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
Stephane Belair, Bernard Bilodeau, Marco Carrera, Natalie Gauthier
Meteorological Research Division
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. Context
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
CMC+GlobSnow (mm)
10 model avg (mm)
10 model avg bias (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 radiative forcings over the period
coupled to a dynamical ocean
observed sea surface temperatures (SSTs)
Mudryk et al Clim Dyn 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 Clim Dyn in press

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

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

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 hindcasts from assimilation runs?

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

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)
TRANSITION: it is in this context that I would like to propose another strategy, on which we have starting working (at least from the point of view of data assimilation)
INFO: what I will discuss here is a way of refining CMC’s forecasts at and near the surface (which is certainly an important component …)
INFO: currently, surface processes are included in atmospheric model so they have to be integrated at the same resolution as the model
INFO: this does not have to be so, and surface could very well be integrated in an off-line manner, using forcing provided by atmos. Model
INFO: this diagram describes such a system, … , coupling is 1-way, and no feedback at this point on the atmospheric runs

20. Ancillary land surface data
The Canada Land Data Assimilation System (CaLDAS)
CaLDAS
OUT IN
Ancillary land surface data
Analyses of…
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
TRANSITION: it is in this context that I would like to propose another strategy, on which we have starting working (at least from the point of view of data assimilation)
INFO: what I will discuss here is a way of refining CMC’s forecasts at and near the surface (which is certainly an important component …)
INFO: currently, surface processes are included in atmospheric model so they have to be integrated at the same resolution as the model
INFO: this does not have to be so, and surface could very well be integrated in an off-line manner, using forcing provided by atmos. Model
INFO: this diagram describes such a system, … , coupling is 1-way, and no feedback at this point on the atmospheric runs
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
TRANSITION: it is in this context that I would like to propose another strategy, on which we have starting working (at least from the point of view of data assimilation)
INFO: what I will discuss here is a way of refining CMC’s forecasts at and near the surface (which is certainly an important component …)
INFO: currently, surface processes are included in atmospheric model so they have to be integrated at the same resolution as the model
INFO: this does not have to be so, and surface could very well be integrated in an off-line manner, using forcing provided by atmos. Model
INFO: this diagram describes such a system, … , coupling is 1-way, and no feedback at this point on the atmospheric runs

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