Rescue and homogenisation of 140 years of glacier mass balance data in Switzerland
- 1Laboratory of Hydraulics, Hydrology and Glaciology, ETH Zürich
- 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
- 3Department of Geosciences, University of Fribourg, Fribourg, Switzerland)
- 1Laboratory of Hydraulics, Hydrology and Glaciology, ETH Zürich
- 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
- 3Department of Geosciences, University of Fribourg, Fribourg, Switzerland)
Abstract. Glacier monitoring in Switzerland has resulted in some of the longest and most complete data series globally. Mass balance observations at individual locations, starting in the 19th century, are the backbone of the monitoring as they represent the raw and original glaciological data demonstrating the response of snow accumulation and snow/ice melt to changes in climate forcing. So far, however, the variety of sources of historic measurements has not been systematically processed and documented. Here, we present a new complete and extensive point glacier mass balance dataset for the Swiss Alps that provides attributes for data quality and corresponding uncertainties. Original sources were digitized or re-assessed to validate or to correct existing entries and to identify metadata. The sources of data are highly diverse and stem from almost 140 years of records, originating from handwritten field notes, unpublished project documents, various digital sources, published reports, as well as meta-knowledge of the observers. The project resulted in data series with metadata for 63 individual Swiss glaciers, including more than 60'000 point observations of mass balance. Data were systematically analyzed and homogenized, e.g. by supplementing partly missing information based on correlations inferred from direct measurements. A system to estimate uncertainty in all individual observations was developed indicating that annual point balance is measured with a typical error of 0.07 m water equivalent (w.e.), while the average error in winter snow measurements is 0.20 m w.e. Our dataset permits further investigating the climate change impacts on Swiss glaciers. Results show an absence of long-term trends in snow accumulation over glaciers, while melt rates have substantially increased over the last three decades.
Lea Geibel et al.
Status: open (extended)
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RC1: 'Comment on essd-2022-56', Mauri Pelto, 10 Mar 2022
reply
Geibel et al (2022) through data rescue, detailed metadata documentation, and record homogenization provide an enhanced and valuable record of Swiss glacier mass balance. This provides both a better record and a good template for such endeavors in other locations. Having worked with such data and produced data that needs this attention, I can attest to the difficulties they systematically overcame to produce a usable and sharable record. The specific comments below are all minor. They aim is to provide further clarity both to the data analysis, big picture trends and potential simplification to the process.
Specific Comments:
32: Provide WGMS (2021) reference.
72: “..of an extensive glacier monitoring effort/program/network”.
73: reword “To date mass balance measurements have been performed on more than 60 individual glaciers, most are short time series with 20 glaciers having been monitored for at least three decades.”
127: Is it worth explaining how potential errors can be avoided/identified? “Beginning from a point of known depth such as the snowline or from a snowpit. Measuring at a consistent interval and using the average of 2 or 3 probes within 25 m (Pelto et al, 2013).
140: Is it worth noting here, or just near line 250, that both end of the winter snowpack and end of summer snowpack density vary from point to point but have a relatively limited mean range.
168: How many of these were along consistent transects where the general route is known?
250: I agree with your approach to documenting and reporting density observations as well as examination of their variation in Figure 4. In terms of the broader import of not having a density observation, is this worth more context? You note the variation in density, does this apply to just point or the mean for a glacier? For most glaciers where detailed observations exist the variation in end of winter and end of summer snowpack density is limited. Fausto et al (2018) found that on the GIS snow density within 0.1 m of the surface had an average value of 315 kgm−3 + 44 kg m−3. Further they found insignificant annual air temperature dependency and suggested using a constant density was likely more appropriate for modelling than modelling surface density. On alpine glaciers density measurements for snow during the accumulation season have limited relation to elevation or snow depth (Machguth et al. 2006; McGrath et al. 2015; Sold et al. 2016). By mid-summer on temperate glaciers the density of retained accumulation has a similar behavior approaching a consistent mean value for specific glaciers and icefields that are between 550 and 600 kgm−3 across western North America (Bidlake et al. 2010; Pelto et al. 2013; Beedle et al. 2014; Pelto, et al. 2019).
326: How many measurements were complemented or corrected for missing information?
330: Do you have a specific example where the intermediate measurements are valuable, such as many on a specific glacier or during a specific time interval?
370: Figure 6 is incredibly valuable. I would encourage using a mechanism to expand the lateral and vertical extent for glaciers with more than 10 years of record. Is a landscape mode for a page allowedusable in ESSD to accommodate a particularly wide figure?
408: Indicate the timing of this transition from accumulation to ablation at these two sites.
410: For visualization of the trends in winter and summer I suggest adding a figure or panel with all of the summer record and winter records of the glaciers in Figure 7 on the same plot. This allows seeing how similar trends are.
429: That summer balance changes are the key has been noted in many alpine regions around the world, is that worth noting here? WGMS GGCB #4 (2021) illustrates seasonal balance for the regions with long balance records from more than a couple glaciers 3.1 (Alaska), 3.2 (Western North America), 3.7 (Scandinavia) and 3.8 (Central Europe), all show this declining summer balance trend and limited winter balance trend.
452: Not sure why this would be expected in a snowpack that is at 0 C. There is certainly a documented evolution of density to a through mid-summer, but after that it is more about removable of thickness than any densification/refreezing processes. No need to address unless you see value in addressing based on local observations.
492: The lack of temporal change in density and limited EOS and EOW density all argue that applying a standard density would be appropriate to substitute for in-situ observations. Could reference Sold et al. (2016) here since they had a similar result with no trends in density spatially or with altitude.
References:
Beedle, M., Menounos, B., and Wheate, R.:An evaluation of mass-balance methods applied to Castle creek Glacier, British Columbia, Canada. Journal of Glaciology 60(220): 262–276. https://doi.org/10.3189/2014JoG13J091, 2014.
Bidlake, W., Josberger, E., and Savoca, M.: Modeled and measured glacier change and related glaciological, hydrological, and meteorological conditions at South Cascade Glacier, Washington, balance and water years 2006 and 2007. USGS Investigations Report 2010–5143.
Fausto, R., Box, J., Vandecrux, B., van As D, Steffen K, MacFerrin M, Machguth H, Colgan W, Koenig L, McGrath D, Charalampidis C, and Braithwaite R.: A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling. Frontiers in Earth Science 6: 51. https://doi.org/10.3389/feart.2018.00051, 2018.
Machguth, H., Eisenm O., Paul, F., and Hoelzle, M. Strong spatial variability of snow accumulation observed with helicopter-borne GPR on two adjacent Alpine glaciers. Geophysical Research Letters 33: , L13503. https://doi.org/10.1029/2006GL026576, 2006.
McGrath, D., Sass, L., O’Neel, S., Arendt, A., Wolken, G. and Gusmeroli, A.: End-of-winter snow depth variability on glaciers in Alaska. Journal of Geophysical Research - Earth Surface 120: 1530–1550. https://doi.org/10.1002/2015JF003539, 2015.
Pelto, M., Kavanaugh, J., and McNeil, C.: Juneau Icefield Mass Balance Program 1946–2011, Earth Syst. Sci. Data, 5, 319–330, https://doi.org/10.5194/essd-5-319-2013, 2013.
Pelto, B., Menounos, B., and Marshall, S.: Multi-year evaluation of airborne geodetic surveys to estimate seasonal mass balance, Columbia and Rocky Mountains, Canada. The Cryosphere, 13, 1709–1727. https://doi.org/10.5194/tc-13-1709-2019, 2019.
Sold, L., Huss, M., Machguth, H., Joerg, P., Leysinger Vieli, G., Linsbauer, A., Salzmann, N., Zemp, M., and Hoelzle, M.: Mass balance re-analysis of Findelengletscher, Switzerland; benefits of extensive snow accumulation measurements, Frontiers in Earth Science, 4, 18, https://doi.org/10.3389/feart.2016.00018, 2016.
WGMS 2021. Global Glacier Change Bulletin No. 4 (2018–2019). Zemp, M., Nussbaumer, S.U., Gärtner-Roer, I., Bannwart, J., Paul, F., and Hoelzle, M. (eds.), ISC/IUGG/UNEP/UNESCO/WMO,World Glacier Monitoring Service, Zurich, Switzerland, 278 pp., publication based on database version: doi:10.5904/wgms-fog-2021-05.
Lea Geibel et al.
Data sets
Swiss Glacier Point Mass Balance Observations (release 2021) GLAMOS - Glacier Monitoring Switzerland https://doi.glamos.ch/data/massbalance_point/massbalance_point_2021_r2021.html
Lea Geibel et al.
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