Natural wetlands constitute the largest and most uncertain source of 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 data from 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 at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).

During August 2016, a quasi-stationary high-pressure system spreading over Central and North-Eastern Europe, caused weather conditions that allowed for 24/7 observations of aerosol optical properties by using a complex multi-wavelength PollyXT lidar system with Raman, polarization and water vapour capabilities, based at the European Aerosol Research Lidar Network (EARLINET network) urban site in Warsaw, Poland. During 24–30 August 2016, the lidar-derived products (boundary layer height, aerosol optical depth, Ångström exponent, lidar ratio, depolarization ratio) were analysed in terms of air mass transport (HYSPLIT model), aerosol load (CAMS data) and type (NAAPS model) and confronted with active and passive remote sensing at the ground level (PolandAOD, AERONET, WIOS-AQ networks) and aboard satellites (SEVIRI, MODIS, CATS sensors). Optical properties for less than a day-old fresh biomass burning aerosol, advected into Warsaw’s boundary layer from over Ukraine, were compared with the properties of long-range transported 3–5 day-old aged biomass burning aerosol detected in the free troposphere over Warsaw. Analyses of temporal changes of aerosol properties within the boundary layer, revealed an increase of aerosol optical depth and Ångström exponent accompanied by an increase of surface PM₁₀ and PM_2.5. Intrusions of advected biomass burning particles into the urban boundary layer seem to affect not only the optical properties observed but also the top height of the boundary layer, by moderating its increase.

Peatlands are ecosystems for which carbon budget relies strongly on the meteorological and hydrological conditions. Here, using a manipulative field experiment, we measured ecosystem respiration (R_ECO) over two years (2013–2014) in a poor fen in Poland to estimate the carbon emission in a changing climate. The experiment consisted of warming (open-top chambers - OTC) and water table manipulation. The application of OTC increased the mean values of daily maximum air temperature by approx. 1.1–1.8 °C. Warming or the increased water table depth separately resulted in an increase in ecosystem respiration by approx. 0.1 μmol CO₂ m⁻² s⁻¹ and 0.3 μmol CO₂ m⁻² s⁻¹, respectively. However, our results show also the additive nature of warming and water table drawdown impact on daily R_ECO during the studied years (2013–0.80 μmol CO₂ m⁻² s⁻¹ and 2014–1.16 μmol CO₂ m⁻² s⁻¹). With the natural dry period event which occurred in 2014, the seasonal R_ECO increased by approx. 0.2 μmol CO₂ m⁻² s⁻¹ as compared to the previous year. Projected global warming will therefore significantly enhance C loss from poor fens in this region of Europe.

Peatlands play an important role in the global carbon cycle due to the high carbon storage in the substrate. Ecosystem production depends, for example, on the solar energy amount that reaches the vegetation, however the diffuse component of this flux can substantially increase ecosystem net productivity. This phenomenon is observed in different ecosystems, but the study of the atmosphere optical properties on peatland production is lacking. In this paper, the presented methodology allowed us to disentangle the diffuse radiation impact on the net ecosystem production (NEP) of Rzecin peatland, Poland. It allowed us to assess the impact of the atmospheric scattering process determined by the aerosol presence in the air mass. An application of atmospheric radiation transfer (ART) and ecosystem production (EP) models showed that the increase of aerosol optical thickness from 0.09 to 0.17 caused NEP to rise by 3.4–5.7%. An increase of the diffusion index (DI) by 0.1 resulted in an NEP increase of 6.1–42.3%, while a DI decrease of 0.1 determined an NEP reduction of −49.0 to −10.5%. These results show that low peatland vegetation responds to changes in light scattering. This phenomenon should be taken into account when calculating the global CO₂ uptake estimation of such ecosystems.

Natural wetlands constitute the largest and most uncertain source of methane (CH₄) to the atmosphere and a large fraction of them are in the northern latitudes. These emissions are typically estimated using process (bottom-up) or inversion (top-down) models, yet the two are not independent of each other since the top-down estimates rely on the a priori estimation of these emissions coming from the process models. Hence, independent validation data of the large-scale emissions would be needed. Here we utilize 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) during years 2013 and 2014. The predictive performance of the RF model is evaluated using the leave-one-site-out cross-validation scheme and the performance (Nash-Sutcliffe model efficiency = 0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide or studies where process models are compared against site-level CH₄ emission data. Three wetland maps are utilized in the upscaling and the annual emissions for the northern wetlands yield 31.7 (22.3–41.2, 95 % confidence interval), 30.6 (21.4–39.9) or 37.6 (25.9–49.5) Tg(CH₄) yr⁻¹, depending on the map used. To evaluate the uncertainties of the upscaled product it is also compared against two process models (LPX-Bern and WetCHARTs) and methodological issues related to CH₄ flux upscaling are discussed. The monthly upscaled CH₄ flux data product is available for further usage at: https://doi.org/10.5281/zenodo.2560164.