Snow Cover: Current Capabilities, Gaps and Issues (Canadian Perspective) Anne Walker Climate Research Branch, Meteorological Service of Canada IGOS-Cryosphere Theme Workshop, Kananaskis, Alberta, Canada, March 2-4, 2005 Environment Canada Environnement Canada

Importance of Snow Cover  Largest areal extent of any component of the cryosphere (mean max. extent of ~47 x 10 6 km 2 )  High spatial and temporal variability in properties  Impacts both global/regional energy and water cycles  high reflectance, thermal insulation, storage of water Key variables:  extent (areal coverage), depth, water equivalent (water content), wet/dry state, grain size  Snowfall/solid precipitation Information requirements:  indicator of climate variability and change  Input/validation of models – NWP, hydrological, climate  Environmental monitoring/prediction – flood forecasting, severe weather (blowing snow), soil moisture/drought, forest fire risk, wildlife  Economic – hydropower production/management, agriculture, tourism

CliC Requirements for Observations and Monitoring  Validation of coupled climate models (gridded hemispheric-global datasets from observations)  Improved understanding of processes and improved model parameterizations (detailed field datasets)  Monitoring variability and change (long-term, homogeneous data series)  Diagnostic studies of climate-cryosphere interactions (combination of re-analyses, data and modelling)

Canadian Science Issues Related to Snow Observing Systems  Quantifying the spatial and temporal variability in snow properties (water resource planning, GCM/RCM evaluation, input to NWP)  Quantifying the spatial and temporal variability of liquid and solid precipitation (essential input to climate and hydrological models, operational decision making)  Improved understanding of snow interception, sublimation and redistribution (improved representation of snow in climate and hydrological models)

Snow: In Situ Observing Networks in Canada  temperature and precipitation network (MSC)  hourly/synoptic meteorological observations (MSC)  “snow on ground” (depth) network (MSC)  snow course observations (Provinces, MSC, hydro companies)

Current MSC Snow Depth Network Significant data sparse areas Network biased to coastal locations in Arctic Network biased to low elevations in cordillera

All active Synoptic Stations north of 50 N as of 29 Oct 2001 (WMO Publication No. 9 Volume A) Active Synoptic Stations

MSC networks are under pressure

Satellite Remote Sensing  alternative information source for remote areas where conventional data are sparse or unavailable  yr data record for satellite-derived cryospheric information (sea ice, snow cover)  high repeat coverage of large regions (daily)  diurnal trends from multiple daytime passes  consistent spatial info. across coverage  gridded information for input/validation of models (climate, land surface process, hydrology, etc.)  requires development of retrieval techniques (algorithms) to derive information on snow cover properties  research MODIS image - composite

Snow: Remote Sensing/Satellite Capabilities Snow Extent – Areal Coverage  optical (visible/infrared) – AVHRR, Landsat, MODIS  30m to 1 km spatial information  long history of standard snow products (NOAA snow charts back to 1960’s)  dependent on solar illumination, limited by cloud cover NOAA daily IMS snow chart Global Daily Snow Cover from MODIS (Red – snow, Blue – clouds)

Snow: Remote Sensing/Satellite Capabilities Snow Depth/Snow Water Equivalent  passive microwave – only proven satellite technique for SWE retrieval  historical record back to 1978 (SMMR, SSM/I) available in consistent 25 km grid format  requires regionally-tuned algorithms to take into account landscape effects, variation in physical properties  validation a challenge!  On-going research into SWE retrieval from active microwave (SAR) – offers higher spatial resolution capability SSM/I SWE map for Canadian prairie region (produced by MSC weekly for 15+ years) Global SWE map from AMSR-E (limited validation)

Climate Research Applications of Passive Microwave SWE  Availability of SMMR and SSM/I in consistent gridded format (EASE- Grid)  25 winter seasons (1978/79 – 2002/03)  Investigation of spatial and temporal variations in snow cover in relation to climate/atmospheric circulation  Evaluation of climate model snow cover outputs – GCM, RCM Pentad winter season (DJF) SWE anomalies produced using passive microwave satellite data time series. Dashed line denotes transition from SMMR to SSM/I. Merging Conventional ( ) and Passive Microwave (1978 – 2002) Time Series

Summary of Measurement Capabilities ParameterTemporal Requirements AccuracyIn SituCurrent SatellitesFuture Satellites ExtentDaily to Monthly10%NoMODIS (500 m) AVHRR (1 km) SSM/I (25 km) AMSR-E (10-25 km) VIIRS CMIS Depth/WEDaily to Weekly10%YesSSM/I (25 km) AMSR-E (10-25 km) CMIS CLPP (500 m – 5 km) Radarsat-2 Grain SizeWeekly to MonthlyMODIS ASTER Wet/Dry StateDaily to WeeklyNoSSM/I AMSR-E Radarsat Envisat CMIS Radarsat-2 CLPP AlbedoDaily5%NoMODIS ASTER Landsat VIIRS Solid Precipitation (Snowfall) 6 hourly to Daily5% of absolute (corrected for errors) YesNoneCloudsat E-GPM/CGPM

Issues Related to Snow Observing Systems 1. Decline in in situ capabilities  decreasing networks  effects of automation  loss of manual measurements (e.g. snow survey), poor understanding of automated sensors  solid precipitation measurement 2. Development/validation of satellite remote sensing capabilities  validation of current snow retrieval products (esp. SWE)  support of new satellite systems (e.g. E-GPM/CGPM for solid precipitation)  support of algorithm development research 3. Data gaps in northern latitudes (> 60 N)  sparse in situ measurements  challenge to validate satellite retrievals 4. Development of techniques to merge in situ measurements and satellite retrievals 5. Canadian GCOS Cryosphere Plan – detailed summary of cryospheric data requirements and issues