We determine the annual timing of spring recovery from spaceborne microwave radiometer observations across northern hemisphere boreal evergreen forests for 1979-2014. We find a trend of advanced spring recovery of carbon uptake for this period, with a total average shift of 8.1 d (2.3 d/decade). We use this trend to estimate the corresponding changes in gross primary production (GPP) by applying in situ carbon flux observations. Micrometeorological CO 2 measurements at four sites in northern Europe and North America indicate that such an advance in spring recovery would have increased the January-June GPP sum by 29 g·C·m carbon uptake | earth observation | snowmelt H igh-latitude warming and an associated reduction in spring snow cover are expected to have complex impacts on regional climate patterns and ecological responses (1, 2). The timing of spring recovery of photosynthesis in boreal evergreen forest following snowmelt is one of the major factors affecting the carbon balance across high latitudes (3-5). The integrated effect of anthropogenic impacts on climate causes the total radiative forcing to be positive, which increases the heat balance of the atmosphere. The largest individual cause of warming is the anthropogenic increase in CO 2 concentration (6), which is controlled by the response of the global carbon cycle to anthropogenic CO 2 emissions. Recent studies have documented, with very high confidence, that the world's forests constitute an important carbon sink (4, 6, 7). The annual total boreal forest net carbon sink is estimated to be 0.5 ± 0.1 Pg·C·y −1 for the 2000-2007 period (4). Based on coupled carbon cycle-climate modeling, this sink is estimated to be increasing by 0.014 Pg·C·y −1 for boreal North America including all land areas and by 0.018 Pg·C·y −1 for boreal Asia, respectively (7). However, considerable uncertainty remains regarding the magnitude of this terrestrial sink, particularly how it changes with time due to external climate drivers including the timing of spring snowmelt. It is vital for future climate scenarios to reduce the forest sink uncertainty and obtain information on the spatiotemporal variability and trends. High-latitude warming over the land surface is associated with observed reductions in spring snow extent through earlier snowmelt (2, 8-10). Advanced snowmelt across the boreal zone has a potentially major impact on the carbon balance (11-14). Here, we combine spatially continuous time series of satellite-derived snowmelt data (clearance from the landscape, that is, the time when fractional snow cover reaches zero) with point-wise carbon flux observations to address this open question. This is performed by quantifying the relationship between the observed declines in spring snow cover extent (due to earlier snowmelt) and the carbon balance in springtime. ResultsWe define the dynamics of boreal forest carbon uptake by using the change of snow clearance day (SCD) as a proxy indicator for changes in evergreen boreal forest spring recovery (SR) of photosynthesis, def...

Abstract. Wetlands are one of the most significant natural sources of methane (CH 4 ) to the atmosphere. They emit CH 4 because decomposition of soil organic matter in waterlogged anoxic conditions produces CH 4 , in addition to carbon dioxide (CO 2 ). Production of CH 4 and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH 4 emissions are thus needed for upscaling observations to estimate present CH 4 emissions and for producing scenarios of future atmospheric CH 4 concentrations. Aiming at a CH 4 model that can be added to models describing peatland carbon cycling, we composed a model called HIMMELI that describes CH 4 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 CH 4 , CO 2 , and oxygen (O 2 ) 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 CH 4 -related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH 4 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 CH 4 emissions largely depend on the prescribed rate of anoxic respiration; (3) the sensitivity of Published by Copernicus Publications on behalf of the European Geosciences Union. 4666M. Raivonen et al.: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands the total CH 4 emission to other input variables is mainly mediated via the concentrations of dissolved gases, in particular, the O 2 concentrations that affect the CH 4 production and oxidation rates; (4) with given input respiration, the peat column description does not significantly affect the simulated CH 4 emissions in this model version.

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Abstract. Estimating methane (CH 4 ) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH 4 emission model to improve the quality of the predictions and to understand the limitations of such models.The detailed process model that we analyze contains descriptions for CH 4 production from anaerobic respiration, CH 4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland.The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH 4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertainties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd-up and emIssion for peatlands (sqHIMMELI).The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that the early spring net primary production could be used to predict parameters affecting the annual methane production.Even though the calibration is specific to the Siikaneva site, the hierarchical modeling approach is well suited for larger-scale studies and the results of the estimation pave way for a regional or global-scale Bayesian calibration of wetland emission models.