We present a global distribution of surface methane (CH₄) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH₄ (CTE-CH₄) data assimilation system. In CTE-CH₄, anthropogenic and biospheric CH₄ emissions are simultaneously estimated based on constraints of global atmospheric in situ CH₄ observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH₄ to surface in situ observations, vertical profiles of CH₄ made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH₄) from the Total Carbon Column Observing Network (TCCON), and XCH₄ from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH₄ is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH₄ yr⁻¹, with an increase of 18 Tg CH₄ yr⁻¹ from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations ( < 2 Tg CH₄ yr⁻¹ difference), and much smaller than suggested by EDGAR v4.2 FT2010 inventory (33 Tg CH₄ yr⁻¹), which was used for prior anthropogenic emission estimates. The result is in good agreement with other published estimates from inverse modelling studies (16–20 Tg CH₄ yr⁻¹). However, this study could not conclusively separate a small trend in biospheric emissions (−5 to +6.9 Tg CH₄ yr⁻¹) from the much larger trend in anthropogenic emissions (15–27 Tg CH₄ yr⁻¹). Finally, we find that the global and North American CH₄ balance could be closed over this time period without the previously suggested need to strongly increase anthropogenic CH₄ emissions in the United States. With further developments, especially on the treatment of the atmospheric CH₄ sink, we expect the data assimilation system presented here will be able to contribute to the ongoing interpretation of changes in this important greenhouse gas budget.

Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional climate models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction (BC) of RCM temperature and precipitation for Finland is carried out using different versions of the distribution based scaling (DBS) method. The DBS-adjusted RCM data are used as input of a hydrological model to simulate changes in discharges of four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS-adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period 1961–2000 and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the standard deviation of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

Wetlands are one of the most significant natural sources of methane (CH₄) to the atmosphere. They emit CH₄ because decomposition of soil organic matter in waterlogged anoxic conditions produces CH₄, in addition to carbon dioxide (CO₂). Production of CH₄ and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH₄ emissions are thus needed for upscaling observations to estimate present CH₄ emissions and for producing scenarios of future atmospheric CH₄ concentrations. Aiming at a CH₄ model that can be added to models describing peatland carbon cycling, we composed a model called HIMMELI that describes CH₄ build-up in and emissions from peatland soils. It is not a full peatland carbon cycle model but it requires the rate of anoxic soil respiration as input. Driven by soil temperature, leaf area index (LAI) of aerenchymatous peatland vegetation, and water table depth (WTD), it simulates the concentrations and transport of CH₄, CO₂, and oxygen (O₂) in a layered one-dimensional peat column. Here, we present the HIMMELI model structure and results of tests on the model sensitivity to the input data and to the description of the peat column (peat depth and layer thickness), and demonstrate that HIMMELI outputs realistic fluxes by comparing modeled and measured fluxes at two peatland sites. As HIMMELI describes only the CH₄-related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH₄ models connected to complete peatland carbon models, which is usually the case. Our results indicated that (1) the model is flexible and robust and thus suitable for different environments; (2) the simulated CH₄ emissions largely depend on the prescribed rate of anoxic respiration; (3) the sensitivity of the total CH₄ emission to other input variables is mainly mediated via the concentrations of dissolved gases, in particular, the O₂ concentrations that affect the CH₄ production and oxidation rates; (4) with given input respiration, the peat column description does not significantly affect the simulated CH₄ emissions in this model version.