A Methodology for Providing Surface-Cover-Corrected Net Radiation at Heterogeneous Eddy-Covariance Sites

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CO2 and CH4 exchanges between moist moss tundra and atmosphere on Kapp Linne, Svalbard

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 mu mol 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 mu mol 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 (R-eco), 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 R-eco in the interval 0-4.5 degrees C; thereafter, the difference is small up to about 6 degrees C and then begins to rise rapidly for R-eco. The consequence of this, for a small increase in air temperature of 1 degrees (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

Spatial variability of CO \u3c inf\u3e 2 uptake in polygonal tundra: Assessing low-frequency disturbances in eddy covariance flux estimates

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 gCm-2 to the annual CO22 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 gCm2). Due to differences in light-use efficiency, wetter areas with lowcentered 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 2K 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

The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF)

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. Chlorophyll fluorescenceSolar Induced Fluorescence (SIF)ReflectancePhotosynthetic functionMERIS terrestrial chlorophyll index (MTCI)High-Arctic tundrapublishedVersio

Snowpack fluxes of methane and carbon dioxide from high Arctic tundra

Measurements of the land-atmosphere exchange of the greenhouse gases methane (CH4) and carbon dioxide (CO2) in high Arctic tundra ecosystems are particularly difficult in the cold season, resulting in large uncertainty on flux magnitudes and their controlling factors during this long, frozen period. We conducted snowpack measurements of these gases at permafrost-underlain wetland sites in Zackenberg Valley (NE Greenland, 74°N) and Adventdalen Valley (Svalbard, 78°N), both of which also feature automatic closed chamber flux measurements during the snow-free period. At Zackenberg, cold season emissions were 1 to 2 orders of magnitude lower than growing season fluxes. Perennially, CH4 fluxes resembled the same spatial pattern, which was largely attributed to differences in soil wetness controlling substrate accumulation and microbial activity. We found no significant gas sinks or sources inside the snowpack but detected a pulse in the δ13C-CH4 stable isotopic signature of the soil's CH4 source during snowmelt, which suggests the release of a CH4 reservoir that was strongly affected by methanotrophic microorganisms. In the polygonal tundra of Adventdalen, the snowpack featured several ice layers, which suppressed the expected gas emissions to the atmosphere, and conversely lead to snowpack gas accumulations of up to 86 ppm CH4 and 3800 ppm CO2 by late winter. CH4 to CO2 ratios indicated distinctly different source characteristics in the rampart of ice-wedge polygons compared to elsewhere on the measured transect, possibly due to geomorphological soil cracks. Collectively, these findings suggest important ties between growing season and cold season greenhouse gas emissions from high Arctic tundra

Snow-vegetation-atmosphere interactions in alpine tundra

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 three years (2019&ndash;2021) of continuous eddy covariance flux measurements of 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 one-month delay in melt-out date, which falls on the 92nd-percentile in the distribution of yearly melt-out dates in the period 2001&ndash;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 one 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 the ecosystem type (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 the high correlation of flux magnitudes (correlation coefficient r = 0.92 for NEE and r = 0.89 for ET) between both areas. The one-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 (&minus;31 to &minus;6 gC m&minus;2 yr&minus;1) to a source (34 to 20 gC m&minus;2 yr&minus;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.</p

Climate–ecosystem modelling made easy: The Land Sites Platform

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.publishedVersio

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994-2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm(-2)) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.An international team of researchers finds high potential for improving climate projections by a more comprehensive treatment of largely ignored Arctic vegetation types, underscoring the importance of Arctic energy exchange measuring stations.Peer reviewe