We measured CO2 and CH4 fluxes using chambers and eddy covariance (only CO2) from a moist moss tundra in Svalbard. The average net ecosystem exchange (NEE) during the summer (9 June–31 August) was ...negative (sink), with -0.139 ± 0.032 µmol m-2 s-1 corresponding to -11.8 g C m-2 for the whole summer. The cumulated NEE over the whole growing season (day no. 160 to 284) was -2.5 g C m-2. The CH4 flux during the summer period showed a large spatial and temporal variability. The mean value of all 214 samples was 0.000511 ± 0.000315 µmol m-2 s-1, which corresponds to a growing season estimate of 0.04 to 0.16 g CH4 m-2. Thus, we find that this moss tundra ecosystem is closely in balance with the atmosphere during the growing season when regarding exchanges of CO2 and CH4. The sink of CO2 and the source of CH4 are small in comparison with other tundra ecosystems in the high Arctic.Air temperature, soil moisture and the greenness index contributed significantly to explaining the variation in ecosystem respiration (Reco), while active layer depth, soil moisture and the greenness index were the variables that best explained CH4 emissions. An estimate of temperature sensitivity ofReco and gross primary productivity (GPP) showed that the sensitivity is slightly higher for GPP than for Reco in the interval 0–4.5 ∘C; thereafter, the difference is small up to about 6 ∘C and then begins to rise rapidly for Reco. The consequence of this, for a small increase in air temperature of 1∘ (all other variables assumed unchanged), was that the respiration increased more than photosynthesis turning the small sink into a small source (4.5 g C m-2) during the growing season. Thus, we cannot rule out that the reason why the moss tundra is close to balance today is an effect of the warming that has already taken place in Svalbard.
Terrestrial Arctic ecosystems play a key role in the global carbon (C) cycle, as they store a large amount of organic matter in permafrost. Among regions with continuous permafrost, Svalbard has one ...of the warmest permafrost and may provide a template of the environmental responses of Arctic regions to future climate change.
We analyze the CO2 fluxes at a polygonal tundra site in Adventdalen (Svalbard) during one full growing season across a vegetation and environmental gradient to understand how the interaction of different abiotic (thaw depth, ground surface temperature (GST), soil moisture, photosynthetic active radiation - PAR) and biotic (leaf area index (LAI), and plant phenology) factors affect the CO2 fluxes and identify the drivers of Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER).
Three distinct periods (early, peak, and late) characterized the growing season based on plant phenology and the main environmental conditions. Comparing early, peak and late season, both NEE and ER exhibited specific patterns: ER shown high values since the early season, only slightly increased at peak, and then decreased drastically in the late season, with GST being the most important driver of ER. The drivers of NEE changed during the season: thaw depth, PAR and GST during the early season, LAI at peak, and PAR during the late season. These data allow to highlight that the thawing and freezing of the upper part of the active layer during the early and late season controls ER, possibly due to the response of microbial respiration in the upper part of the soil. Especially during the late season, despite the fully developed active layer (reaching its highest thawing depth), the freezing of the uppermost 2 cm of soil induced the drastic decrease of the respiratory efflux. In addition, the seasonal C balance of our plots indicated a seasonal source at our plots, in apparent contrast with previous eddy covariance (EC) measurements from a wetter area nearby. This difference implies that drier ecosystems act as sources while wetter ecosystems are sinks, suggesting that a drying trend in polygonal tundra could switch these ecosystems from CO2 sinks to sources in a feedback to future climate change.
•We emphasize the role of the uppermost soil layer controlling CO2 fluxes in the Arctic.•The drivers of NEE and ER change during the growing season (early, peak, late).•Thaw depth, GST, LAI and PAR in turn drive NEE; GST always drives ER.•At late season despite the maximum thawing the uppermost soil freezing controls ER.•The shift from wet to dry vegetation can switch ecosystems from CO2 sinks to sources.
CO2 release from thawing permafrost is both a consequence of, and a driver for, global warming, making accurate information on the Arctic carbon cycle essential for climate predictions. Eddy ...covariance data obtained from Bayelva (Svalbard) in 2015, using well‐established processing and quality control techniques, indicate that most of the annual net CO2 uptake is due to high CO2 flux events in winter that are associated with strong winds and probably relate to technical limitations of the gas analyzer. Emission events may relate to either (unidentified) instrumental limitations or to physical processes such as CO2 advection. Excluding the high winter uptake events yields an annual CO2 budget close to zero; whether or not these events are included can, therefore, have a considerable effect on carbon budget calculations. Further investigation will be crucial to pinpoint the factors causing these high CO2 flux events and to derive scientifically substantiated flux processing standards.
Plain Language Summary
Global warming is making Arctic soils thaw, with formerly frozen organic material decomposing and producing the greenhouse gas CO2. This CO2 release further amplifies the rise in temperature. In order to predict how our climate will develop in the future, we, therefore, need to investigate how much CO2 is released into the atmosphere and how much is taken up by plants. Strong CO2 release or uptake signals are not expected during the Arctic winter due to the reduced microbial and plant activity but have nevertheless been observed at Arctic sites. We have investigated CO2 exchanges during the winter of 2015 at the Bayelva site, Svalbard, using the eddy covariance technique. We found that high levels of CO2 emission and uptake occurred during periods with high wind speed and have a significant impact on the calculated net annual CO2 exchange. The apparent CO2 uptake is likely to be an artefact resulting from technical limitations of the instruments, while the high levels of CO2 emission are probably a result of physical processes. However, known physical mechanisms alone, such as episodic outbursts of CO2 stored within the snow, cannot adequately explain our observations. Additional measurements will be required to identify the processes at play.
Key Points
High levels of CO2 exchange during the Arctic winter, associated with high wind speeds, have a marked effect on the annual carbon budget
Conventional flux measurement and calculation techniques are subject to large uncertainties under Arctic low‐flux conditions
Local abiotic processes cannot explain the high‐flux events, suggesting advective flux contributions or unidentified instrumental limitations
Abstract
CO
2
release from thawing permafrost is both a consequence of, and a driver for, global warming, making accurate information on the Arctic carbon cycle essential for climate predictions. ...Eddy covariance data obtained from Bayelva (Svalbard) in 2015, using well‐established processing and quality control techniques, indicate that most of the annual net CO
2
uptake is due to high CO
2
flux events in winter that are associated with strong winds and probably relate to technical limitations of the gas analyzer. Emission events may relate to either (unidentified) instrumental limitations or to physical processes such as CO
2
advection. Excluding the high winter uptake events yields an annual CO
2
budget close to zero; whether or not these events are included can, therefore, have a considerable effect on carbon budget calculations. Further investigation will be crucial to pinpoint the factors causing these high CO
2
flux events and to derive scientifically substantiated flux processing standards.
Plain Language Summary
Global warming is making Arctic soils thaw, with formerly frozen organic material decomposing and producing the greenhouse gas CO
2
. This CO
2
release further amplifies the rise in temperature. In order to predict how our climate will develop in the future, we, therefore, need to investigate how much CO
2
is released into the atmosphere and how much is taken up by plants. Strong CO
2
release or uptake signals are not expected during the Arctic winter due to the reduced microbial and plant activity but have nevertheless been observed at Arctic sites. We have investigated CO
2
exchanges during the winter of 2015 at the Bayelva site, Svalbard, using the eddy covariance technique. We found that high levels of CO
2
emission and uptake occurred during periods with high wind speed and have a significant impact on the calculated net annual CO
2
exchange. The apparent CO
2
uptake is likely to be an artefact resulting from technical limitations of the instruments, while the high levels of CO
2
emission are probably a result of physical processes. However, known physical mechanisms alone, such as episodic outbursts of CO
2
stored within the snow, cannot adequately explain our observations. Additional measurements will be required to identify the processes at play.
Key Points
High levels of CO
2
exchange during the Arctic winter, associated with high wind speeds, have a marked effect on the annual carbon budget
Conventional flux measurement and calculation techniques are subject to large uncertainties under Arctic low‐flux conditions
Local abiotic processes cannot explain the high‐flux events, suggesting advective flux contributions or unidentified instrumental limitations
Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms.
The ...scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure.
Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE).
A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations.
The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space.
Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations.
It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone.
Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread.
Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes.
In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE).
While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.
The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow–vegetation–atmosphere interactions across a range ...of spatio-temporal scales. To explore the role of snow cover for the land–atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined 3 years (2019–2021) of continuous eddy covariance flux measurements of the net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a 1-month delay in melt-out date, which falls in the 92nd percentile in the distribution of yearly melt-out dates in the period 2001–2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to 1 month later than the other ecosystem types. While the summertime average normalized difference vegetation index (NDVI) was reduced considerably during the extreme-snow year 2020, it reached the same maximum as in the other years for all but one of the ecosystem types (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model, as well as by the high correlation of flux magnitudes (correlation coefficient r=0.92 for NEE and r=0.89 for ET) between both areas. The 1-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021 and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (−31 to −6 gC m−2 yr−1) to a source (34 to 20 gC m−2 yr−1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.
The representation of snow processes in most large-scale hydrological and climate models is known to introduce considerable uncertainty into the predictions and projections of water availability. ...During the critical snowmelt period, the main challenge in snow modeling is that net radiation is spatially highly variable for a patchy snow cover, resulting in large horizontal differences in temperatures and heat fluxes. When a wind blows over such a system, these differences can drive advection of sensible and latent heat from the snow-free areas to the snow patches, potentially enhancing the melt rates at the leading edge and increasing the variability of subgrid melt rates. To get more insight into these processes, we examine the melt along the upwind and downwind edges of a 50 m long snow patch in the Finseelvi catchment, Norway, and try to explain the observed behavior with idealized simulations of heat fluxes and air movement over patchy snow. The melt of the snow patch was monitored from 11 June until 15 June 2019 by making use of height maps obtained through the photogrammetric structure-from-motion principle. A vertical melt of 23 ± 2.0 cm was observed at the upwind edge over the course of the field campaign, whereas the downwind edge melted only 3 ± 0.4 cm. When comparing this with meteorological measurements, we estimate the turbulent heat fluxes to be responsible for 60 % to 80 % of the upwind melt, of which a significant part is caused by the latent heat flux. The melt at the downwind edge approximately matches the melt occurring due to net radiation. To better understand the dominant processes, we represented this behavior in idealized direct numerical simulations, which are based on the measurements on a single snow patch by Harder et al. (2017) and resemble a flat, patchy snow cover with typical snow patch sizes of 15, 30, and 60 m. Using these simulations, we found that the reduction of the vertical temperature gradient over the snow patch was the main cause of the reductions in vertical sensible heat flux over distance from the leading edge, independent of the typical snow patch size. Moreover, we observed that the sensible heat fluxes at the leading edge and the decay over distance were independent of snow patch size as well, which resulted in a 15 % and 25 % reduction in average snowmelt for, respectively, a doubling and quadrupling of the typical snow patch size. These findings lay out pathways to include the effect of highly variable turbulent heat fluxes based on the typical snow patch size in large-scale hydrological and climate models to improve snowmelt modeling.
The large spatial variability in Arctic tundra complicates the representative assessment of CO2 budgets. Accurate measurements of these heterogeneous landscapes are, however, essential to ...understanding their vulnerability to climate change. We surveyed a polygonal tundra lowland on Svalbard with an unmanned aerial vehicle (UAV) that mapped ice-wedge morphology to complement eddy covariance (EC) flux measurements of CO2. The analysis of spectral distributions showed that conventional EC methods do not accurately capture the turbulent CO2 exchange with a spatially heterogeneous surface that typically features small flux magnitudes. Nonlocal (low-frequency) flux contributions were especially pronounced during snowmelt and introduced a large bias of −46 gC m−2 to the annual CO2 budget in conventional methods (the minus sign indicates a higher uptake by the ecosystem). Our improved flux calculations with the ogive optimization method indicated that the site was a strong sink for CO2 in 2015 (−82 gC m−2). Due to differences in light-use efficiency, wetter areas with low-centered polygons sequestered 47 % more CO2 than drier areas with flat-centered polygons. While Svalbard has experienced a strong increase in mean annual air temperature of more than 2 K in the last few decades, historical aerial photographs from the site indicated stable ice-wedge morphology over the last 7 decades. Apparently, warming has thus far not been sufficient to initiate strong ice-wedge degradation, possibly due to the absence of extreme heat episodes in the maritime climate on Svalbard. However, in Arctic regions where ice-wedge degradation has already initiated the associated drying of landscapes, our results suggest a weakening of the CO2 sink in polygonal tundra.
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