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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Jan 2020

14 Jan 2020

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A revised version of this preprint is currently under review for the journal TC.

GrSMBMIP: Intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice sheet

Xavier Fettweis1, Stefan Hofer1,2, Uta Krebs-Kanzow3, Charles Amory1, Teruo Aoki4,5, Constantijn J. Berends6, Andreas Born7,8, Jason E. Box9, Alison Delhasse1, Koji Fujita10, Paul Gierz3, Heiko Goelzer6,11, Edward Hanna12, Akihiro Hashimoto4, Philippe Huybrechts13, Marie-Luise Kapsch14, Michalea D. King15, Christoph Kittel1, Charlotte Lang1, Peter L. Langen16,17, Jan T. M. Lenaerts18, Glen E. Liston19, Gerrit Lohmann3, Sebastian H. Mernild20,21,22,23, Uwe Mikolajewicz14, Kameswarrao Modali14, Ruth H. Mottram16, Masashi Niwano4, Brice Noël6, Jonathan C. Ryan24, Amy Smith25, Jan Streffing3, Marco Tedesco26, Willem Jan van de Berg6, Michiel van den Broeke6, Roderik S. W. van de Wal6,27, Leo van Kampenhout6, David Wilton28, Bert Wouters29, Florian Ziemen14, and Tobias Zolles7,8 Xavier Fettweis et al.
  • 1University of Liège, Department of Geography, Belgium
  • 2School of Geographical Sciences, University of Bristol, UK
  • 3Alfred-Wegener-Institut, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
  • 4Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
  • 5National Institute of Polar Research, Tachikawa, Japan
  • 6Institute for Marine and Atmospheric research Utrecht, Utrecht University, the Netherlands
  • 7Department of Earth Science, University of Bergen, Bergen, Norway
  • 8Bjerknes Centre for Climate Research, Bergen, Norway
  • 9Geological Survey of Denmark and Greenland, Copenhagen, Denmark
  • 10Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
  • 11Laboratoire de Glaciologie, Université Libre de Bruxelles, Brussels, Belgium
  • 12School of Geography and Lincoln Centre for Water and Planetary Health, Lincoln, UK
  • 13Earth System Science & Departement Geografie, Vrije Universiteit Brussel, Brussels, Belgium
  • 14Max Planck Institute for Meteorology, Hamburg, Germany
  • 15Byrd Polar and Climate Research Center & School of Earth Sciences, The Ohio State University, Columbus OH, USA
  • 16Danish Meteorological Institute, Copenhagen, Denmark
  • 17PICE, Niels Bohr Institute, University of Copenhagen, Denmark
  • 18Department of Atmospheric and Oceanic Sciences. University of Colorado Boulder, Boulder CO, USA
  • 19Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, USA
  • 20Nansen Environmental and Remote Sensing Center, Bergen, Norway
  • 21Department of Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
  • 22Geophysical Institute, University of Bergen, Bergen, Norway
  • 23Antarctic and Sub-Antarctic Program, Universidad de Magallanes, Punta Arenas, Chile
  • 24Institute at Brown for Environment and Society, Brown University, USA
  • 25Department of Geography, University of Sheffield, UK
  • 26Lamont-Doherty Earth Observatory at Columbia University, New-York, USA
  • 27Department of Physical Geography, Utrecht University, Utrecht, the Netherlands
  • 28Department of Computer Science, University of Sheffield, UK
  • 29Department of Geoscience & Remote Sensing, Delft University of Technology, Delft, the Netherlands

Abstract. The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.

Xavier Fettweis et al.

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Xavier Fettweis et al.

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Latest update: 04 Aug 2020
Publications Copernicus
Short summary
We here evaluate the modelled Greenland ice sheet surface mass balance (SMB) from 5 kinds of models. While the most complex (but computing expensive) models are the best, the faster/simpler models compare also well with observations and have biases of same order than the complex models. This intercomparison suggests that the current modelled mean SMB estimates are reliable but discrepancies in the trend over 2000–2012 suggests that large uncertainty remains in the modelled future SMB changes.
We here evaluate the modelled Greenland ice sheet surface mass balance (SMB) from 5 kinds of...