1 / 17

Photo credit: A. Rees, WLU

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

vila
Télécharger la présentation

Photo credit: A. Rees, WLU

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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

  3. 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

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

  5. Spatial distribution of March SWE obs from Canada and FSU, 1966-1990. Current snow survey network for Alaska Not a data gap… high density of SWE obs exist over Scandinavia

  6. 2.Satellite sources with continuous snow cover information over Arctic:

  7. 3. Other sources:

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

  9. Large differences in mean SCE between datasets over the Arctic

  10. Amount of snow cover “seen” depends on threshold and resolution… IMS-24 SCE = 0 IMS-4 SCE = 150 km2 MODIS SCE = 180 km2 25 km 25 km Error in Arctic ablation season SCE > ± 10% when resolution falls below ~ 25 km

  11. Mean SCE seen by various data sets over NH north of 60° (excl Greenland) for the period 2004-2008 May Average NH SCE (excl PMW) 2004-2008 = 10.3 ± 0.9 million km2 June Average NH SCE (excl PMW) 2004-2008 = 3.7 ± 1.1 million km2

  12. 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?

  13. Mean annual maximum SWE estimated from CMC snow depth analysis, 1999-2006 Mean annual maximum SWE from 14 AR4 GCMs, 1970-1999 Difference (mm) Comparison of CMC est mean monthly max SWE for 1999-2006 with the average model values for the 1970-1999 reference period. On average the models overestimate annual maximum monthly SWE by 16 mm over NH land areas north of 30N.

  14. 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

  15. Application of QuikSCAT for monitoring pan-Arctic melt onset, 2000 - 2005 2001 2000 2002 2003 2004 2005 Source: L. Wang, EC Julian Day

  16. Application of QuikSCAT for mapping spring snow cover – mean spring (MAMJJ) snow cover duration, 2000-2004. Spring SCD (days) Source: Brown et al., 2007

  17. Sample of Canada Centre for Remote Sensing circumpolar dataset of snow disappearance date from Arctic Polar Pathfinder AVHRR data, 1982-2004 Source: H. Zhao and R. Fernandes, CCRS (JGR, 2009)

More Related