We have analyzed decade‐long methane flux data set from a boreal fen, Siikaneva, together with data on environmental parameters and carbon dioxide exchange. The methane flux showed seasonal cycle but ...no systematic diel cycle. The highest fluxes were observed in July–August with average value of 73 nmol m−2 s−1. Wintertime fluxes were small but positive, with January–March average of 6.7 nmol m−2 s−1. Daily average methane emission correlated best with peat temperatures at 20–35 cm depths. The second highest correlation was with gross primary production (GPP). The best correspondence between emission algorithm and measured fluxes was found for a variable‐slope generalized linear model (r2 = 0.89) with peat temperature at 35 cm depth and GPP as explanatory variables, slopes varying between years. The homogeneity of slope approach indicated that seasonal variation explained 79% of the sum of squares variation of daily average methane emission, the interannual variation in explanatory factors 7.0%, functional change 5.3%, and random variation 9.1%. Significant correlation between interannual variability of growing season methane emission and that of GPP indicates that on interannual time scales GPP controls methane emission variability, crucially for development of process‐based methane emission models. Annual methane emission ranged from 6.0 to 14 gC m−2 and was 2.7 ± 0.4% of annual GPP. Over 10‐year period methane emission was 18% of net ecosystem exchange as carbon. The weak relation of methane emission to water table position indicates that space‐to‐time analogy, used to extrapolate spatial chamber data in time, may not be applicable in seasonal time scales.
Plain Language Summary
Methane emission from a boreal wetland was measured over one decade. Methane emission shows strong seasonal cycle, with highest emission in late summer and lowest emission during winter. No diel cycle was observed. The methane emission is an important part of the carbon balance of the wetland as 18% of carbon taken up as carbon dioxide was emitted back into atmosphere as methane. The seasonal cycle of the emission was controlled first by peat temperature and second by ecosystem photosynthesis. The interannual variability of methane emission was more related to photosynthesis. A large part of the interannual variability remained unexplained by the measured environmental parameters.
Key Points
Methane emission was insensitive to water table variations observed during decade‐long period
Soil temperature was the dominant driver of methane emission during shorter periods of time (<1 year), while interannual variation was more related to variation in GPP
Seventy‐nine percent of the variability of daily average methane emission is due to seasonal change of drivers, 7.0% is due to interannual variation of drivers, and 5.3% is due to functional change
Freshwaters bring a notable contribution to the global carbon budget by emitting both carbon dioxide (CO2) and methane (CH4) to the atmosphere. Global estimates of freshwater emissions traditionally ...use a wind-speed-based gas transfer velocity, kCC (introduced by Cole and Caraco, 1998), for calculating diffusive flux with the boundary layer method (BLM). We compared CH4 and CO2 fluxes from BLM with kCC and two other gas transfer velocities (kTE and kHE), which include the effects of water-side cooling to the gas transfer besides shear-induced turbulence, with simultaneous eddy covariance (EC) and floating chamber (FC) fluxes during a 16-day measurement campaign in September 2014 at Lake Kuivajärvi in Finland. The measurements included both lake stratification and water column mixing periods. Results show that BLM fluxes were mainly lower than EC, with the more recent model kTE giving the best fit with EC fluxes, whereas FC measurements resulted in higher fluxes than simultaneous EC measurements. We highly recommend using up-to-date gas transfer models, instead of kCC, for better flux estimates. BLM CO2 flux measurements had clear differences between daytime and night-time fluxes with all gas transfer models during both stratified and mixing periods, whereas EC measurements did not show a diurnal behaviour in CO2 flux. CH4 flux had higher values in daytime than night-time during lake mixing period according to EC measurements, with highest fluxes detected just before sunset. In addition, we found clear differences in daytime and night-time concentration difference between the air and surface water for both CH4 and CO2. This might lead to biased flux estimates, if only daytime values are used in BLM upscaling and flux measurements in general. FC measurements did not detect spatial variation in either CH4 or CO2 flux over Lake Kuivajärvi. EC measurements, on the other hand, did not show any spatial variation in CH4 fluxes but did show a clear difference between CO2 fluxes from shallower and deeper areas. We highlight that while all flux measurement methods have their pros and cons, it is important to carefully think about the chosen method and measurement interval, as well as their effects on the resulting flux.
Peatlands account for a large fraction of global methane (
CH
4
) emissions. These environments exchange
CH
4
with the atmosphere via three main mechanisms: diffusion through the peat and water, ...plant-mediated diffusion, and sporadic release of
CH
4
bubbles. While rapid advances have been made in measuring
CH
4
fluxes above peatlands on sub-daily time scales, partitioning
CH
4
fluxes into ebullition and background diffusion remains a formidable challenge. Such partitioning is becoming necessary for future projection of methane concentration as atmospheric, hydrologic, and edaphic drivers of these two types of methane releases may differ significantly. Using surface renewal theory, a framework for partitioning measured methane fluxes based on the mass transfer mechanism is introduced with the overall objective of characterizing the intermittency of
CH
4
source and its strength at the ground. This approach is tested using a large dataset of measured turbulent air velocity and multiple scalar concentrations (including heat, water vapour, and carbon dioxide) for flow above a boreal peatland in Finland. The transport efficiencies of different gas transfer mechanisms are then evaluated for scalars characterized by background diffusion (e.g., water vapour) or by intermittent sources (e.g., methane). Whether environmental variables such as water-table levels and atmospheric conditions have a signature on the occurrence of
CH
4
hotspots is then investigated. Building upon the classical surface renewal theory, this work introduces a novel approach for inferring the intermittent nature of scalar sources at the ground and for exploring how non-homogeneity affects the efficiency of gas turbulent transport in the atmospheric surface layer.
Large variability is inherent to turbulent flux observations. We review different methods used to estimate the flux random errors. Flux errors are calculated using measured turbulent and simulated ...artificial records. We recommend two flux errors with clear physical meaning: the flux error of the covariance, defining the error of the measured flux as 1 standard deviation of the random uncertainty of turbulent flux observed over an averaging period of typically 30 min to 1 h duration; and the error of the flux due to the instrumental noise. We suggest that the numerical approximation by Finkelstein and Sims (2001) is a robust and accurate method for calculation of the first error estimate. The method appeared insensitive to the integration period and the value 200 s sufficient to obtain the estimate without significant bias for variety of sites and wide range of observation conditions. The filtering method proposed by Salesky et al. (2012) is an alternative to the method by Finkelstein and Sims (2001) producing consistent, but somewhat lower, estimates. The method proposed by Wienhold et al. (1995) provides a good approximation to the total flux random uncertainty provided that independent cross-covariance values far from the maximum are used in estimation as suggested in this study. For the error due to instrumental noise the method by Lenschow et al. (2000) is useful in evaluation of the respective uncertainty. The method was found to be reliable for signal-to-noise ratio, defined by the ratio of the standard deviation of the signal to that of the noise in this study, less than three. Finally, the random uncertainty of the error estimates was determined to be in the order of 10 to 30 % for the total flux error, depending on the conditions and method of estimation.
Nitrous oxide (N2O) is an important greenhouse gas produced in soil and aquatic ecosystems. Its warming potential is 296 times higher than that of CO2. Most N2O emission measurements made so far are ...limited in temporal and spatial resolution causing uncertainties in the global N2O budget. Recent advances in laser spectroscopic techniques provide an excellent tool for area-integrated, direct and continuous field measurements of N2O fluxes using the eddy covariance method. By employing this technique on an agricultural site with four laser-based analysers, we show here that N2O exchange exhibits contrasting diurnal behaviour depending upon soil nitrogen availability. When soil N was high due to fertilizer application, N2O emissions were higher during daytime than during the night. However, when soil N became limited, emissions were higher during the night than during the day. These reverse diurnal patterns supported by isotopic analyses may indicate a dominant role of plants on microbial processes associated with N2O exchange. This study highlights the potential of new technologies in improving estimates of global N2O sources.
Climate change impacts the characteristics of the vegetation carbon-uptake process in the northern Eurasian terrestrial ecosystem. However, the currently available direct CO2 flux measurement ...datasets, particularly for central Siberia, are insufficient for understanding the current condition in the northern Eurasian carbon cycle. Here, we report daily and seasonal interannual variations in CO2 fluxes and associated abiotic factors measured using eddy covariance in a coniferous forest and a bog near Zotino, Krasnoyarsk Krai, Russia, for April to early June, 2013–2017. Despite the snow not being completely melted, both ecosystems became weak net CO2 sinks if the air temperature was warm enough for photosynthesis. The forest became a net CO2 sink 7–16 days earlier than the bog. After the surface soil temperature exceeded ~1 °C, the ecosystems became persistent net CO2 sinks. Net ecosystem productivity was highest in 2015 for both ecosystems because of the anomalously high air temperature in May compared with other years. Our findings demonstrate that long-term monitoring of flux measurements at the site level, particularly during winter and its transition to spring, is essential for understanding the responses of the northern Eurasian ecosystem to spring warming.
We estimated the CH
4
budget in Finland for 2004-2014 using the CTE-CH
4
data assimilation system with an extended atmospheric CH
4
observation network of seven sites from Finland to surrounding ...regions (Hyytiälä, Kjølnes, Kumpula, Pallas, Puijo, Sodankylä, and Utö). The estimated average annual total emission for Finland is 0.6 ± 0.5 Tg CH
4
yr
−1
. Sensitivity experiments show that the posterior biospheric emission estimates for Finland are between 0.3 and 0.9 Tg CH
4
yr
−1
, which lies between the LPX-Bern-DYPTOP (0.2 Tg CH
4
yr
−1
) and LPJG-WHyMe (2.2 Tg CH
4
yr
−1
) process-based model estimates. For anthropogenic emissions, we found that the EDGAR v4.2 FT2010 inventory (0.4 Tg CH
4
yr
−1
) is likely to overestimate emissions in southernmost Finland, but the extent of overestimation and possible relocation of emissions are difficult to derive from the current observation network. The posterior emission estimates were especially reliant on prior information in central Finland. However, based on analysis of posterior atmospheric CH
4
, we found that the anthropogenic emission distribution based on a national inventory is more reliable than the one based on EDGAR v4.2 FT2010. The contribution of total emissions in Finland to global total emissions is only about 0.13%, and the derived total emissions in Finland showed no trend during 2004-2014. The model using optimized emissions was able to reproduce observed atmospheric CH
4
at the sites in Finland and surrounding regions fairly well (correlation
, bias
ppb), supporting adequacy of the observations to be used in atmospheric inversion studies. In addition to global budget estimates, we found that CTE-CH
4
is also applicable for regional budget estimates, where small scale (1
1
in this case) optimization is possible with a dense observation network.
Dynamics of carbon dioxide and energy exchange over a small boreal lake were investigated. Flux measurements have been carried out by the eddy covariance technique during two open‐water periods ...(June–October) at Lake Kuivajärvi in Finland. Sensible heat (H) flux peaked in the early morning, and upward sensible heat flux at night results in unstable stratification over the lake. Minimum H was measured in the late afternoon, often resulting in adiabatic conditions or slightly stable stratification over the lake. The latent heat flux (LE) showed a different pattern, peaking in the afternoon and having a minimum at night. High correlation (r2 = 0.75) between H and water‐air temperature difference multiplied by wind speed (U) was found, while LE strongly correlated with the water vapor pressure deficit multiplied by U (r2 = 0.78). Monthly average values of energy balance closure ranged between 70 and 99%. The lake acted as net source of carbon dioxide, and the measured flux (FCO2) averaged over the two open‐water periods (0.7 µmol m−2 s−1) was up to 3 times higher than those reported in other studies. Furthermore, it was found that during period of high wind speed (>3 m s−1) shear‐induced water turbulence controls the water‐air gas transfer efficiency. However, under calm nighttime conditions, FCO2 was poorly correlated with the difference between the water and the equilibrium CO2 concentrations multiplied by U. Nighttime cooling of surface water enhances the gas transfer efficiency through buoyancy‐driven turbulent mixing, and simple wind speed‐based transfer velocity models strongly underestimate FCO2.
Key Points
Dynamics of CO2 and energy exchange over a boreal lake are studied
The lake acted as net source of carbon dioxide
Nighttime cooling of surface water enhances the gas transfer efficiency
Stably stratified roughness sublayer flows are ubiquitous yet remain difficult to represent in models and to interpret using field experiments. Here, continuous high‐frequency potential temperature ...profiles from the forest floor up to 6.5 times the canopy height observed with distributed temperature sensing (DTS) are used to link eddy topology to roughness sublayer stability correction functions and coupling between air layers within and above the canopy. The experiments are conducted at two forest stands classified as hydrodynamically sparse and dense. Near‐continuous profiles of eddy sizes (length scales) and effective mixing lengths for heat are derived from the observed profiles using a novel conditional sampling approach. The approach utilizes potential temperature isoline fluctuations from a statically stable background state. The transport of potential temperature by an observed eddy is assumed to be conserved (adiabatic movement) and we assume that irreversible heat exchange between the eddy and the surrounding background occurs along the (vertical) periphery of the eddy. This assumption is analogous to Prandtl's mixing‐length concept, where momentum is transported rapidly vertically and then equilibrated with the local mean velocity gradient. A distinct dependence of the derived length scales on background stratification, height above ground, and canopy characteristics emerges from the observed profiles. Implications of these findings for (1) the failure of Monin–Obukhov similarity in the roughness sublayer and (2) above‐canopy flow coupling to the forest floor are examined. The findings have practical applications in terms of analysing similar DTS data sets with the proposed approach, modelling roughness sublayer flows, and interpreting nocturnal eddy covariance measurements above tall forested canopies.
Nocturnal air flows above and within forests are highly complex, due to trees blocking air flow and stably stratified air layers inhibiting vertical movement. These complexities cause severe challenges for micrometeorological models and measurements alike. Here, continuous high‐frequency potential temperature profiles observed with distributed temperature sensing are used to study these flows. Eddy topology is derived from the measurements and used to estimate roughness sublayer stability correction functions and coupling between air layers within and above the canopy.
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