Understanding climate model biases in
Southern Hemisphere mid-latitude
variability
Isla Simpson1
Ted Shepherd2, Peter Hitchcock3, John Scinocca4
(1) LDEO, Columbia University, USA (2) Dept of Meteorology, University of Reading,
UK (3) DAMTP, University of Cambridge, UK (4) CCCma, Environment Canada,
Canada
The Southern Annular Mode
Dominant mode of variability in SH extra-tropical circulation
Climatology
First EOF
ERA-Interim re-analysis
The SAM timescale
Calculate the
autocorrelation
function
 =7 days
Calculate the
e-folding
timescale.
The SAM timescale bias – CMIP3
 Climate models exhibit much too persistent SAM anomalies
in the summer season.
Obs
IPCC
models
Gerber et al (2008)
The SAM timescale bias – CCMVal2
 Climate models exhibit much too persistent SAM anomalies
in the summer season.
Obs
CCMVal
models
Gerber et al (2010)
The SAM timescale bias – CCMVal2
 Climate Models exhibit a SAM that is much too persistent in
the summer season.
Gerber et al (2010)
The SAM timescale bias – CMIP5
 Climate Models exhibit a SAM that is much too persistent in
the summer season.
Many climate forcings produce a mid-latitude circulation
response that projects onto the SAM.
Ozone depletion
Son et al (2010), JGR
Why is this potentially of concern for simulating
forced responses?
May indicate that we’re getting an important process wrong
in the simulation of the SH extra-tropical circulation.
Why is this potentially of concern for simulating
forced responses?
 Eddy Feedbacks
(Lorenz and Hartmann 2001,
2003), Robinson 2000)
 Intraseasonal Forcing e.g.
forcing from the stratosphere
(Keeley et al 2009)
 Dissipative processes e.g.
surface friction
Can we isolate the role for “internal” tropospheric
dynamics on the SAM timescale bias from the influence of
stratospheric variability as an intraseasonal forcing on the
SAM?
stratospheric
influence
onlate
SAMintimescales?
The ASH
vortex breaks
down too
GCMs,
maybe this is resulting in enhanced stratospheric
variability in the summer and contributing to the SAM
timescale bias?
Thought to be stratospheric variability that gives rise
to this maximum…variability in the timing of the
vortex breakdown (Baldwin et al 2003)
The Canadian Middle Atmosphere Model
 Comprehensive stratosphere resolving GCM
 T63L71, lid=0.0006hPa
 Without interactive chemistry
 Prescribed SSTs
 No QBO
 Constant GHG’s (1990’s concentrations)
Model Experiments
 100 year free running control simulation (FREE)
 100 year nudged simulation (NUDGED)
In NUDGED, the zonal mean vorticity, divergence and
temperature in the stratosphere are nudged toward
the zonal mean, seasonally varying climatology of
FREE.
We eliminate zonal mean stratospheric variability but
keep the climatology the same.
The Nudging Process
In spectral space
Only acting on the
zonal mean
(X  X o)
X
  K ( p)
t
N
K
The Nudging Process
(X  X o)
X
  K ( p)
t
N
In spectral space
Only acting on the
zonal mean
climatology
time
FREE and NUDGED have the same climatologies, but
FREE has stratospheric variability, NUDGED does not.
Vortex Breakdown Dates
FREE
NUDGED
ERA-Interim
FREE
FREE
NUDGED
Contribution from stratospheric variability
Stratospheric variability enhances the SAM
timescales in the SH spring.
ERA-Interim
NUDGED
There does seem to be a problem in the “internal”
dynamics of the tropospheric circulation.
Is this caused by climatological circulation biases?
Relationship between climatological jet bias and SAM timescales
If we improve the jet position, do we improve the
timescale of SAM variability?
Kidston and Gerber (2010)
Bias Correcting Experiments
Obtain the mean tendency that is required to bring the
model toward the ERA climatology (Kharin and Scinocca,
2012, GRL)  applying that constant seasonally varying
tendency to the model.
Model Climatology
Observed Climatology
Time
Different from nudging in that variability can still
occur, just around a new climatological state.
Two different experiments
 Bias correcting at all levels – BC
• Both stratospheric and tropospheric variability but around an
improved climatological state. Improved timing of the vortex
breakdown and improved tropospheric jet structure.
 Bias correcting in the troposphere and nudging the
zonal mean toward ERA-Interim in the stratosphere BCNUDG
• Removed stratospheric variability but has an improved
climatological timing of the vortex breakdown. Improved the
tropospheric jet structure.
Improved tropospheric jet structure?
Annual Mean Timescales
Kidston and Gerber (2010)
Annual Mean Timescales
Annual Mean Timescales
Annual Mean Timescales
The SAM timescale bias in CMAM does not seem to
be caused by climatological circulation biases.
Eddy feedback biases?
Eddy feedbacks on the SAM
Eddy forcing of the SAM regressed onto the SAM Index
Eddies driving
the SAM
SAM driving eddies
i.e., a positive
feedback
See Lorenz and Hartmann (2001), Simpson et al (2013)
Quantify the feedback strengths for each simulation and
the reanalysis.
Focus on the DJF season.
Synoptic scale eddy feedback (k>3)
Planetary scale eddy feedback (k=1-3)
Summary of DJF feedback strengths
This is mostly coming
from wavenumber 3
DJF regressions averaged over lags +7 to + 14 days
FREE
ERA
u
-u’v’, k=3
Regressions on the 300hPa (+7 to +14 lag average)
ERA-Interim
-u’v’ (k=1-3)
Regressions on the 300hPa (+7 to +14 lag average)
ERA-Interim
FREE
-u’v’ (k=1-3)
-u’v’ (k=1-3)
Comparison with CMIP-5 historical simulations
 20 models: those with 6 hourly u and v
available
 Quantify DJF feedback strength
Eddy feedback, All k
Eddy feedback, All k
Eddy feedback, k=1-3
Eddy feedback, k=1-3
Virtually all GCMs exhibit this same bias in
planetary wave feedbacks.
Models don’t capture the negative
feedback by planetary scale waves that is
localised to the south west of New
Zealand in the summer season.
Relation to climatological circulation biases?
Our bias corrected runs tell us that climatological
circulation biases are NOT the CAUSE of the eddy
feedback bias.
But the climatological circulation biases and eddy
feedback biases could be related e.g. they could
have a common cause.
Climatologically there is wave activity propagating
into the mid-latitudes to the S-W of New Zealand
ERA
 (u ' v ' )'
FREE
 (u ' v ' )'
There are common climatological biases in the region
around New Zealand
300hPa eddy geopotential height
ERA
FREE-ERA
There are common climatological biases in the region
around New Zealand
300hPa eddy geopotential height
FREE-ERA
CMIP5 - ERA
There are common climatological biases in the region
around New Zealand
300hPa eddy geopotential height
CMIP5 - ERA
CMIP5 CONSENSUS
Climatological jet latitude bias
CMIP-5
ERA
Conclusions
 Overly persistent SAM variability in the SH summer
season is a common model bias.
 The CMAM experiments demonstrated a bias in
internal tropospheric dynamics that is not alleviated
by improving the climatological circulation.
 The problem is associated with a bias in the
feedback by planetary scale waves in the model in
the summer season.
 This is true of the majority of other models in the
CMIP-5 archive.
Conclusions
 In order to have faith in the future predictions for
the SH mid-latitude circulation in the summer
season, we need to understand the planetary wave
feedback localised to the SW of New Zealand and
why it is biased in the models.
But….
 Models do reasonably well at simulating past SAM
trends.
CMIP-5 DJF SAM Trends
 Are we able to simulate recent SH SAM trends
correctly for the correct reason?
 Is our ability to simulate SAM eddy feedbacks
correctly somehow less important than we imagine
for our ability to simulate forced responses?
Gillett and Fyfe (2013), GRL
Seasonal Variation in Timescales
Eddy feedback, k>3
Eddy feedback, k=>3
Climatologically there is wave activity propagating
into the mid-latitudes to the S-W of New Zealand
ERA
 (u ' v ' )'
FREE
 (u ' v ' )'
Evidence for this relationship in simplified GCM
strat-trop coupling experiments
Response to Forcing
Dynamical Core Experiments
Timescale of natural variability
Simpson et al (2010)