the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mass Balance of the Greenland and Antarctic Ice Sheets from 1992 to 2020
- 1Centre for Polar Observation and Modelling, University of Leeds, Leeds, United Kingdom
- 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
- 3Institute of Environmental Geosciences, Université Grenoble Alpes, Grenoble, France
- 4Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, The Netherlands
- 5Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
- 6Polar Science Center, University of Washington, Seattle, United States
- 7Department of Geology, University at Buffalo, Buffalo, United States
- 8School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
- 9Earth System Science, University of California Irvine, Irvine, United States
- 10Earth Science and Observation Center, CIRES, University of Colorado Boulder, Boulder, United States
- 11Faculty of Civil Engineering and Geoscience, Delft University of Technology, Delft, The Netherlands
- 12Geodesy and Earth Observations, Technical University of Denmark, Lyngby, Denmark
- 13Department of Geography, Durham University, Durham, United Kingdom
- 14Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
- 15Glaciology and Climate, Geological Survey of Denmark and Greenland, Copenhagen, Denmark
- 16Spatial Geophysics and Oceanography Studies Laboratory, Toulouse, France
- 17ESA-ESRIN, Frascati, Italy
- 18Geography, University of Liège, Liège, Belgium
- 19Mullard Space Science Laboratory, University College London, West Sussex, United Kingdom
- 20University of Edinburgh, Edinburgh, United Kingdom
- 21Aerospace Engineering, Georgia Institute of Technology, Atlanta, United States
- 22Department of Geosciences, University of Arizona, Tucson, United States
- 23Glaciology, Alfred-Wegener-Institute Helmholtz-Center for Polar and Marine Research, Bremerhaven, Germany
- 24Satellite-based Climate Monitoring, Deutscher Wetterdienst, Offenbach/Main, Germany
- 25Department of Environmental Science, iClimate, Aarhus University, Roskilde, Denmark
- 26Department of Physics and Physical Oceanography, Memorial University, St. John’s, Canada
- 27Geodesy and Geophysics Laboratory, NASA GSFC, Greenbelt, United States
- 28Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
- 29Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
- 30SDU Climate Cluster, University of Southern Denmark, Odense, Denmark
- 31Research and Development Department, Danish Meteorological Institute, Copenhagen, Denmark
- 32isardSAT, Guildford, United Kingdom
- 33Physics, University of Toronto, Toronto, Canada
- 34Center for Space Research, University of Texas at Austin, Austin, United States
- 35Seoul National University, Seoul, South Korea
- 36Department SpE, Faculty of Aerospace Engineering, TU Delft, Delft, The Netherlands
- 37Dipartimento di Fisica e Astronomia, Alma Mater Studiorum Università di Bologna, Bologna, Italy
- 38Applied Physics Laboratory, University of Washington, Seattle, United States
- 39Interdisciplinary Centre for Water Research, Indian Institute of Science, Bengaluru, India
- 1Centre for Polar Observation and Modelling, University of Leeds, Leeds, United Kingdom
- 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
- 3Institute of Environmental Geosciences, Université Grenoble Alpes, Grenoble, France
- 4Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, The Netherlands
- 5Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
- 6Polar Science Center, University of Washington, Seattle, United States
- 7Department of Geology, University at Buffalo, Buffalo, United States
- 8School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
- 9Earth System Science, University of California Irvine, Irvine, United States
- 10Earth Science and Observation Center, CIRES, University of Colorado Boulder, Boulder, United States
- 11Faculty of Civil Engineering and Geoscience, Delft University of Technology, Delft, The Netherlands
- 12Geodesy and Earth Observations, Technical University of Denmark, Lyngby, Denmark
- 13Department of Geography, Durham University, Durham, United Kingdom
- 14Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
- 15Glaciology and Climate, Geological Survey of Denmark and Greenland, Copenhagen, Denmark
- 16Spatial Geophysics and Oceanography Studies Laboratory, Toulouse, France
- 17ESA-ESRIN, Frascati, Italy
- 18Geography, University of Liège, Liège, Belgium
- 19Mullard Space Science Laboratory, University College London, West Sussex, United Kingdom
- 20University of Edinburgh, Edinburgh, United Kingdom
- 21Aerospace Engineering, Georgia Institute of Technology, Atlanta, United States
- 22Department of Geosciences, University of Arizona, Tucson, United States
- 23Glaciology, Alfred-Wegener-Institute Helmholtz-Center for Polar and Marine Research, Bremerhaven, Germany
- 24Satellite-based Climate Monitoring, Deutscher Wetterdienst, Offenbach/Main, Germany
- 25Department of Environmental Science, iClimate, Aarhus University, Roskilde, Denmark
- 26Department of Physics and Physical Oceanography, Memorial University, St. John’s, Canada
- 27Geodesy and Geophysics Laboratory, NASA GSFC, Greenbelt, United States
- 28Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
- 29Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
- 30SDU Climate Cluster, University of Southern Denmark, Odense, Denmark
- 31Research and Development Department, Danish Meteorological Institute, Copenhagen, Denmark
- 32isardSAT, Guildford, United Kingdom
- 33Physics, University of Toronto, Toronto, Canada
- 34Center for Space Research, University of Texas at Austin, Austin, United States
- 35Seoul National University, Seoul, South Korea
- 36Department SpE, Faculty of Aerospace Engineering, TU Delft, Delft, The Netherlands
- 37Dipartimento di Fisica e Astronomia, Alma Mater Studiorum Università di Bologna, Bologna, Italy
- 38Applied Physics Laboratory, University of Washington, Seattle, United States
- 39Interdisciplinary Centre for Water Research, Indian Institute of Science, Bengaluru, India
Abstract. Ice losses from the Greenland and Antarctic Ice Sheets have accelerated since the 1990s, accounting for a significant increase in global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume and in Earth’s gravity field. Between 1992 and 2020, the ice sheets contributed 21.0 ± 1.9 mm to global mean sea-level, with the rate of mass loss rising from 105 Gt yr-1 between 1992 and 1996 to 372 Gt yr-1 between 2016 and 2020. In Greenland, the rate of mass loss is 169 ± 9 Gt yr-1 between 1992 and 2020 but there are large inter-annual variations in mass balance with mass loss ranging from 86 Gt yr-1 in 2017 to 444 Gt yr-1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (-82 ± 9 Gt yr-1) and to a lesser extent from the Antarctic Peninsula (-13 ± 5 Gt yr-1). East Antarctica remains close to a state of balance (3 ± 15 Gt yr-1), but is the most uncertain component of Antarctica’s mass balance.
Inès N. Otosaka et al.
Status: final response (author comments only)
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CC1: 'Comment on essd-2022-261', Kenneth Mankoff, 03 Sep 2022
I want to start declaring some conflicts of interest I have with this paper, but I have thoughts and comments that I would like to give, so I am providing this public review.
Conflicts: I am the Chief Editor (Ice) for ESSD, but have declined to edit this paper because I contributed to some data used in it - but not enough that I should be a co-author. I am also a contributor to the next version of IMBIE. I also make reference to my own recent paper and suggest citation of it, and correction of some of your text based on it. Please take all this into consideration while reading my review, comments, and suggestions. I am choosing to submit this as a "community comment" not an "Chief editor comment".
+ "A to B", in this case "1992 to 2020" is ambiguous. Or at least not as clear as it could be. Is 2020 included or not? I suggest changing all "to" to "through", as in "1992 through 2020".
+ Last line of abstract should follow ESSD standard: Cite data product.
+ L101-103: Again, "through" rather than "to" so it is clear the last year is included.
+ L135: When discussing IO method you should probably cite Mankoff /et al./ (2021). L137 mentions "year-to-year" but Mankoff /et al./ (2021) show that IO can provide daily estimates of mass change. L138 " The technique provides moderate (annual) temporal sampling" <-- Or daily, or whatever resolution the RCMs output. I do take 12-day velocity data and resample to daily, which you may have issue with. But even 12 day is more frequent than annual.
+ Paragraph 1 of section 2 is methods or background, not data.
+ Paragraph 2 of section 2 is intro to data. It would be good to talk about the actual input data. Your Appendix Table A1 is appropriate as an Appendix in other journals, but is the core of an ESSD product, and should not be hidden in an Appendix. This should be in Section 2, "Data".
+ Feel free to split "Data" into "Input Data" and "Output Data".
+ Paragraph 3 of Data talks about masks and ROIs. Can you share these? I think not, because each data set used their own and then told you the area of the basins, but did not provide you with the boundaries themselves (is this correct?). But it may be worth pointing out that many different input products may have used many different masks.
+ No mention of peripheral glaciers, and their inclusion or exclusion from each of the 50 products. Does this explain some of the disagreements?
+ I believe RACMO has a binary ice sheet mask: 1 or 0. On the other hand, MAR has a floating point mask, and it is up to MAR users to decide if the cutoff for "ice sheet" is 0.5 or some other value. Is this worth discussing? Does this explain some of the differences between estimates?
+ Table 1: "X" and gray is redundant. Could be visually cleaner if you just did gray and no "X"?
+ 3 Methods: I am happy to see that you shared your code. Maybe mention this here, and even reference specific functions in the code? Code should not just be on GitHub, where it is likely to change. Or if it is, reference a specific git hash. Or export from GitHub and release a 'frozen' version on Zenodo or some other service where you can DOI your code.
+ IMBIE has the opportunity here to do really transformative "open science" and set a standard for how it could be done. Can you ask all 50 data providers if they are willing to share (publish) the data that they provided with you? If so, you could provide the input data, and the full processing pipeline to generate the output data. This would let people re-run the analysis but with different methods and assumptions, if they choose.
+ L196/197: " The associated error is calculated as the root mean square of the contributing time-series errors." I take this to mean that errors reduce in quadrature? And that as you add more data products, your errors decrease? I am not sure that assuming all errors are random, and that more measurements reduces error, is reasonable. It is quite likely that there are some biases in the data, that remain with the same sign through time, or are the same for different products.
+ L202: See previous comment.
+ Fig 1: Can remove all but one Y axis labels (L & R) since they are all the same.
+ Fig 1: 2020 shows 1 method, but the bar is 'black' implying 'all'. Does this mean "all" is not "all methods" but "average" or "mean" or "median" of "all available data in a given year"? Or something else?
+ Section 5 Discussion Paragraph 2 and Figure 4: I'm not sure this is relevant or appropriate for ESSD - It is science outside of the dataset. I would reframe Section 5 Paragraph 1 as "Validation" - basically admitting you cannot easily validate this against anything because you've incorporated all datasets, or if you did validate against the one not incorporated (Mankoff /et al./, 2021) it would only be useful in pointing out issues with that dataset, not the 50 that make up your dataset. Perhaps the last paragraph of Section 4 could be combined with this - there you basically validate against the last version of IMBIE. I'm not sure this is a "Result".
+ L398 Acknowledgements: This should probably be more comprehensive given the length of your author list.
+ Figure A1: This highlights what I believe is a significant deficiency in your error handling. It appears that when you have fewer products, your errors decrease. Shouldn't your uncertainty increase when you're relying on only 1 product?
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CC2: 'Reply on CC1', Kenneth Mankoff, 03 Sep 2022
One additional comment: ISO 8601 is a really nice date standard. Is there a reason for using yyyy.dec rather than yyyy-mm-dd?
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AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
We would like to thank Anny Cazenave and Ellyn Enderlin for their referee comments and Ken Mankoff, Romain Hugonnet, and Etienne Berthier for their community comments and suggestions. Please find attached our response in the attached file.
Best wishes, Inès Otosaka, on behalf of the IMBIE Team
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AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
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AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
We would like to thank Anny Cazenave and Ellyn Enderlin for their referee comments and Ken Mankoff, Romain Hugonnet, and Etienne Berthier for their community comments and suggestions. Please find attached our response in the attached file.
Best wishes, Inès Otosaka, on behalf of the IMBIE Team
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CC2: 'Reply on CC1', Kenneth Mankoff, 03 Sep 2022
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CC3: '“Peripheral glaciers matter” by Hugonnet R. & Berthier E.', Etienne Berthier, 12 Sep 2022
We commend the authors for continuing to develop the IMBIE effort and provide a multi-technique estimate of ice sheet mass balances. We leave it to the reviewers to evaluate the study in detail.
The authors explain how the two ice sheets were split into different basins (i.e. two sets of ice sheet drainage basins were used). However, as in earlier IMBIE studies, they did not explain how they took into account (or not) the mass changes of glaciers peripheral to the ice sheets (Rastner et al., 2012; Pfeffer et al., 2014; Gardner et al., 2013). This issue is important because the three techniques have different spatial resolution and hence varying capabilities to separate the mass changes from the main ice sheets and the glaciers lying at their periphery. Our understanding is that gravimetric studies include peripheral glaciers, altimetric studies exclude peripheral glaciers, and input–output studies do both. Therefore, there might be important systematic errors in the IMBIE estimates.
This is relevant for both ice sheets, but especially for the Greenland Ice Sheet where the losses from peripheral glaciers amounted to 36 ± 6 Gt/yr (95% confidence) during 2000–2019 (Hugonnet et al., 2021). This was independently assessed at 27 ± 12 Gt/yr during 2003–2010 and 42 ± 12 Gt/yr during 2019–2022 (Khan et al., 2022). A loss of 36 Gt/yr translates to about 19% of the overall Greenland Ice Sheet mass loss over the period of 2000–2019, and is more than twice the uncertainty range of ± 16 Gt/yr provided by Otasaka et al. for 1992–2020. We foresee that removing the mass contribution of peripheral glaciers (in particular for gravimetry-based estimates of Greenland and the Antarctic Peninsula) will increase the uncertainties.
To conclude, the authors should provide a clear definition of the Greenland and Antarctic ice masses for which they estimate mass losses for each of the applied techniques. This would avoid double counting the mass change from peripheral glaciers when IMBIE results are combined with glacier-specific mass change estimates to evaluate closure of the sea level budget.
References
Gardner, A. S., et al.: A Reconciled Estimate of Glacier Contributions to Sea Level Rise: 2003 to 2009, Science, 340, 852–857, https://doi.org/10.1126/science.1234532, 2013.
Hugonnet, R., et al.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021.
Khan, S. A., et al.: Accelerating Ice Loss From Peripheral Glaciers in North Greenland, Geophysical Research Letters, 49, e2022GL098915, https://doi.org/10.1029/2022GL098915, 2022.
Pfeffer, W. T., et al. : The Randolph Glacier Inventory: a globally complete inventory of glaciers, J. Glaciol., 60, 537–552, https://doi.org/10.3189/2014JoG13J176, 2014.
Rastner, P., et al.: The first complete inventory of the local glaciers and ice caps on Greenland, The Cryosphere, 6, 1483–1495, https://doi.org/10.5194/tc-6-1483-2012, 2012.
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AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
We would like to thank Anny Cazenave and Ellyn Enderlin for their referee comments and Ken Mankoff, Romain Hugonnet, and Etienne Berthier for their community comments and suggestions. Please find attached our response in the attached file.
Best wishes, Inès Otosaka, on behalf of the IMBIE Team
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AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
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RC1: 'Comment on essd-2022-261', Anny Cazenave, 26 Sep 2022
This study is an update of the previous IMBIE assessments of the Greenland and Antarctica mass balances based on different space-based estimates provided by several groups worldwide. The new time series of combined ice sheet mass balances are extremely useful for the community, in particular for scientists interested in studying the global mean sea level budget. The paper is clearly written and should be published after accounting for a few minor corrections.
My main comment concerns the systematic differences reported by the authors between the three methods used for estimating the Greenland and Antarctica mass balances (IOM, altimetry and GRACE) as well as on the solutions dispersion within each method. As shown in the present study, satellite altimetry provides more dispersed solutions (lines 240-241) than the other two methods, while the IOM approach leads to systematically lower estimates than altimetry and space gravimetry (Fig.2). The first IMBIE assessment was published 10 years ago and I am sure that the authors have investigated the reasons for such discrepancies. I thus recommend that a discussion be added in the present paper on the potential causes of the reported dispersion of altimetry solutions and of the systematic discrepancies between the 3 methods. A few words on perspectives to reduce them in the future (if possible) would also be welcome. I would also suggest that you show (e.g., in a Supplementary Material section) the different mass balance time series for each method separately (not only annual rates estimates as in Appendix A).
Minor comments:
-In the abstract, ice mass loss values are either positive or negative. Please use the same sign for all
-Lines 89 to 100: for non experts, explain what is the GIA correction and how it affects each method
Line 93: quote GRACE after 'space gravimetry'
Line 126: clarify the sentence '...orbit crossing' (e.g., difference in ice sheet elevation at a crossover point between ascending and descending satellite passes)
Line 143: Quote land hydrology when refering to leakage of mass trends in the climate system
Lines 145 to 149: it seems that you use the words 'satellite gravimetry when you refer to GRACE and GRACE FO when you refer to GRACE Follow On. Space gravimetry' is the generic term. Indice more clearly is all 'space gravimetry' estimates include GRACE FO
Fig.4: The figure caption is quite brief and not fully clear. Indicate that the starting points of the curves shown in the right hand side panels are the 2030 values of the left hand side panels
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AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
We would like to thank Anny Cazenave and Ellyn Enderlin for their referee comments and Ken Mankoff, Romain Hugonnet, and Etienne Berthier for their community comments and suggestions. Please find attached our response in the attached file.
Best wishes, Inès Otosaka, on behalf of the IMBIE Team
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AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
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RC2: 'Comment on essd-2022-261', Ellyn Enderlin, 07 Nov 2022
Summary
The paper describes the process by which 50 mass balance time series for the ice sheets were combined to produce a consensus estimate of ice sheet mass loss since the early 1990s and then summarizes the results and their implications. They find that the scientific community is in generally good agreement regarding rates of mass loss within and across the three different methodologies used for these estimates – altimetry, input-output, and gravimetry – with the largest disagreements for the East Antarctic Ice Sheet. The paper is concise and generally well-written with several summary tables and figures that aid the presentation. I appreciate the complex data wrangling that likely took place to produce this paper and thank the authors for producing an updated IMBIE dataset. I have a few major comments regarding the presentation of numbers throughout the text as well as some minor figure recommendations, as described below.
Major Comments
- At the end of the Data section, two different basin definitions are described but then it isn’t clear how these are used in the analysis. The data all seem to be split according to GrIS, APIS, WAIS, and EAIS, not these smaller drainage basins. When are these different basins used? The basin use should be clarified in the Methods section.
- In the dataset descriptions and the results, you state that the input-output method provides annual temporal resolution but in the methods your explanation of the dataset integration describes all estimates as monthly. Are the input-output datasets monthly? Do they have regular temporal intervals? It would be helpful to add temporal sampling flag or some other indicator of temporal resolution to Table 1. Similarly, these datasets are described as all relying on the same SMB model. That model should be explicitly stated since SMB is a tremendously important component of GrIS mass loss.
- Throughout the results, I was uncertain how to interpret some of the metrics presented as summaries for the datasets and their intercomparison. It seems like the maximum difference in datasets is often reported. Why is this used and not the median or the trend? Why report the average of the standard deviations of the datasets? For small sample sizes, the average may be highly skewed. Finally, what are the metrics presented for the aggregate datasets? Are they the mean +/- standard deviation? Is the standard deviation calculated using the standard deviations of the independent datasets or is it a metric of variability over time for the aggregate dataset?
- At the beginning of the discussion, the aggregate rates of mass loss are compared to trends in global sea level rise. In addition to their contribution to the trend, it would be helpful to know what fraction of annual sea level rise is driven by ice sheet mass loss.
Minor Comments
- lines 64-68: In the abstract you switch between stating mass change for GrIS as a positive mass loss number and for Antarctica as negative numbers to also indicate mass loss. Make sure you are consistent with sign convention throughout.
- line 99: I prefer the use of the Oxford coma in sentences because I think it makes them easier to read. It is apparently not favored by these authors and I normally accept that stylistic preference, but there are several instances in this paper where the additional coma would help with sentence flow. For example, I had to read this particular sentence a few times. I recommend it is changed to “…geophysical corrections, SMB models, or GIA models in …”
- line 152: Instead of “1 input-output method estimate” you could say “the input-output method estimate”
- lines 125-144: I appreciate the summary of the methods and their strengths and weaknesses!
- line 190: How did you quantify linear model structural error?
- line 223: Why are you only reporting differences from 2007-2011? The previous sentence states the datasets have a much longer period of overlap.
- Figure 1: While I really like the idea of this table, I struggled to see the aggregate average (black) when there are a large number of gravimetry estimates (green). Consider changing the shade or saturation of the green color. The aggregate average also needs to be stated in the caption and the difference in y-axis scaling should be noted as well. This is a stylist preference but I recommend only plotting the y-axis labels once per side to reduce clutter.
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AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
We would like to thank Anny Cazenave and Ellyn Enderlin for their referee comments and Ken Mankoff, Romain Hugonnet, and Etienne Berthier for their community comments and suggestions. Please find attached our response in the attached file.
Best wishes, Inès Otosaka, on behalf of the IMBIE Team
-
AC1: 'Response to referee and community comments', Ines Otosaka, 04 Dec 2022
We would like to thank Anny Cazenave and Ellyn Enderlin for their referee comments and Ken Mankoff, Romain Hugonnet, and Etienne Berthier for their community comments and suggestions. Please find attached our response in the attached file.
Best wishes, Inès Otosaka, on behalf of the IMBIE Team
Inès N. Otosaka et al.
Data sets
Antarctic and Greenland Ice Sheet mass balance 1992-2020 for IPCC AR6 The IMBIE Team https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452
Model code and software
IMBIE2 Processor The IMBIE Team https://github.com/IMBIE/imbie
Inès N. Otosaka et al.
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