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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 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 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

The NEBIE plot network is a stand-scale, multi-agency research project designed to compare the ecological effects of a range of silvicultural treatments in northern temperate and boreal forest regions of Ontario, Canada. While research on silviculture intensities has been previously conducted, the NEBIE plot network is at a larger scale, and covers a wider range of intensities in a variety of northern temperate and boreal forest types. Details about experimental design, treatment designs and research sites, are presented in a companion paper which is published in this edition of The Forestry Chronicle. The operational scale of treatment plots allow for assessment of a variety of forest values. We used a criteria and indicator approach to organize long-term research studies on the network sites, with the goal of providing scientific findings that would inform forest policy. Pre-treatment, and 2-, 5-, and 10-year post-harvesting data have been collected. These initial data add to existing information on the effects of intensification of silviculture on biological diversity, forest productivity, ecosystem health and vitality, soil and water resources, contribution of enhanced forest management global carbon cycles, and long-term multiple socio-economic benefits of northern forests.

Abstract Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process‐based model TRIPLEX‐GHG was developed by coupling it with the new MEND (Microbial‐ENzyme‐mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX‐MICROBE) shows considerable improvement over the previous version (TRIPLEX‐GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well‐regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated by Xu et al. (2014). We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC), and mineral‐associated organic carbon (MOC). However, our work represents the first step toward a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles. , Key Points Traditional soil carbon models are lacking in their representation of key microbial processes that control the soil carbon response to global climate change A Ecosystem model (TRIPLEX‐MICROBE) offers considerable improvement over a previous version (TRIPLEX‐GHG) in simulating soil organic carbon Our work is the first step toward a new generation of ecosystem process models that integrate key microbial processes into soil carbon cycles

Abstract Over the last few decades, there has been an increasing number of controlled‐manipulative experiments to investigate how plants and soils might respond to global change. These experiments typically examined the effects of each of three global change drivers [i.e., nitrogen (N) deposition, warming, and elevated CO 2 ] on primary productivity and on the biogeochemistry of carbon (C), N, and phosphorus (P) across different terrestrial ecosystems. Here, we capitalize on this large amount of information by performing a comprehensive meta‐analysis (>2000 case studies worldwide) to address how C:N:P stoichiometry of plants, soils, and soil microbial biomass might respond to individual vs. combined effects of the three global change drivers. Our results show that (i) individual effects of N addition and elevated CO 2 on C:N:P stoichiometry are stronger than warming, (ii) combined effects of pairs of global change drivers (e.g., N addition + elevated CO 2 , warming + elevated CO 2 ) on C:N:P stoichiometry were generally weaker than the individual effects of each of these drivers, (iii) additive interactions (i.e., when combined effects are equal to or not significantly different from the sum of individual effects) were more common than synergistic or antagonistic interactions, (iv) C:N:P stoichiometry of soil and soil microbial biomass shows high homeostasis under global change manipulations, and (v) C:N:P responses to global change are strongly affected by ecosystem type, local climate, and experimental conditions. Our study is one of the first to compare individual vs. combined effects of the three global change drivers on terrestrial C:N:P ratios using a large set of data. To further improve our understanding of how ecosystems might respond to future global change, long‐term ecosystem‐scale studies testing multifactor effects on plants and soils are urgently required across different world regions.