Arctic Snow Cover Monitoring

Chris Derksen and Ross Brown

Climate Research Division

Environment Canada

Thanks to our data providers:

Rutgers Global Snow Lab ● National Snow and Ice Data

Center ● World Climate Research Programme Working

Group on Coupled Modelling ● University of East Anglia –

Climatic Research Unit ● NASA Global Modeling and

Assimilation Office ● European Centre for Midrange

Weather Forecasting

Outline

• How do recent reductions in observed

spring snow cover extent (SCE)

compare to CMIP5 simulations? (historical + RCP8.5)

• Why are observed trends in the dates of

snow cover onset (relatively little

change) and snow cover

disappearance (widespread significant

trends to earlier loss of snow cover) so

different?

• What is the state of our observational

capability to assess CMIP5 simulations

of snow water equivalent (SWE)?

• A few suggestions for GCW…

• Binary snow extent (weekly) derived by analysts from primarily optical satellite imagery

since 1966.

• Maintained at Rutgers U by David Robinson http://climate.rutgers.edu/snowcover/].

• Subject to intensive validation, and is widely used in the climate community.

NA monthly

SCE anomalies

EUR monthly

SCE anomalies

Observed Arctic Snow Cover Extent –

NOAA CDR Dataset

Model Acronym Institution n lat n lon Resolution

(°)

CanESM2 CCCma (Canadian Centre for Climate Modelling and

Analysis, Canada)

64 128 2.81 x 2.81

CCSM4 NCAR (National Center for Atmospheric Research, USA) 192 288 0.93 x 1.25

CNRM-CM5 CNRM-CERFACS (Centre National de Recherches

Meteorologiques / Centre Europeen de Recherche et

Formation Avancees en Calcul Scientifique, France)

128 256 1.40 x 1.40

GISS-E2-R NASA-GISS (NASA-Goddard Institute for Space Studies,

USA)

90 144 2.00 x 2.50

INMCM4 Institute for Numerical Mathematics, Russian Academy of

Sciences, Russia

120 180 1.50 x 2.00

MIROC5 AORI (Atmosphere and Ocean Research Institute, The

University of Tokyo, Japan), NIES (National Institute

for Environmental Studies, Japan), and JAMSTEC

(Japan Agency for Marine-Earth Science and

Technology)

128 256 1.40 x 1.40

MPI-ESM-LR Max Planck Institute for Meteorology, Germany 96 192 1.88 x 1.88

MRI-CGCM3 Meteorological Research Institute, Tsukuba, Japan 160 320 1.13 x 1.13

CMIP5 Climate Model Simulations of

Arctic Snow Cover (historical + rcp8.5)

Historical + projected (8 CMIP5 models; rcp85 scenario) and observed (NOAA snow

chart CDR) snow cover extent for April, May and June for the Northern Hemisphere

Snow covered area is normalized by the maximum area simulated by each model.

• Observations are mostly within ±1 standard deviation of the multi-model ensemble in

April and May, but start to diverge from the model consensus in recent years.

• Marked reductions in June SCE observed since 2005 fall below the zone of model

consensus defined by +/-1 standard deviation from the multi-model ensemble mean.

Simulated vs. Observed Arctic Snow

Cover – CMIP5

Derksen, C Brown, R (2012) Geophys. Res. Letters

Changes in Snow vs. Sea Ice Extent

• Over the 1979 – 2012 time period, June snow extent is decreasing at a rate of -17.6%

per decade (relative to 1979-2000 mean).

• September sea ice extent is decreasing at -13.0% per decade over the same time

period.

Northern hemisphere June snow cover; June and September

Arctic sea ice extent, 1979-2012

Derksen, C Brown, R (2012) Geophys. Res. Letters

How reliable are the NOAA Snow Chart

CDR Trends?

• Similar anomaly trend results obtained

with three independent datasets. • Tendency for NOAA to consistently map

less spring snow (~0.5 to 1 million km2)

than the multi-dataset average since 2007.

• Accounting for this difference reduces the

June SCE trend to -15.0% per decade.

Standardized anomaly time series of

Northern Hemisphere SCE, 1981-2012,

from the NOAA snow chart CDR (blue),

MERRA (red) and ERAint (green)

Northern Hemisphere SCE time series,

1981-2012, for the NOAA snow chart CDR

(red) and average of

NOAA+MERRA+ERAint (blue)

NA monthly

SCE anomalies

EUR monthly

SCE anomalies

Observed Arctic Snow Cover Extent –

NOAA CDR Dataset

• Warming temperature trends in both spring and fall, so why the seasonal difference in

SCE anomalies?

• CMIP5 model output suggests seasonally symmetric reductions in SCE.

There is evidence of a

tendency in the NOAA

snow chart data record to

map relatively more snow

over Eurasia in the snow

onset period than other

datasets, which results in

an artificial trend (~+1.0

million km2 per decade)

October snow cover.

Explaining Seasonal Asymmetry in Arctic

Snow Cover Duration Trends

Difference between NOAA snow chart CDR and other

independent SCE datasets, 1982-2005

Static adjustment of -1.0 x 106km2 per

decade applied to the NOAA time series

An arbitrarily adjusted NOAA time

series:

1. agrees closely with multiple

evaluation datasets

2. is significantly correlated with

surface temperature

• Higher MODIS

cloud fraction in the

snow cover onset

period creates

challenges for

NOAA analysts

Explaining Seasonal Asymmetry in Arctic

Snow Cover Duration Trends

October May

Snow cover

onset date is

less strongly

coupled to air

temperature

than snow-off

date [weaker

feedbacks;

requires Tair <

0 AND precip >

0 to initiate

snow cover].

Date of freeze onset

vs. start of snow cover

Melt onset vs. end of

snow cover

Explaining Seasonal Asymmetry in Arctic

Snow Cover Duration Trends

MERRA

ERA-interim

reconstruction

Multi-Dataset SWE Time Series Dataset Method Domain Time Period Resolution Reference

GlobSnow Passive microwave + climate station Pan-Arctic 1980-2011 25 km Takala et al., 2011

ERA-int Snow accumulation model driven by

reanalysis temp and precip

Pan-Arctic 1980-2012 ~275 km Uppala et al., 2005

B2003 Surface analysis + snow model North America 1979-1997 ~35 km Brown et al, 2003

CMC Surface analysis + snow model Pan-Arctic 1998-2012 ~35 km Brasnett, 1999

MERRA SWE field from reanalysis Pan-Arctic 1980-2012 ~35 km Rienecker et al., 2011

Seasonal SWEmax time series (+/- standard error) from multi-datasets

• SWE datasets are reasonably consistent (+/- 10 to 25 mm) with no significant trends in the

multi-dataset average; SWEmax shows a step change over Eurasia in the late 1990s.

Mean annual maximum SWE (1998/99 – 2009/10) from CMC (left),

GlobSnow (middle), and merged dataset (right).

Hemispheric Snow Water Equivalent

Data for CMIP5 Evaluation

GlobSnow: combines satellite passive microwave measurements, forward snow

emission model simulations, and climate station snow depth observations in an

assimilation framework (lacks information in areas with complex relief).

CMC: snow depth observations combined with a first guess using a simple snow

accumulation and melt model (underestimates SWE over northern latitudes).

RMSE= 47 mm RMSE= 92 mm

GlobSnow Standard Algorithm

Takala M et al (2011) Remote Sens. Environ.

GlobSnow Algorithm

Evaluation

(Greater than 100 000 samples from across FSU)

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

• The multi-model ensemble agree more closely with the observed data

than any individual model

10-model ensemble mean annual max monthly SWE (SWEmax) evaluation results

Primary Objective:

Quantify amount and variability of freshwater stored in seasonal snow packs,

and snow accumulation on glaciers

Scientific Impact:

To improve hydrological and climate modelling and Numerical Weather

Prediction by incorporation of direct observations of snow mass and snow

mass variability

Instrument:

SAR at Ku-(17.2 GHz) and X-Band (9.6 GHz)

Repeat Time:

3 and 15 days

Spatial Resolution:

50 x 50 m, Swath ≥100 km, Products at 500m

Cold Regions High

Resolution Hydrological

Observatory (CoReH20)

Conclusions

• The rate of June snow cover extent reductions

(-17.6% per decade since 1979) is greater

than the rate of summer ice loss (-13.0% per

decade over the same time period). Both are

declining at rates that exceed CMIP5 model

projections [Stroeve et al 2012, Derksen and

Brown 2012].

• The observed seasonal asymmetry in snow

cover extent response to warming Arctic

temperatures is due to: (1) internal bias in the

NOAA snow chart time series; (2) weak

coupling between snow cover and air

temperature at the start of the snow season.

• New SWE products (i.e. ‘GlobSnow’ merged

with the CMC analysis) are providing an

improved basis for model evaluation

Recommendations for

GCW Snow-Watch 1. Through dataset intercomparisons, develop high quality validation data sets

covering a range of spatial scales.

Anomalies of June Arctic SCE, 1982-2002

-3

-2

-1

0

1

2

3

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

SC

E s

td a

no

maly

NOAA

NCEP

ERA-40

ERA-40rec

CCRS

PMW

Avg

Various estimates of June 2002 Arctic snow cover extent (ranges from 2.3 to 5.4 million sq km)

Address:

1. regions and times when between-dataset

variance is the highest (i.e. snow cover onset

and melt periods over Arctic).

2. strong interannual variability: compare “good”

and “bad” years to identify issues

3. is the multi-dataset average a better estimate

of the “true” snow cover?

2. Provide near real time SCE and SWE anomaly tracking.

-requires continuous monitoring by credible, media savvy people (like

Dave Robinson at Rutgers)

2011 North American Snow Cover Extent (excl Greenland)

0

2

4

6

8

10

12

14

16

18

20

1 31 61 91 121 151 181 211 241 271 301 331 361

Julian Day

SC

E (

millio

n s

q k

m)

NOAA mly avg (1981-2010)

NOAA mly min (1966-2010)

NOAA mly max (1966-2010)

IMS-24 km

CMC > 0 cm

Earlier than

normal melt

Comparison of NA

SCE from CMC daily

snow depth analysis

and NOAA IMS-24

km daily snow cover

product

Below average

SCE during snow

cover onset period

Recommendations for

GCW Snow-Watch

3. Conduct an evaluation of snow reconstructions from detailed physical

snow process models (driven by reanalysis) in order to determine the

reliability and consistency of precipitation (and SWE) products.

Trend in simulated maximum winter SWE

(cm/decade) over 1979-2009 from Liston and

Hiemstra (2011) [Arctic snow process model

driven with MERRA reanalysis fields

downscaled to 10 km]

Recommendations for

GCW Snow-Watch

Questions?

Interannual Variability:

Observations vs. Simulations

Standard deviation in monthly SCE from NOAA

observations and CMIP5 model simulations.

• CMIP5 simulations underestimate variability in the shoulder seasons.

• Peak spring variability in observations occurs in June; May for CMIP5.

Derksen, C Brown, R (2012) Geophys. Res. Letters

Linear trends (1967-2011): NOAA snow chart CDR

Arctic snow cover extent (km2 x 106 x decade-1);

CRUtem3v surface temperature over land areas

(ºC x decade-1).

Fall and spring Arctic snow cover

duration anomalies, 1967-2012

(NOAA Snow Chart CDR)

Derksen, C Brown, R (2012) BAMS

• Warming temperature trends in both spring and fall,

so why the seasonal difference in snow cover

duration anomalies?

• CMIP5 model output suggests seasonally

symmetric reductions in SCE.

Arctic Snow Cover Duration and

Temperature Trends SCE SAT

Month NA EUR NA EUR

September -0.02 -0.01 0.42 0.30

October 0.03 -0.02 0.38 0.53

November 0.07 0.01 0.50 0.53

April -0.18 -0.43 0.53 0.50

May -0.24 -0.58 0.30 0.46

June -0.35 -0.78 0.35 0.35

Trends in Passive Microwave Derived

Melt Onset

Trend P values (blue for P<0.05) Linear least squares trend (1979-

2011) for local significance (days/year)

+1

-1

0

Wang L et al (in press) Geophys. Res. Letters

Correlations of melt onset date and surface air temperature, 1979-2011.

(Correlations less than -0.3 are statistically significant at 95% confidence level)

March April

May June

Correlations Between Melt Onset Date

and Surface Air Temperature

Wang L et al (in press) Geophys. Res. Letters

Satellite brightness temperatures

(19 and 37 GHz)

Snow depth observations from synoptic weather

stations (ECMWF)

Observed field produced by kriging synoptic

weather station observations. Estimate of

kriging interpolation variance also obtained.

Weather station measurements of snow depth

used as input to forward snow emission model

simulations. Model fit to satellite measurements

by fluctuating the effective grain size.

Spatially continuous background field of

effective grain size (including a variance field)

produced by kriging.

Radiometer field produced through forward Tb

simulations using the kriged effective grain size.

Model fit to satellite measurements by

fluctuating SWE.

Assimilation procedure adaptively weighs the

observed and radiometer fields to estimate final SWE

and a measure of statistical uncertainty.

GlobSnow SWE Algorithm

2. Promote dataset intercomparisons to characterize the uncertainty in data

products

95% confidence interval in NA mean

SCE and between-dataset standard

deviation (% of mean SCE) for 6 SCE

datasets over 2001-2008 period

(MODIS, CMC, IMS-24, ERAint

reconstruction, PMW, NCEP proxy)

Stdev < 10% from Dec to April

Recommendations for

GCW Snow-Watch

3. Provide near real time information on hemispheric snow depth (or

SWE) anomalies.

-Highly visible product with many potential applications (e.g. water

resources, agriculture, forest fire potential)

Dec Feb April

Monthly snow depth anomaly (%) over 2011-2012 snow season from CMC daily

snow depth analysis (1999-2010 reference period) (Derksen and Brown, BAMS State

of Climate, 2012)

Recommendations for

GCW Snow-Watch