Arctic snow in a changing cryosphere:
What have we learned from observations
and CMIP5 simulations?
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
1. Snow in the context of a changing
cryosphere
2. Overview of ‘observational’ snow
analyses
• Validation approaches
• Inter-dataset agreement
3. Observations versus CMIP5
simulations
Climate Change and the Cryosphere
Spring snow cover
Summer sea ice
Sea level
Observational time series
Trends in surface temperature 1901–2012
IPCC AR5 Summary for Policy Makers Figure 3
IPCC AR5 WG1 Chapter 2 Figure 2.21
Arctic Sea Ice Volume
Arctic sea ice volume anomalies from the Pan-Arctic Ice Ocean
Modeling and Assimilation System (PIOMAS )
U. Washington Polar Science Center
Canadian Arctic Sea Ice
Canadian Arctic Sea Ice Trends from the
Canadian Ice Service Digital Archive
Howell et al 2013 (updated)
Greenland Ice Sheet Mass Balance
Monthly changes in the total mass (Gt) of the Greenland ice sheet estimated
from GRACE measurements.
Tedesco et al., 2013 NOAA Arctic Report Card
Arctic Ice Caps and Glaciers
Mean annual (red) and cumulative (blue) mass balance from 1989-2011
from Arctic glaciers reported to the World Glacier Monitoring Service by January 2013.
Sharp et al., 2013 NOAA Arctic Report Card
Cryosphere Contribution to
Sea Level Rise
Rate of ice sheet loss in sea level equivalent averaged over 5-year periods.
IPCC AR5 WG1 Figure 4.17
Arctic Terrestrial Snow
Snow cover extent (SCE) anomaly time series, 1967-2013 (with respect to 1988–
2007) from the NOAA snow chart CDR. Solid line denotes 5-yr running mean.
• Over the 1979 – 2013 time period, NH June snow extent decreased at
a rate of -19.9% per decade (relative to 1981-2010 mean).
• September sea ice extent decreased at-13.0% per decade.
Derksen, C Brown, R (2012) Geophys. Res. Letters
Active Layer Thickness
Active layer thickness from Siberian stations, 1950 to 2008
IPCC AR5 WG1 Figure 4.23d
Snow – An Important Hydrological
Resource
NASA Earth Observatory
Snow – Highly Variable in Space and
Time
Focus on Arctic land
areas, during the spring
season (AMJ):
100% snow cover at the
beginning of April;
Nearly all snow gone by
end of June.
Part 2: Overview of ‘observational’ snow analyses
• Validation approaches
• Inter-dataset agreement
Hemispheric Snow Datasets
The challenge is not a lack of data…
Description
Period
Resolution
Data Source
NOAA weekly snow/no-snow
1966-2013
190.5 km
Rutgers University, Robinson et al [1993]
NOAA IMS daily 24 km snow/no-snow
1997-2004
24 km
National Snow and Ice Data Center (NSIDC),
Ramsay [1998]
NOAA IMS daily 4 km snow/no-snow
2004-2013
4 km
NSIDC, Helfrich et al [2007]
AVHRR Pathfinder daily snow/no-snow
1982-2004
5 km
Canada Centre for Remote Sensing, Zhao and
Fernandes, [2009]
MODIS 0.05° snow cover fraction
2000-2013
~5 km
NSIDC, Hall et al [2006]
ERA-40 reconstructed snow cover duration
(temperature-index snow model)
1957-2002
~275 km (5 km Environment Canada, Brown et al [2010]
elev. adjustment)
QuikSCAT derived snow-off date
2000-2010
~5 km
Environment Canada, Wang et al [2008]
Daily snow depth analysis (in situ obs + snow
1998-2013
model forced by GEM forecast temp/precip fields)
~35 km
Canadian Meteorological Centre, Brasnett [1999]
Daily snow depth analysis (in situ obs + snow
model forced by reanalysis temp/precip fields)
1979-1998
~35 km
Environment Canada, Brown et al [2003]
MERRA reanalysis snow water equivalent
(CATCHMENT LSM)
1979-2013
0.5 x 0.67 deg
NASA, Rienecker et al [2011]
ERA-interim reanalysis snow water equivalent
(HTESSEL LSM)
1979-2010
~80 km
ECMWF, Balsamo et al [2013]
GLDAS reanalysis snow water equivalent
(Noah LSM)
1948-2000
1948-2010
1.0 x 1.0 deg
0.25 x 0.25 deg
NASA, Rodell et al [2004]
SnowModel driven by MERRA atmospheric
reanalysis snow water equivalent
1979-2009
10 km
Colorado State, Liston and Hiemstra [2011]
GlobSnow snow water equivalent (satellite
passive microwave + climate station obs)
1978-2013
25 km
Finnish Meteorological Institute, Takala et al [2011]
Validating Snow Products with Ground
Measurements
•
•
•
•
•
Lack of in situ observations
‘Snapshot’ datasets
Spatial representativeness?
Measurement deficiencies
Poor reporting practices (non-zero
snow depth)
Challenges to Validating Gridded Snow
Products with Ground Measurements
This is what product users want to see:
This is the reality:
180
160
Old Jack Pine
1975 Harvest
0.25
140
SWE (mm)
120
Relative Frequency
1994 Harvest
2002 Harvest
GlobSnow SWE V0.9.2
100
80
60
40
20
0.20
n= ~5000
0.15
0.10
0.05
0.00
0
<20
305 317 329 341 353 365 12 24
36 48
60 72
84
40
60
80
100
120
140
160
180
200 >200
96 108 120
Day of Year 2006/07
Time series for the former BERMS sites
SWE (mm)
Spatial sampling across one grid cell
‘Validating’ Gridded Snow Products via
Multi-Dataset Comparisons
NH June SCE time series, 1981-2012
NOAA snow chart CDR (red); average of NOAA,
MERRA, ERAint (blue)
•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
NH SCE trend from -1.27 km2 x 106 to -1.12 km2
x 106
EUR Oct SCE: difference between NOAA snow
chart CDR and 4 independent datasets, 19822005
• Evidence of an artificial trend (~+1.0 million
km2 per decade) in October snow cover.
Brown, R Derksen, C (2013) Env. Res. Letters
A New Multi-Dataset Arctic SCE Anomaly
Time Series
April
May
June
Part 3: Observations versus CMIP5
simulations
Simulated vs. Observed Arctic SCE
1. NOAA CDR
NA
EUR
Historical + projected (16 CMIP5 models; rcp85 scenario) and observed (NOAA snow chart CDR)
snow cover extent for April, May and June.
SCE normalized by the maximum area simulated by each model.
Updated from Derksen, C Brown, R (2012) Geophys. Res. Letters
Simulated vs. Observed Arctic SCE
NA
1. NOAA CDR
2. Liston & Hiemstra
3. MERRA
4. GLDAS-Noah
5. ERA-int Recon.
EUR
Historical + projected (16 CMIP5 models; rcp85 scenario) and multi-observational snow cover
extent for April, May and June.
SCE normalized by the maximum area simulated by each model.
Arctic SCE and Surface Temperature
Trends: 1980-2009
SCE
Tsurf
• Simulations
slightly
underestimate
observed spring
SCA reductions
NA
• Similar range in
observed versus
simulated SCA
trends
• Observed Arctic
temperature
trends are
captured by the
CMIP5 ensemble
range
EUR
1. CRU
2. GISS
3. MERRA 4. ERA-int
Why do CMIP5 models underestimate
observed spring SCE reductions?
North America
Eurasia
Model vs observed temperature sensitivity (dSCE/dTs), 1981-2010
• Models exhibit lower temperature sensitivity (change in SCE per deg C warming)
than observations
• Magnitude of observational dSCE/dTs depends on choice of observations (both
snow and temperature)
Understanding CMIP5 SCE Projections
• Projected changes in snow cover for individual models are predictable based on
the characteristics of historical simulations.
• Consistent with a priori expectations, models project greater DSCE with:
-greater standard deviation (s)
-greater dSCA/dTs
-stronger historical trends
Future Work
•
•
CMIP5 models do fairly good job of replicating
the mean seasonal cycle of SWE over the Arctic
but the maximum is higher than observations,
and the models underestimate the rate of spring
depletion.
Shallow snow albedo and excess precipitation
frequency may together act to keep albedo
higher – simulated snow melt is not ‘patchy’.
Conclusions
• The rate of June snow cover extent loss (-19.9% per decade since 1979) is
greater than the rate of summer ice loss (-13.0% per decade).
• Arctic surface temperatures in the spring are well simulated by CMIP5 models,
but they exhibit reduced snow cover extent sensitivity to temperature compared
to observations.
• Interannual variability (s), temperature sensitivity (dSCE/dTs), and historical
trends are good predictors of DSCE projections to 2050.
• The spread between 5 observational datasets (mean; variability) is
approximately the same as across 16 CMIP5 models.
A climate modeling group would never run one model once, and
claim this is the best result.
Why do we gravitate towards this approach with observational
analyses?
Questions?
Snow Cover Extent:
Inter-Dataset Variability
June snow cover extent (2002)
2004-2008
CMC
IMS-24
May Avg SCE
9.0
11.0
June Avg SCE
3.0
5.1
IMS-4
NCEP
NOAA
10.6
9.6
10.2
11.6
10.2
10.2
10.3 ± 0.80
4.7
2.3
2.8
4.8
3.1
3.4
3.7 ± 1.06
Brown et al., 2010, J. Geophys. Res.
PMW
QSCAT
Avg ± 1
std
MODIS
Observed vs. Simulated SCE Variability
CMIP5 versus NOAA
Liston and Hiemstra
1. NOAA CDR
1. NOAA CDR
2. Liston & Hiemstra
All Observations
1. NOAA CDR
2. Liston & Hiemstra
3. MERRA
4. GLDAS-Noah
5. ERA-int Recon.
• The NOAA CDR is an outlier with respect to interannual variability
Understanding CMIP5 SCE Projections
Ratio of interannual variability relative to observations (NOAA) for fall and spring
• The 3 models with <5% difference in variability for spring versus fall are the 3
top ranked models at reproducing the spatial pattern of mean snow cover
duration over Arctic land areas (vs. NOAA)