Photo credit: A. Rees, WLU The challenge of evaluating RCM snow cover simulations over northern high latitudes Ross D. Brown, Climate Research Division, Environment Ouranos, Montréal

The problem… Snow cover extent variations over high latitudes are difficult to monitor for a number of reasons… Strong local controls on snow cover (wind, topography, vegetation, proximity to open water…) Patchy spring snow cover (scaling and sensor resolution issues) Frequent cloud cover during snow cover onset and melt periods Large gaps in surface observing network; unrepresentative sites Snowpack structure and lake ice pose challenges for passive m/w Confusion of lake ice and snow cover during melt season Historical operational products such as NOAA weekly product include changes in analysis procedures, spatial resolution and the amount and resolution of available satellite imagery over time (re-charting work by Dave Robinson attempting to address this) Relatively small area of snow cover during melt season in Arctic; errors potentially large % of mean SCE

What snow information exists over the Arctic and how good is it? 1. In situ:  daily snow depths, bi-weekly snow surveys, snow pillows  sparse data coverage over Arctic especially northern Canada  daily depths are point observations take at open locations and may not be representative especially in regions with high winds and frequent drifting snow  can obtain longer length scales with derived snow cover variables such as snow cover duration and start/end dates of snow cover  NO pan-Arctic dataset… Russia and Scandinavian countries have a merged SWE dataset but is not public; Canada and FSU data published to 2004; US data dispersed over a number of agencies MSC snow sampler

Spatial distribution of daily snow depth observations in the Global Summary of the Day dataset

Spatial distribution of March SWE obs from Canada and FSU, Not a data gap… high density of SWE obs exist over Scandinavia Current snow survey network for Alaska

2. Satellite sources with continuous snow cover information over Arctic: Data sourceVariablePeriodResoln.Source CCRS AVHRRSnow-off date kmZhao and Fernandes (2009) IMS daily 24 kmDaily snow-no snow kmNSIDC [Ramsey (1998)] IMS daily 4 kmDaily snow-no snow kmNSIDC [Helfrich et al., 2007] MODIS monthly 0.05° (MOD10CM Version 5) Snow cover fraction ~5 kmNSIDC [Hall et al., (2006)] NOAAWeekly snow- no snow km Rutgers U. [Robinson et al., 1993] Passive m/wSWE, snow cover extent kmNSIDC [Armstrong and Brodzik(2005)] QSCATSnow-off date ~5 kmWang et al. (2008)

Data sourceVariablePeriodResolutionSource CMC AnalysisDaily snow depth (estimated SWE) ~35 kmCanadian Met. Centre [Brasnett, 1999] CRCM4.1driven with ERA-40 over North America Daily snow depth, SWE, snow cover fraction ~50 kmOuranos Climate Simulation Group [Caya and Laprise, 1999] ERA-40 Reanalysis snow depths Daily snow depth ~275 kmECMWF [Uppala et al, 2005] ERA-40 reconstructed snow cover Daily snow depth ~275 km (with 5 km elevation adjustment) Reconstructed snow depth from 6-hourly temp and precip NCEP Reanalysis spring thaw date Snow-off date ~275 km0°C crossing date used as proxy for snow cover melt date 3. Other sources:

Temporal distribution of NH snow cover data sets (CRCM4 only available for North America) Problem that dataset temporal coverage is quite variable…

Large differences in mean SCE between datasets over the Arctic

40%0% 40% 0% 50% 40%0% 80% 40%0% 100%80%40%0% 25 km IMS-24SCE = 0 IMS-4 SCE = 150 km 2 MODISSCE = 180 km 2 Amount of snow cover “seen” depends on threshold and resolution… Error in Arctic ablation season SCE > ± 10% when resolution falls below ~ 25 km

JuneNOAANCEPPMWQSCATMODISIMS-24IMS-4CMC Average Stdev Mean SCE seen by various data sets over NH north of 60° (excl Greenland) for the period MayNOAANCEPPMWQSCATMODISIMS-24IMS-4CMC Average Stdev May Average NH SCE (excl PMW) = 10.3 ± 0.9 million km 2 June Average NH SCE (excl PMW) = 3.7 ± 1.1 million km 2

Development of reliable gridded SWE information is particularly problematic over the Arctic :  sparse obs, problems of data representativeness  PMW has not yet shown it can provide reliable SWE estimates over Arctic  CMC analysis affected by data biases and excessive melt from degree- day melt algorithm  snow depth estimates available from laser and radar altimetry but not enough in time and space for circumpolar RCM evaluation  SWE estimates from GRACE could be used for basin-averaged analysis of snow water storage  downscaled snow cover information from reanalyses with snow/hydrological models including key Arctic processes (blowing snow, sublimation) is a potential solution but then how reliable is the precipitation?

Mean annual maximum SWE estimated from CMC snow depth analysis, Mean annual maximum SWE from 14 AR4 GCMs, Comparison of CMC est mean monthly max SWE for with the average model values for the reference period. On average the models overestimate annual maximum monthly SWE by 16 mm over NH land areas north of 30N. Difference (mm)

Conclusions: Evaluation of RCM snow simulations in the Arctic is a challenge! Are in good shape for evaluating snow-off dates with new CCRS dataset, Quikscat and PMW (snow-on dates more of challenge) Also in good shape for evaluating monthly snow cover fraction with MODIS monthly and IMS 24-km products (but only have ~10 years data) Snow depth and SWE are more problematic - downscaling with high resolution Arctic snow process models is one approach e.g. PBSM Pomeroy et al., SnowTran-3D Liston and Sturm

Application of QuikSCAT for monitoring pan-Arctic melt onset, Source: L. Wang, EC Julian Day

Spring SCD (days) Application of QuikSCAT for mapping spring snow cover – mean spring (MAMJJ) snow cover duration, Source: Brown et al., 2007

Sample of Canada Centre for Remote Sensing circumpolar dataset of snow disappearance date from Arctic Polar Pathfinder AVHRR data, Source: H. Zhao and R. Fernandes, CCRS (JGR, 2009)