356360 Arctic Sea Ice Thickness Subseasonal Predictability: Comparing CFSv2 Operational Forecasts with CryoSat-2/SMOS Satellite Data

Sunday, 6 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Robert Grimm, Northern Vermont University - Lyndon, Lyndonville, VT

Advancing the understanding of changing Arctic sea ice and its influence on the global climate system relies upon the accuracy of sea ice predictability. Sea ice thickness functions as a vital parameter and stands to gain the most improvement as an operationally forecasted polar variable. Utilizing CryoSat-2/SMOS merged satellite data, this research study analyzed 26 consecutive weeks (October-April 2016/17) of mean Arctic sea ice thickness and statistically evaluated observations to detect subseasonal predictability lead-times. Autocorrelation analyses found hemispheric Arctic observations possessing statically significant lead-times of approximately two weeks. While assessments of individual Arctic sea regions and grid points discovered reduced predictability of one week forecast lead-times. Exceptions to this included the Kara and Barents Sea regions, producing highly statically significant autocorrelations and matching hemispheric Arctic predictability. Subsequently, these two Arctic regions possessed minimum bias (less than 0.1m) when compared with CFSv2 operational forecasts. Whereas all other Arctic regions possessed strong biases. Overall, the study determined that CFSv2 vastly over- and under-modelled the sea ice thickness parameter, with significant bias correction deemed necessary to verify sea ice thickness predictability lead-times as determined from CryoSat-2/SMOS data.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner