the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Snow-vegetation-atmosphere interactions in alpine tundra
Kristoffer Aalstad
Yeliz A. Yilmaz
Astrid Vatne
Andrea L. Popp
Peter Horvath
Anders Bryn
Ane Victoria Vollsnes
Sebastian Westermann
Terje Koren Berntsen
Frode Stordal
Lena Merete Tallaksen
Abstract. 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 (2019–2021) 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 2001–2021. 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 (−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.
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Norbert Pirk et al.
Status: final response (author comments only)
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RC1: 'Comment on bg-2023-21', Anonymous Referee #1, 14 Mar 2023
The manuscript by Pirk et al. investigates links between net ecosystem CO2 and water vapour exchange and snowpack dynamics at an alpine tundra over a three-year observation period. The authors combine observations using the eddy covariance technique with remote sensing observations of snow cover and of vegetation indices and with land cover classifications. One of their main findings is that the site turned into an annual net CO2 source during the year with most snow accumulation and consequently the latest snowmelt date, while the site was a net CO2 sink during the other years when snow cover was close to the long-term mean. Understanding interactions between snowpack dynamics and land-atmosphere exchange of CO2 and water vapour is crucial to better predict climate change impacts on alpine ecosystems and the topic of this study is thus timely and addresses an important research topic. However, in my opinion, the study would strongly benefit from clearly defined research questions that better link to the authors’ analyses. The submitted manuscript presents various analyses that are only loosely connected. The lack of coherence makes it difficult for the reader to grasp the most important findings/implications of this study. The authors may consider reframing the study and to link each analysis to a specific objective. For example, it remains unclear how the spatial variability in melt-out dates across the landscape links to interannual variability in NEE and ET. Furthermore, the authors demonstrate a link between snow accumulation and synoptic atmospheric circulation patterns. However, it remains unclear how this analysis then links to the flux tower measurements. The authors present a range of interesting findings that would have a much stronger impact if they were logically connected.
Specific comments
In some cases, the author report correlation coefficients (r) and in some cases the coefficient of determination (R2). I would recommend a consistent use of one of the two metrics.
Line 51-55: It remains unclear how the analysis of water-use efficiency contributes to the main goals of this study.
Line 77-84: Here, listing of the main objectives would help framing the study. As it is written now, it emphasises the “exploration” of various datasets, but I think the logical links between these analyses need to be explained.
Line 92: I do not think that the “minimum 30-min average” is a good metric to support the statement that winters are mild.
Line 120: How were these limits determined?
Line 224-225: This statement should be supported by observations. What is the contribution of February to total winter snowfall events?
Figure 2: How was NDVI treated when ground was snow covered? This analysis would be strengthened if statistical analyses of differences between land cover types would be presented.
3.2 Flux dynamics in the two footprints: The authors analyse two footprints separately, which is a reasonable approach if underlying land cover composition is very different. However, the authors find very similar NEE and ET dynamics and it remains unclear what the added value of this analysis is for the study. A better explanation of the separate treatment of the two footprints would be useful.
Line 328: Perhaps rephrase: “in order to maximize leaf area”
Line 372: Gap-filling algorithms like Marginal Distribution Sampling (MDS) do not prescribe functional relationships.
Citation: https://doi.org/10.5194/bg-2023-21-RC1 -
RC2: 'Comment on bg-2023-21', Anonymous Referee #2, 18 Mar 2023
Pirk and others explore the response of a Norwegian alpine tundra ecosystem to a year with anomalously late snowmel. The Introduction bounced around a bit between different topics including carbon flux, plant succession, global change, hydrology, remote sensing, and more. All of these things are interconnected of course, and the Introduction was very nicely cited, but the topics could be linked more clearly to point toward the particular topic of this study. As a consequence it wasn't entirely clear why the landsat, sentinel, and modis observations were used when modis measures more frequently at coarser scales and landsat and sentinel measure less frequently at finer scales, and how these observations fit together. Was MODIS for historical melt out dates and how were melt out dates characterized for the 16 day landsat overpass? The results are interesting but I had a difficult time understanding how everything fit together.
Are fig. 5 c and d on log scales?
The eddy covariance measurements were discussed nicely and the gapfilling approach was well suited to the site.
All in all with some restructuring and focus on a consistent narrative the manuscript will be publishable as it makes some interesting points.
Citation: https://doi.org/10.5194/bg-2023-21-RC2
Norbert Pirk et al.
Norbert Pirk et al.
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