Modeling Applications of the ESA GlobSnow Data Record

1. Modeling Applications of the ESA GlobSnow Data Record Chris Derksen, Ross Brown, Bill Merryfield Climate Research Division Environment Canada Lawrence Mudryk, Paul Kushner, Department of Physics University of Toronto StephaneBelair, Bernard Bilodeau, Marco Carrera, Natalie Gauthier Meteorological Research Division Environment Canada Kari Luojus and the GlobSnow SWE team at FMI

2. Outline • Strengths and weaknesses of the GlobSnow SWE record for modeling applications • Use of GlobSnow SWE data for modeling applications: • Evaluation of coupled climate model simulations • Land surface initialization – CanSIPS • Land surface data assimilation - CaLDAS

3. Contextwww.cansise.ca The Canadian Sea Ice and Snow Evolution (CanSISE) Network seeks to advance seasonal to multidecadal prediction of Arctic sea ice and snow in Canada’s sub-Arctic, alpine, and seasonally snow covered regions. It will also quantify and exploit, for prediction purposes, the role that Northern Hemisphere snow and sea ice processes play in climate variability and change.

4. The GlobSnow Data Record and Modeling Applications • By utilizing climate station observations of snow depth, the GlobSnow SWE record has better retrieval performance and uncertainty characterization compared to standalone passive microwave products. • NWP and climate modeling applications have very different requirements: • -best instantaneous retrieval (NWP) versus homogeneous time series (climate) • -latency: near real time versus re-processed archives • -use of the GlobSnow processor system versus final products • -For NWP applications new analyses must be shown to be superior to existing operational schemes • -For climate applications new products can make an immediate contribution to efforts to observationally characterize SWE variability and trends • For Hemispheric modeling applications, the alpine mask is problematic.

5. Impact of Radiometer Derived Information Difference between final assimilated SWE and background SWE from interpolated synoptic weather station data. • The impact of climate station snow depth observations is high

6. Consistency of Climate Station Observations • CMC and GlobSnow datasets utilize climate station observations of snow depth. • The impact of variability in the number of climate stations on time series homogeneity remains to be fully quantified. • The mean number of stations reporting through the month of April varies by +/- 70 across Eurasia, and +/- 40 across North America. Mean (with max/min) of number of Arctic stations reporting snow depth within April. North America Eurasia

7. Filling in the GlobSnow Mountain Mask Mean annual maximum SWE (1998/99 – 2009/10) from CMC (left), GlobSnow (middle), and merged dataset (right). A simple GlobSnow + CMC merging procedure was used: In areas where information was available in both datasets, the SWE estimates were averaged; the CMC SWE estimates were retained in areas masked in the GlobSnow product

8. CMIP5 Simulated vs. Observed Arctic Snow Water Equivalent 10 model avg bias (mm) CMC+GlobSnow (mm) 10 model avg (mm) Annual maximum monthly SWE (SWEmax) • Models overestimate SWEmax over Arctic land areas/high elevation regions • The multi-model ensemble agrees more closely with the observed data than any individual model

9. Large Ensemble Experiment Simulated variability and trends in Northern Hemisphere seasonal snow cover analyzed in large ensembles of climate integrations of the National Center for Atmospheric Research’s Community Earth System Model. Two 40-member ensembles driven by historical radiativeforcingsover the period 1981-2010. coupled to a dynamical ocean observed sea surface temperatures (SSTs) Mudryk et al ClimDyn in press

10. Snow Climatology and Variability: CCSM4 Simulations vs. Observations Annual cycle of snow cover extent (SCE; NOAA snow chart CDR and snow water equivalent (SWE; GlobSnow) for NH (black), NA (red), and EUR (blue). Ensemble mean coupled experiment (solid), ensemble mean uncoupled experiment (dotted). Mudryk et al ClimDyn in press

11. Snow Water Equivalent Trends:CCSM4 Simulations vs. Observations Coupled Uncoupled Observations Mudryk et al ClimDyn in press

12. Snow Water Equivalent Trends:CCSM4 Simulations vs. Observations Simulations identify positive winter and spring SWE trends over much of the Arctic. Observations identify predominantly negative trends, particularly for NA Mudryk et al ClimDyn in press

13. Simulated Snowfall Trends Positive Arctic SWE trends are the result of positive OND and JFM snowfall trends which are very difficult to verify with observations. Mudryk et al ClimDyn in press

14. Evaluation of Snow Initial Conditions in Canadian Seasonal to Interannual Prediction System (CanSIPS) How close are initial conditions to observations of SWE? Assimilation Runs Hindcasts serve as initial conditions for hindcasts 1 year duration begin on 1st of month assimilate observed T, u, v, q, SST, sea ice Observations Historical Runs GlobSnow (station + PMW) MERRA (reanalysis) CMC (station + snow model) …and others freely running CanCM3\CanCM4

15. Springtime Bias in SWE Initial Conditions

16. Springtime Bias in SWE Initial Conditions • Generally too much NH SWE from February to May in CanCM3/CanCM4 assimilation run climatologies. Somewhat reduced in CanCM4 consistent with differences in temperature biases. • Mean drift of hindcastsfrom assimilation runs?

17. Evaluation of Snow Initial Conditions in Canadian Seasonal to Interannual Prediction System (CanSIPS) How quickly do hindcasts drift from initial conditions to model climatology? Assimilation Runs Hindcasts serve as initial conditions for hindcasts 1 year duration begin on 1st of month assimilate observed T, u, v, q, SST, sea ice Observations Historical Runs GlobSnow (station + PMW) MERRA (reanalysis) CMC (station + snow model) …and others freely running CanCM3\CanCM4

18. Mean Drift of Hindcasts from Assimilation Runs

19. Progress in the Assimilation of GlobSnow SWE in the Canadian Land Data Assimilation System Currently OP: OI assimilation of snow depth surface observations (Brasnett 1999) Now being implemented: Ensemble OI w/ Canadian Land Data Assimilation System Tested: Assimilation of GlobSnow products (CaLDAS)

20. The Canada Land Data Assimilation System (CaLDAS) OUT CaLDAS IN Analyses of… Ancillary land surface data LAND MODEL (SPS) Orography, vegetation, soils, water fraction, ... xb Surface Temperature Soil moisture Snow depth or SWE Vegetation* ASSIMILATION EnKF + EnOI Atmospheric forcing y OBS T, q, U, V, Pr, SW, LW EnKF xa = xb+ K { y – H(xb) } Observations Screen-level (T, Td) Surface stations snow depth L-band passive (SMOS, SMAP) Microwave SWE (AMSR-E) *Optical / IR (MODIS, VIIRS) Combined products (GlobSnow) with K = BHT ( HBHT+R)-1 *) not done yet…

21. GlobSnow-2 in CaLDAS Mean snow depth OBS Bias Open Loop GlobSnow STDE CaLDAS-GS (CAREFUL… GlobSnow experiments not leave-one-out)

22. Moving Forward… • Implementing the GlobSnow SWE operator at CMC has proven challenging • Direct assimilation of microwave Tbs (not retrievals) is the next step • First guess from snow model and MW emission model • Snow model being improved (part of new land surface scheme at EC) • Utilize microwave forward modeling with HUT as in the GlobSnow retrieval

23. Conclusions • NWP and climate modeling applications have different requirements for observational snow products • The development and validation of new SWE products (i.e. GlobSnow) can have an immediate impact on seasonal to multi-decadal model evaluations by adding a new observational ‘member’ to multi-dataset time series. • GlobSnow v2.0 used to evaluate CMIP5 historical simulations, large ensemble member experiments, and initial conditions for seasonal forecasting • Greater implementation and validation challenges are a reality for NWP applications.

24. Questions?

25. CMIP5 Simulated vs. GlobSnow Arctic Snow Water Equivalent January April