the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jul 2024
Ice thickness and bed topography of Jostedalsbreen ice cap, Norway
Abstract. We present an extensive dataset of ice thickness measurements from Jostedalsbreen ice cap, mainland Europe's largest glacier. The dataset consists of more than 351 000 point values of ice thickness distributed along ~1100 km profile segments that cover most of the ice cap. Ice thickness was measured during field campaigns in 2018, 2021, 2022, and 2023 using various ground-penetrating radar (GPR) systems with frequencies ranging between 2.5 and 500 MHz. The large majority of ice thickness observations were collected in spring using either snowmobiles (90 %) or a helicopter-based radar system (8 %), while summer measurements were carried out on foot (2 %). To ensure accessibility and ease of use, metadata were attributed following the GlaThiDa dataset and follows the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles. Our findings show that glacier ice of more than 400 m thickness is found in the upper regions of large outlet glaciers, with a maximum ice thickness of ~630 m in the Tunsbergdalsbreen outlet glacier accumulation area. Thin ice of less than 50 m covers narrow regions joining the central part of Jostedalsbreen with its northern and southern parts, making the ice cap vulnerable to break-up with future climate warming. Using the point values of ice thickness as input to an ice thickness model, we compute 10 m grids of ice thickness and bed topography that cover the entire ice cap. From these distributed datasets we find that Jostedalsbreen has a mean ice thickness of 154 m ±22 m and a present (~2020) ice volume of 70.6 ±10.2 km3. Locations of depressions in the map of bed topography are used to delimitate the locations of potential future lakes, consequently providing a glimpse of the landscape if the entire Jostedalsbreen melts away. Together, the comprehensive ice thickness point values and ice cap-wide grids serve as a baseline for future climate change impact studies at Jostedalsbreen.
All data are available for download at https://doi.org/10.58059/yhwr-rx55 (Gillespie et al., 2024).
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RC1: 'Comment on essd-2024-167', Anonymous Referee #1, 17 Jul 2024
General
Gillespie et al. present an extensive collection of ice thickness observations on Jostedalsbreen ice cap
obtained using ground penetrating radar (by scooter, by helicopter and by foot) between 2018 and
2023. They derive an impressive coverage of the ice cap with 90% of the area now being within
300 m of a data point. The point observations are used to calculate a distributed map of ice thickness
and bed topography based on a thickness inversion approach. Uncertainties are presented for both
the observations as well as the modelled thicknesses. The final ice volume of 70±10.2 km2 is closely
aligned with previously published estimates. The distributed bed and thickness maps are thought to
form a valuable input for future modelling studies of the ice cap as well as for predicting landscape
change, e.g. with regard to future lakes and possible break-up points of the ice cap.
I would like to commend the authors for their efforts to obtain such a wealth and density of
thickness observations which may well be unique for an ice cap of this size. A transparent account
of error sources and related uncertainties is evident throughout the manuscript and lends trust to
presented results. The manuscript is well written and clear, with appropriate figures and tables. The
provided data is easily accessible and well documented. All in all, the significant improvements in
coverage and accuracy compared to the previously available thickness observations of Jostedalsbreen
surely warrant publication in ESSD. I have only minor comments with the exception of one related to
the inverse modelling approach.2 Specific comments
2.1 Inverse modelling
The inverse approach chosen is based on Huss and Farinotti (2012), a method which reduces glacier
shape to a flow-line and is often thought to not be the ideal tool for ice caps (e.g. its comparatively
low performance there is already discussed in paragraph 25 of the original publication, but also in,
e.g., Millan et al. (2022); Frank and Pelt (2024)). Previously published ice thickness maps of ice
caps based on this method (e.g. Farinotti et al., 2019) are not seldom characterized by unrealistic
bed shapes and issues at ice divides. Regarding the latter, the authors here aim to alleviate the
problems by resorting to an iterative procedure that involves interpolation of nearby GPR profiles and
”estimated thicknesses on ice divides based on local knowledge” - doesn´t sound entirely convincing.
Nevertheless, this strategy is helpful when looking at the output, yet there remain small inconsistencies
at glacier boundaries. Regarding the overall appearance of the bed shape and thickness distribution,
after plotting in QGIS, I find that it does not look realistic. For sure, the thickness observations are
well matched, but overall clear ”stripes” are visible that appear as if someone had drawn with a thick
pencil. So, my general question is: why did the authors settle for this approach and not for other
methods on distributed grids, possibly even one that is specifically designed for assimilating thickness
observations (e.g. Morlighem et al., 2017; Fürst et al., 2017; Jouvet, 2023)? Looking at the modelled
thickness field, I have doubts that the flux divergence would look somewhat realistic when put into a
distributed ice flow model. This could turn out to be an issue if, as suggested in the introduction, this
bed shape is used for prognostic simulations. In fact, is the final thickness distribution mass conserving in any way? For the plausibility of the location of future lakes, the unrealistic bed shape also doesn´t
help (although I am aware that there is always a relatively large uncertainty for such a product).
Besides this general comment on the choice of inverse method, I find that section 3.7 in parts is
unclear and should be revised (c.f. specific comments). Simply, while I understand that the method
has already been presented in other publications and hence some omission of details is justified, I
currently don´t find myself able to understand all steps taken. Not the least, more information on key
model parameters should be presented .2.2 Other specific comments
L36f: Slightly re-formulate to make clear that future studies can use the outputs from this work for
climate change impact studies. This is not done here.
L40: There are also other reasons for glacier mass loss than atmospheric temperature increase (e.g.
increased ocean temperature for marine-terminating glaciers). Please re-phrase.
L 52: A newer version of the GlaThiDa with continuous additions of published thickness obser-
vations is available under https://gitlab.com/wgms/glathida. Consider referring to updated num-
bers from there as well. More generally, besides publishing the data on the repository given in the
manuscript, would you consider actually feeding it into the GlaThiDa?
L 169f: Here and in other places, the helicopter radar system is described as new/newly developed.
However, not much details regarding its novelty are given. So, what exactly are the novelties? And has
the system been thoroughly tested beyond what is mentioned in L 365ff? Also, what was the travel
speed?
L 274: You mention that you could infer information on glacier thermal regime from the collected
GPR data. Was this done systematically, and if so, would it be worthwhile adding more information
on that in the manuscript? Could be quite interesting!
L 434: The inversion method was originally developed for global applications and was relying on
some large-scale input products and coarse assumptions to infer parameters such as basal sliding, ice
viscosity and apparent mass balance. Was this global setup used to infer these parameters here as
well, or were they based on more local knowledge/input products (e.g. the mass balance observations
on Jostedalsbreen)? Also, in Huss and Farinotti (2012), one basal parameter per glacier was chosen,
not a longitudinal trend as stated here. Please state or provide a reference on how the basal sliding
distribution was derived.
L 444: Please state that this integrated form of Glen´s flow law assumes parallel flow and thus in
essence is the shallow ice approximation. In my view, this information is important for readers to be
able to evaluate potential errors of the model output.
L 449: Could you please comment on your choice of inferring ice thickness on a 10 m grid? There is
a physical limit to how much spatial detail one can obtain from a thickness inversion, at best horizontal
features of >1 times the ice thickness can be resolved (e.g. Gudmundsson, 2003). Isn´t the choice of a
10 m grid unnecessary? Or even, doesn´t it give the wrong impression that such small features should
actually be detectable in your thickness map? You later mention that you smooth the bed topography
(but not the thicknesses, why?) with a 50-100 m filter which, however, still in many places is lower
than one ice thickness.
L 463ff: Here you mention that the apparent mass balance gradient is optimized to reduce the misfit
to the thickness observations. In the next sentence, there is talk of two gradients that are subsequently
computed. Please clarify the discrepancy on the number of gradients. Furthermore, it is not clear
to me why the apparent mass balance is first optimized, and then calculated again. What exactly is
done when it is calculated after the optimization? Lastly, you assume a balanced mass budget over the
entire glacier unit. Instead of an assumption, isn´t that a necessity that follows from mass conservation?L 473ff: Is the difference field smooth? It sounds as if that wouldn´t necessarily be the case, e.g.
one observation point may be well matched, whereas the neighboring one isn´t. How does that affect
the extrapolation? And why is the ice thickness distribution after this step not going through the
observations, if you apply point-wise corrections? And on a different note: I am somewhat confused
about the terms extra- and interpolation (here and in other places of this section). Please clarify what
you mean when you interpolate vs when you extrapolate, and between what or where to you do that.L 479ff: Here I cannot follow any longer. I take that after step ii, you already have a thickness
distribution that is based on all available thickness observations. However, now again thickness ob-
servations in combination with model results are used for some more interpolation. Why and how is
that done? Where do you interpolate between? What happens within the buffer? Do you simply set
the thickness there to the observed one? And ultimately, is your final thickness distribution consistent
with the ice dynamics and apparent mass balance of the inversion? If not, what distinguishes your
approach from a statistical interpolation?
L 484: Please provide more information on what is meant by ”local knowledge” and how that
information was transformed to quantitative values.
L 516: Another way of assessing uncertainties would be to remove some of the thickness observa-
tions from the data set, re-run the inversion and test how the resulting thickness field compares at
those spots. Could that be worthwile?
L 594f: You mention that the old data set has considerable uncertainties in many places, and thus
you limit your comparison to an area which is more certain. I would argue that there is no reason
for that. Indeed, to get a full understanding of how much better your new data set is plus to allow
users of the old data set to judge how ”bad” the old data was, I think it would be meaningful to
undertake a full comparison. A simple scatter plot of old vs new thicknesses (at places where the two
are reasonably close together, or using your interpolated bed map) would already be helpful in my view.3 Technical comments
L 26: I think adding a reference for the GlaThiDa is appropriate here, as not all readers can be assumed
to know what that is
L 62: Fürst et al. (2017) actually assimilates thickness observations, so should not be mentioned
here
L 74: remain, not remains
L 141 ”continues to this day”: consider rephrasing, e.g. ”continue to do so to this day”
L149: In contrast to other place/glacier names mentioned in the text, Bødalsbreen is not labeled
on the figures
L 629: Consider including also a comparison to Farinotti et al. (2019) and Millan et al. (2022)4 Figures and Tables
Table 2: In the GlaThiDa, the attribute field for elevation is ELEVATION, whereas it is GNSS ELEVATION
here. For simplicity of usage, consider aligning with the GlaThiDaReferences
Farinotti D, Huss M, Fürst JJ, Landmann J, Machguth H, Maussion F, Pandit A. 2019. A consensus
estimate for the ice thickness distribution of all glaciers on Earth. Nature Geoscience 12:168–173.
doi:10.1038/s41561-019-0300-3.
Frank T, Pelt WJJv. 2024. Ice volume and thickness of all Scandinavian glaciers and ice caps. Journal
of Glaciology :1–14doi:10.1017/jog.2024.25.
Fürst JJ, Gillet-Chaulet F, Benham TJ, Dowdeswell JA, Grabiec M, Navarro F, Pettersson R, Moholdt
G, Nuth C, Sass B, Aas K, Fettweis X, Lang C, Seehaus T, Braun M. 2017. Application of a two-
step approach for mapping ice thickness to various glacier types on Svalbard. The Cryosphere
11:2003–2032. doi:10.5194/tc-11-2003-2017.
Gudmundsson GH. 2003. Transmission of basal variability to a glacier surface. Journal of Geophysical
Research: Solid Earth 108. doi:10.1029/2002JB002107.
Huss M, Farinotti D. 2012. Distributed ice thickness and volume of all glaciers around the globe.
Journal of Geophysical Research: Earth Surface 117. doi:10.1029/2012JF002523.
Jouvet G. 2023. Inversion of a Stokes glacier flow model emulated by deep learning. Journal of
Glaciology 69:13–26. doi:10.1017/jog.2022.41.
Millan R, Mouginot J, Rabatel A, Morlighem M. 2022. Ice velocity and thickness of the world’s glaciers.
Nature Geoscience 15:124–129. doi:10.1038/s41561-021-00885-z.
Morlighem M, Williams CN, Rignot E, An L, Arndt JE, Bamber JL, Catania G, Chauch´e N,
Dowdeswell JA, Dorschel B, Fenty I, Hogan K, Howat I, Hubbard A, Jakobsson M, Jordan TM,
Kjeldsen KK, Millan R, Mayer L, Mouginot J, No¨el BPY, O’Cofaigh C, Palmer S, Rysgaard S,
Seroussi H, Siegert MJ, Slabon P, Straneo F, van den Broeke MR, Weinrebe W, Wood M, Zin-
glersen KB. 2017. BedMachine v3: Complete Bed Topography and Ocean Bathymetry Mapping
of Greenland From Multibeam Echo Sounding Combined With Mass Conservation. Geophysical
Research Letters 44:11,051–11,061. doi:10.1002/2017GL074954Citation: https://doi.org/10.5194/essd-2024-167-RC1 - RC2: 'Comment on essd-2024-167', Anonymous Referee #2, 26 Jul 2024
-
AC1: 'Reply on RC1 and RC2', Mette K Gillespie, 05 Sep 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-167/essd-2024-167-AC1-supplement.pdf
Status: closed
-
RC1: 'Comment on essd-2024-167', Anonymous Referee #1, 17 Jul 2024
General
Gillespie et al. present an extensive collection of ice thickness observations on Jostedalsbreen ice cap
obtained using ground penetrating radar (by scooter, by helicopter and by foot) between 2018 and
2023. They derive an impressive coverage of the ice cap with 90% of the area now being within
300 m of a data point. The point observations are used to calculate a distributed map of ice thickness
and bed topography based on a thickness inversion approach. Uncertainties are presented for both
the observations as well as the modelled thicknesses. The final ice volume of 70±10.2 km2 is closely
aligned with previously published estimates. The distributed bed and thickness maps are thought to
form a valuable input for future modelling studies of the ice cap as well as for predicting landscape
change, e.g. with regard to future lakes and possible break-up points of the ice cap.
I would like to commend the authors for their efforts to obtain such a wealth and density of
thickness observations which may well be unique for an ice cap of this size. A transparent account
of error sources and related uncertainties is evident throughout the manuscript and lends trust to
presented results. The manuscript is well written and clear, with appropriate figures and tables. The
provided data is easily accessible and well documented. All in all, the significant improvements in
coverage and accuracy compared to the previously available thickness observations of Jostedalsbreen
surely warrant publication in ESSD. I have only minor comments with the exception of one related to
the inverse modelling approach.2 Specific comments
2.1 Inverse modelling
The inverse approach chosen is based on Huss and Farinotti (2012), a method which reduces glacier
shape to a flow-line and is often thought to not be the ideal tool for ice caps (e.g. its comparatively
low performance there is already discussed in paragraph 25 of the original publication, but also in,
e.g., Millan et al. (2022); Frank and Pelt (2024)). Previously published ice thickness maps of ice
caps based on this method (e.g. Farinotti et al., 2019) are not seldom characterized by unrealistic
bed shapes and issues at ice divides. Regarding the latter, the authors here aim to alleviate the
problems by resorting to an iterative procedure that involves interpolation of nearby GPR profiles and
”estimated thicknesses on ice divides based on local knowledge” - doesn´t sound entirely convincing.
Nevertheless, this strategy is helpful when looking at the output, yet there remain small inconsistencies
at glacier boundaries. Regarding the overall appearance of the bed shape and thickness distribution,
after plotting in QGIS, I find that it does not look realistic. For sure, the thickness observations are
well matched, but overall clear ”stripes” are visible that appear as if someone had drawn with a thick
pencil. So, my general question is: why did the authors settle for this approach and not for other
methods on distributed grids, possibly even one that is specifically designed for assimilating thickness
observations (e.g. Morlighem et al., 2017; Fürst et al., 2017; Jouvet, 2023)? Looking at the modelled
thickness field, I have doubts that the flux divergence would look somewhat realistic when put into a
distributed ice flow model. This could turn out to be an issue if, as suggested in the introduction, this
bed shape is used for prognostic simulations. In fact, is the final thickness distribution mass conserving in any way? For the plausibility of the location of future lakes, the unrealistic bed shape also doesn´t
help (although I am aware that there is always a relatively large uncertainty for such a product).
Besides this general comment on the choice of inverse method, I find that section 3.7 in parts is
unclear and should be revised (c.f. specific comments). Simply, while I understand that the method
has already been presented in other publications and hence some omission of details is justified, I
currently don´t find myself able to understand all steps taken. Not the least, more information on key
model parameters should be presented .2.2 Other specific comments
L36f: Slightly re-formulate to make clear that future studies can use the outputs from this work for
climate change impact studies. This is not done here.
L40: There are also other reasons for glacier mass loss than atmospheric temperature increase (e.g.
increased ocean temperature for marine-terminating glaciers). Please re-phrase.
L 52: A newer version of the GlaThiDa with continuous additions of published thickness obser-
vations is available under https://gitlab.com/wgms/glathida. Consider referring to updated num-
bers from there as well. More generally, besides publishing the data on the repository given in the
manuscript, would you consider actually feeding it into the GlaThiDa?
L 169f: Here and in other places, the helicopter radar system is described as new/newly developed.
However, not much details regarding its novelty are given. So, what exactly are the novelties? And has
the system been thoroughly tested beyond what is mentioned in L 365ff? Also, what was the travel
speed?
L 274: You mention that you could infer information on glacier thermal regime from the collected
GPR data. Was this done systematically, and if so, would it be worthwhile adding more information
on that in the manuscript? Could be quite interesting!
L 434: The inversion method was originally developed for global applications and was relying on
some large-scale input products and coarse assumptions to infer parameters such as basal sliding, ice
viscosity and apparent mass balance. Was this global setup used to infer these parameters here as
well, or were they based on more local knowledge/input products (e.g. the mass balance observations
on Jostedalsbreen)? Also, in Huss and Farinotti (2012), one basal parameter per glacier was chosen,
not a longitudinal trend as stated here. Please state or provide a reference on how the basal sliding
distribution was derived.
L 444: Please state that this integrated form of Glen´s flow law assumes parallel flow and thus in
essence is the shallow ice approximation. In my view, this information is important for readers to be
able to evaluate potential errors of the model output.
L 449: Could you please comment on your choice of inferring ice thickness on a 10 m grid? There is
a physical limit to how much spatial detail one can obtain from a thickness inversion, at best horizontal
features of >1 times the ice thickness can be resolved (e.g. Gudmundsson, 2003). Isn´t the choice of a
10 m grid unnecessary? Or even, doesn´t it give the wrong impression that such small features should
actually be detectable in your thickness map? You later mention that you smooth the bed topography
(but not the thicknesses, why?) with a 50-100 m filter which, however, still in many places is lower
than one ice thickness.
L 463ff: Here you mention that the apparent mass balance gradient is optimized to reduce the misfit
to the thickness observations. In the next sentence, there is talk of two gradients that are subsequently
computed. Please clarify the discrepancy on the number of gradients. Furthermore, it is not clear
to me why the apparent mass balance is first optimized, and then calculated again. What exactly is
done when it is calculated after the optimization? Lastly, you assume a balanced mass budget over the
entire glacier unit. Instead of an assumption, isn´t that a necessity that follows from mass conservation?L 473ff: Is the difference field smooth? It sounds as if that wouldn´t necessarily be the case, e.g.
one observation point may be well matched, whereas the neighboring one isn´t. How does that affect
the extrapolation? And why is the ice thickness distribution after this step not going through the
observations, if you apply point-wise corrections? And on a different note: I am somewhat confused
about the terms extra- and interpolation (here and in other places of this section). Please clarify what
you mean when you interpolate vs when you extrapolate, and between what or where to you do that.L 479ff: Here I cannot follow any longer. I take that after step ii, you already have a thickness
distribution that is based on all available thickness observations. However, now again thickness ob-
servations in combination with model results are used for some more interpolation. Why and how is
that done? Where do you interpolate between? What happens within the buffer? Do you simply set
the thickness there to the observed one? And ultimately, is your final thickness distribution consistent
with the ice dynamics and apparent mass balance of the inversion? If not, what distinguishes your
approach from a statistical interpolation?
L 484: Please provide more information on what is meant by ”local knowledge” and how that
information was transformed to quantitative values.
L 516: Another way of assessing uncertainties would be to remove some of the thickness observa-
tions from the data set, re-run the inversion and test how the resulting thickness field compares at
those spots. Could that be worthwile?
L 594f: You mention that the old data set has considerable uncertainties in many places, and thus
you limit your comparison to an area which is more certain. I would argue that there is no reason
for that. Indeed, to get a full understanding of how much better your new data set is plus to allow
users of the old data set to judge how ”bad” the old data was, I think it would be meaningful to
undertake a full comparison. A simple scatter plot of old vs new thicknesses (at places where the two
are reasonably close together, or using your interpolated bed map) would already be helpful in my view.3 Technical comments
L 26: I think adding a reference for the GlaThiDa is appropriate here, as not all readers can be assumed
to know what that is
L 62: Fürst et al. (2017) actually assimilates thickness observations, so should not be mentioned
here
L 74: remain, not remains
L 141 ”continues to this day”: consider rephrasing, e.g. ”continue to do so to this day”
L149: In contrast to other place/glacier names mentioned in the text, Bødalsbreen is not labeled
on the figures
L 629: Consider including also a comparison to Farinotti et al. (2019) and Millan et al. (2022)4 Figures and Tables
Table 2: In the GlaThiDa, the attribute field for elevation is ELEVATION, whereas it is GNSS ELEVATION
here. For simplicity of usage, consider aligning with the GlaThiDaReferences
Farinotti D, Huss M, Fürst JJ, Landmann J, Machguth H, Maussion F, Pandit A. 2019. A consensus
estimate for the ice thickness distribution of all glaciers on Earth. Nature Geoscience 12:168–173.
doi:10.1038/s41561-019-0300-3.
Frank T, Pelt WJJv. 2024. Ice volume and thickness of all Scandinavian glaciers and ice caps. Journal
of Glaciology :1–14doi:10.1017/jog.2024.25.
Fürst JJ, Gillet-Chaulet F, Benham TJ, Dowdeswell JA, Grabiec M, Navarro F, Pettersson R, Moholdt
G, Nuth C, Sass B, Aas K, Fettweis X, Lang C, Seehaus T, Braun M. 2017. Application of a two-
step approach for mapping ice thickness to various glacier types on Svalbard. The Cryosphere
11:2003–2032. doi:10.5194/tc-11-2003-2017.
Gudmundsson GH. 2003. Transmission of basal variability to a glacier surface. Journal of Geophysical
Research: Solid Earth 108. doi:10.1029/2002JB002107.
Huss M, Farinotti D. 2012. Distributed ice thickness and volume of all glaciers around the globe.
Journal of Geophysical Research: Earth Surface 117. doi:10.1029/2012JF002523.
Jouvet G. 2023. Inversion of a Stokes glacier flow model emulated by deep learning. Journal of
Glaciology 69:13–26. doi:10.1017/jog.2022.41.
Millan R, Mouginot J, Rabatel A, Morlighem M. 2022. Ice velocity and thickness of the world’s glaciers.
Nature Geoscience 15:124–129. doi:10.1038/s41561-021-00885-z.
Morlighem M, Williams CN, Rignot E, An L, Arndt JE, Bamber JL, Catania G, Chauch´e N,
Dowdeswell JA, Dorschel B, Fenty I, Hogan K, Howat I, Hubbard A, Jakobsson M, Jordan TM,
Kjeldsen KK, Millan R, Mayer L, Mouginot J, No¨el BPY, O’Cofaigh C, Palmer S, Rysgaard S,
Seroussi H, Siegert MJ, Slabon P, Straneo F, van den Broeke MR, Weinrebe W, Wood M, Zin-
glersen KB. 2017. BedMachine v3: Complete Bed Topography and Ocean Bathymetry Mapping
of Greenland From Multibeam Echo Sounding Combined With Mass Conservation. Geophysical
Research Letters 44:11,051–11,061. doi:10.1002/2017GL074954Citation: https://doi.org/10.5194/essd-2024-167-RC1 - RC2: 'Comment on essd-2024-167', Anonymous Referee #2, 26 Jul 2024
-
AC1: 'Reply on RC1 and RC2', Mette K Gillespie, 05 Sep 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-167/essd-2024-167-AC1-supplement.pdf
Data sets
Jostedalsbreen ice thickness and bed topography data Mette Kusk Gillespie et al. https://doi.org/10.58059/yhwr-rx55
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