Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH₄). Increased wetland CH₄ emissions could act as a positive feedback to future warming. The Wetland and Wetland CH₄ Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH₄ emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO₂) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO₂ concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. <br><br> Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH₄ emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH₄ emissions, but large variation between the models remains. For annual global CH₄ emissions, the models vary by ±40% of the all-model mean (190 Tg CH₄ yr⁻¹). Second, all models show a strong positive response to increased atmospheric CO₂ concentrations (857 ppm) in both CH₄ emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH₄ fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH₄ fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale c...

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This is a repository copy of Acceleration of global N2O emissions seen from two decades of atmospheric inversion.

We undertook this study to increase our understanding of how forest clearing for pasture in the Brazilian Amazon is affecting N20 fluxes from tropical soils. More specifically, we wanted to do four things: (1) develop an understanding of the dynamics of N20 fluxes during pasture establishment in the Amazon; (2) evaluate the finding that intact forests have 34,179

Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH₄). Increased wetland CH₄ emissions could act as a positive feedback to future warming. The Wetland and Wetland CH₄ Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large scale wetland characteristics and corresponding CH₄ emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO₂) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO₂ concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location with some models simulating wetland area prognostically, while other models relied on remotely-sensed inundation datasets, or an approach intermediate between the two. <br><br> Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH₄ emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH₄ emissions, but large variation between the models remains. For annual global CH₄ emissions, the models vary by ±40 % of the all model mean (190 Tg CH₄ yr^&minus;1). Second, all models show a strong positive response to increased atmospheric CO₂ concentrations (857 ppm) in both CH₄ emissions and wetland area. In response to increasing global temperatures (+3.4 % globally spatially uniform), on average, the models decreased wetland area and CH₄ fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH₄ fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatia...

We use the Terrestrial Ecosystem Model (TEM, Version 4.1) and the land cover data set of the international geosphere-biosphere program to investigate how increasing atmospheric CO 2 concentration and climate variability during 1900-1994 affect the carbon storage of terrestrial ecosystems in the conterminous USA, and how carbon storage has been affected by land-use

Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for Alaska, we 5 provide a baseline of terrestrial carbon cycle structural and parametric uncertainty, defined as the multi-model standard deviation () against the mean (x) for each quantity. Mean annual uncertainty (/x) was largest for net ecosystem exchange (NEE) (−0.01±0.19 kgCm−2 yr−1), then net primary production (NPP) (0.14± 0.33 kgCm−2 yr−1), autotrophic respiration (Ra) (0.09±0.20 kgCm−2 yr−1), gross pri10 mary production (GPP) (0.22±0.50 kgCm−2 yr−1), ecosystem respiration (Re) (0.23± 0.38 kgCm−2 yr−1), CH4 flux (2.52±4.02 gCH4m−2 yr−1), heterotrophic respiration (Rh) (0.14±0.20 kgCm−2 yr−1), and soil carbon (14.0±9.2 kgCm−2). The spatial patterns in regional carbon stocks and fluxes varied widely with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as 15 carbon neutral. Additionally, a feedback (i.e., sensitivity) analysis was conducted of 20th century NEE to CO2 fertilization () and climate ( ), which showed that uncertainty in was 2x larger than that of , with neither indicating that the Alaskan Arctic is shifting towards a certain net carbon sink or source. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE 20 was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic

Abstract. Wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of methane emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This risk is particularly relevant at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over the next 100 years. However, global models disagree as to the magnitude and spatial distribution of emissions, due to uncertainties in wetland area and emissions per unit area and a scarcity of in situ observations. Recent intensive field campaigns across the West Siberian Lowland (WSL) make this an ideal region over which to assess the performance of large-scale process-based wetland models in a high-latitude environment. Here we present the results of a follow-up to the Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP), focused on the West Siberian Lowland (WETCHIMP-WSL). We assessed 21 models and 5 inversions over this domain in terms of total CH4 emissions, simulated wetland areas, and CH4 fluxes per unit wetland area and compared these results to an intensive in situ CH4 flux dataset, several wetland maps, and two satellite inundation products. We found that: (a) despite the large scatter of individual estimates, 12 year mean estimates of annual total emissions over the WSL from forward models (5.34 ± 0.54 Tg CH4 y-1), inversions (6.06 ± 1.22 Tg CH4 y-1), and in situ observations (3.91 ± 1.29 Tg CH4 y-1) largely agreed, (b) forward models using inundation products alone to estimate wetland areas suffered from severe biases in CH4 emissions, (c) the interannual timeseries of models that lacked either soil thermal physics appropriate to the high latitudes or realistic emissions from unsaturated peatlands tended to be dominated by a single environmental driver (inundation or air temperature), unlike those of inversions and more sophisticated forward models, (d) differences in biogeochemical schemes across models had relatively smaller influence over performance; and (e) multi-year or multi-decade observational records are crucial for evaluating models' responses to long-term climate change.