The FLUXNET2015 dataset provides ecosystem-scale data on CO₂, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Methane (CH₄) emissions from natural landscapes constitute roughly half of global CH₄ contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH₄ flux are ideal for constraining ecosystem-scale CH₄ emissions due to quasi-continuous and high-temporal-resolution CH₄ flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH₄ Version 1.0, the first open-source global dataset of CH₄ EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH₄ includes half-hourly and daily gap-filled and non-gap-filled aggregated CH₄ fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH₄ representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH₄ Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH₄ emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH₄ fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH₄ emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20^∘ S to 20^∘ N) the spring onset of elevated CH₄ emissions starts 3 d earlier, and the CH₄ emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH₄ emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH₄ emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH₄ emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH₄ emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH₄ modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH₄ peak). FLUXNET-CH₄ is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH₄ cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH₄ data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2 kPa, peatland ET exceeds forest ET by up to 30%. Future (2091–2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections.

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).

Four years of continuous aerosol number size distribution measurements from the Arctic Climate Observatory in Tiksi, Russia, are analyzed. Tiksi is located in a region where in situ information on aerosol particle properties has not been previously available. Particle size distributions were measured with a differential mobility particle sizer (in the diameter range of 7–500 nm) and with an aerodynamic particle sizer (in the diameter range of 0.5–10 μm). Source region effects on particle modal features and number, and mass concentrations are presented for different seasons. The monthly median total aerosol number concentration in Tiksi ranges from 184 cm⁻³ in November to 724 cm⁻³ in July, with a local maximum in March of 481 cm⁻³. The total mass concentration has a distinct maximum in February–March of 1.72–2.38 μg m⁻³ and two minimums in June (0.42 μg m⁻³) and in September–October (0.36–0.57 μg m⁻³). These seasonal cycles in number and mass concentrations are related to isolated processes and phenomena such as Arctic haze in early spring, which increases accumulation and coarse-mode numbers, and secondary particle formation in spring and summer, which affects the nucleation and Aitken mode particle concentrations. Secondary particle formation was frequently observed in Tiksi and was shown to be slightly more common in marine, in comparison to continental, air flows. Particle formation rates were the highest in spring, while the particle growth rates peaked in summer. These results suggest two different origins for secondary particles, anthropogenic pollution being the important source in spring and biogenic emissions being significant in summer. The impact of temperature-dependent natural emissions on aerosol and cloud condensation nuclei numbers was significant: the increase in both the particle mass and the CCN (cloud condensation nuclei) number with temperature was found to be higher than in any previous study done over the boreal forest region. In addition to the precursor emissions of biogenic volatile organic compounds, the frequent Siberian forest fires, although far away, are suggested to play a role in Arctic aerosol composition during the warmest months. Five fire events were isolated based on clustering analysis, and the particle mass and cloud condensation nuclei number were shown to be somewhat affected by these events. In addition, during calm and cold months, aerosol concentrations were occasionally increased by local aerosol sources in trapping inversions. These results provide valuable information on interannual cycles and sources of Arctic aerosols.