- CanSISE

Report
CanSISE conference, Victoria
Yara Mohajerani
May 9, 2014
OBSERVED ROBUSTNESS OF
INTERANNUAL VARIABILITY OF
OCTOBER EURASIAN
SNOWCOVER AND ITS
CLIMATE LINKAGE
Outline

Background
 Eurasian
October snow and wintertime climate
 Snow cover datasets



Evaluation of snow cover datasets
Climate linkage
Conclusion
Snow-Climate Connection


Vast and highly variable
snow cover
Positive October snow
anomaly  negative AO
phase in the following winter
Effect of October snow on AO
Cohen et al 2007, Figure 6, page
5342
Inconsistencies in snow data



Known
inconsistencies in
snowcover data
NOAA shows
false positive
trend
NOAA widely
used in climateconnection
studies
Snow Cover Extent Anomaly
Brown and Derksen (2013) Figure 2, page 4
Project Overview
1.
Validation and evaluation of satellite and
reanalysis datasets against ground measurements
(for ground station locations)

2.
Need global coverage, but can only validate data with local
observations
Application of revised datasets to the issue of
relationships between October Eurasian snow
cover and northern hemisphere winter circulation
(sea level pressure and free atmospheric
geopotential) from 1980 to 2011.
Snow Cover Fraction (SCF) datasets
Measurement Type
Name
Monthly SCF Definition
Reanalysis (based on ERAint
temperature and precipitation)
BROWN
Reanalysis
MERRA
% of days in a month with ≥ 2cm
snow depth
% of days in a month with
SWE ≥4mm
PMW
% of days in a month with
SWE ≥7.5mm
NOAA
% of days in month that a cell has
more than 50% snow covered
Passive Microwave Satellite
Measurements
Visible Satellite Measurements
Ground Measurements of snow
RU
depth over Russia
% of days in a month with >=2 cm
snow depth.
Consistency
Consistency (15%)
S(t) time series
Dataset
SCF
Change (%)
MERRA
-8.2
BROWN
-11.4
NOAA
16.7
RU
-7.5
Correlation of snow indices
RU
MERRA-RU
BROWN-RU
NOAA-RU
PMW-RU
MERRA-EU
BROWN-EU
NOAA-EU
MERRA-RU
0.97
0.94
0.85
0.77
0.87
0.78
0.73
0.96
0.83
0.78
0.90
0.80
0.73
BROWN-RU
0.82
0.82
0.90
0.87
0.72
All correlations significant at the 0.05 confidence level.
NOAA-RU
0.68
0.72
0.68
0.88
PMW-RU
0.71
0.70
0.72
MERRA-EU
0.93
0.70
Snow-Climate Connection
Regression: 2-meter Temperature
Regression: Sea Level Pressure
Regression: Zonal mean geopotential
[Z]
Snowcover and AO index
Snow climate connection discussion



MERRA has weaker correlation than NOAA
BROWN has very weak correlation
Time frame matters: MERRA and NOAA more
similar in JF than DJF
Conclusions





Internal inconsistencies in NOAA: false positive trend
MERRA and BROWN have best agreement with insitu data (respectively)
Dataset bias (NOAA) possible in previous climate
linkage studies
Relationship still evident between October Eurasian
snow and wintertime circulation
The extent of this relationship should be taken with
caution due to inconsistencies in snowcover data
Acknowledgements






Professor Paul J. Kushner
Dr. Chris Derksen
Dr. Ross Brown
Centre for Global Change Science, University of
Toronto
The Canadian Sea Ice and Snow Evolution (CanSISE)
Department of Physics, University of Toronto
References

Watanabe, M., and T. Nitta (1998): Relative impacts of snow and sea surface temperature anomalies on an extreme phase in the winter atmospheric circulation. J.
Climate, 11, 2837–2857.

Cohen J., and D. Entekhabi (1999): Eurasian snow cover variability and Northern Hemisphere climate predictability. Geophys. Res. Lett., 26, 345–348.

Cohen J. et al (2007) Stratosphere–Troposphere Coupling and Links with Eurasian Land Surface Variability. J. Climate, Volume 20, 5335-5343, 1 NOVEMBER 2007.

Wallace, J. M., Hobbs, P. V. (2006): Atmospheric Science: An Introductory Survey. Second Edition, University of Washington. Copyright ©2006 Elsevier Inc.
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
Dee, D.P., “The ERA-Interim reanalysis: configuration and performance of the data assimilation system.” Q.J.R.Meteorol.Soc.137:553–597, April 2011
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Supplementary Material
SCF Histogram
Regression: Zonally Asymmetric
geopotential [Z]*

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