Natural climate variability is an important aspect of future projections of snow water resources and rain-on-snow events
- 1Swiss Federal Institute for Forest, Snow and Landscape Research, 8903 Birmensdorf, Switzerland
- 2WSL Institute for Snow and Avalanche Research SLF, 7260 Davos, Switzerland
- 3Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland
- 4Institute of Earth Surface Dynamics, University of Lausanne, 1015 Lausanne, Switzerland
- deceased, March 2021
- 1Swiss Federal Institute for Forest, Snow and Landscape Research, 8903 Birmensdorf, Switzerland
- 2WSL Institute for Snow and Avalanche Research SLF, 7260 Davos, Switzerland
- 3Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland
- 4Institute of Earth Surface Dynamics, University of Lausanne, 1015 Lausanne, Switzerland
- deceased, March 2021
Abstract. Climate projection studies of future changes in snow conditions and resulting rain-on-snow (ROS) flood events are subject to large uncertainties. Typically, emission scenario uncertainties and climate model uncertainties are included. This is the first study on this topic to also include quantification of natural climate variability, which is the dominant uncertainty for precipitation at local scales with large implications for e.g. runoff projections. To quantify natural climate variability, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 × 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The climate change signal for snow water resources stands out as early as mid-century from the noise originating from the three sources of uncertainty investigated, namely uncertainty in emission scenarios, uncertainty in climate models, and natural climate variability. For ROS events, a climate change signal toward more frequent and intense events was found for an RCP 8.5 scenario at high elevations at the end of the century, consistently with other studies. However, for ROS events with a substantial contribution of snowmelt to runoff (>20 %), the climate change signal was largely masked by sources of uncertainty. Only those ROS events where snowmelt does not play an important role during the event will occur considerably more frequently in the future, while ROS events with substantial snowmelt contribution will mainly occur earlier in the year but not more frequently. There are two reasons for this: first, although it will rain more frequently in midwinter, the snowpack will typically still be too cold and dry and thus cannot contribute significantly to runoff; second, the very rapid decline in snowpack toward early summer, when conditions typically prevail for substantial contributions from snowmelt, will result in a large decrease in ROS events at that time of the year. Finally, natural climate variability is the primary source of uncertainty in projections of ROS metrics until the end of the century, contributing more than 70 % of the total uncertainty. These results imply that both the inclusion of natural climate variability and the use of a snow model, which includes a physically-based processes representation of water retention, are important for ROS projections at the local scale.
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Michael Schirmer et al.
Status: final response (author comments only)
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RC1: 'Comment on tc-2021-276', Anonymous Referee #1, 02 Oct 2021
- AC1: 'Reply on RC1', Michael Schirmer, 23 Dec 2021
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RC2: 'Comment on tc-2021-276', Anonymous Referee #2, 11 Oct 2021
This study assesses projected evolutions of snow-related events in a small alpine region located in Switzerland, using a simulation chain composed of dynamical climate simulations, a stochastic precipitation generator, a snow model. This study provides interesting results about these possible future events, and the methodological choices seem reasonable, at least for the simulations, but there are two main aspects of the manuscript that need to be improved.
1. Presentation of the methodology
Section 2 is difficult to follow for several reasons. The first reason is that the different subsections 2.2, 2.3, 2.4 do not follow a logical order. When the snow model is described, we do not know how its inputs (total precipitation, air temperature, etc.) are obtained, or their spatial resolution. Another example, factors of change are first introduced in Subsection 2.3 whereas they are obtained from climate model outputs in Subsection 2.4. I advise following the order of the simulation chain: 1/Climate models, 2/ Weather generator, 3/ Snow model.
Secondly, while I understand that all the details of the methodology cannot be provided, the current presentation lacks important information. In particular, from Table 1, it seems that the different precipitation products are used to fit different properties of the precipitation fields (i.e. monthly mean rainfall using optimal interpolated fields, mean areal rainfall using weather radar data). Does it mean that the variability of precipitation at a monthly scale (mean, variance, skewness, etc.) is reproduced using these optimal interpolated fields? What information is used to reproduce statistical properties at a finer resolution (hourly, daily)? For example, how the largest (“extreme”) values at daily and sub-daily scales are reproduced? Since this is an important aspect of the study which focuses on intense rain-on-snow events, it needs to be clarified. It was also unclear if there is any information of snow data at a daily scale. To my knowledge, weather radar data do not provide this kind of information. At a monthly scale, it is indicated at l. 110-111 that “optimal interpolation (OI) of snow depth sensor data and a gridded precipitation product, RhiresD) are used, but in Table 1, the line “Optimal interpolated fields” indicates that it is used to fit “Monthly mean rainfall”, not snow, so that it is unclear if these OI fields provide total precipitation values or only rainfall. I am not sure where the product RhiresD appears in Table 1. What should be clarified is the list of the statistical properties (statistic, spatial and temporal resolution) of snow and rain that are fitted (and simulated) by the weather generator, and what source of information is used for each of these statistics.
At l. 131, it is indicated that factors of change are calculated, but no details are provided. For example, the factors of change are usually computed with respect to a reference period, but I could not find this information.
2. Uncertainty assessment
The uncertainty assessment really puzzled me. There is a large number of publications on uncertainty partitioning for climate model simulations (Déqué et al., 2007; Hawkins and Sutton, 2009; Northrop and Chandler, 2014; and many others). These papers all apply an Analysis of Variance (ANOVA) method which provides a clear and rigorous framework in order to obtain a total variance and its components. The different contributions logically sum to one. I do not really understand the approach proposed in Fatichi et al. (2016) which is based on the evaluation of percentile ranges. At l. 154, it is indicated that the 5-95th percentiles obtained from the ten climate models actually refer to the minimum and the maximum, which seems to be a major flaw of the method. Low and high percentiles cannot be obtained from a very limited number of climate simulations (even if you emulate these simulations) and the evaluation of the dispersion (variance) is the best that you can obtain. Secondly, I cannot understand how we can interpret the different contributions if they do not sum to one (l. 167). Fractional uncertainty, as a percentage (e.g. Fig. 3 in Hawkins and Sutton, 2009) provides a direct assessment of the most important contributors to the uncertainty. At l. 164-165, it is indicated that “weights [are used] to avoid overweighting days with only low climate change signal uncertainty”. I do not see the problem of having a low climate change signal uncertainty, and why it becomes a problem using your approach. For all these reasons, I strongly recommend using a standard ANOVA approach for the uncertainty assessment.
3. Minor comments:
- All figure captions: Usually “(a)”, “(b)”, etc. are placed before the description of the respective subpanels.
- Figure 2: The labels of the y-axis are ILWR and ISWR for panels (c) and (d) whereas in the caption, it is inverted.
- 260: “by definition, only determined by natural variability”: I am not sure what you mean by “definition”. There is also an important part of model uncertainty for the current climate periods. This kind of uncertainty is usually removed mechanically using factors of change (as you did probably). A clarification would be appreciated here.
- 14 – l. 293: I guess “Figure 7” is missing.
- 17: Figure 9 is not presented and described.
References
Déqué, M., D. P. Rowell, D. Lüthi, F. Giorgi, J. H. Christensen, B. Rockel, D. Jacob, E. Kjellström, M. de Castro, and B. van den Hurk. 2007. “An Intercomparison of Regional Climate Simulations for Europe: Assessing Uncertainties in Model Projections.” Climatic Change 81 (1): 53–70. https://doi.org/10.1007/s10584-006-9228-x.
Hawkins, E., and R. Sutton. 2009. “The Potential to Narrow Uncertainty in Regional Climate Predictions.” Bulletin of the American Meteorological Society 90 (8): 1095–1107. https://doi.org/10.1175/2009BAMS2607.1.
Northrop, Paul J., and Richard E. Chandler. 2014. “Quantifying Sources of Uncertainty in Projections of Future Climate.” Journal of Climate 27 (23): 8793–8808. https://doi.org/10.1175/JCLI-D-14-00265.1.
- AC2: 'Reply on RC2', Michael Schirmer, 23 Dec 2021
Michael Schirmer et al.
Data sets
Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century Michael Schirmer; Adam Winstral; Tobias Jonas; Paolo Burlando; Nadav Peleg https://www.envidat.ch/#/metadata/multiple-realizations-of-daily-swe-swi-and-rain-projections
Michael Schirmer et al.
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