A one-point measurement of net radiation is typically not representative of radiative energy available for the turbulent exchange of latent and sensible heat at eddy-covariance sites with ...heterogeneous surface cover. We propose a methodology for providing surface-cover-corrected net radiation matching the footprint of turbulent fluxes at a heterogenous eddy-covariance site. This is demonstrated at a complex sub-alpine site in southern central Norway over a week. The methodology is assessed by comparing the energy balance closure calculated with the regular one-point net radiation measurement at the flux tower against the surface-cover-corrected net radiation. The assessment indicates a decrease in the energy imbalance by 8% when assessed with the energy balance ratio, but no improvement is revealed when assessed with regression methods. However, only a small dataset serves as basis for this demonstration, and the findings therefore cannot necessarily be generalized. Further testing and application of the methodology is required to understand the full effect of surface-cover-correcting mismatching footprints of turbulent fluxes and net radiation at heterogeneous eddy-covariance sites.
Extensive regions in the permafrost zone are projected to become climatically unsuitable to sustain permafrost peatlands over the next century, suggesting transformations in these landscapes that can ...leave large amounts of permafrost carbon vulnerable to post‐thaw decomposition. We present 3 years of eddy covariance measurements of CH4 and CO2 fluxes from the degrading permafrost peatland Iškoras in Northern Norway, which we disaggregate into separate fluxes of palsa, pond, and fen areas using information provided by the dynamic flux footprint in a novel ensemble‐based Bayesian deep neural network framework. The 3‐year mean CO2‐equivalent flux is estimated to be 106 gCO2 m−2 yr−1 for palsas, 1,780 gCO2 m−2 yr−1 for ponds, and −31 gCO2 m−2 yr−1 for fens, indicating that possible palsa degradation to thermokarst ponds would strengthen the local greenhouse gas forcing by a factor of about 17, while transformation into fens would slightly reduce the current local greenhouse gas forcing.
Plain Language Summary
Arctic and sub‐arctic regions on the southern border of the permafrost zone often feature peatlands with a patchy surface of peat mounds, thaw ponds, and surrounding fens. As the permafrost underneath peat mounds thaws, these areas transform and can change their emission or uptake of greenhouse gases like CO2 and methane. Assessing this gas exchange on the patchy surface is difficult because our measurement techniques cannot directly observe the variability in space and time. We collected 3 years of gas exchange measurements at a Norwegian permafrost peatland and developed a new method using a collection of uncertainty‐aware neural networks to predict the greenhouse gas exchange of different surface types. Our work suggests that large amounts of methane are emitted by ponds and fens, while the elevated peat mounds have almost no methane emissions. For CO2, we see that ponds are strong emitters, while fens take up substantial amounts as their vegetation absorbs this gas. We are still unsure when the peat mounds will collapse and if they turn into ponds or fens, but we can say that pond formation would give a 17 fold increase in greenhouse gas emissions, while fen formation would slightly reduce today's emissions of permafrost peatlands.
Key Points
Eddy covariance fluxes are disaggregated for different surfaces using Bayesian neural networks to derive uncertainty‐aware carbon balances
While palsa areas have a near‐zero annual methane balance, the fens and ponds that form upon palsa degradation emit large amounts of methane
Fens compensate for methane emissions with strong annual CO2 sinks, while ponds appear as strong, yet uncertain, CO2 emission hotspots
A hyperspectral field sensor (FloX) was installed in Adventdalen (Svalbard, Norway) in 2019 as part of the Svalbard Integrated Arctic Earth Observing System (SIOS) for monitoring vegetation phenology ...and Sun-Induced Chlorophyll Fluorescence (SIF) of high-Arctic tundra. This northernmost hyperspectral sensor is located within the footprint of a tower for long-term eddy covariance flux measurements and is an integral part of an automatic environmental monitoring system on Svalbard (AsMovEn), which is also a part of SIOS. One of the measurements that this hyperspectral instrument can capture is SIF, which serves as a proxy of gross primary production (GPP) and carbon flux rates. This paper presents an overview of the data collection and processing, and the 4-year (2019–2021) datasets in processed format are available at: https://thredds.met.no/thredds/catalog/arcticdata/infranor/NINA-FLOX/raw/catalog.html associated with https://doi.org/10.21343/ZDM7-JD72 under a CC-BY-4.0 license. Results obtained from the first three years in operation showed interannual variation in SIF and other spectral vegetation indices including MERIS Terrestrial Chlorophyll Index (MTCI), EVI and NDVI. Synergistic uses of the measurements from this northernmost hyperspectral FLoX sensor, in conjunction with other monitoring systems, will advance our understanding of how tundra vegetation responds to changing climate and the resulting implications on carbon and energy balance.
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.
Emissions of biogenic volatile organic compounds (BVOCs) are a crucial component of biosphere-atmosphere interactions. In northern latitudes, climate change is amplified by feedback processes in ...which BVOCs have a recognized, yet poorly quantified role, mainly due to a lack of measurements and concomitant modeling gaps. Hence, current Earth system models mostly rely on temperature responses measured on vegetation from lower latitudes, rendering their predictions highly uncertain. Here, we show how tundra isoprene emissions respond vigorously to temperature increases, compared to model results. Our unique dataset of direct eddy covariance ecosystem-level isoprene measurements in two contrasting ecosystems exhibited
(the factor by which the emission rate increases with a 10 °C rise in temperature) temperature coefficients of up to 20.8, that is, 3.5 times the
of 5.9 derived from the equivalent model calculations. Crude estimates using the observed temperature responses indicate that tundra vegetation could enhance their isoprene emissions by up to 41% (87%)-that is, 46% (55%) more than estimated by models-with a 2 °C (4 °C) warming. Our results demonstrate that tundra vegetation possesses the potential to substantially boost its isoprene emissions in response to future rising temperatures, at rates that exceed the current Earth system model predictions.
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
We measured CO.sub.2 and CH.sub.4 fluxes using chambers and eddy covariance (only CO.sub.2) 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.sup.-2 s.sup.-1 corresponding to -11.8 g C m.sup.-2 for the whole summer. The cumulated NEE over the whole growing season (day no. 160 to 284) was -2.5 g C m.sup.-2 . The CH.sub.4 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.sup.-2 s.sup.-1, which corresponds to a growing season estimate of 0.04 to 0.16 g CH.sub.4 m.sup.-2 . Thus, we find that this moss tundra ecosystem is closely in balance with the atmosphere during the growing season when regarding exchanges of CO.sub.2 and CH.sub.4 . The sink of CO.sub.2 and the source of CH.sub.4 are small in comparison with other tundra ecosystems in the high Arctic.
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 of
Reco 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.
Data assimilation techniques that integrate available observations with snow models have been proposed as a viable option to simultaneously help constrain model uncertainty and add value to ...observations by improving estimates of the snowpack state. However, the propagation of information from spatially sparse observations in high-resolution simulations remains an under-explored topic. To remedy this, the development of data assimilation techniques that can spread information in space is a crucial step. Herein, we examine the potential of spatio-temporal data assimilation for integrating sparse snow depth observations with hyper-resolution (5 m) snow simulations in the Izas central Pyrenean experimental catchment (Spain). Our experiments were developed using the Multiple Snow Data Assimilation System (MuSA) with new improvements to tackle the spatio-temporal data assimilation. Therein, we used a deterministic ensemble smoother with multiple data assimilation (DES-MDA) with domain localization.
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.
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