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Accurate national methane (CH4) emission estimates are essential for tracking progress towards climate goals. This study investigated Finnish CH4 emissions from 2000–2021 using bottom-up and top-down approaches. We evaluated the ability of a global atmospheric inverse model CarbonTracker Europe – CH4 to estimate CH4 emissions within a single country. We focused on how different priors and their uncertainties affect the optimised emissions and showed that the optimised anthropogenic and natural CH4 emissions were strongly dependent on the prior emissions. However, while the range of CH4 estimates was large, the optimised emissions were more constrained than the bottom-up estimates. Further analysis showed that the optimisation aligned the trends of anthropogenic and natural CH4 emissions and improved the modelled seasonal cycles of natural emissions. Comparison of atmospheric CH4 observations with model results showed no clear preference between anthropogenic inventories (EDGAR v6 and CAMS-REG), but results using the highest natural prior (JSBACH–HIMMELI) agreed best with observations, suggesting that process-based models may underestimate CH4 emissions from Finnish peatlands or unaccounted sources such as freshwater emissions. Additionally, using an uncertainty estimate based on a process-based model ensemble for natural CH4 emissions seemed to be advantageous compared to the standard uncertainty definition. The average total posterior emission of the ensemble from one inverse model with different priors was similar to the average of the ensemble including different inverse models but similar priors. Thus, a single inverse model using a range of priors can be used to reliably estimate CH4 emissions when an ensemble of different models is unavailable.

Wetland methane responses to temperature and precipitation are studied in a boreal wetland-rich region in northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5, and CLM5) are compared to multi-model means of ecosystem models and atmospheric inversions from the Global Carbon Project and upscaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, are used as priors in atmospheric inversions with Carbon Tracker Europe–CH4 (CTE-CH4) in order to find out how the assimilation of atmospheric concentration data changes the flux estimates and how this alters the interpretation of the flux responses to temperature and precipitation. Inversion moves wetland emissions of both models towards co-limitation by temperature and precipitation. Between 2000 and 2018, periods of high temperature and/or high precipitation often resulted in increased emissions. However, the dry summer of 2018 did not result in increased emissions despite the high temperatures. The process models show strong temperature and strong precipitation responses for the region (51 %–91 % of the variance explained by both). The month with the highest emissions varies from May to September among the models. However, multi-model means, inversions, and upscaled eddy covariance flux observations agree on the month of maximum emissions and are co-limited by temperature and precipitation. The setup of different emission components (peatland emissions, mineral land fluxes) has an important role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the regional representation of wet and dry mineral soils and periodic flooding which contribute to the seasonality and magnitude of methane fluxes. The realistic representation of temperature dependence of the peat soil fluxes is also important. Furthermore, it is important to use process-based descriptions for both mineral and peat soil fluxes to simulate the flux responses to climate drivers.

Peatland management practices, such as drainage and restoration, have a strong effect on boreal peatland methane (CH4) fluxes. Furthermore, CH4 fluxes are strongly controlled by local environmental conditions, such as soil hydrology, temperature and vegetation, which are all experiencing considerable changes due to climate change. Both management practices and climate change are expected to influence peatland CH4 fluxes during this century, but the magnitude and net impact of these changes is still insufficiently understood. In this study, we simulated the impacts of two forest management practices, rotational forestry and continuous cover forestry, as well as peatland restoration, on hypothetical forestry-drained peatlands across Finland using the land surface model JSBACH (Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg) coupled with the soil carbon model YASSO and a peatland methane model HIMMELI (Helsinki Model of Methane Buildup and Emission for Peatlands). We further simulated the impacts of climatic warming using two RCP (Representative Concentration Pathway) emission scenarios, RCP2.6 and RCP4.5. We investigated the responses of CH4 fluxes, soil water-table level (WTL), soil temperatures and soil carbon dynamics to changes in management practices and climate. Our results show that management practices have a strong impact on peatland WTLs and CH4 emissions that continues for several decades, with emissions increasing after restoration and clearcutting. Towards the end of the century, WTLs increase slightly, likely due to increasing precipitation. CH4 fluxes have opposing trends in restored and drained peatlands. In restored peatlands, CH4 emissions decrease towards the end of the century following decomposition of harvest residue in the top peat layers despite increasing WTLs, while in drained peatland forests sinks get weaker and occasional emissions become more common, likely due to rising WTLs and soil temperatures. The strength of these trends varies across the country, with CH4 emissions from restored peatlands decreasing more strongly in southern Finland, and forest soil CH4 sinks weakening most in northern Finland.

Forest fire dynamics are expected to alter due to climate change. Despite the projected increase in precipitation, rising temperatures will amplify forest fire risk from the present to the end of the century. Here, we analysed changes in fire season, the number of fires, and burnt area in Fennoscandia from 1951 to 2100. Regional simulations from the JSBACH–SPITFIRE ecosystem model (where SPITFIRE stands for SPread and InTensity of FIRE) were performed under two climate change forcing scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5) and three global climate driver models (CanESM2, CNRM-CM5, and MIROC5) with a 0.5° resolution. Simulations were forced by downscaled and bias-corrected EURO-CORDEX data. Generally, as a consequence of the projected longer fire season and drier fuel, the probability of fires is projected to increase. However, changes in fire season, the number of fires, and burnt area are highly dependent on climate projections and location. The fire season is estimated to increase on average from 20 ± 7 to 52 ± 12 d, starting from 10 ± 9 to 23 ± 11 d earlier and ending from 10 ± 10 to 30 ± 16 d later, compared to the reference period (1981–2010), by the end of the century (2071–2100). The results for Finland indicate a change in the number of fires, ranging from −7 ± 4 % to 98 ± 56 %, and a change in burnt area, ranging from −19 ± 24 % to 87 ± 42 %. These findings contribute to a better understanding of potential changes in the future fire seasons of northern Europe.

Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its global stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023), provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use updated national GHG inventories (NGHGIs) reported by Annex I parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available biennial update reports (BURs) reported by non-Annex I parties. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH4 emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH4 yr−1) and the mean of the BU CH4 emissions (17.8 (16–19) Tg CH4 yr−1) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH4 yr−1 and 24 (22–25) Tg CH4 yr−1 for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH4 yr−1 (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH4 emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.12818506 (Petrescu et al., 2024).

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8±55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4590875 (Petrescu et al., 2020b).

The trajectories of soil carbon in our changing climate are of the utmost importance as soil is a substantial carbon reservoir with a large potential to impact the atmospheric carbon dioxide (CO2) burden. Atmospheric CO2 observations integrate all processes affecting carbon exchange between the surface and the atmosphere and therefore are suitable for carbon cycle model evaluation. In this study, we present a framework for how to use atmospheric CO2 observations to evaluate two distinct soil carbon models (CBALANCE, CBA, and Yasso, YAS) that are implemented in a global land surface model (JSBACH). We transported the biospheric carbon fluxes obtained by JSBACH using the atmospheric transport model TM5 to obtain atmospheric CO2. We then compared these results with surface observations from Global Atmosphere Watch stations, as well as with column XCO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The seasonal cycles of atmospheric CO2 estimated by the two different soil models differed. The estimates from the CBALANCE soil model were more in line with the surface observations at low latitudes (0–45∘ N) with only a 1 % bias in the seasonal cycle amplitude, whereas Yasso underestimated the seasonal cycle amplitude in this region by 32 %. Yasso, on the other hand, gave more realistic seasonal cycle amplitudes of CO2 at northern boreal sites (north of 45∘ N) with an underestimation of 15 % compared to a 30 % overestimation by CBALANCE. Generally, the estimates from CBALANCE were more successful in capturing the seasonal patterns and seasonal cycle amplitudes of atmospheric CO2 even though it overestimated soil carbon stocks by 225 % (compared to an underestimation of 36 % by Yasso), and its estimations of the global distribution of soil carbon stocks were unrealistic. The reasons for these differences in the results are related to the different environmental drivers and their functional dependencies on the two soil carbon models. In the tropics, heterotrophic respiration in the Yasso model increased earlier in the season since it is driven by precipitation instead of soil moisture, as in CBALANCE. In temperate and boreal regions, the role of temperature is more dominant. There, heterotrophic respiration from the Yasso model had a larger seasonal amplitude, which is driven by air temperature, compared to CBALANCE, which is driven by soil temperature. The results underline the importance of using sub-annual data in the development of soil carbon models when they are used at shorter than annual timescales.

Forest ecosystems are already responding to changing environmental conditions that are driven by increased atmospheric CO2 concentrations. These developments affect how societies can utilise and benefit from the woodland areas in the future, be it for example climate change mitigation as carbon sinks, lumber for wood industry, or preserved for nature tourism and recreational activities. We assess the effect and the relative magnitude of different uncertainty sources in ecosystem model simulations from the year 1980 to 2100 for two Finnish boreal forest sites. The models used in this study are the land ecosystem model JSBACH and the forest growth model PREBAS. The considered uncertainty sources for both models are model parameters and four prescribed climates with two RCP (representative concentration pathway) scenarios. Usually, model parameter uncertainty is not included in these types of uncertainty studies. PREBAS simulations also include two forest management scenarios. We assess the effect of these sources of variation at four different points in time on several ecosystem indicators, e.g. gross primary production (GPP), ecosystem respiration, soil moisture, recurrence of drought, length of the vegetation active period (VAP), length of the snow melting period and the stand volume. The uncertainty induced by the climate models remains roughly the same throughout the simulations and is overtaken by the RCP scenario impact halfway through the experiment. The management actions are the most dominant uncertainty factors for Hyytiälä and as important as RCP scenarios at the end of the simulations, but they contribute only half as much for Sodankylä. The parameter uncertainty is the least influential of the examined uncertainty sources, but it is also the most elusive to estimate due to non-linear and adverse effects on the simulated ecosystem indicators. Our analysis underlines the importance of carefully considering the implementation of forest use when simulating future ecosystem conditions, as human impact is evident and even increasing in boreal forested regions.

We calibrated the JSBACH model with six different stomatal conductance formulations using measurements from 10 FLUXNET coniferous evergreen sites in the boreal zone. The parameter posterior distributions were generated by the adaptive population importance sampler (APIS); then the optimal values were estimated by a simple stochastic optimisation algorithm. The model was constrained with in situ observations of evapotranspiration (ET) and gross primary production (GPP). We identified the key parameters in the calibration process. These parameters control the soil moisture stress function and the overall rate of carbon fixation. The JSBACH model was also modified to use a delayed effect of temperature for photosynthetic activity in spring. This modification enabled the model to correctly reproduce the springtime increase in GPP for all conifer sites used in this study. Overall, the calibration and model modifications improved the coefficient of determination and the model bias for GPP with all stomatal conductance formulations. However, only the coefficient of determination was clearly improved for ET. The optimisation resulted in best performance by the Bethy, Ball–Berry, and the Friend and Kiang stomatal conductance models. We also optimised the model during a drought event at a Finnish Scots pine forest site. This optimisation improved the model behaviour but resulted in significant changes to the parameter values except for the unified stomatal optimisation model (USO). Interestingly, the USO demonstrated the best performance during this event.

The surface albedo time series, CLARA-A2 SAL, was used to study trends in the snowmelt start and end dates, the melting season length and the albedo value preceding the melt onset in Finland during 1982–2016. In addition, the melt onset from the JSBACH land surface model was compared with the timing of green-up estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Moreover, the melt onset was compared with the timing of the greening up based on MODIS data. Similarly, the end of snowmelt timing predicted by JSBACH was compared with the melt-off dates based on the Finnish Meteorological Institute (FMI) operational in situ measurements and the Fractional Snow Cover (FSC) time-series product provided by the EU FP7 CryoLand project. It was found that the snowmelt date estimated using the 20 % threshold of the albedo range during the melting period corresponded well to the melt estimate of the permanent snow layer. The longest period, during which the ground is continuously half or more covered by snow, defines the permanent snow layer (Solantie et al., 1996). The greening up followed within 5–13 days the date when the albedo reached the 1 % threshold of the albedo dynamic range during the melting period. The time difference between greening up and complete snowmelt was smaller in mountainous areas than in coastal areas. In two northern vegetation map areas (Northern Karelia–Kainuu and Southwestern Lapland), a clear trend towards earlier snowmelt onset (5–6 days per decade) and increasing melting season length (6–7 days per decade) was observed. In the forested part of northern Finland, a clear decreasing trend in albedo (2 %–3 % per decade in absolute albedo percentage) before the start of the melt onset was observed. The decreasing albedo trend was found to be due to the increased stem volume.

The regional climate model REMO was coupled with the FLake lake model to include an interactive treatment of lakes. Using this new version, the Fenno-Scandinavian climate and lake characteristics were studied in a set of 35-year hindcast simulations. Additionally, sensitivity tests related to the parameterization of snow albedo were conducted. Our results show that overall the new model version improves the representation of the Fenno-Scandinavian climate in terms of 2 m temperature and precipitation, but the downside is that an existing wintertime cold bias in the model is enhanced. The lake surface water temperature, ice depth and ice season length were analyzed in detail for 10 Finnish, 4 Swedish and 2 Russian lakes and 1 Estonian lake. The results show that the model can reproduce these characteristics with reasonably high accuracy. The cold bias during winter causes overestimation of ice layer thickness, for example, at several of the studied lakes, but overall the values from the model are realistic and represent the lake physics well in a long-term simulation. We also analyzed the snow depth on ice from 10 Finnish lakes and vertical temperature profiles from 5 Finnish lakes and the model results are realistic.

Abstract We present a 4-dimensional back reaction analysis of de Sitter space for a conformally coupled scalar field in the presence of vacuum energy initialized in the Bunch–Davies vacuum. In contrast to the usual semi-classical prescription, as the source term in the Friedmann equations we use expectation values where the unobservable information hidden by the cosmological event horizon has been neglected i.e. coarse grained over. It is shown that in this approach the energy-momentum is precisely thermal with constant temperature despite the dilution from the expansion of space due to a flux of energy radiated from the horizon. This leads to a self-consistent solution for the Hubble rate, which is gradually evolving and at late times deviates significantly from de Sitter. Our results hence imply de Sitter space to be unstable in this prescription. The solution also suggests dynamical vacuum energy: the continuous flux of energy is balanced by the generation of negative vacuum energy, which accumulatively decreases the overall contribution. Finally, we show that our results admit a thermodynamic interpretation which provides a simple alternate derivation of the mechanism. For very long times the solutions coincide with flat space.

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.

The influence of drought on plant functioning has received considerable attention in recent years, however our understanding of the response of carbon and water coupling to drought in terrestrial ecosystems still needs to be improved. A severe soil moisture drought occurred in southern Finland in the late summer of 2006. In this study, we investigated the response of water use efficiency to summer drought in a boreal Scots pine forest (Pinus sylvestris) on the daily time scale mainly using eddy covariance flux data from the Hyytiälä (southern Finland) flux site. In addition, simulation results from the JSBACH land surface model were evaluated against the observed results. Based on observed data, the ecosystem level water use efficiency (EWUE; the ratio of gross primary production, GPP, to evapotranspiration, ET) showed a decrease during the severe soil moisture drought, while the inherent water use efficiency (IWUE; a quantity defined as EWUE multiplied with mean daytime vapour pressure deficit, VPD) increased and the underlying water use efficiency (uWUE, a metric based on IWUE and a simple stomatal model, is the ratio of GPP multiplied with a square root of VPD to ET) was unchanged during the drought. The decrease in EWUE was due to the stronger decline in GPP than in ET. The increase in IWUE was because of the decreased stomatal conductance under increased VPD. The unchanged uWUE indicates that the trade-off between carbon assimilation and transpiration of the boreal Scots pine forest was not disturbed by this drought event at the site. The JSBACH simulation showed declines of both GPP and ET under the severe soil moisture drought, but to a smaller extent compared to the observed GPP and ET. Simulated GPP and ET led to a smaller decrease in EWUE but a larger increase in IWUE because of the severe soil moisture drought in comparison to observations. As in the observations, the simulated uWUE showed no changes in the drought event. The model deficiencies exist mainly due to the lack of the limiting effect of increased VPD on stomatal conductance during the low soil moisture condition. Our study provides a deeper understanding of the coupling of carbon and water cycles in the boreal Scots pine forest ecosystem and suggests possible improvements to land surface models, which play an important role in the prediction of biosphere–atmosphere feedbacks in the climate system.

Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf-level chlorophyll fluorescence measurements (Chl F) to evaluate the performance of the SIF module at a coniferous forest at Hyytiälä, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite-observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. At regional scale the model was able to simulate observed SIF averaged over 5 years with r2 of 0.86. The GOME-2-based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation). The observed SIF captured the seasonality of the photosynthesis at site scale and showed feasibility for use in improving of model seasonality at site and regional scale.

We examined parameter optimisation in the JSBACH (Kaminski et al., 2013; Knorr and Kattge, 2005; Reick et al., 2013) ecosystem model, applied to two boreal forest sites (Hyytiälä and Sodankylä) in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon-exchange-related formulations, and optimised them using the adaptive Metropolis (AM) algorithm for Hyytiälä with a 5-year calibration period (2000–2004) followed by a 4-year validation period (2005–2008). Sodankylä acted as an independent validation site, where optimisations were not made. The tuning provided estimates for full distribution of possible parameters, along with information about correlation, sensitivity and identifiability. Some parameters were correlated with each other due to a phenomenological connection between carbon uptake and water stress or other connections due to the set-up of the model formulations. The latter holds especially for vegetation phenology parameters. The least identifiable parameters include phenology parameters, parameters connecting relative humidity and soil dryness, and the field capacity of the skin reservoir. These soil parameters were masked by the large contribution from vegetation transpiration. In addition to leaf area index and the maximum carboxylation rate, the most effective parameters adjusting the gross primary production (GPP) and evapotranspiration (ET) fluxes in seasonal tuning were related to soil wilting point, drainage and moisture stress imposed on vegetation. For daily and half-hourly tunings the most important parameters were the ratio of leaf internal CO2 concentration to external CO2 and the parameter connecting relative humidity and soil dryness. Effectively the seasonal tuning transferred water from soil moisture into ET, and daily and half-hourly tunings reversed this process. The seasonal tuning improved the month-to-month development of GPP and ET, and produced the most stable estimates of water use efficiency. When compared to the seasonal tuning, the daily tuning is worse on the seasonal scale. However, daily parametrisation reproduced the observations for average diurnal cycle best, except for the GPP for Sodankylä validation period, where half-hourly tuned parameters were better. In general, the daily tuning provided the largest reduction in model–data mismatch. The models response to drought was unaffected by our parametrisations and further studies are needed into enhancing the dry response in JSBACH.

Droughts can have an impact on forest functioning and production, and even lead to tree mortality. However, drought is an elusive phenomenon that is difficult to quantify and define universally. In this study, we assessed the performance of a set of indicators that have been used to describe drought conditions in the summer months (June, July, August) over a 30-year period (1981–2010) in Finland. Those indicators include the Standardized Precipitation Index (SPI), the Standardized Precipitation–Evapotranspiration Index (SPEI), the Soil Moisture Index (SMI), and the Soil Moisture Anomaly (SMA). Herein, regional soil moisture was produced by the land surface model JSBACH of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM). Results show that the buffering effect of soil moisture and the associated soil moisture memory can impact on the onset and duration of drought as indicated by the SMI and SMA, while the SPI and SPEI are directly controlled by meteorological conditions. In particular, we investigated whether the SMI, SMA and SPEI are able to indicate the Extreme Drought affecting Forest health (EDF), which we defined according to the extreme drought that caused severe forest damages in Finland in 2006. The EDF thresholds for the aforementioned indicators are suggested, based on the reported statistics of forest damages in Finland in 2006. SMI was found to be the best indicator in capturing the spatial extent of forest damage induced by the extreme drought in 2006. In addition, through the application of the EDF thresholds over the summer months of the 30-year study period, the SPEI and SMA tended to show more frequent EDF events and a higher fraction of influenced area than SMI. This is because the SPEI and SMA are standardized indicators that show the degree of anomalies from statistical means over the aggregation period of climate conditions and soil moisture, respectively. However, in boreal forests in Finland, the high initial soil moisture or existence of peat often prevent the EDFs indicated by the SPEI and SMA to produce very low soil moisture that could be indicated as EDFs by the SMI. Therefore, we consider SMI is more appropriate for indicating EDFs in boreal forests. The selected EDF thresholds for those indicators could be calibrated when there are more forest health observation data available. Furthermore, in the context of future climate scenarios, assessments of EDF risks in northern areas should, in addition to climate data, rely on a land surface model capable of reliable prediction of soil moisture.

Land cover changes can impact the climate by influencing the surface energy and water balance. Naturally treeless or sparsely treed peatlands were extensively drained to stimulate forest growth in Finland over the second half of 20th century. The aim of this study is to investigate the biogeophysical effects of peatland forestation on regional climate in Finland. Two sets of 18-year climate simulations were done with the regional climate model REMO by using land cover data based on pre-drainage (1920s) and post-drainage (2000s) Finnish national forest inventories. In the most intensive peatland forestation area, located in the middle west of Finland, the results show a warming in April of up to 0.43 K in monthly-averaged daily mean 2 m air temperature, whereas a slight cooling from May to October of less than 0.1 K in general is found. Consequently, snow clearance days over that area are advanced up to 5 days in the mean of 15 years. No clear signal is found for precipitation. Through analysing the simulated temperature and energy balance terms, as well as snow depth over five selected subregions, a positive feedback induced by peatland forestation is found between decreased surface albedo and increased surface air temperature in the snow-melting period. Our modelled results show good qualitative agreements with the observational data. In general, decreased surface albedo in the snow-melting period and increased evapotranspiration in the growing period are the most important biogeophysical aspects induced by peatland forestation that cause changes in climate. The results from this study can be further integrally analysed with biogeochemical effects of peatland forestation to provide background information for adapting future forest management to mitigate climate warming effects. Moreover, they provide insights about the impacts of projected forestation of tundra at high latitudes due to climate change.

In the analyses of VOC fluxes measured above plant canopies, one usually assumes the flux above canopy to equal the exchange at the surface. Thus one assumes the chemical degradation to be much slower than the turbulent transport. We used a stochastic Lagrangian transport model in which the chemical degradation was described as first order decay in order to study the effect of the chemical degradation on above canopy fluxes of chemically reactive species. With the model we explored the sensitivity of the ratio of the above canopy flux to the surface emission on several parameters such as chemical lifetime of the compound, friction velocity, stability, and canopy density. Our results show that friction velocity and chemical lifetime affected the loss during transport the most. The canopy density had a significant effect if the chemically reactive compound was emitted from the forest floor. We used the results of the simulations together with oxidant data measured during HUMPPA-COPEC-2010 campaign at a Scots pine site to estimate the effect of the chemistry on fluxes of three typical biogenic VOCs, isoprene, α-pinene, and β-caryophyllene. Of these, the chemical degradation had a major effect on the fluxes of the most reactive species β-caryophyllene, while the fluxes of α-pinene were affected during nighttime. For these two compounds representing the mono- and sesquiterpenes groups, the effect of chemical degradation had also a significant diurnal cycle with the highest chemical loss at night. The different day and night time loss terms need to be accounted for, when measured fluxes of reactive compounds are used to reveal relations between primary emission and environmental parameters.

Eddy covariance (EC) flux measurements of nitrous oxide (N2O) obtained by using a 3-D sonic anemometer and a tunable diode laser gas analyzer for N2O were investigated. Two datasets (Sorø, Denmark and Kalevansuo, Finland) from different measurement campaigns including sub-canopy flux measurements of energy and carbon dioxide are discussed with a focus on selected quality control aspects and flux error analysis. Although fast response trace gas analyzers based on spectroscopic techniques are increasingly used in ecosystem research, their suitability for reliable estimates of EC fluxes is still limited, and some assumptions have to be made for filtering and processing data. The N2O concentration signal was frequently dominated by offset drifts (fringe effect), which can give an artificial extra contribution to the fluxes when the resulting concentration fluctuations are correlated with the fluctuations of the vertical wind velocity. Based on Allan variance analysis of the N2O signal, we found that a recursive running mean filter with a time constant equal to 50 s was suitable to damp the influence of the periodic drift. Although the net N2O fluxes over the whole campaign periods were quite small at both sites (~5 μg N m−2 h−1 for Kalevansuo and ~10 μg N m−2 h−1 for Sorø), the calculated sub-canopy EC fluxes were in good agreement with those estimated by automatic soil chambers. However, EC N2O flux measurements show larger random uncertainty than the sensible heat fluxes, and classification according to statistical significance of single flux values indicates that downward N2O fluxes have larger random error.

In this study we introduce a comparison method for footprint model results by evaluating the performance of conventional Lagrangian stochastic (LS) footprint models that use parameterised flow field characteristics with results of a Lagrangian trajectory model embedded in a large eddy simulation (LES) framework. The two conventional models follow the particles backward and forward in time while the trajectories in LES only evolve forward in time. We assess their performance in two unstably stratified boundary layers at observation levels covering the whole depth of the atmospheric boundary layer. We present a concept for footprint model comparison that can be applied for 2-D footprints and demonstrate that comparison of only cross wind integrated footprints is not sufficient for purposes facilitating two dimensional footprint information. Because the flow field description among the three models is most realistic in LES we use those results as the reference in the comparison. We found that the agreement of the two conventional models against the LES is generally better for intermediate measurement heights and for the more unstable case, whereas the two conventional flux footprint models agree best under less unstable conditions. The model comparison in 2-D was found quite sensitive to the grid resolution.

We applied a site evaluation approach combining Lagrangian Stochastic footprint modeling with a quality assessment approach for eddy-covariance data to 25 forested sites of the CarboEurope-IP network. The analysis addresses the spatial representativeness of the flux measurements, instrumental effects on data quality, spatial patterns in the data quality, and the performance of the coordinate rotation method. Our findings demonstrate that application of a footprint filter could strengthen the CarboEurope-IP flux database, since only one third of the sites is situated in truly homogeneous terrain. Almost half of the sites experience a significant reduction in eddy-covariance data quality under certain conditions, though these effects are mostly constricted to a small portion of the dataset. Reductions in data quality of the sensible heat flux are mostly induced by characteristics of the surrounding terrain, while the latent heat flux is subject to instrumentation-related problems. The Planar-Fit coordinate rotation proved to be a reliable tool for the majority of the sites using only a single set of rotation angles. Overall, we found a high average data quality for the CarboEurope-IP network, with good representativeness of the measurement data for the specified target land cover types.

We measured the fluxes of several hydrocarbon species above a Scots pine (Pinus sylvestris) stand using disjunct eddy covariance technique with proton transfer reaction – mass spectrometry. The measurements were conducted during four days in July at SMEAR II research station in Hyytiälä, Finland. Compounds which showed significant emission fluxes were methanol, acetaldehyde, acetone, and monoterpenes. A stochastic Lagrangian transport model with simple chemical degradation was applied to assess the sensitivity of the above canopy fluxes to chemistry. According to the model, the chemical degradation had a minor effect on the fluxes measured in this study but may have a major effect on the vertical flux profiles of more reactive compounds, such as sesquiterpenes. The monoterpene fluxes derived using M81 and M137 had a systematic difference with the latter one being higher. These fluxes followed the traditional exponential temperature dependent emission algorithm but were considerably higher than the fluxes measured before at the same site. The normalized monoterpene emission potentials at 30°C, obtained using the temperature dependence coefficient of 0.09°C−1, were 2.0 μg gdw−1 h−1 and 2.5 μg gdw−1 h−1, for fluxes derived using M81 and M137.

Spring time nitrous oxide (N2O) emissions from an old beech (Fagus sylvatica L.) forest were measured with eddy covariance (EC) and chamber techniques. The aim was to obtain information on the spatial and temporal variability in N2O emissions and link the emissions to soil environmental parameters. Mean N2O fluxes over the five week measurement period were 5.6±1.1, 10±1 and 16±11 μg N m−2 h−1 from EC, automatic chamber and manual chambers, respectively. High temporal variability characterized the EC fluxes in the trunk-space. To reduce this variability, resulting mostly from random uncertainty due to measuring fluxes close to the detection limit, we averaged the fluxes over one day periods. The variability in the chamber measurements was much smaller and dominated by high small scale spatial variability. The highest emissions measured by the EC method occurred during the first week of May when the trees were leafing and the soil moisture content was at its highest. If chamber techniques are used to estimate ecosystem level N2O emissions from forest soils, placement of the chambers should be considered carefully to cover the spatial variability in the soil N2O emissions. The EC technique, applied in this study, is a promising alternative tool to measure ecosystem level N2O fluxes in forest ecosystems. To our knowledge, this is the first study to demonstrate that the EC technique can be used to measure N2O fluxes in the trunk-space of a forest.