J8.1A Relationship Between Snow Cover and Temperature Trends in Observational and Earth-System Model Ensembles

Thursday, 26 January 2017: 10:30 AM
Conference Center: Skagit 3 (Washington State Convention Center )
Paul J. Kushner, Univ. of Toronto, Toronto, ON, Canada; and L. Mudryk, C. Derksen, R. Brown, and C. Thackeray

Having been able to continuously map Northern Hemisphere snow cover extent for a half century, we are well positioned to compare and contrast several recently developed snow datasets for the purpose of climate analysis. Using ensemble approaches enables estimation of mean and uncertainty of these trends in both observations and earth-systems models (ESMs), and more precise quantification of the physical coupling between temperature and snow cover trends. We compare temperature and snow cover extent trends from three distinct types of ensembles over the 1981-2010 period: 1) an observation-based ensemble using five estimates of surface temperature trends as well as seven estimates of snow cover trends [including the NOAA CDR and snow cover extent derived from several multidecadal snow-water equivalent datasets]; 2) An ensemble of CMIP5 ESMs that simultaneously samples internal variability and the effect of differences in ESM construction; and 3) large “initial condition” ensembles of two different ESMs that cleanly sample model generated internal climate variability. Observation-based estimates of snow cover trends are consistent across the datasets except in the fall season of snow onset. In observations and the ESMs, variability in snow cover trends is strongly coherent with variability in temperature. The observations and ESMs are mutually consistent at the hemispheric scale and over Arctic regions, but models show stronger snow cover loss for the given amount of historical warming in midlatitude and alpine regions. It is argued that about half the spread in CMIP5 snow cover trends in this period could arise from the impacts of internal variability.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner