Preprints
https://doi.org/10.5194/tc-2021-331
https://doi.org/10.5194/tc-2021-331

  27 Oct 2021

27 Oct 2021

Review status: this preprint is currently under review for the journal TC.

Improving model-satellite comparisons of sea ice melt onset with a satellite simulator

Abigail Smith1,a, Alexandra Jahn1, Clara Burgard2,b, and Dirk Notz2,3 Abigail Smith et al.
  • 1Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado Boulder, USA
  • 2Max Planck Institute for Meteorology, Hamburg, Germany
  • 3Center for Earth System Research and Sustainability (CEN), University of Hamburg, Germany
  • anow at: National Center for Atmospheric Research (NCAR), Boulder, USA
  • bnow at: Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France

Abstract. Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and therefore must define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator (ARC3O) to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using CESM2 ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place.

Abigail Smith et al.

Status: open (until 22 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Abigail Smith et al.

Data sets

DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures, Version 5 Meier, W. N., H. Wilcox, M. A. Hardman, and J. S. Stewart https://nsidc.org/data/NSIDC-0001/versions/5

AMSR-E/Aqua Daily L3 25 km Brightness Temperature & Sea Ice Concentration Polar Grids, Version 3 Cavalieri, D. J., T. Markus, and J. C. Comiso https://nsidc.org/data/ae_si25#

Model code and software

Original ARC3O Clara Burgard https://arc3o.readthedocs.io/en/latest/

ARC3O-related code adapted and created for this study Abigail Smith https://drive.google.com/drive/folders/1sY6_Jh5Y6Lw2omvKtmhLYblrsIdZO8gn?usp=sharing

Abigail Smith et al.

Viewed

Total article views: 246 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
204 40 2 246 16 0 2
  • HTML: 204
  • PDF: 40
  • XML: 2
  • Total: 246
  • Supplement: 16
  • BibTeX: 0
  • EndNote: 2
Views and downloads (calculated since 27 Oct 2021)
Cumulative views and downloads (calculated since 27 Oct 2021)

Viewed (geographical distribution)

Total article views: 236 (including HTML, PDF, and XML) Thereof 236 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Nov 2021
Download
Short summary
The timing of Arctic sea ice melt each year is an important metric for assessing how sea ice in climate models compares to satellite observations. Here, we utilize a new tool for creating more direct comparisons between climate models projections and satellite observations of Arctic sea ice, such that the melt onset dates are defined the same way. This tool allows us to identify climate model biases more clearly and gain more information about what the satellites are observing.