the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A decrease in rockfall probability under climate change conditions in Germany
Katrin M. Nissen
Martina Wilde
Thomas M. Kreuzer
Annika Wohlers
Bodo Damm
Uwe Ulbrich
Abstract. The effect of climate change on rockfall in the German low mountain regions is investigated following two different approaches. The first approach uses a logistic regression model that describes the combined effect of precipitation, freezethaw cycles and fissure water on rockfall probability. The climate change signal for past decades is analysed by applying the model to meteorological observations. The possible effect of climate change until the end of the century is explored by applying the statistical model to the output of a multimodel ensemble of 23 regional climate scenario simulations. It is found that the number of days per year exhibiting an aboveaverage probability for rockfall has been mostly decreasing during the last decades. Statistical significance is, however, present only at few sites. A robust and statistically significant decrease can be seen in the RCP8.5 climate scenario simulations for Germany and neighbouring regions, locally falling below 10 % when comparing the last 30 years of the 20th century to the last 30 years of the 21st century. The most important factor determining the projected decrease in rockfall probability is a reduction in the number of freezethaw cycles expected under future climate conditions.
For the second approach four largescale meteorological patterns that are associated with enhanced rockfall probability are identified from reanalysis data. The frequency of all four patterns exhibits a seasonal cycle that maximizes in the cold half of the year (winter/spring). Trends in the number of days that can be assigned to these patterns are determined both in meteorological reanalysis data and in climate simulations. In the reanalysis no statistically significant trend is found. For the future scenario simulations all climate models show a statistically significant decrease in the number of rockfall promoting weather situations.

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Katrin M. Nissen et al.
Interactive discussion
Status: closed

RC1: 'Comment on egusphere202394', Anonymous Referee #1, 01 Mar 2023
I’ve read with interest this article. It presents an application of two different methods to evaluate the impact of climate change on rockfall probability in Germany. The article is clear, overall wellwritten, and wellstructured, following the IMRAD structure. The literature review is appropriate. The figures are sufficiently good.
I think that the manuscript deserves publication in NHESS journal after minor revisions. I listed below some specific comments and some suggestions for technical corrections.1. Introduction
 Line 39. Some interesting references: https://doi.org/10.1007/s1106901730033 ; https://doi.org/10.5194/nhess1620852016 ; https://doi.org/10.1016/j.scitotenv.2014.02.102
2. Data
 Line 60. Do you have any information on the accuracy with which the rockfalls were mapped or localized spatially?
 Line 74. Any consideration regarding the use of rainfall data with a daily temporal resolution for rockfalls?
 Line 76. Are the EOBS and REGNIE spatial resolutions in accordance with the spatial resolution of the rockfall database? Or perhaps they are too coarse?
 Line 85. Why did you decide to use the 19712000 period and not the "common" 30year period 19812010?
 Line 88. This is interesting about RCP8.5: https://www.nature.com/articles/d41586020001773. However, why have you not used also another RCP?
3. Methods
Two general comments:
 Have you checked the presence of change points and structural breaks rockfall time series (e.g. Pettitt test)?
 Have you checked if the series can be correlated (e.g. if a correlation coefficient can be calculated or if a Kendall test (correlation rank) can be applied)?
 Line 103. Why 9 days? Probably it was defined in Nissen et al 2022, however, I would suggest adding some details here.
 Line 111. The observational period is 19502020, while the period considered for the presentday greenhouse as forcing is 19712000. Some details should be added regarding this inhomogeneity.
 Lines 162169. These sentences are not very clear.
4. Results
 Line 179. “The trend is statistically significant only at few sites”. It seems they are very few. How many? What's the percentage with respect of the total?
6. Conclusions
I would suggest adding in the conclusions section more findings and the main novelty of the present work. Eventually also limitations (also considering my comments above) and future perspectives could be added.
Technical corrections
 Use either “rockfalls” or “rock falls” to be consistent in the whole text.
 Line 1. change “rockfalls” or “rockfall probability”
 Line 116. perhaps “statistically significant”?
 Figures 1 and 2. I'd suggest adding in the caption the projection used.
 Figure 3. Check the abbreviations in the caption and in the figure legend.
 Figure 4. I would separate the two labels in each bar vertically, for better readability.
 Figure 5. I would reduce the font of the 1,2,3,6, labels. Perhaps the a), b), c), and d) labels should be added.
 Figure 6 I would add the abbreviations in the caption, e.g. (FTC, light blue) and (PRECIP, dark blue).Citation: https://doi.org/10.5194/egusphere202394RC1 
AC2: 'Reply on RC1', Katrin Nissen, 05 Jun 2023
Dear reviewer, we thank you for reviewing our manuscript and for your constructive comments. We added our responses below the individual comments.
Introduction
 Line 39. Some interesting references: https://doi.org/10.1007/s1106901730033 ; https://doi.org/10.5194/nhess1620852016 ; https://doi.org/10.1016/j.scitotenv.2014.02.102
Thank you for pointing these out. We will include them in a revised version of the manuscript.
2. Data Line 60. Do you have any information on the accuracy with which the rockfalls were mapped or localized spatially?
For the events from the landslide database for Germany the exact point of initiation is recorded. It was determined either by digital mapping or by field inspection. For the events provided by the German railway company the impact location on the railway infrastructure is listed. Line 74. Any consideration regarding the use of rainfall data with a daily temporal resolution for rockfalls?
In Nissen et al. 2022 we compared hourly rainfall and daily rainfall as predictors for the logistic regression model. It turned out that daily precipitation lead to a better fit of the model. Line 76. Are the EOBS and REGNIE spatial resolutions in accordance with the spatial resolution of the rockfall database? Or perhaps they are too coarse?
Both EOBS and REGNIE are constructed by interpolating station data to a regular grid considering orographical conditions. The value in a grid box represents the mean over the area covered by the grid box. Its accuracy depends for example on the station density in the vicinity and the spatial homogeneity of the variable. It will deviate to some extent from the exact conditions. As the exact values at the rockfall sites are not known we consider these datasets the best option we have for this kind of analysis. The results from the logistic regression model (Nissen et al. 2022) confirm that the datasets are useful for our purpose. Line 85. Why did you decide to use the 19712000 period and not the "common" 30year period 19812010?
The EUROCORDEX historical period ends in 2005. Line 88. This is interesting about RCP8.5: https://www.nature.com/articles/d41586020001773. However, why have you not used also another RCP?
As we have stated in the manuscript the idea was to capture the upper range of the potential changes. In our study area the effect of climate change on the two meteorological variables that drive the changes in rockfall probability  (extreme) precipitation and temperature  point into the same direction for the RCP4.5 and the RCP8.5 scenario (https://doi.org/10.1007/s1011301304992 and https://doi.org/10.5194/nhess1711772017). Changes are just less pronounced in RCP4.5 compared to RCP8.5. We therefore expect that applying the statistical model to RCP4.5 simulations would also show a reduction in rockfall probability with a similar spatial distribution than the one shown in our manuscript, just weaker and with lower statistical significance. We will add a statement to the discussion section.3. Methods
Two general comments:
 Have you checked the presence of change points and structural breaks rockfall time series (e.g. Pettitt test)?
The rockfall time series is shown in Nissen et al. 2022 and described there in more detail. An important fact is that the rockfall database is not comprehensive. It shows an increase in the number of recorded events with time that is not due to climatic conditions but reflects the fact that data on rockfall events were more readily available
in recent years. Structural breaks are therefore present. We will add a sentence to our manuscript. Have you checked if the series can be correlated (e.g. if a correlation coefficient can be calculated or if a Kendall test (correlation rank) can be applied)?
As stated before, the time series is not comprehensive. We have therefore not attempted any correlation analysis. In Nissen et al. 2022 we show that a relationship exits between the meteorological predictor variables and rockfall probability using Weights of Evidence. Line 103. Why 9 days? Probably it was defined in Nissen et al 2022, however, I would suggest adding some details here.
The 9day period is a result of Nissen et al. 2022. Slightly longer or shorter periods give very similar results but the best model fit for the logistic regression model was achieved using 9 days. We will add a sentence to the manuscript to clarify this. Line 111. The observational period is 19502020, while the period considered for the presentday greenhouse as forcing is 19712000. Some details should be added regarding this inhomogeneity.
For the observational period we analysed trends which requires a long continuos time series. For the greenhouse gas experiments we compare two time slices that need to be long enough to account for natural variability but short enough to be able to neglect trends. Both methods are common practice. Lines 162169. These sentences are not very clear.
We will rewrite this section:
"To evaluate the ability of a classification to discriminate between favourable and unfavourable meteorological conditions with respect to rockfall probability,the nonrockfall days were assigned to the existing clusters if E < E_thres or to the new group "Other". For each cluster a X^{2} test was applied to determine if the probability for rockfall occurrence in the days belonging to the cluster differs significantly from p_clim. Only the clusters that are associated with a rockfall probability higher than p_clim, pass the X^{2}test at the 95% significance level, and contain at least 10 rockfall days were regarded as relevant. The mean probability for rockfall in the relevant clusters was determined (p_clusters). The probability increase (p_clusters  p_clim) was taken as a measure for quality criterium a).
The number of rockfall days in the relevant classes was used as quality criterium b).
Criterium c) was determined by a subjective visual inspection of the centroids. We found that for more than 4 relevant classes centroids resembled each other showing only slight shifts in the location of the low pressure systems. Therefore, classifications with more than 4 relevant classes were dismissed. "4. Results
 Line 179. “The trend is statistically significant only at few sites”. It seems they are very few. How many? What's the percentage with respect of the total?
Only 9%6. Conclusions
I would suggest adding in the conclusions section more findings and the main novelty of the present work. Eventually also limitations (also considering my comments above) and future perspectives could be added.
We will extend the conclusions.Citation: https://doi.org/10.5194/egusphere202394AC2

AC2: 'Reply on RC1', Katrin Nissen, 05 Jun 2023

RC2: 'Comment on egusphere202394', Anonymous Referee #2, 24 May 2023
The authors investigated the probability of rockfall under changing climate conditions in Germany. For this purpose, they employed two different approaches.
First, a logistic regression model is developed that allows to model the probability of rockfall as a function of different influencing variables. Next, this model is applied to a multimode ensemble of regional climate simulations to analyze the effect of climate change. For the case considered here, the probability of rockfall tends to decrease.
In a second approach, the probability of rockfall is assigned to different meteorological patterns and then again applied to future climate scenarios. Again, a decrease in the probability of rockfall is observed under certain weather scenarios.Recommendation:
I have no doubt that the study makes a valuable contribution to the NHESS Special Issue. The article is clearly and concisely written, pleasant to read, and highlights an important aspect of the probability of rockfall. The focus here is on rockfall processes in low mountain regions where permafrost does not play a major role. Thus, the study clearly deviates from the generalized statements in the IPCC report. The approach, the methods and the conclusions are consistent and conclusive. The study thus extends the current state of research and also addresses a societyrelevant topic.
In my opinion, the article can be published with some minor corrections or clarifications.
General comments:
Line 75: The given values are not the horizontal resolution, but the grid spacing of the interpolation method. Actual resolution of processes may be very different from this. I cannot judge well whether this has an effect on the study here.
Line 90: Instead of choosing the first ensemble member, wouldn't it be interesting to run the method several times with changing ensemble members? This would give an indication of the stability of the solution.
Line 103: Why exactly are nine days selected for the freezethaw cycles? How does this fit with the patterns for just one day below? The issue has been addressed, but perhaps some additional comments could be added here, with a look ahead to further work if necessary.Line 135: That only twothirds of the models point in the right direction, at all, seems like a weak signal at first glance. Perhaps more could be explained about this. The question is also how many point significantly in the other direction (if relevant, see line 180).
Line 136: The Monte Carlo method could be explained in more detail. At this point, it would also be interesting to see what the autocorellation of the time series looks like, which may have an influence on the assesment of statistical significance.
Line 152: When fitting a statistical model, how is it justified to take out the data that does not fit the model? Maybe I have overlooked something here, but it seems like circular reasoning to me.
Line 180: Just for clarification, so there are no statistically significant positive trends or are they just not shown?
Technical corrections:
Line 18: "as landslide are/is"?
Line 151: Maybe describe g and ng briefly.
Figure 5: Legend for moisture field is missing.
All illustrations: Font sizes and line widths vary widely between figures. Perhaps it is possible to harmonized this a little.
Citation: https://doi.org/10.5194/egusphere202394RC2 
AC1: 'Reply on RC2', Katrin Nissen, 02 Jun 2023
Dear reviewer, we thank you for reviewing our manuscript and for your constructive comments. We added our responses below the individual comments.
Line 75: The given values are not the horizontal resolution, but the grid spacing of the interpolation method. Actual resolution of processes may be very different from this. I cannot judge well whether this has an effect on the study here.
This is correct. As for both variables the interpolation considers elevation, we think that using the gridded data provides more reliable estimates for the event location than using station data from the closest station. We will rephrase the description.
Line 90: Instead of choosing the first ensemble member, wouldn't it be interesting to run the method several times with changing ensemble members? This would give an indication of the stability of the solution.
There are only 4 combinations (out of 23) of regional and global models for which more then one ensemble simulation was available. The forcing is limited to two different global models (out of 8). We think that the gain in confidence that can be achieved by sampling these 4 simulations in the multimodel ensemble is small and not representative. We have therefore decided to apply the multimodel ensemble approach that is stateoftheart in most IPCC studies and refrain from this extra analysis.
Line 103: Why exactly are nine days selected for the freezethaw cycles? How does this fit with the patterns for just one day below? The issue has been addressed, but perhaps some additional comments could be added here, with a look ahead to further work if necessary.
The 9day period is a result of Nissen et al. 2022. Slightly longer or shorter periods give very similar results but the best model fit for the logistic regression model was achieved using 9 days. We will add a sentence to the manuscript to clarify this.
Line 135: That only twothirds of the models point in the right direction, at all, seems like a weak signal at first glance. Perhaps more could be explained about this. The question is also how many point significantly in the other direction (if relevant, see line 180).
We like your suggestion on how to display the model disagreement. We attached a figure showing the number of models that point significantly in the other direction. In the areas showing a robust and statistically significant signal in Fig. 2 the numbers are very low (01 models). We will add a sentence to the manuscript.
Line 136: The Monte Carlo method could be explained in more detail. At this point, it would also be interesting to see what the autocorellation of the time series looks like, which may have an influence on the assessment of statistical significance.
There will clearly be some autocorrelation as our statistical model includes preconditions calculated over a period of time (freezethaw cycles in the previous 9 days and moisture preconditions calculated from the conditions in the previous 5 days). We have chosen to test for significance using the Monte Carlo technique as this method can be applied regardless of the data distribution. In order to explain the method in more detail an example might help: Assume the number of favourable days at one grid point in model A in the historical period is 2000 and in the scenario period it is 1500. Together there are 2500 events and the difference between the two periods is D=500. Each of the 2500 events will now be individually assigned to one of the two periods by the random generator and the Difference Drandom between the two periods is determined. This process is repeated 100 times producing 100 values for Drandom. If D is larger or smaller than 95% of these values, D is regarded as statistically significant at the 95 % level.
We will include this example into our manuscript.
Line 152: When fitting a statistical model, how is it justified to take out the data that does not fit the model? Maybe I have overlooked something here, but it seems like circular reasoning to me.
We don't aim at fitting a statistical model. Instead we investigate if there are groups of events that have occurred under similar largescale weather conditions (patterns). Events that don't fit into groups are removed. We will rephrase the text to make this more clear.Line 180: Just for clarification, so there are no statistically significant positive trends or are they just not shown?
As stated, there are no statistically significant positive trends for the period 19502020.

AC1: 'Reply on RC2', Katrin Nissen, 02 Jun 2023
Interactive discussion
Status: closed

RC1: 'Comment on egusphere202394', Anonymous Referee #1, 01 Mar 2023
I’ve read with interest this article. It presents an application of two different methods to evaluate the impact of climate change on rockfall probability in Germany. The article is clear, overall wellwritten, and wellstructured, following the IMRAD structure. The literature review is appropriate. The figures are sufficiently good.
I think that the manuscript deserves publication in NHESS journal after minor revisions. I listed below some specific comments and some suggestions for technical corrections.1. Introduction
 Line 39. Some interesting references: https://doi.org/10.1007/s1106901730033 ; https://doi.org/10.5194/nhess1620852016 ; https://doi.org/10.1016/j.scitotenv.2014.02.102
2. Data
 Line 60. Do you have any information on the accuracy with which the rockfalls were mapped or localized spatially?
 Line 74. Any consideration regarding the use of rainfall data with a daily temporal resolution for rockfalls?
 Line 76. Are the EOBS and REGNIE spatial resolutions in accordance with the spatial resolution of the rockfall database? Or perhaps they are too coarse?
 Line 85. Why did you decide to use the 19712000 period and not the "common" 30year period 19812010?
 Line 88. This is interesting about RCP8.5: https://www.nature.com/articles/d41586020001773. However, why have you not used also another RCP?
3. Methods
Two general comments:
 Have you checked the presence of change points and structural breaks rockfall time series (e.g. Pettitt test)?
 Have you checked if the series can be correlated (e.g. if a correlation coefficient can be calculated or if a Kendall test (correlation rank) can be applied)?
 Line 103. Why 9 days? Probably it was defined in Nissen et al 2022, however, I would suggest adding some details here.
 Line 111. The observational period is 19502020, while the period considered for the presentday greenhouse as forcing is 19712000. Some details should be added regarding this inhomogeneity.
 Lines 162169. These sentences are not very clear.
4. Results
 Line 179. “The trend is statistically significant only at few sites”. It seems they are very few. How many? What's the percentage with respect of the total?
6. Conclusions
I would suggest adding in the conclusions section more findings and the main novelty of the present work. Eventually also limitations (also considering my comments above) and future perspectives could be added.
Technical corrections
 Use either “rockfalls” or “rock falls” to be consistent in the whole text.
 Line 1. change “rockfalls” or “rockfall probability”
 Line 116. perhaps “statistically significant”?
 Figures 1 and 2. I'd suggest adding in the caption the projection used.
 Figure 3. Check the abbreviations in the caption and in the figure legend.
 Figure 4. I would separate the two labels in each bar vertically, for better readability.
 Figure 5. I would reduce the font of the 1,2,3,6, labels. Perhaps the a), b), c), and d) labels should be added.
 Figure 6 I would add the abbreviations in the caption, e.g. (FTC, light blue) and (PRECIP, dark blue).Citation: https://doi.org/10.5194/egusphere202394RC1 
AC2: 'Reply on RC1', Katrin Nissen, 05 Jun 2023
Dear reviewer, we thank you for reviewing our manuscript and for your constructive comments. We added our responses below the individual comments.
Introduction
 Line 39. Some interesting references: https://doi.org/10.1007/s1106901730033 ; https://doi.org/10.5194/nhess1620852016 ; https://doi.org/10.1016/j.scitotenv.2014.02.102
Thank you for pointing these out. We will include them in a revised version of the manuscript.
2. Data Line 60. Do you have any information on the accuracy with which the rockfalls were mapped or localized spatially?
For the events from the landslide database for Germany the exact point of initiation is recorded. It was determined either by digital mapping or by field inspection. For the events provided by the German railway company the impact location on the railway infrastructure is listed. Line 74. Any consideration regarding the use of rainfall data with a daily temporal resolution for rockfalls?
In Nissen et al. 2022 we compared hourly rainfall and daily rainfall as predictors for the logistic regression model. It turned out that daily precipitation lead to a better fit of the model. Line 76. Are the EOBS and REGNIE spatial resolutions in accordance with the spatial resolution of the rockfall database? Or perhaps they are too coarse?
Both EOBS and REGNIE are constructed by interpolating station data to a regular grid considering orographical conditions. The value in a grid box represents the mean over the area covered by the grid box. Its accuracy depends for example on the station density in the vicinity and the spatial homogeneity of the variable. It will deviate to some extent from the exact conditions. As the exact values at the rockfall sites are not known we consider these datasets the best option we have for this kind of analysis. The results from the logistic regression model (Nissen et al. 2022) confirm that the datasets are useful for our purpose. Line 85. Why did you decide to use the 19712000 period and not the "common" 30year period 19812010?
The EUROCORDEX historical period ends in 2005. Line 88. This is interesting about RCP8.5: https://www.nature.com/articles/d41586020001773. However, why have you not used also another RCP?
As we have stated in the manuscript the idea was to capture the upper range of the potential changes. In our study area the effect of climate change on the two meteorological variables that drive the changes in rockfall probability  (extreme) precipitation and temperature  point into the same direction for the RCP4.5 and the RCP8.5 scenario (https://doi.org/10.1007/s1011301304992 and https://doi.org/10.5194/nhess1711772017). Changes are just less pronounced in RCP4.5 compared to RCP8.5. We therefore expect that applying the statistical model to RCP4.5 simulations would also show a reduction in rockfall probability with a similar spatial distribution than the one shown in our manuscript, just weaker and with lower statistical significance. We will add a statement to the discussion section.3. Methods
Two general comments:
 Have you checked the presence of change points and structural breaks rockfall time series (e.g. Pettitt test)?
The rockfall time series is shown in Nissen et al. 2022 and described there in more detail. An important fact is that the rockfall database is not comprehensive. It shows an increase in the number of recorded events with time that is not due to climatic conditions but reflects the fact that data on rockfall events were more readily available
in recent years. Structural breaks are therefore present. We will add a sentence to our manuscript. Have you checked if the series can be correlated (e.g. if a correlation coefficient can be calculated or if a Kendall test (correlation rank) can be applied)?
As stated before, the time series is not comprehensive. We have therefore not attempted any correlation analysis. In Nissen et al. 2022 we show that a relationship exits between the meteorological predictor variables and rockfall probability using Weights of Evidence. Line 103. Why 9 days? Probably it was defined in Nissen et al 2022, however, I would suggest adding some details here.
The 9day period is a result of Nissen et al. 2022. Slightly longer or shorter periods give very similar results but the best model fit for the logistic regression model was achieved using 9 days. We will add a sentence to the manuscript to clarify this. Line 111. The observational period is 19502020, while the period considered for the presentday greenhouse as forcing is 19712000. Some details should be added regarding this inhomogeneity.
For the observational period we analysed trends which requires a long continuos time series. For the greenhouse gas experiments we compare two time slices that need to be long enough to account for natural variability but short enough to be able to neglect trends. Both methods are common practice. Lines 162169. These sentences are not very clear.
We will rewrite this section:
"To evaluate the ability of a classification to discriminate between favourable and unfavourable meteorological conditions with respect to rockfall probability,the nonrockfall days were assigned to the existing clusters if E < E_thres or to the new group "Other". For each cluster a X^{2} test was applied to determine if the probability for rockfall occurrence in the days belonging to the cluster differs significantly from p_clim. Only the clusters that are associated with a rockfall probability higher than p_clim, pass the X^{2}test at the 95% significance level, and contain at least 10 rockfall days were regarded as relevant. The mean probability for rockfall in the relevant clusters was determined (p_clusters). The probability increase (p_clusters  p_clim) was taken as a measure for quality criterium a).
The number of rockfall days in the relevant classes was used as quality criterium b).
Criterium c) was determined by a subjective visual inspection of the centroids. We found that for more than 4 relevant classes centroids resembled each other showing only slight shifts in the location of the low pressure systems. Therefore, classifications with more than 4 relevant classes were dismissed. "4. Results
 Line 179. “The trend is statistically significant only at few sites”. It seems they are very few. How many? What's the percentage with respect of the total?
Only 9%6. Conclusions
I would suggest adding in the conclusions section more findings and the main novelty of the present work. Eventually also limitations (also considering my comments above) and future perspectives could be added.
We will extend the conclusions.Citation: https://doi.org/10.5194/egusphere202394AC2

AC2: 'Reply on RC1', Katrin Nissen, 05 Jun 2023

RC2: 'Comment on egusphere202394', Anonymous Referee #2, 24 May 2023
The authors investigated the probability of rockfall under changing climate conditions in Germany. For this purpose, they employed two different approaches.
First, a logistic regression model is developed that allows to model the probability of rockfall as a function of different influencing variables. Next, this model is applied to a multimode ensemble of regional climate simulations to analyze the effect of climate change. For the case considered here, the probability of rockfall tends to decrease.
In a second approach, the probability of rockfall is assigned to different meteorological patterns and then again applied to future climate scenarios. Again, a decrease in the probability of rockfall is observed under certain weather scenarios.Recommendation:
I have no doubt that the study makes a valuable contribution to the NHESS Special Issue. The article is clearly and concisely written, pleasant to read, and highlights an important aspect of the probability of rockfall. The focus here is on rockfall processes in low mountain regions where permafrost does not play a major role. Thus, the study clearly deviates from the generalized statements in the IPCC report. The approach, the methods and the conclusions are consistent and conclusive. The study thus extends the current state of research and also addresses a societyrelevant topic.
In my opinion, the article can be published with some minor corrections or clarifications.
General comments:
Line 75: The given values are not the horizontal resolution, but the grid spacing of the interpolation method. Actual resolution of processes may be very different from this. I cannot judge well whether this has an effect on the study here.
Line 90: Instead of choosing the first ensemble member, wouldn't it be interesting to run the method several times with changing ensemble members? This would give an indication of the stability of the solution.
Line 103: Why exactly are nine days selected for the freezethaw cycles? How does this fit with the patterns for just one day below? The issue has been addressed, but perhaps some additional comments could be added here, with a look ahead to further work if necessary.Line 135: That only twothirds of the models point in the right direction, at all, seems like a weak signal at first glance. Perhaps more could be explained about this. The question is also how many point significantly in the other direction (if relevant, see line 180).
Line 136: The Monte Carlo method could be explained in more detail. At this point, it would also be interesting to see what the autocorellation of the time series looks like, which may have an influence on the assesment of statistical significance.
Line 152: When fitting a statistical model, how is it justified to take out the data that does not fit the model? Maybe I have overlooked something here, but it seems like circular reasoning to me.
Line 180: Just for clarification, so there are no statistically significant positive trends or are they just not shown?
Technical corrections:
Line 18: "as landslide are/is"?
Line 151: Maybe describe g and ng briefly.
Figure 5: Legend for moisture field is missing.
All illustrations: Font sizes and line widths vary widely between figures. Perhaps it is possible to harmonized this a little.
Citation: https://doi.org/10.5194/egusphere202394RC2 
AC1: 'Reply on RC2', Katrin Nissen, 02 Jun 2023
Dear reviewer, we thank you for reviewing our manuscript and for your constructive comments. We added our responses below the individual comments.
Line 75: The given values are not the horizontal resolution, but the grid spacing of the interpolation method. Actual resolution of processes may be very different from this. I cannot judge well whether this has an effect on the study here.
This is correct. As for both variables the interpolation considers elevation, we think that using the gridded data provides more reliable estimates for the event location than using station data from the closest station. We will rephrase the description.
Line 90: Instead of choosing the first ensemble member, wouldn't it be interesting to run the method several times with changing ensemble members? This would give an indication of the stability of the solution.
There are only 4 combinations (out of 23) of regional and global models for which more then one ensemble simulation was available. The forcing is limited to two different global models (out of 8). We think that the gain in confidence that can be achieved by sampling these 4 simulations in the multimodel ensemble is small and not representative. We have therefore decided to apply the multimodel ensemble approach that is stateoftheart in most IPCC studies and refrain from this extra analysis.
Line 103: Why exactly are nine days selected for the freezethaw cycles? How does this fit with the patterns for just one day below? The issue has been addressed, but perhaps some additional comments could be added here, with a look ahead to further work if necessary.
The 9day period is a result of Nissen et al. 2022. Slightly longer or shorter periods give very similar results but the best model fit for the logistic regression model was achieved using 9 days. We will add a sentence to the manuscript to clarify this.
Line 135: That only twothirds of the models point in the right direction, at all, seems like a weak signal at first glance. Perhaps more could be explained about this. The question is also how many point significantly in the other direction (if relevant, see line 180).
We like your suggestion on how to display the model disagreement. We attached a figure showing the number of models that point significantly in the other direction. In the areas showing a robust and statistically significant signal in Fig. 2 the numbers are very low (01 models). We will add a sentence to the manuscript.
Line 136: The Monte Carlo method could be explained in more detail. At this point, it would also be interesting to see what the autocorellation of the time series looks like, which may have an influence on the assessment of statistical significance.
There will clearly be some autocorrelation as our statistical model includes preconditions calculated over a period of time (freezethaw cycles in the previous 9 days and moisture preconditions calculated from the conditions in the previous 5 days). We have chosen to test for significance using the Monte Carlo technique as this method can be applied regardless of the data distribution. In order to explain the method in more detail an example might help: Assume the number of favourable days at one grid point in model A in the historical period is 2000 and in the scenario period it is 1500. Together there are 2500 events and the difference between the two periods is D=500. Each of the 2500 events will now be individually assigned to one of the two periods by the random generator and the Difference Drandom between the two periods is determined. This process is repeated 100 times producing 100 values for Drandom. If D is larger or smaller than 95% of these values, D is regarded as statistically significant at the 95 % level.
We will include this example into our manuscript.
Line 152: When fitting a statistical model, how is it justified to take out the data that does not fit the model? Maybe I have overlooked something here, but it seems like circular reasoning to me.
We don't aim at fitting a statistical model. Instead we investigate if there are groups of events that have occurred under similar largescale weather conditions (patterns). Events that don't fit into groups are removed. We will rephrase the text to make this more clear.Line 180: Just for clarification, so there are no statistically significant positive trends or are they just not shown?
As stated, there are no statistically significant positive trends for the period 19502020.

AC1: 'Reply on RC2', Katrin Nissen, 02 Jun 2023
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