Understanding climate model biases in Southern Hemisphere mid-latitude variability
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Transcript of Understanding climate model biases in Southern Hemisphere mid-latitude variability
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
Calculate the e-folding timescale.
=7 days
The SAM timescale bias – CMIP3 Climate models exhibit much too persistent SAM anomalies in the summer season.
Gerber et al (2008)
Obs
IPCC models
The SAM timescale bias – CCMVal2 Climate models exhibit much too persistent SAM anomalies in the summer season.
Obs
CCMVal modelsGerber et al (2010)
Climate Models exhibit a SAM that is much too
persistent in the summer season.
Gerber et al (2010)
The SAM timescale bias – CCMVal2
Climate Models exhibit a SAM that is much too
persistent in the summer season.
The SAM timescale bias – CMIP5
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)
Dissipative processes e.g. surface friction
Intraseasonal Forcing e.g. forcing from the stratosphere(Keeley et al 2009)
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?
A stratospheric influence on SAM timescales?
Thought to be stratospheric variability that gives rise to this maximum…variability in the timing of the vortex breakdown (Baldwin et al 2003)
The SH vortex breaks down too late in GCMs, maybe this is resulting in enhanced stratospheric variability in the summer and contributing to the SAM timescale bias?
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
N
oXXpK
t
X
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Only acting on the zonal mean
K
The Nudging Process
In spectral space
N
oXXpK
t
X
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Only acting on the zonal mean
timeclimatology
FREE and NUDGED have the same climatologies, but FREE has stratospheric variability, NUDGED
does not.Vortex Breakdown DatesFREE NUDGED
ERA-Interim
FREE
FREE
NUDGED
Contribution from stratospheric variability
Stratospheric variability enhances the SAM timescales in the SH spring.
NUDGED
ERA-Interim
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
Kidston and Gerber (2010)
If we improve the jet position, do we improve the timescale of SAM variability?
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
Different from nudging in that variability can still occur, just around a new climatological state.
Time
Bias Correcting Experiments
Two different experiments
Bias correcting at all levels – BC
Bias correcting in the troposphere and nudging the zonal mean toward ERA-Interim in the stratosphere - BCNUDG
• Both stratospheric and tropospheric variability but around an improved climatological state. Improved timing of the vortex breakdown and improved tropospheric jet structure.
• 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
Eddies driving the SAM
SAM driving eddies i.e., a positive feedback
See Lorenz and Hartmann (2001), Simpson et al (2013)
Eddy forcing of the SAM regressed onto the SAM Index
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
ERA
u
-u’v’, k=3
FREE
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 ZealandERA ) '''( vu
FREE) '''( vu
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
Gillett and Fyfe (2013), GRL
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?
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 ZealandERA ) '''( vu
FREE) '''( vu
Evidence for this relationship in simplified GCM strat-trop coupling experiments
Dynamical Core Experiments
Timescale of natural variability
Resp
onse
to
Forc
ing
Simpson et al (2010)