the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Aug 2021
Effects of climate change on the valley glaciers of the Italian Alps
Abstract. The behaviour of the valley glaciers of the Italian Alps as a result of the climate changes expected for the 21st century has been investigated. From 1980 to 2017 the average length reductions of these glaciers has been 16 % and their average areal reduction around 22 %, much smaller than the overall glacier retreat of the Alps. Their mean observed shortening was about 500 m for a temperature increase of 1.4 °C. To quantify the valley glacier life expectancy, a model estimating their length variations from the air temperature variations of the EuroCordex climatological projections of six different models under RCP4.5 and RCP8.5 scenarios has been used. The ensemble mean temperatures in the Italian Alps region under these scenarios indicate increases of temperature of ~2 °C and ~4 °C from 2018 to 2100 respectively. In both scenarios, the glacier model projections show a constant retreat until the eighties, weakening towards the end of the century. As expected, it resulted more severe under the RCP8.5 (from 22 % to 48 %) than under the RCP4.5 (from 10 % to 25 %) scenario, with a mean length shortening of 35 % and 13 % respectively by 2100. The model used estimates that the majority of the valley glaciers could better resist the climate change.
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RC1: 'Comment on tc-2021-241', Michael Kuhn, 07 Sep 2021
Based on measurements from 1980 to 2017, this paper gives a survey of general properties of the largest Italian valley glaciers that obviously distinguish them from slope glaciers. Six Eurocordex climate projections are applied to these valley glaciers for the period 2018 to 2100. The analysis is based on annual length measurements and records of temperature and precipitation. The projection up to 2100 uses a model proposed by Oerlemans which has two characteristic parameters, climate sensitivity of glacier length (m K-1) and response time, which are valid for length variations with a standard deviation much smaller than glacier length, which is true for the Italian valley glaciers.
Valuable results of this study are summarized in this manuscript on lines 324 to 329: “a) under the RCP8.5 scenario, the mean retreat velocity of the glaciers is similar to that of the period 1980 to 2017 obtained from the observed data; b) given the form of the model used, the main forcing is the air temperature variation, while total precipitation changes have a weak impact on the results; c) the impact of different climatological models on the simulated results is less than 10%; d) according to the model, under the RCP8.5 scenario the majority of the valley glaciers (about 80%) will resist the climate change experiencing retreats less than50% of their 2017 length and thus probably maintaining their characteristics of valley glaciers.”
There are two aspects that may be improved in the final publication: (1) changes of length are expressed in percent. In Table 1 a column of absolute length changes in m in addition to relative changes in % would help the readers to give weight to the results. (2) is the commitment of mass balance of the positive years before 1980 significant for the projections to 2018 - 2100? And one suggestion: (3) can you provide links to relevant glacier photographs?
I am confident that these three points can be accomplished without much effort.
A few clarifications or corrections are required as follows.
Line 54 such as their
57 other more sophisticated
72 in areas
78 that the model
Figure 1 explain the color code in the legend
Table 1 add delta length in meters
170 – 173 explain this in more detail
189 how was the slope defined, (top – end) / dh ? was it area-weighted?
Figure 6 This is Fig. 5, apparently with the legend of Fig. 6 which is missing.
235 while their primary classification as valley vs. slope glaciers
255 the response times (equ. 3) Did Paul et al. 2004 use the same definition of response time?
316 glaciers seem able
317 present at the end of the 21st century
334 Although the effect of climate change indicates a severe ice mass decline for the Alps
337 probably more resistant to climate change
375 glacier
406 Knoll before Kuhn
506 Check Figures 5 and 6, one of the two panels is missing.
With best wishes for the finalization of this manuscript, Michael Kuhn
Citation: https://doi.org/10.5194/tc-2021-241-RC1 -
AC1: 'Reply on RC1', Sandra Donnici, 09 Sep 2021
We read your comment to our manuscript with great interest:
- We added the suggested column in Table 1
- Given the size of response times of the considered glaciers, we do not think that positive mass balances before 1980 can influence the projections to 2018-2100.
- Glacier photographs are published in the reports of annual glaciological surveys (http://www.glaciologia.it/en/i-ghiacciai-italiani/le-campagne-glaciologiche/). We added a link in chapter 3.1.
Line 255: our response time matches the definition of the response time in Coogley et al. (2011), applicable to both lengths and volumes, and is comparable with the mean values given by Paul et al. (2004), which are indicative for the generality of Alpine mountain glaciers.
Thank you for your suggestions, we added all the required corrections to the manuscript.
Best wishes,
Sandra Donnici
Citation: https://doi.org/10.5194/tc-2021-241-AC1 -
RC2: 'Reply on AC1', Michael Kuhn, 20 Sep 2021
I recommend to publish the revised manuscript in its present form
Citation: https://doi.org/10.5194/tc-2021-241-RC2
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AC1: 'Reply on RC1', Sandra Donnici, 09 Sep 2021
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RC3: 'Comment on tc-2021-241', Anonymous Referee #2, 30 Oct 2021
Review to Serandrei-Barbero et al. (2021): "Effects of climate change on the valley glaciers of the Italian Alps" submitted to The Cryosphere.
The authors present a study of the future glacier length evolution of the Italian valley glaciers feeding a simple glacier model with future climate projections taken from the EURO-CORDEX initiative for RCP4.5 and RCP8.5 scenarios. The main result of the study is that until the end of the century the Italian valley glaciers (representing roughly 26% of the total Italian glacier area) will preserve about 50% of their length of 2017 and thus, their retreat will be slower than other glaciers in the Alps.
Overall, I rate this manuscript not ready for publication and due to the sum of inconsistencies I even suggest a rejection. My main points of concern are:
Missing scientific rigor: The introduction (L21) starts with a misconception. Glacier fluctuations are not a result of air temperature and precipitation variability, they are a result of complex climate-glacier interactions. We just prefer to make glacier fluctuations a proxy of air temperature and precipitation, because we usually have long-term observations of or established scaling functions for them. Hence, simple glacier models were established for conceptual understanding. However, we must be careful when interpreting (putative) results of (highly) parametrized processes or inverting them. Fig. 4 is an example of such a putative result. The correlation between the slope and the climate sensitivity is not a result of the study, it is a result of the model design. Because the simplified model defines the climate sensitivity as a function of the slope (Oerlemans, 2001), we detect it as correlation in the data. Cause and effect must not be interchanged.
Additionally, the chosen method seems not to be state of the art anymore. Meanwhile ice thickness estimates are available (Farinotti et al., 2017) opening the path to models deriving glacier volume changes (e.g. Maussion et al., 2019), having a higher significance than glacier length changes.Missing model calibration: A previous study of the same authors (Zecchetto et al., 2017) calibrated an existing method of glacier length change modelling (Oerlemans, 2005) on smaller glaciers in the Italian Alps for air temperature reconstructions. Now, the author team applies the same method without further amendments on the larger Italian valley glaciers. While the model is calibrated on shorter and steeper glaciers, it is applied on longer and flatter glaciers, although the authors would have all the data to calibrate the model on the valley glaciers, too. The missing model calibration might explain the low climate sensitivities and short response times compared to the original model publication (Oerlemans, 2005) and definitely impacts the results of the study and the conclusions the authors draw.
Missing error estimation: Because the model is not calibrated, there is no model error reported. The uncertainties given in Fig. 8 are induced by the different CORDEX ensemble members, but not by the model. Robust scientific results rely on a rigorous error estimation, would have helped to phrase a stronger discussion section.
References
Farinotti, D., Brinkerhoff, D. J., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., Gillet-Chaulet, F., Girard, C., Huss, M., Leclercq, P. W., Linsbauer, A., Machguth, H., Martin, C., Maussion, F., Morlighem, M., Mosbeux, C., Pandit, A., Portmann, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Sanchez, O., Stentoft, P. A., Singh Kumari, S., van Pelt, W. J. J., Anderson, B., Benham, T., Binder, D., Dowdeswell, J. A., Fischer, A., Helfricht, K., Kutuzov, S., Lavrentiev, I., McNabb, R., Gudmundsson, G. H., Li, H. and Andreassen, L. M.: How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment, The Cryosphere, 11(2), 949–970, doi:10.5194/tc-11-949-2017, 2017.
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T. and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geoscientific Model Development, 12(3), 909–931, doi:10.5194/gmd-12-909-2019, 2019.
Oerlemans, J.: Glaciers and Climate Change, A.A Balkema, Lisse., 2001.
Oerlemans, J.: Extracting a climate signal from 169 glacier records., Science, 308(5722), 675–677, doi:10.1126/science.1107046, 2005.
Zecchetto, S., Serandrei-Barbero, R. and Donnici, S.: Temperature reconstruction from the length fluctuations of small glaciers in the eastern Alps (northeastern Italy), Climate Dynamics, 49(1–2), 363–374, doi:10.1007/s00382-016-3347-5, 2017.
Citation: https://doi.org/10.5194/tc-2021-241-RC3 -
AC2: 'Reply on RC3', Sandra Donnici, 08 Nov 2021
Review to Serandrei-Barbero et al. (2021): "Effects of climate change on the valley glaciers of the Italian Alps" submitted to The Cryosphere.
The authors present a study of the future glacier length evolution of the Italian valley glaciers feeding a simple glacier model with future climate projections taken from the EURO-CORDEX initiative for RCP4.5 and RCP8.5 scenarios. The main result of the study is that until the end of the century the Italian valley glaciers (representing roughly 26% of the total Italian glacier area) will preserve about 50% of their length of 2017 and thus, their retreat will be slower than other glaciers in the Alps.
This is in agreement with what measured on the ground: on the valley glaciers here considered, from 1980 to 2017 the mean length loss is 16%. Their average areal shrinkage of 22% between 1980 and 2015 (Table 1) shows their smaller retreat with respect to the general shrinkage in the European Alps estimated around 40% as a lower bound.
The lower retreat of valley glaciers was also highlighted by considering together valley and mountain glaciers small to medium-sized (Serandrei-Barbero et al., 2019).
However, as the valley glaciers considered here are medium to large in size, it is possible that this may contribute to their lower retreat (at lines 243-247 of the text: In general larger glaciers show regresses inversely proportional to their size and this could significantly contribute to their expected minor retreat).
We include this consideration also in the conclusions.
We realize that the reviewer does not agree with our results. We are also sorry that the reasons for these conclusions are based on some misunderstandings of the text. Our results do not contradict the general belief that climate change will destroy all the glaciers, but rather indicate a longer survival of valley glaciers than mountain glaciers (and this different behavior has already been demonstrated by field monitoring performed in the past decades).
We are aware that the model used in the work is by far simpler than the models quoted here, but we doubt that projections like those reported in this paper may be reached using other models requiring data not available for the Italian Alps.
Overall, I rate this manuscript not ready for publication and due to the sum of inconsistencies I even suggest a rejection. My main points of concern are:
Missing scientific rigor: The introduction (L21) starts with a misconception. Glacier fluctuations are not a result of air temperature and precipitation variability, they are a result of complex climate-glacier interactions.
Of course, anyone dealing with glaciers is aware of the complexity of climate-glacier interactions (at l. 75 of the manuscript: thus, implicitly, that the model does not account for all the non-linear and local factors influencing a glacier’s life). In the text, we wrote that the air temperature and the variability of precipitation are the main parameters that influence the fluctuations of glaciers, but not the only ones. Furthermore, the importance of temperature is well known (Leclercq&Oerlemans 2012), since the past fluctuations of glaciers, used as a proxy of temperature, reproduce very well the instrumental record of the last century.
We just prefer to make glacier fluctuations a proxy of air temperature and precipitation, because we usually have long-term observations of or established scaling functions for them. Hence, simple glacier models were established for conceptual understanding. However, we must be careful when interpreting (putative) results of (highly) parametrized processes or inverting them. Fig. 4 is an example of such a putative result. The correlation between the slope and the climate sensitivity is not a result of the study, it is a result of the model design. Because the simplified model defines the climate sensitivity as a function of the slope (Oerlemans, 2001), we detect it as correlation in the data. Cause and effect must not be interchanged.
We did not say in any part of the paper that Fig. 4 is a result of the study, and we believe this is an inference of the reviewer that the text does not support. As in the text, The climate sensitivity Cs (Eq. 2) depends on the glacier slope and the total annual precipitation and therefore Fig. 4 is just Eq. 2 applied to our glaciers, which provides an estimate of the rate of change of Cs with slope.
Additionally, the chosen method seems not to be state of the art anymore. Meanwhile ice thickness estimates are available (Farinotti et al., 2017) opening the path to models deriving glacier volume changes (e.g. Maussion et al., 2019), having a higher significance than glacier length changes.You are right. With respect to the models of new generation, our approach does not represent the status of the art. But this does not mean that more complex models would provide better projections, also given the uncertainties on the several parameters needed to run them. The ground data contains many gaps and inconsistencies and, despite the efforts of the Glacier Monitoring Service, not all glacier outlines are available. Our aim was to use available and verifiable data so that we could have a reliable database: the glacier lengths fulfil this premise and are available on almost all Italian valley glaciers.
We include part of these consideration in the Introduction, where Farinotti et al. (2017) and Maussion et al., (2019) are cited.
Missing model calibration: A previous study of the same authors (Zecchetto et al., 2017) calibrated an existing method of glacier length change modelling (Oerlemans, 2005) on smaller glaciers in the Italian Alps for air temperature reconstructions.
Now, the author team applies the same method without further amendments on the larger Italian valley glaciers. While the model is calibrated on shorter and steeper glaciers,
As the reviewer probably knows, model calibration is a statistical procedure, which minimizes the differences between the model and the data through coefficients. In our case, the model (eq. 1) was compared with the data of 3 glaciers, and the coefficients c1 and c2 of Eqs. 2 and 3 were estimated as c1= 0.0078 ± 0.0004 and c2= 1.35 ± 0.14 by means of least-squares regression of the function. These values, of course, are mean values and satisfy differently the glaciers, but we cannot have N coefficients for N glaciers.
The glaciers used for calibrations are on average smaller (from 1060 m to 2192 m) than those of the present work (from 1712 m to 5357 m), but not steeper.
it is applied on longer and flatter glaciers, although the authors would have all the data to calibrate the model on the valley glaciers, too. The missing model calibration might explain the low climate
Model calibration is not missing and we wonder how the reviewer could expect larger values of Cs and τ and why. Our new calibration in Zecchetto et al, 2017 has been shown to work on glaciers longer than those used for calibration (2880 m, 1267 m, 1933 m). This was done because the Oerlemans 2005 calibration did not suit with the glaciers of our region and cannot be used for them.
sensitivities and short response times compared to the original model publication (Oerlemans, 2005) and definitely impacts the results of the study and the conclusions the authors draw.
Oerlemans, 2005 studied 169 glaciers over the world with lengths from 0.3 km to 45 km, while Leclercq&Oerlemens (2012) used 309 glaciers for temperature reconstruction. As far as we know, their coefficients of calibration (0.00204 for Cs and 19.4 for τ) were obtained from fourteen glaciers and then used for all the glaciers. Our procedure was quite similar to that of these quoted works, but with a smaller glacier data set.
Of course the values of Cs and τ impact on the results. It seems to us that the reviewer is disturbed by the conclusions of possible survival of the valley glaciers on the Italian Alps.
Missing error estimation: Because the model is not calibrated, there is no model error reported. The uncertainties given in Fig. 8 are induced by the different CORDEX ensemble members, but not by the model. Robust scientific results rely on a rigorous error estimation, would have helped to phrase a stronger discussion section.
We do not see the relationship between calibration and model error. The model error can be evaluated only thought the uncertainty of Cs, τ and temperature.
References
Farinotti, D., Brinkerhoff, D. J., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., Gillet-Chaulet, F., Girard, C., Huss, M., Leclercq, P. W., Linsbauer, A., Machguth, H., Martin, C., Maussion, F., Morlighem, M., Mosbeux, C., Pandit, A., Portmann, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Sanchez, O., Stentoft, P. A., Singh Kumari, S., van Pelt, W. J. J., Anderson, B., Benham, T., Binder, D., Dowdeswell, J. A., Fischer, A., Helfricht, K., Kutuzov, S., Lavrentiev, I., McNabb, R., Gudmundsson, G. H., Li, H. and Andreassen, L. M.: How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment, The Cryosphere, 11(2), 949–970, doi:10.5194/tc-11-949-2017, 2017.
Leclercq, P. W., Oerlemans, J.: Global and hemispheric temperature reconstruction from glacier length fluctuations, Clim Dynam, 38(5-6), 1065-1079, https://doi.org/10.1007/s00382-011-1145-7, 2012.
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T. and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geoscientific Model Development, 12(3), 909–931, doi:10.5194/gmd-12-909-2019, 2019.
Oerlemans, J.: Glaciers and Climate Change, A.A Balkema, Lisse., 2001.
Oerlemans, J.: Extracting a climate signal from 169 glacier records., Science, 308(5722), 675–677, doi:10.1126/science.1107046, 2005.
Serandrei-Barbero, R., Donnici, S., and Zecchetto, S.: Projected effects of temperature changes on the Italian Western Tauri glaciers (Eastern Alps), J Glaciol, 1-10, https://doi.org/10.1017/jog.2019.7, 2019.
Zecchetto, S., Serandrei-Barbero, R. and Donnici, S.: Temperature reconstruction from the length fluctuations of small glaciers in the eastern Alps (northeastern Italy), Climate Dynamics, 49(1–2), 363–374, doi:10.1007/s00382-016-3347-5, 2017.
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AC2: 'Reply on RC3', Sandra Donnici, 08 Nov 2021
Status: closed
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RC1: 'Comment on tc-2021-241', Michael Kuhn, 07 Sep 2021
Based on measurements from 1980 to 2017, this paper gives a survey of general properties of the largest Italian valley glaciers that obviously distinguish them from slope glaciers. Six Eurocordex climate projections are applied to these valley glaciers for the period 2018 to 2100. The analysis is based on annual length measurements and records of temperature and precipitation. The projection up to 2100 uses a model proposed by Oerlemans which has two characteristic parameters, climate sensitivity of glacier length (m K-1) and response time, which are valid for length variations with a standard deviation much smaller than glacier length, which is true for the Italian valley glaciers.
Valuable results of this study are summarized in this manuscript on lines 324 to 329: “a) under the RCP8.5 scenario, the mean retreat velocity of the glaciers is similar to that of the period 1980 to 2017 obtained from the observed data; b) given the form of the model used, the main forcing is the air temperature variation, while total precipitation changes have a weak impact on the results; c) the impact of different climatological models on the simulated results is less than 10%; d) according to the model, under the RCP8.5 scenario the majority of the valley glaciers (about 80%) will resist the climate change experiencing retreats less than50% of their 2017 length and thus probably maintaining their characteristics of valley glaciers.”
There are two aspects that may be improved in the final publication: (1) changes of length are expressed in percent. In Table 1 a column of absolute length changes in m in addition to relative changes in % would help the readers to give weight to the results. (2) is the commitment of mass balance of the positive years before 1980 significant for the projections to 2018 - 2100? And one suggestion: (3) can you provide links to relevant glacier photographs?
I am confident that these three points can be accomplished without much effort.
A few clarifications or corrections are required as follows.
Line 54 such as their
57 other more sophisticated
72 in areas
78 that the model
Figure 1 explain the color code in the legend
Table 1 add delta length in meters
170 – 173 explain this in more detail
189 how was the slope defined, (top – end) / dh ? was it area-weighted?
Figure 6 This is Fig. 5, apparently with the legend of Fig. 6 which is missing.
235 while their primary classification as valley vs. slope glaciers
255 the response times (equ. 3) Did Paul et al. 2004 use the same definition of response time?
316 glaciers seem able
317 present at the end of the 21st century
334 Although the effect of climate change indicates a severe ice mass decline for the Alps
337 probably more resistant to climate change
375 glacier
406 Knoll before Kuhn
506 Check Figures 5 and 6, one of the two panels is missing.
With best wishes for the finalization of this manuscript, Michael Kuhn
Citation: https://doi.org/10.5194/tc-2021-241-RC1 -
AC1: 'Reply on RC1', Sandra Donnici, 09 Sep 2021
We read your comment to our manuscript with great interest:
- We added the suggested column in Table 1
- Given the size of response times of the considered glaciers, we do not think that positive mass balances before 1980 can influence the projections to 2018-2100.
- Glacier photographs are published in the reports of annual glaciological surveys (http://www.glaciologia.it/en/i-ghiacciai-italiani/le-campagne-glaciologiche/). We added a link in chapter 3.1.
Line 255: our response time matches the definition of the response time in Coogley et al. (2011), applicable to both lengths and volumes, and is comparable with the mean values given by Paul et al. (2004), which are indicative for the generality of Alpine mountain glaciers.
Thank you for your suggestions, we added all the required corrections to the manuscript.
Best wishes,
Sandra Donnici
Citation: https://doi.org/10.5194/tc-2021-241-AC1 -
RC2: 'Reply on AC1', Michael Kuhn, 20 Sep 2021
I recommend to publish the revised manuscript in its present form
Citation: https://doi.org/10.5194/tc-2021-241-RC2
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AC1: 'Reply on RC1', Sandra Donnici, 09 Sep 2021
-
RC3: 'Comment on tc-2021-241', Anonymous Referee #2, 30 Oct 2021
Review to Serandrei-Barbero et al. (2021): "Effects of climate change on the valley glaciers of the Italian Alps" submitted to The Cryosphere.
The authors present a study of the future glacier length evolution of the Italian valley glaciers feeding a simple glacier model with future climate projections taken from the EURO-CORDEX initiative for RCP4.5 and RCP8.5 scenarios. The main result of the study is that until the end of the century the Italian valley glaciers (representing roughly 26% of the total Italian glacier area) will preserve about 50% of their length of 2017 and thus, their retreat will be slower than other glaciers in the Alps.
Overall, I rate this manuscript not ready for publication and due to the sum of inconsistencies I even suggest a rejection. My main points of concern are:
Missing scientific rigor: The introduction (L21) starts with a misconception. Glacier fluctuations are not a result of air temperature and precipitation variability, they are a result of complex climate-glacier interactions. We just prefer to make glacier fluctuations a proxy of air temperature and precipitation, because we usually have long-term observations of or established scaling functions for them. Hence, simple glacier models were established for conceptual understanding. However, we must be careful when interpreting (putative) results of (highly) parametrized processes or inverting them. Fig. 4 is an example of such a putative result. The correlation between the slope and the climate sensitivity is not a result of the study, it is a result of the model design. Because the simplified model defines the climate sensitivity as a function of the slope (Oerlemans, 2001), we detect it as correlation in the data. Cause and effect must not be interchanged.
Additionally, the chosen method seems not to be state of the art anymore. Meanwhile ice thickness estimates are available (Farinotti et al., 2017) opening the path to models deriving glacier volume changes (e.g. Maussion et al., 2019), having a higher significance than glacier length changes.Missing model calibration: A previous study of the same authors (Zecchetto et al., 2017) calibrated an existing method of glacier length change modelling (Oerlemans, 2005) on smaller glaciers in the Italian Alps for air temperature reconstructions. Now, the author team applies the same method without further amendments on the larger Italian valley glaciers. While the model is calibrated on shorter and steeper glaciers, it is applied on longer and flatter glaciers, although the authors would have all the data to calibrate the model on the valley glaciers, too. The missing model calibration might explain the low climate sensitivities and short response times compared to the original model publication (Oerlemans, 2005) and definitely impacts the results of the study and the conclusions the authors draw.
Missing error estimation: Because the model is not calibrated, there is no model error reported. The uncertainties given in Fig. 8 are induced by the different CORDEX ensemble members, but not by the model. Robust scientific results rely on a rigorous error estimation, would have helped to phrase a stronger discussion section.
References
Farinotti, D., Brinkerhoff, D. J., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., Gillet-Chaulet, F., Girard, C., Huss, M., Leclercq, P. W., Linsbauer, A., Machguth, H., Martin, C., Maussion, F., Morlighem, M., Mosbeux, C., Pandit, A., Portmann, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Sanchez, O., Stentoft, P. A., Singh Kumari, S., van Pelt, W. J. J., Anderson, B., Benham, T., Binder, D., Dowdeswell, J. A., Fischer, A., Helfricht, K., Kutuzov, S., Lavrentiev, I., McNabb, R., Gudmundsson, G. H., Li, H. and Andreassen, L. M.: How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment, The Cryosphere, 11(2), 949–970, doi:10.5194/tc-11-949-2017, 2017.
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T. and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geoscientific Model Development, 12(3), 909–931, doi:10.5194/gmd-12-909-2019, 2019.
Oerlemans, J.: Glaciers and Climate Change, A.A Balkema, Lisse., 2001.
Oerlemans, J.: Extracting a climate signal from 169 glacier records., Science, 308(5722), 675–677, doi:10.1126/science.1107046, 2005.
Zecchetto, S., Serandrei-Barbero, R. and Donnici, S.: Temperature reconstruction from the length fluctuations of small glaciers in the eastern Alps (northeastern Italy), Climate Dynamics, 49(1–2), 363–374, doi:10.1007/s00382-016-3347-5, 2017.
Citation: https://doi.org/10.5194/tc-2021-241-RC3 -
AC2: 'Reply on RC3', Sandra Donnici, 08 Nov 2021
Review to Serandrei-Barbero et al. (2021): "Effects of climate change on the valley glaciers of the Italian Alps" submitted to The Cryosphere.
The authors present a study of the future glacier length evolution of the Italian valley glaciers feeding a simple glacier model with future climate projections taken from the EURO-CORDEX initiative for RCP4.5 and RCP8.5 scenarios. The main result of the study is that until the end of the century the Italian valley glaciers (representing roughly 26% of the total Italian glacier area) will preserve about 50% of their length of 2017 and thus, their retreat will be slower than other glaciers in the Alps.
This is in agreement with what measured on the ground: on the valley glaciers here considered, from 1980 to 2017 the mean length loss is 16%. Their average areal shrinkage of 22% between 1980 and 2015 (Table 1) shows their smaller retreat with respect to the general shrinkage in the European Alps estimated around 40% as a lower bound.
The lower retreat of valley glaciers was also highlighted by considering together valley and mountain glaciers small to medium-sized (Serandrei-Barbero et al., 2019).
However, as the valley glaciers considered here are medium to large in size, it is possible that this may contribute to their lower retreat (at lines 243-247 of the text: In general larger glaciers show regresses inversely proportional to their size and this could significantly contribute to their expected minor retreat).
We include this consideration also in the conclusions.
We realize that the reviewer does not agree with our results. We are also sorry that the reasons for these conclusions are based on some misunderstandings of the text. Our results do not contradict the general belief that climate change will destroy all the glaciers, but rather indicate a longer survival of valley glaciers than mountain glaciers (and this different behavior has already been demonstrated by field monitoring performed in the past decades).
We are aware that the model used in the work is by far simpler than the models quoted here, but we doubt that projections like those reported in this paper may be reached using other models requiring data not available for the Italian Alps.
Overall, I rate this manuscript not ready for publication and due to the sum of inconsistencies I even suggest a rejection. My main points of concern are:
Missing scientific rigor: The introduction (L21) starts with a misconception. Glacier fluctuations are not a result of air temperature and precipitation variability, they are a result of complex climate-glacier interactions.
Of course, anyone dealing with glaciers is aware of the complexity of climate-glacier interactions (at l. 75 of the manuscript: thus, implicitly, that the model does not account for all the non-linear and local factors influencing a glacier’s life). In the text, we wrote that the air temperature and the variability of precipitation are the main parameters that influence the fluctuations of glaciers, but not the only ones. Furthermore, the importance of temperature is well known (Leclercq&Oerlemans 2012), since the past fluctuations of glaciers, used as a proxy of temperature, reproduce very well the instrumental record of the last century.
We just prefer to make glacier fluctuations a proxy of air temperature and precipitation, because we usually have long-term observations of or established scaling functions for them. Hence, simple glacier models were established for conceptual understanding. However, we must be careful when interpreting (putative) results of (highly) parametrized processes or inverting them. Fig. 4 is an example of such a putative result. The correlation between the slope and the climate sensitivity is not a result of the study, it is a result of the model design. Because the simplified model defines the climate sensitivity as a function of the slope (Oerlemans, 2001), we detect it as correlation in the data. Cause and effect must not be interchanged.
We did not say in any part of the paper that Fig. 4 is a result of the study, and we believe this is an inference of the reviewer that the text does not support. As in the text, The climate sensitivity Cs (Eq. 2) depends on the glacier slope and the total annual precipitation and therefore Fig. 4 is just Eq. 2 applied to our glaciers, which provides an estimate of the rate of change of Cs with slope.
Additionally, the chosen method seems not to be state of the art anymore. Meanwhile ice thickness estimates are available (Farinotti et al., 2017) opening the path to models deriving glacier volume changes (e.g. Maussion et al., 2019), having a higher significance than glacier length changes.You are right. With respect to the models of new generation, our approach does not represent the status of the art. But this does not mean that more complex models would provide better projections, also given the uncertainties on the several parameters needed to run them. The ground data contains many gaps and inconsistencies and, despite the efforts of the Glacier Monitoring Service, not all glacier outlines are available. Our aim was to use available and verifiable data so that we could have a reliable database: the glacier lengths fulfil this premise and are available on almost all Italian valley glaciers.
We include part of these consideration in the Introduction, where Farinotti et al. (2017) and Maussion et al., (2019) are cited.
Missing model calibration: A previous study of the same authors (Zecchetto et al., 2017) calibrated an existing method of glacier length change modelling (Oerlemans, 2005) on smaller glaciers in the Italian Alps for air temperature reconstructions.
Now, the author team applies the same method without further amendments on the larger Italian valley glaciers. While the model is calibrated on shorter and steeper glaciers,
As the reviewer probably knows, model calibration is a statistical procedure, which minimizes the differences between the model and the data through coefficients. In our case, the model (eq. 1) was compared with the data of 3 glaciers, and the coefficients c1 and c2 of Eqs. 2 and 3 were estimated as c1= 0.0078 ± 0.0004 and c2= 1.35 ± 0.14 by means of least-squares regression of the function. These values, of course, are mean values and satisfy differently the glaciers, but we cannot have N coefficients for N glaciers.
The glaciers used for calibrations are on average smaller (from 1060 m to 2192 m) than those of the present work (from 1712 m to 5357 m), but not steeper.
it is applied on longer and flatter glaciers, although the authors would have all the data to calibrate the model on the valley glaciers, too. The missing model calibration might explain the low climate
Model calibration is not missing and we wonder how the reviewer could expect larger values of Cs and τ and why. Our new calibration in Zecchetto et al, 2017 has been shown to work on glaciers longer than those used for calibration (2880 m, 1267 m, 1933 m). This was done because the Oerlemans 2005 calibration did not suit with the glaciers of our region and cannot be used for them.
sensitivities and short response times compared to the original model publication (Oerlemans, 2005) and definitely impacts the results of the study and the conclusions the authors draw.
Oerlemans, 2005 studied 169 glaciers over the world with lengths from 0.3 km to 45 km, while Leclercq&Oerlemens (2012) used 309 glaciers for temperature reconstruction. As far as we know, their coefficients of calibration (0.00204 for Cs and 19.4 for τ) were obtained from fourteen glaciers and then used for all the glaciers. Our procedure was quite similar to that of these quoted works, but with a smaller glacier data set.
Of course the values of Cs and τ impact on the results. It seems to us that the reviewer is disturbed by the conclusions of possible survival of the valley glaciers on the Italian Alps.
Missing error estimation: Because the model is not calibrated, there is no model error reported. The uncertainties given in Fig. 8 are induced by the different CORDEX ensemble members, but not by the model. Robust scientific results rely on a rigorous error estimation, would have helped to phrase a stronger discussion section.
We do not see the relationship between calibration and model error. The model error can be evaluated only thought the uncertainty of Cs, τ and temperature.
References
Farinotti, D., Brinkerhoff, D. J., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., Gillet-Chaulet, F., Girard, C., Huss, M., Leclercq, P. W., Linsbauer, A., Machguth, H., Martin, C., Maussion, F., Morlighem, M., Mosbeux, C., Pandit, A., Portmann, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Sanchez, O., Stentoft, P. A., Singh Kumari, S., van Pelt, W. J. J., Anderson, B., Benham, T., Binder, D., Dowdeswell, J. A., Fischer, A., Helfricht, K., Kutuzov, S., Lavrentiev, I., McNabb, R., Gudmundsson, G. H., Li, H. and Andreassen, L. M.: How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment, The Cryosphere, 11(2), 949–970, doi:10.5194/tc-11-949-2017, 2017.
Leclercq, P. W., Oerlemans, J.: Global and hemispheric temperature reconstruction from glacier length fluctuations, Clim Dynam, 38(5-6), 1065-1079, https://doi.org/10.1007/s00382-011-1145-7, 2012.
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T. and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geoscientific Model Development, 12(3), 909–931, doi:10.5194/gmd-12-909-2019, 2019.
Oerlemans, J.: Glaciers and Climate Change, A.A Balkema, Lisse., 2001.
Oerlemans, J.: Extracting a climate signal from 169 glacier records., Science, 308(5722), 675–677, doi:10.1126/science.1107046, 2005.
Serandrei-Barbero, R., Donnici, S., and Zecchetto, S.: Projected effects of temperature changes on the Italian Western Tauri glaciers (Eastern Alps), J Glaciol, 1-10, https://doi.org/10.1017/jog.2019.7, 2019.
Zecchetto, S., Serandrei-Barbero, R. and Donnici, S.: Temperature reconstruction from the length fluctuations of small glaciers in the eastern Alps (northeastern Italy), Climate Dynamics, 49(1–2), 363–374, doi:10.1007/s00382-016-3347-5, 2017.
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AC2: 'Reply on RC3', Sandra Donnici, 08 Nov 2021
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