Articles | Volume 16, issue 8
https://doi.org/10.5194/tc-16-3235-2022
https://doi.org/10.5194/tc-16-3235-2022
Research article
 | 
12 Aug 2022
Research article |  | 12 Aug 2022

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

Abigail Smith, Alexandra Jahn, Clara Burgard, and Dirk Notz

Related authors

Seasonal transition dates can reveal biases in Arctic sea ice simulations
Abigail Smith, Alexandra Jahn, and Muyin Wang
The Cryosphere, 14, 2977–2997, https://doi.org/10.5194/tc-14-2977-2020,https://doi.org/10.5194/tc-14-2977-2020, 2020
Short summary
Definition differences and internal variability affect the simulated Arctic sea ice melt season
Abigail Smith and Alexandra Jahn
The Cryosphere, 13, 1–20, https://doi.org/10.5194/tc-13-1-2019,https://doi.org/10.5194/tc-13-1-2019, 2019
Short summary

Related subject area

Discipline: Sea ice | Subject: Arctic (e.g. Greenland)
Sea-ice conditions from 1880 to 2017 on the Northeast Greenland continental shelf: a biomarker and observational record comparison
Joanna Davies, Kirsten Fahl, Matthias Moros, Alice Carter-Champion, Henrieka Detlef, Ruediger Stein, Christof Pearce, and Marit-Solveig Seidenkrantz
The Cryosphere, 18, 3415–3431, https://doi.org/10.5194/tc-18-3415-2024,https://doi.org/10.5194/tc-18-3415-2024, 2024
Short summary
The radiative and geometric properties of melting first-year landfast sea ice in the Arctic
Nathan J. M. Laxague, Christopher J. Zappa, Andrew R. Mahoney, John Goodwin, Cyrus Harris, Robert E. Schaeffer, Roswell Schaeffer Sr., Sarah Betcher, Donna D. W. Hauser, Carson R. Witte, Jessica M. Lindsay, Ajit Subramaniam, Kate E. Turner, and Alex Whiting
The Cryosphere, 18, 3297–3313, https://doi.org/10.5194/tc-18-3297-2024,https://doi.org/10.5194/tc-18-3297-2024, 2024
Short summary
Improving short-term sea ice concentration forecasts using deep learning
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024,https://doi.org/10.5194/tc-18-2161-2024, 2024
Short summary
Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)
Dunwang Lu, Jianqiang Liu, Lijian Shi, Tao Zeng, Bin Cheng, Suhui Wu, and Manman Wang
The Cryosphere, 18, 1419–1441, https://doi.org/10.5194/tc-18-1419-2024,https://doi.org/10.5194/tc-18-1419-2024, 2024
Short summary
Sea-ice variations and trends during the Common Era in the Atlantic sector of the Arctic Ocean
Ana Lúcia Lindroth Dauner, Frederik Schenk, Katherine Elizabeth Power, and Maija Heikkilä
The Cryosphere, 18, 1399–1418, https://doi.org/10.5194/tc-18-1399-2024,https://doi.org/10.5194/tc-18-1399-2024, 2024
Short summary

Cited articles

Barber, D., Fung, A., Grenfell, T., Nghiem, S., Onstott, R., Lytle, V., Perovich, D., and Gow, A.: The role of snow on microwave emission and scattering over first-year sea ice, IEEE T. Geosci. Remote, 36, 1750–1763, https://doi.org/10.1109/36.718643, 1998. a
Bliss, A. C., Miller, J. A., and Meier, W. N.: Comparison of passive microwave-derived early melt onset records on Arctic sea ice, Remote Sens., 9, 1–23, https://doi.org/10.3390/rs9030199, 2017. a, b
Bodas-Salcedo, A., M.J., W., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Burgard, C.: Read the Docs: Arctic Ocean Observation Operator, arc3o, https://arc3o.readthedocs.io/en/latest/ (last access: October 2021), 2020. a
Burgard, C., Notz, D., Pedersen, L. T., and Tonboe, R. T.: The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output, The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, 2020a. a, b, c, d, e, f, g, h, i
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 model 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.