the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Surface mass balance modelling of the Juneau Icefield highlights the potential for rapid ice loss by the mid-21st century
Abstract. Plateau icefields are large stores of freshwater, preconditioned to enhanced mass loss due to their gently sloping accumulation areas. Accurately modelling the mass-balance of these icefields is therefore vital for obtaining projections of their future contribution to sea-level rise. Here, we use the COupled Snowpack and Ice surface energy and mass-balance model in PYthon (COSIPY) to simulate the historical and potential future mass balance of the Juneau Icefield, Alaska – a high elevation (>1200 m) plateau icefield. We force the model with dynamically downscaled climate simulations, pertaining to both the past and potential future (RCP 8.5) conditions. The rich dataset of surface mass balance observations of the Juneau Icefield allows us to tune COSIPY, providing confidence in our future predictions and highlighting changes to the icefield between the years 1980 and 2019. Icefield-wide negative mass balances were simulated from the start of the 21st century, as many glaciers transitioned from positive to negative mass-balances. Under RCP8.5, the model simulates increasing negative mass balance across Juneau Icefield, with the entire icefield potentially displaying a negative mass balance by the mid-21st century. This simulated loss of accumulation is driven by increased temperatures and reduced amounts of snowfall, exposing more of the icefield to thinning. Ice thinning is likely to be exacerbated by the exposure of ice to melting across the plateau surface, and prolonged melt may lead to an increase in disconnections, splitting glaciers between their accumulation and ablation areas at icefalls. The similar hypsometry of other high latitude plateau icefields and ice caps may mean that similar processes will act to determine their potential fate in our changing climate.
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Interactive discussion
Status: closed
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CC1: 'Comment on tc-2023-33', Andrew Bliss, 17 Mar 2023
How do your historical results compare to Young et al 2021?
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020WR027404
Citation: https://doi.org/10.5194/tc-2023-33-CC1 - AC1: 'Reply on CC1', Jeremy Ely, 12 Jul 2023
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RC1: 'Comment on tc-2023-33', Anonymous Referee #1, 26 Apr 2023
Ryan et al. give new estimates about the surface mass balance of the Juneau Icefield at the north American east coast. The use the Coupled Snowpack and Ice surface energy and mass-balance model in Python to generate a data set of the surface mass and energy balance in present day and future. For calibration and validation of COSIPY, they used input data from the CFSR Reanalysis, for future projections in the period 2031 to 2060 they used input data from output of RCP8.5 scenarios of GFDL-CM3 and NCAR-CCSM4 (CMIP5 models). The validation showed promising results. Future projections indicated largescale ablation and the risk of glaciers which loose connection to the ice source region. The COSIPY output for future projections gives insights in possible outcomes assuming different climate sensitivities.
The manuscript fills a gap on the mass balance of the Juneau Icefield. This was done by using CMIP5 models as input to COSIPY. Therefore, it is a valuable addition to science. The dataset can give estimates of the future development of the ice sheet and invites for novel research.
Major comments
- You applied bias correction on the downscaled model input. How did the bias correction improve the input data?
- Can you give further explanation for the decrease in total precipitation (Fig 10e)?
- In Fig 10 there is a strong decrease in total precipitation but only small decrease in snowfall. Would this lead to more precipitation falling as snow in future? Which implication does that have? Is it removed by increased snowmelt?
- In some figures the units are not matching.
Minor comments:
- Why did you choose GFDL-CM3 and NCAR-CCSM4? Did you evaluate other models as well?
- Can you specify which of the outlet glaciers are marine-terminating or have floating glacier tongues?
L. 124: roughness length decline?
L. 131: “… shapefile of the region of interest …” can be rephrased. The shapefile defines the grid points on which COSIPY is applied (glacier mask)
L. 218 & Fig 4: Is the unit correct? 3060 mm a-1 seems high.
L. 280: predicted -> projected to?
L. 292: towards to end -> towards the end?
L. 296: prediction -> projection?
L. 326-328: the sentence is unclear. Can you please rephrase it?
L. 340: Please check the panels in the figure reference?
L. 433: multi-model mean SMB
Fig 10: Are snowmelt and snowfall in the same unit (mm w.eq. vs. m w.eq.)?
Citation: https://doi.org/10.5194/tc-2023-33-RC1 - AC1: 'Reply on CC1', Jeremy Ely, 12 Jul 2023
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RC2: 'Comment on tc-2023-33', Tobias Sauter, 08 May 2023
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2023-33/tc-2023-33-RC2-supplement.pdf
- AC1: 'Reply on CC1', Jeremy Ely, 12 Jul 2023
Interactive discussion
Status: closed
-
CC1: 'Comment on tc-2023-33', Andrew Bliss, 17 Mar 2023
How do your historical results compare to Young et al 2021?
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020WR027404
Citation: https://doi.org/10.5194/tc-2023-33-CC1 - AC1: 'Reply on CC1', Jeremy Ely, 12 Jul 2023
-
RC1: 'Comment on tc-2023-33', Anonymous Referee #1, 26 Apr 2023
Ryan et al. give new estimates about the surface mass balance of the Juneau Icefield at the north American east coast. The use the Coupled Snowpack and Ice surface energy and mass-balance model in Python to generate a data set of the surface mass and energy balance in present day and future. For calibration and validation of COSIPY, they used input data from the CFSR Reanalysis, for future projections in the period 2031 to 2060 they used input data from output of RCP8.5 scenarios of GFDL-CM3 and NCAR-CCSM4 (CMIP5 models). The validation showed promising results. Future projections indicated largescale ablation and the risk of glaciers which loose connection to the ice source region. The COSIPY output for future projections gives insights in possible outcomes assuming different climate sensitivities.
The manuscript fills a gap on the mass balance of the Juneau Icefield. This was done by using CMIP5 models as input to COSIPY. Therefore, it is a valuable addition to science. The dataset can give estimates of the future development of the ice sheet and invites for novel research.
Major comments
- You applied bias correction on the downscaled model input. How did the bias correction improve the input data?
- Can you give further explanation for the decrease in total precipitation (Fig 10e)?
- In Fig 10 there is a strong decrease in total precipitation but only small decrease in snowfall. Would this lead to more precipitation falling as snow in future? Which implication does that have? Is it removed by increased snowmelt?
- In some figures the units are not matching.
Minor comments:
- Why did you choose GFDL-CM3 and NCAR-CCSM4? Did you evaluate other models as well?
- Can you specify which of the outlet glaciers are marine-terminating or have floating glacier tongues?
L. 124: roughness length decline?
L. 131: “… shapefile of the region of interest …” can be rephrased. The shapefile defines the grid points on which COSIPY is applied (glacier mask)
L. 218 & Fig 4: Is the unit correct? 3060 mm a-1 seems high.
L. 280: predicted -> projected to?
L. 292: towards to end -> towards the end?
L. 296: prediction -> projection?
L. 326-328: the sentence is unclear. Can you please rephrase it?
L. 340: Please check the panels in the figure reference?
L. 433: multi-model mean SMB
Fig 10: Are snowmelt and snowfall in the same unit (mm w.eq. vs. m w.eq.)?
Citation: https://doi.org/10.5194/tc-2023-33-RC1 - AC1: 'Reply on CC1', Jeremy Ely, 12 Jul 2023
-
RC2: 'Comment on tc-2023-33', Tobias Sauter, 08 May 2023
The comment was uploaded in the form of a supplement: https://tc.copernicus.org/preprints/tc-2023-33/tc-2023-33-RC2-supplement.pdf
- AC1: 'Reply on CC1', Jeremy Ely, 12 Jul 2023
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Ryan N. Ing
Julie M. Jones
Bethan J. Davies
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