The Snow Product Intercomparison Experiment (SnowPEx) Chris Derksen Climate Research Division Environment Canada

Historical + projected (16 CMIP5 models; rcp85 scenario) and observed (NOAA snow chart CDR) snow cover extent for April, May and June. SCE normalized by the maximum area simulated by each model. Simulated vs. Observed Arctic SCE Updated from Derksen, C Brown, R (2012) Geophys. Res. Letters NA EUR 1.NOAA CDR

Historical + projected (16 CMIP5 models; rcp85 scenario) and multi-observational snow cover extent for April, May and June. SCE normalized by the maximum area simulated by each model. Simulated vs. Observed Arctic SCE NA EUR 1.NOAA CDR 2.Liston & Hiemstra 3.MERRA 4.GLDAS-Noah 5.ERA-int Recon.

Arctic SCE and Surface Temperature Trends: NA EUR SCETsurf Simulations slightly underestimate observed spring SCA reductions Similar range in observed versus simulated SCA trends Observed Arctic temperature trends are captured by the CMIP5 ensemble range 1. CRU 2. GISS 3. MERRA 4. ERA-int

Why do CMIP5 models underestimate observed spring SCE reductions? North AmericaEurasia Model vs observed temperature sensitivity (dSCE/dTs), Models exhibit lower temperature sensitivity (change in SCE per deg C warming) than observations Magnitude of observational dSCE/dTs depends on choice of observations (both snow and temperature)

Background WMO Global Cryosphere Watch snow workshop held at Environment Canada in January One of the actions was to: “Initiate a satellite snow products evaluation/intercomparison activity” Support for European participation provided by the European Space Agency; Canadian and American participation is in-kind. Proposal submitted to ESA in December 2013; initial scoping meeting held at ESRIN in January Initial scope will be hemispheric snow extent (1 km) and snow water equivalent (25 km) products, derived primarily from earth observation. Reanalysis/model approaches will be included for SWE.

Objectives Utilize ground reference measurements to perform a standardized assessment of all datasets. Inter-compare time series to determine agreement relative to multi-dataset statistics. Derive trends with uncertainty estimates.

First Workshop July 2014 – Washington DC Finalize participating datasets Standardize intercomparison protocol

Relevance to CanSISE Area A: CanSIPS initialization Area B: Detection and attribution study