Improving model-satellite comparisons of sea ice melt onset with a satellite simulator
- 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
- 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.
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Abigail Smith et al.
Status: final response (author comments only)
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RC1: 'Comment on tc-2021-331', Anonymous Referee #1, 20 Dec 2021
Review of "Improving model-satellite comparisons of sea ice melt onset with a satellite simulator" by A. Smith at el.
The paper uses a brightness temperature satellite simulator to diagnose the timing of melt onset in model simulations, and compare this new metric to other metrics of melt onset. Overall, the paper is well written and worthy of publication subject to the minor corrections listed below:
Line 49: Write out the ACR3O acronym here as this is its first use.
Line 61: What method of regridding was used?
Line 73: "Each .." Describe the method in slightly more detail - How does brightness temperature change as melt occurs?
Line 85: "Surface temperture" - clarify whether this is ice, snow or either surface temperature.
Line 123: What is the justification for the step function as opposed to another functional form?
Line 138: "So here" -> "Therefore" or another replacement.
Line 141: "CICE provides..." I believe CICE can provide the number of layers specificed so clarify this isn't the only choice possible. Maybe something like: "CICE was configured to provide"
Lines 140-150: Clarify what thermodynamic model CICE is used? Mushy layer? Bitz-Lipscomb 1999?
Line 152: Lagrangian tracking - I am unfamilar with this functionality in CICE - is it documented/referenced somewhere? There is a FY ice tracer, but it is not lagragian tracked, instead advected with the Eulerian transport scheme.
Line 154: Clarify what the correlation length is.
Line 159: "(greater than 30cm)(Fig. S2)" -> "(greater than 30cm; Fig. S2)"
Line 180-185: The two thresholds are presumably justified from a bimodal nature of the brightness temeperatures. It would be useful to see histograms demonstrating this bimodal nature.
Line 185: "with boundary" -> "with the boundary"
Figures 1, 5, 6, 8: Add the field name and units to the colorbars.
Figures 3, 4, 7: Add markers to the map of the Arctic ocean showing exactly where the a,b,c,d points are.
References: "SIMIP community ... 2020": This doesn't appear to be how to cite: https://agupubs.onlinelibrary.wiley.com/action/showCitFormats?doi=10.1029%2F2019GL086749
- AC1: 'Reply on RC1', Abigail Smith, 14 Apr 2022
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RC2: 'Comment on tc-2021-331', Anonymous Referee #2, 21 Dec 2021
Review of “Improving model-satellite comparisons of sea ice melt onset with a satellite simulator”
This paper describes a novel use of model simulated brightness temperatures to compute a new metric to identify the timing of earliest snowmelt on Arctic sea ice. The paper compares the simulated brightness temperatures and earliest snowmelt dates with brightness temperatures and a melt onset data set from satellite data.
The paper is well written, and the study is clearly presented. I agree with comments from the prior reviewer and recommend that this paper is published pending a few additional clarifications as described in my comments below.
L61: Why was 2003 selected as the sample year? Is it representative of a normal (non-anomalous) melt onset year? Or something else?
L73: Why is the Steele et al. 2019 dataset used instead of the Markus melt onset product directly? Are they not the same data?
L206-207: To what extent do you think error in the observations or in the simulated brightness temperature contribute to the divergence between the simulated and observed brightness temperatures in the central Arctic (i.e, Figure 3d) seen after the SIC declines? What might the physical reason for this big difference be?
L288: Please add proper names for the geographic locations that are considered “inflow regions”.
- AC2: 'Reply on RC2', Abigail Smith, 14 Apr 2022
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.
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