Modeling Applications of the ESA GlobSnow Data RecordChris Derksen, Ross Brown, Bill MerryfieldClimate Research DivisionEnvironment Canada

Lawrence Mudryk, Paul Kushner, Department of PhysicsUniversity of Toronto

Stephane Belair, Bernard Bilodeau, Marco Carrera, Natalie GauthierMeteorological Research DivisionEnvironment Canada

Kari Luojus and the GlobSnow SWE team at FMI

Outline1. Strengths and weaknesses of the

GlobSnow SWE record for modeling applications

2. Use of GlobSnow SWE data for modeling applications:

• Evaluation of coupled climate model simulations

• Land surface initialization – CanSIPS

• Land surface data assimilation - CaLDAS

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.

Contextwww.cansise.ca

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.

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

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1990 1993 1996 1999 2002 2005 2008Stat

ions

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epth

•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.

Consistency of Climate Station Observations

Mean (with max/min) of number of Arctic stations reporting snow depth within April.

Eurasia

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ions

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epth

North America

Mean annual maximum SWE (1998/99 – 2009/10) from CMC (left), GlobSnow (middle), and merged dataset (right).

Filling in the GlobSnow Mountain Mask

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

CMC+GlobSnow (mm)

10 model avg (mm)

10 model avg bias (mm)

CMIP5 Simulated vs. Observed Arctic Snow Water Equivalent

• 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

Annual maximum monthly SWE (SWEmax)

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 1981-2010.1. coupled to a dynamical ocean2. observed sea surface temperatures (SSTs)

Mudryk et al Clim Dyn in press

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).

Snow Climatology and Variability: CCSM4 Simulations vs. Observations

Mudryk et al Clim Dyn in press

Snow Water Equivalent Trends:CCSM4 Simulations vs. Observations

CoupledUncoupledObservations

Mudryk et al Clim Dyn in press

Snow Water Equivalent Trends:CCSM4 Simulations vs. Observations

Observations identify predominantly negative trends, particularly for NA

Simulations identify positive winter and spring SWE trends over much of the Arctic.

Mudryk et al Clim Dyn in press

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

Evaluation of Snow Initial Conditions in Canadian Seasonal to Interannual Prediction System (CanSIPS) How close are initial conditions to observations of SWE?

HindcastsAssimilation Runs

Historical Runsfreely running CanCM3\CanCM4

ObservationsGlobSnow (station + PMW)MERRA (reanalysis)CMC (station + snow model)…and others

1 year durationserve as initial conditions for hindcasts

assimilate observed T, u, v, q, SST, sea ice

begin on 1st of month

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?

Springtime Bias in SWE Initial Conditions

How quickly do hindcasts drift from initial conditions to model climatology?

HindcastsAssimilation Runs

Historical Runsfreely running CanCM3\CanCM4

ObservationsGlobSnow (station + PMW)MERRA (reanalysis)CMC (station + snow model)…and others

1 year durationserve as initial conditions for hindcasts

assimilate observed T, u, v, q, SST, sea ice

begin on 1st of month

Evaluation of Snow Initial Conditions in Canadian Seasonal to Interannual Prediction System (CanSIPS)

Mean Drift of Hindcasts from Assimilation Runs

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)

Progress in the Assimilation of GlobSnow SWE in the Canadian Land Data Assimilation System

The Canada Land Data Assimilation System (CaLDAS)

LANDMODEL(SPS)

OBS

ASSIMILATIONEnKF + EnOI

xb

y

EnKFxa = xb+ K { y – H(xb) }

K = BHT ( HBHT+R)-1

with

CaLDASIN OUT

Ancillary land surface data

Atmospheric forcing

Observations

Surface Temperature

Soil moistureSnow depth or

SWE

Vegetation*Screen-level (T, Td)Surface stations snow depthL-band passive (SMOS, SMAP)Microwave SWE (AMSR-E)*Optical / IR (MODIS, VIIRS)Combined products (GlobSnow)

T, q, U, V, Pr, SW, LW

Orography, vegetation, soils, water fraction, ...

Analyses of…

*) not done yet…

Open Loop

CaLDAS-GS

OBS

Mean snow depth

Bias

STDE

GlobSnow

(CAREFUL… GlobSnow experiments not leave-one-out)

GlobSnow-2 in CaLDAS

• 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

Moving Forward…

• 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.

Conclusions

Questions?

CMIP5 Simulated vs. GlobSnow Arctic Snow Water Equivalent

January April