to be further improved. Based on existing studies, climate change scenarios are used in future simulation applications to construct a biomonitoring network platform integrating observations and predictions for better conservation of species diversity.

Abstract Globally, livestock grazing is an important management factor influencing soil degradation, soil health and carbon (C) stocks of grassland ecosystems. However, the effects of grassland types, grazing intensity and grazing duration on C stocks are unclear across large geographic scales. To provide a more comprehensive assessment of how grazing drives ecosystem C stocks in grasslands, we compiled and analyzed data from 306 studies featuring four grassland types across China: desert steppes, typical steppes, meadow steppes and alpine steppes. Light grazing was the best management practice for desert steppes (< 2 sheep ha −1 ) and typical steppes (3 to 4 sheep ha −1 ), whereas medium grazing pressure was optimal for meadow steppes (5 to 6 sheep ha −1 ) and alpine steppes (7 to 8 sheep ha −1 ) leading to the highest ecosystem C stocks under grazing. Plant biomass (desert steppes) and soil C stocks (meadow steppes) increased under light or medium grazing, confirming the ‘ intermediate disturbance hypothesis ’. Heavy grazing decreased all C stocks regardless of grassland ecosystem types, approximately 1.4 Mg ha −1 per year for the whole ecosystem. The regrowth and regeneration of grasslands in response to grazing intensity (i.e., grazing optimization ) depended on grassland types and grazing duration. In conclusion, grassland grazing is a double-edged sword. On the one hand, proper management (light or medium grazing) can maintain and even increase C stocks above- and belowground, and increase the harvested livestock products from grasslands. On the other hand, human-induced overgrazing can lead to rapid degradation of vegetation and soils, resulting in significant carbon loss and requiring long-term recovery. Grazing regimes (i.e., intensity and duration applied) must consider specific grassland characteristics to ensure stable productivity rates and optimal impacts on ecosystem C stocks. Graphical Abstract