Snow Cover: Current Capabilities, Gaps Snow Cover: Current Capabilities, Gaps and Issues and Issues

(Canadian Perspective)(Canadian Perspective)

Anne Walker

Climate Research Branch, Meteorological Service of Canada

IGOS-Cryosphere Theme Workshop, Kananaskis, Alberta, Canada, March 2-4, 2005

EnvironmentCanada

EnvironnementCanada

Importance of Snow CoverImportance of Snow Cover

Largest areal extent of any component of the cryosphere (mean max. extent of ~47 x 106 km2)

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 MonitoringCliC 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 Canadian Science Issues Related to Snow Observing SystemsSystems

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: Snow: In SituIn Situ Observing Networks in Canada 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).

90° N

75° N

60° N

45° N

2477

Active Synoptic Stations

MSC networks are under pressure

Satellite Remote SensingSatellite Remote Sensing

alternative information source for remote areas where conventional data are sparse or unavailable

20-30+ 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 CapabilitiesSnow: 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 chartGlobal Daily Snow Cover from MODIS

(Red – snow, Blue – clouds)

Snow: Remote Sensing/Satellite CapabilitiesSnow: 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 SWEClimate 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

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Pentad winter season (DJF) SWE anomalies produced using passive microwave satellite data time series. Dashed line denotes transition from SMMR to SSM/I.

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Passive MicrowavePassive Microwave 5-yr Moving AverageHistoricalHistorical 5-yr Moving Average

Merging Conventional (1915-1992) and Passive Microwave (1978 – 2002) Time Series

Summary of Measurement CapabilitiesSummary of Measurement Capabilities

Parameter Temporal Requirements

Accuracy In Situ Current Satellites Future Satellites

Extent Daily to Monthly 10% No MODIS (500 m)

AVHRR (1 km)

SSM/I (25 km)

AMSR-E (10-25 km)

VIIRS

CMIS

Depth/WE Daily to Weekly 10% Yes SSM/I (25 km)

AMSR-E (10-25 km)

CMIS

CLPP (500 m – 5 km)

Radarsat-2

Grain Size Weekly to Monthly MODIS

ASTER

Wet/Dry State Daily to Weekly No SSM/I

AMSR-E

Radarsat

Envisat

CMIS

Radarsat-2

CLPP

Albedo Daily 5% No MODIS

ASTER

Landsat

VIIRS

Solid Precipitation (Snowfall)

6 hourly to Daily 5% of absolute (corrected for errors)

Yes None Cloudsat

E-GPM/CGPM

Issues Related to Snow Observing SystemsIssues 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