Astrid Vatne

• Pirk, Norbert; Aalstad, Kristoffer; Yilmaz, Yeliz A.; Vatne, Astrid; Popp, Andrea & Horvath, Peter [Show all 12 contributors for this article] (2023). Snow-vegetation-atmosphere interactions in alpine tundra. Biogeosciences. ISSN 1726-4170. 20(11), p. 2031–2047. doi: 10.5194/bg-20-2031-2023. Full text in Research Archive Show summary

  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.

• Pirk, Norbert; Aalstad, Kristoffer; Westermann, Sebastian; Vatne, Astrid; van Hove, Alouette & Tallaksen, Lena Merete [Show all 8 contributors for this article] (2022). Inferring surface energy fluxes using drone data assimilation in large eddy simulations. Atmospheric Measurement Techniques. ISSN 1867-1381. 15(24), p. 7293–7314. doi: 10.5194/amt-15-7293-2022. Full text in Research Archive Show summary

  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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• Vatne, Astrid; Vollsnes, Ane Victoria; Pirk, Norbert & Tallaksen, Lena M. (2023). Drought effects on dwarf birch leaves.
• Engeland, Kolbjørn; Erlandsen, Helene B.; Gelati, Emiliano; Huang, Shaochun; Narayanappa, Devaraju & Pirk, Norbert [Show all 10 contributors for this article] (2023). Intercomparison of local evapotranspiration estimates at high latitudes.
• Torgersen, Eivind; Vatne, Astrid & Vollsnes, Ane Victoria (2022). Planter er ikke bare passive mottakere av klimaendringer. Titan.uio.no.
• Engeland, Kolbjørn; Aas, Kjetil Schanke; Erlandsen, Helene Birkelund; Gelati, Emiliano; Huang, Shaochun & Narayanappa, Devaraju [Show all 11 contributors for this article] (2022). LATICE MIP evapotranspiration – A model intercomparison project for evapotranspiration estimates at high latitudes.
• Gullvåg, Rebekka; Vollsnes, Ane Victoria; Bryn, Anders; Brysting, Anne Krag & Vatne, Astrid (2021). Effects of high-latitude light conditions on stomatal conductance and photosynthesis in white clover (Trifolium repens) .
• Vollsnes, Ane Victoria & Vatne, Astrid (2020). Field work at Iškoras and input to transpiration modelling.

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