Abstract. As soil microbial respiration is the major component of land CO2 emissions, differences in the functional dependence of respiration on soil moisture among Earth system models (ESMs) contributes significantly to the uncertainties in their projections. Using soil organic C (SOC) stocks and CO2 data from a boreal forest\u2013mire ecotone in Finland and Bayesian data assimilation, we revised the original precipitation-based monotonic saturation dependency of the Yasso07 soil carbon model using the non-monotonic Ricker function based on soil volumetric water content. We fit the revised functional dependency of moisture to the observed microbial respiration and SOC stocks and compared its performance against the original Yasso07 model and the version used in the JSBACH land surface model with a reduction constant for decomposition rates in wetlands. The Yasso07 soil\u00a0C model coupled with the calibrated unimodal Ricker moisture function with an optimum in well-drained soils accurately reconstructed observed SOC stocks and soil CO2 emissions and clearly outperformed previous model versions on paludified organo-mineral soils in forested peatlands and water-saturated organic soils in mires. The best estimate of the posterior moisture response of decomposition used both measurements of SOC stocks and CO2 data from the full range of moisture conditions (from dry and xeric to wet and water-saturated soils). We observed unbiased residuals of SOC and CO2 data modelled with the moisture optimum in well-drained soils, suggesting that this modified function accounts more precisely for the long-term SOC change dependency according to ecosystem properties as well as the contribution of short-term CO2 responses including extreme events. The optimum moisture for decomposition in boreal forests was found in well-drained soils instead of the mid-range between dry and water-saturated conditions as is commonly assumed among soil\u00a0C and ESMs. Although the unimodal moisture modifier with an optimum in well-drained soils implicitly incorporates robust biogeochemical mechanisms of SOC accumulation and CO2 emissions, it needs further evaluation with large-scale data to determine if its use in land surface models will decrease the uncertainty in projections.