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Abstract. In the global methane budget, the largest natural sourceis attributed to wetlands, which encompass all ecosystems composed ofwaterlogged or inundated ground, capable of methane production. Among them,northern peatlands that store large amounts of soil organic carbon have beenfunctioning, since the end of the last glaciation period, as long-termsources of methane (CH4) and are one of the most significant methanesources among wetlands. To reduce uncertainty of quantifying methane flux in theglobal methane budget, it is of significance to understand the underlyingprocesses for methane production and fluxes in northern peatlands. A methanemodel that features methane production and transport by plants, ebullitionprocess and diffusion in soil, oxidation to CO2, and CH4 fluxes tothe atmosphere has been embedded in the ORCHIDEE-PEAT land surface modelthat includes an explicit representation of northern peatlands.ORCHIDEE-PCH4 was calibrated and evaluated on 14 peatland sites distributedon both the Eurasian and American continents in the northern boreal andtemperate regions. Data assimilation approaches were employed to optimizedparameters at each site and at all sites simultaneously. Results show thatmethanogenesis is sensitive to temperature and substrate availability overthe top 75 cm of soil depth. Methane emissions estimated using single siteoptimization (SSO) of model parameters are underestimated by 9 g CH4 m−2 yr−1 on average (i.e., 50 % higher than the site average ofyearly methane emissions). While using the multi-site optimization (MSO),methane emissions are overestimated by 5 g CH4 m−2 yr−1 onaverage across all investigated sites (i.e., 37 % lower than the siteaverage of yearly methane emissions). 

Abstract In eddy covariance measurements, the storage flux represents the variation in time of the dry molar fraction of a given gas in the control volume representative of turbulent flux. Depending on the time scale considered, and on the height above ground of the measurements, it can either be a major component of the overall net ecosystem exchange or nearly negligible. Instrumental configuration and computational procedures must be optimized to measure this change at the time step used for the turbulent flux measurement. Three different configurations are suitable within the Integrated Carbon Observation System infrastructure for the storage flux determination: separate sampling, subsequent sampling and mixed sampling. These configurations have their own advantages and disadvantages, and must be carefully selected based on the specific features of the considered station. In this paper, guidelines about number and distribution of vertical and horizontal sampling points are given. Details about suitable instruments, sampling devices, and computational procedures for the quantification of the storage flux of different GHG gases are also provided. 

Abstract The eddy covariance is a powerful technique to estimate the surface-atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO 2 , water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in terms of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in e.g . ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO 2 and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-the-art processing methods. 

Abstract. Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934). 

Abstract The Integrated Carbon Observation System Research Infrastructure aims to provide long-term, continuous observations of sources and sinks of greenhouse gases such as carbon dioxide, methane, nitrous oxide, and water vapour. At ICOS ecosystem stations, the principal technique for measurements of ecosystem-atmosphere exchange of GHGs is the eddy-covariance technique. The establishment and setup of an eddy-covariance tower have to be carefully reasoned to ensure high quality flux measurements being representative of the investigated ecosystem and comparable to measurements at other stations. To fulfill the requirements needed for flux determination with the eddy-covariance technique, variations in GHG concentrations have to be measured at high frequency, simultaneously with the wind velocity, in order to fully capture turbulent fluctuations. This requires the use of high-frequency gas analysers and ultrasonic anemometers. In addition, to analyse flux data with respect to environmental conditions but also to enable corrections in the post-processing procedures, it is necessary to measure additional abiotic variables in close vicinity to the flux measurements. Here we describe the standards the ICOS ecosystem station network has adopted for GHG flux measurements with respect to the setup of instrumentation on towers to maximize measurement precision and accuracy while allowing for flexibility in order to observe specific ecosystem features. 

The main source of soil moisture variability in savanna ecosystems is pulsed rainfall. Rainfall pulsing impacts water‐stress durations, soil moisture switching between wet‐to‐dry and dry‐to‐wet states, and soil moisture spectra as well as derived measures from it such as soil moisture memory. Rainfall pulsing is also responsible for rapid changes in grassland leaf area and concomitant changes in evapotranspirational (ET) losses, which then impact soil moisture variability. With the use of a hierarchy of models and soil moisture measurements, temporal variability in root‐zone soil moisture and water‐stress periods are analysed at four African sites ranging from grass to miombo savannas. The normalized difference vegetation index (NDVI) and potential ET (PET)‐adjusted ET model predict memory timescale and dry persistence in agreement with measurements. The model comparisons demonstrate that dry persistence and mean annual dry periods must account for seasonal and interannual changes in maximum ET represented by NDVI and to a lesser extent PET. Interestingly, the precipitation intensity and soil moisture memory were linearly related across three savannas with ET/infiltration ∼ 1.0. This relation and the variability of length and timing of dry periods are also discussed.

 

Abstract. Methane (CH4) emissions from natural landscapes constituteroughly half of global CH4 contributions to the atmosphere, yet largeuncertainties remain in the absolute magnitude and the seasonality ofemission quantities and drivers. Eddy covariance (EC) measurements ofCH4 flux are ideal for constraining ecosystem-scale CH4emissions due to quasi-continuous and high-temporal-resolution CH4flux measurements, coincident carbon dioxide, water, and energy fluxmeasurements, lack of ecosystem disturbance, and increased availability ofdatasets over the last decade. Here, we (1) describe the newly publisheddataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset ofCH4 EC measurements (available athttps://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4includes half-hourly and daily gap-filled and non-gap-filled aggregatedCH4 fluxes and meteorological data from 79 sites globally: 42freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drainedecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverageglobally because the majority of sites in FLUXNET-CH4 Version 1.0 arefreshwater wetlands which are a substantial source of total atmosphericCH4 emissions; and (3) we provide the first global estimates of theseasonal variability and seasonality predictors of freshwater wetlandCH4 fluxes. Our representativeness analysis suggests that thefreshwater wetland sites in the dataset cover global wetland bioclimaticattributes (encompassing energy, moisture, and vegetation-relatedparameters) in arctic, boreal, and temperate regions but only sparselycover humid tropical regions. Seasonality metrics of wetland CH4emissions vary considerably across latitudinal bands. In freshwater wetlands(except those between 20∘ S to 20∘ N) the spring onsetof elevated CH4 emissions starts 3 d earlier, and the CH4emission season lasts 4 d longer, for each degree Celsius increase in meanannual air temperature. On average, the spring onset of increasing CH4emissions lags behind soil warming by 1 month, with very few sites experiencingincreased CH4 emissions prior to the onset of soil warming. Incontrast, roughly half of these sites experience the spring onset of risingCH4 emissions prior to the spring increase in gross primaryproductivity (GPP). The timing of peak summer CH4 emissions does notcorrelate with the timing for either peak summer temperature or peak GPP.Our results provide seasonality parameters for CH4 modeling andhighlight seasonality metrics that cannot be predicted by temperature or GPP(i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resourcefor diagnosing and understanding the role of terrestrial ecosystems andclimate drivers in the global CH4 cycle, and future additions of sitesin tropical ecosystems and site years of data collection will provide addedvalue to this database. All seasonality parameters are available athttps://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021).Additionally, raw FLUXNET-CH4 data used to extract seasonality parameterscan be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a completelist of the 79 individual site data DOIs is provided in Table 2 of this paper. 

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO₂ fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (V_cmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r² =  0.76; Nash–Sutcliffe modeling efficiency, MEF  =  0.76) and ecosystem respiration (ER, r² =  0.78, MEF  =  0.75), with lesser accuracy for latent heat fluxes (LE, r² =  0.42, MEF  =  0.14) and and net ecosystem CO₂ exchange (NEE, r² =  0.38, MEF  =  0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r² values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r² values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r² < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized V_cmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V_cmax value.