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a core project of
2018WCRPspon col July2018 01 1

Development of sea-ice satellite emulators

Abigail Ahlert and Clara Burgard  

The scientific questions that will be studied
* Why do climate models in CMIP3 and CMIP5 have limited skill in reproducing the sea ice relationships observed in satellite data?
* Why do climate models show wide variations in future projections of sea ice cover? 
* How sensitive are brightness temperatures to the physical processes of sea ice and snow on sea ice? What snow and sea ice processes are represented by changes in satellitederived brightness temperatures (i.e. snow melt, ice growth, etc.)? 
* Satellite observations and climate models define some sea ice parameters differently, and are often on different spatial and temporal resolutions. How can we better compare satellite data and climate model output?  

The processes that will be investigated 
* Passive microwave brightness temperature sensitivity 
* Changes in sea ice extent and concentration 
* Definitions of and changes in sea ice melt onset, freeze-up and melt season length
* Spatial and temporal evolution of snow properties on sea ice  

The type of analyses that will be conducted
We will use thermodynamic models and emission models to understand the importance of different parameters for the simulation of brightness temperatures. Based on that knowledge, we aim to create a passive microwave emulator, which will use climate model-generated input to calculate brightness temperatures. From there, it will calculate sea ice fields derived from these emulated brightness temperatures. The passive microwave emulator will provide new comparisons for radiance data, sea ice extent, melt and freeze onset and snow properties between satellite observations and climate models.      

References to earlier works
Bench, K. (2016), Quantifying Seasonal Skill In Coupled Sea Ice Models Using Freeboard Measurements From Spaceborne Laser Altimeters, Master’s thesis, Naval Postgraduate School, Monterey, California. (http://hdl.handle.net/10945/49374)

Bliss, A., J. Miller, W. Meier. (2017). Comparison of passive microwave-derived early melt onset records on Arctic sea ice, Remote Sensing, 9(3), 199, doi:10.3390/rs9030199

Jahn, A., K. Sterling, M. Holland, J. Kay, J. Maslanik, C. Bitz, D. Bailey, J. Stroeve, E. Hunke, W. Lipscomb, D. Pollak. (2012). Late-twentieth-century simulation of arctic sea ice and ocean properties in the CCSM4, Journal of Climate, 25, 1431-1452, doi: 10.1175/JCLI-D-11-00201.1
Markus, T., J. C. Stroeve, and J. Miller (2009), Recent changes in Arctic sea ice melt onset, freezeup, and melt season length, J. Geophys. Res., 114, C12024, doi:10.1029/2009JC005436.
Notz, D. (2017). How well must climate models agree with observations? Philosophical Transactions of the Royal Society A, 373(2052), doi: 10.1098/rsta.2014.0164

Pan, J., M. Durand, M. Sandells, J. Lemmetyinen, E. Kim, J. Pulliainen, A. Kontu, C. Derksen. (2016). Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation, IEEE Transactions on Geosciences, 54(4), 2001-2019, doi: 10.1109/TGRS.2015.2493505

Stroeve, J. C., A. D. Crawford, and S. Stammerjohn (2016), Using timing of ice retreat to predict timing of fall freeze-up in the Arctic, Geophys. Res. Lett., 43, 6332–6340, doi:10.1002/2016GL069314.

Stroeve, J. C., T. Markus, L. Boisvert, J. Miller, and A. Barrett (2014), Changes in Arctic melt season and implications for sea ice loss, Geophys. Res. Lett., 41, 1216–1225, doi:10.1002/2013GL058951.

Tonboe, R. T., Dybkjær, G. and Høyer, J. L. (2011), Simulations of the snow covered sea ice surface temperature and microwave effective temperature. Tellus A, 63: 1028–1037. doi:10.1111/j.1600-0870.2011.00530.x

Tonboe, R. T. (2010), The simulated sea ice thermal microwave emission at window and sounding frequencies. Tellus A, 62: 333–344. doi:10.1111/j.1600-0870.2010.00434.x