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Abstract. In the global methane budget, the largest natural sourceis attributed to wetlands, which encompass all ecosystems composed ofwaterlogged or inundated ground, capable of methane production. Among them,northern peatlands that store large amounts of soil organic carbon have beenfunctioning, since the end of the last glaciation period, as long-termsources of methane (CH4) and are one of the most significant methanesources among wetlands. To reduce uncertainty of quantifying methane flux in theglobal methane budget, it is of significance to understand the underlyingprocesses for methane production and fluxes in northern peatlands. A methanemodel that features methane production and transport by plants, ebullitionprocess and diffusion in soil, oxidation to CO2, and CH4 fluxes tothe atmosphere has been embedded in the ORCHIDEE-PEAT land surface modelthat includes an explicit representation of northern peatlands.ORCHIDEE-PCH4 was calibrated and evaluated on 14 peatland sites distributedon both the Eurasian and American continents in the northern boreal andtemperate regions. Data assimilation approaches were employed to optimizedparameters at each site and at all sites simultaneously. Results show thatmethanogenesis is sensitive to temperature and substrate availability overthe top 75 cm of soil depth. Methane emissions estimated using single siteoptimization (SSO) of model parameters are underestimated by 9 g CH4 m−2 yr−1 on average (i.e., 50 % higher than the site average ofyearly methane emissions). While using the multi-site optimization (MSO),methane emissions are overestimated by 5 g CH4 m−2 yr−1 onaverage across all investigated sites (i.e., 37 % lower than the siteaverage of yearly methane emissions). 

Abstract. Natural wetlands constitute the largest and most uncertain sourceof methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance datafrom 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available athttps://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).