clivar enso workshop, paris, nov. 2010 enso in gcms: overview, progress and challenges eric...
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CLIVAR ENSO workshop, Paris, Nov. 2010
ENSO in GCMs: overview, progress and challenges
Eric Guilyardi IPSL/LOCEAN, Paris, France & NCAS-Climate, Univ. Reading, UK
1. ENSO in coupled GCMs
2. Atmosphere feedbacks during ENSO• Dynamical (Bjerknes) feedback• Heat flux feedback
3. Strategies to improve ENSO in models
CLIVAR ENSO workshop, Paris, Nov. 2010 2
El Niño in coupled GCMs – mean state
Trade winds too strong
Both mean and annual cycle
Guilyardi et al. (BAMS 2009)
Tau
x N
iño
4 (
Pa)
IPCC-class models
OBS.
Zonal wind stress in central Pacific (mean and annual cycle)
CLIVAR ENSO workshop, Paris, Nov. 2010 3
El Niño in coupled GCMs - amplitude
ENSO amplitude in IPCC AR4 : much too large diversity !
Standard deviation SSTA (C)
EN
SO
Am
pli
tud
e
picntrl2xco2
CLIVAR ENSO workshop, Paris, Nov. 2010 4
El Niño in coupled GCMs - frequency
Maximum power of Niño3 SSTA spectra
AchutaRao & Sperber (2006)
IPCC TAR: to high frequency
CMIP3: improved towards low frequency but still large diversity
CLIVAR ENSO workshop, Paris, Nov. 2010 5
Westward zonal extension
Too small meridional extension
El Niño in coupled GCMs - structure and timing
With impacts on periodicity
(Capotondi et al. 2007)
SST standard deviation
e.g.: Model events terminate in West rather than in East Pacific
Leloup et al. (2008)
Time sequence of El Niño/La Niña also has errors
El Niño termination spatial characteristics
CLIVAR ENSO workshop, Paris, Nov. 2010 6
El Niño in coupled GCMs - summary
Clear improvement since ~15 years
• some models get Mean and Annual cycle and ENSO right !
but:
• Amplitude: models diversity much larger than (recent) observed diversity
• Frequency: progress towards low frequency/wider spectra but still errors
• Seasonal phase lock: very few models have the spring relaxation and the
winter variability maximum
• Structure and timing: westward extension and narrowing around
equator, issues with time sequence (onset, termination)
• Modes: very few model exhibits the diversity of observed ENSO modes;
most are locked into a S-mode (coherent with too strong trade winds)
• Teleconnections: ENSO influence over-dominantGuilyardi et al. (BAMS 2009)
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Ocean response to τ and HF anomalies•Upwelling, mixing, ("thermocline feedback", "cold tongue dynamics") (Meehl al. 2001, Burgers & van Oldenborgh 2003)
•Zonal advection (Picaut al. 1997)
•Wave dynamics •Energy Dissipation (Fedorov 2006)
van Oldenborgh al. 2005
Atmosphere response to SSTA•Bjerknes wind stress feedback (van Oldenborgh al. 2005, Guilyardi 2006)
•Meridional response of wind stress (An & Wang 2000, Capotondi al. 2006, Merryfield 2006)
•Radiative and cloud feedbacks (Sun al. 2006, Bony al. 2006, Sun et al. 2009)
Other processes:
• NL dynamical heating (∇xT + U in phase, An & Jin 2004)
• "Multiplicative noise" - MJO (Lengaigne et al. 2004, Perez et al. 2005, Philip & van Oldenborgh 2007)
Attributing ENSO errors: physical mechanisms
CLIVAR ENSO workshop, Paris, Nov. 2010 8
• Other studies confirm this result (e.g. Schneider 2002, Kim et al. 2008, Neale et
al. 2008, Sun et al. 2009, Shin et al. 2010,...)
Atmosphere GCM has a dominant role
Guilyardi et al. (2004)
Q1: Why this dominant role ? (ENSO theories focus on ocean role)
Q2: How to attribute ENSO errors to model systematic errors ?
Attributing ENSO errors: the role of the atmosphere
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The BJ coupled-stability index for ENSO IBJ
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
α: atmosphere heat flux feedback
μa: Bjerknes feedback or “coupling strength”
Mean advection and upwelling (damping)
Zonal advection
feedback
Ekman pumping feedback
Thermocline feedback
αis a negative feedback (damping)
μ is a positive feedback (amplification)
Jin et al. (2006), Kim et al. (2010a,b)
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Atmosphere feedbacks during ENSO
Dynamical: Bjerknes feedback
East-west SST gradient
Trade winds
Equatorial upwelling in the east
Heat flux feedback
SST increase in the east
Modified heat fluxes (SHF, LHF)
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Evaluating the Bjerknes feedback
• Monthly variability = measure of seasonal phase lock• Bjerknes amplification stronger in July-December
• Obs. values of vary from 10 to 15 (10-3 N.m-2/C)
Niño 3 SST anomaly
Niñ
o 4
Tau
X a
no
mal
y
Seasonal evolution of
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Evaluating the heat flux feedback α
• Defined as slope of heat flux QA = F(SSTA)• α varies from -10 W.m-2/C to -30 W.m-2/C• Damping stronger in January-May
Niño 3 SST anomaly
Niñ
o 3
Hea
t F
lux
ano
mal
y
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IPSL/Tiedke (TI)(0.3 C) – old scheme
IPSL (KE)Kerry Emanuel(1.0 C) - in IPCC
Impact of atmosphere convection scheme on ENSO
Observations(0.9 C) - HadiSST1.1
ENSO has disappeared !
What role for α and μ?
Hourdin et al. (2006) Braconnot et al. (2007)
Guilyardi et al. J.Clim (2009b)
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BJ index for KE and TI
Linear theory: α dominant factor in TI/KE difference
Guilyardi et al. J.Clim (2009b)
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~10 -18 0.9
4 -5 1.0
4 -20 0.3
El Niño
Amplitude
KE
TI
Obs
10-3 N.m-2/C W.m-2/C oC
Compute and in the KE and TI simulations
Error compensation !
Can we relate this change to physical processes ?
Getting the right ENSO amplitude for the wrong reasons !
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Seasonal evolution of feedbacks
• Shortwave HF feedback SW in second half of year explains most of the difference
-25 W.m-2/C
<=>
1oC/month in SST cooling !!!
(MXL 50 m,
SSTA of 2oC)
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SW feedback distribution
• Point-wise regression of SHF anomaly vs. SSTA (correl. less than 0.2 blanked out)
• Negative feedback (blue) = convective/ascent regime
• Positive feedback (red/orange) = subsidence regime
• ERA40 has large errors in East Pacific (Cronin et al. 2006)
• AMIP KE closer to ISCCP
• AMIP TI has too strong convection
• In KE, subsidence/+ve SW invades central Pacific
• In TI, convection/-ve SW invades east Pacific
• Coupled vs. forced (Yu & Kirtman 2007)
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Composite analysis of large-scale regimes during El Niño
Subsidence
Convection
in far east Pacific• Positive SW has the potential to amplify El Niño
during growing phase, i.e. before convective/ascent threshold is reached
• AMIP KE potential much larger than that of AMIP TI
• KE and TI have opposite regimes during ENSO in far east Pacific
• TI ascent threshold 2oC lower than KE
TI physics triggers convection too easily which prevents ENSO from developping
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Can we test this, i.e. suppress ENSO in KE ?• Perform KE run with increased SW
• Interannual Flux Correction:
• SHFO= SHFSCKE + SW
mod (SSTO-SSTSCKE)
• SWmod = -15 W.m
-2
• Mean state (SC) unchanged
~10
-180.9
El NiñoAmplitude
4 -51.0
4 -200.3
5 -210.4
Obs
KE
TI
KE mod TI
10-3 N/m2/C W/m2/C oC
ENSO gone as well !
KE
TI
KE
mod TI ?
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Summary
• El Niño in IPCC-class GCMs: progress but still major errors (too much diversity) and atmosphere GCM is a dominant contributor
• Dynamical positive () and heat flux negative (α) feedbacks both likely to control El Niño properties in CGCMs
• Both feedbacks are usually too weak in models (poster by James Lloyd)
• In the IPSL-CM4 KE model, weak and α compensate each other
• When the convection scheme is changed to Tiedke (TI), α is increased and ENSO is damped (convective regime stronger)
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Summary cont’d• Errors in these feedbacks:
• can be indentified in AMIP mode (good for model development) • have an impact on the mean as well (improve the mean and you’ll
improve ENSO)
• Improving ENSO in coupled GCMs requires:• Getting the right amplitude for the right reasons !• Increased atmosphere GCM horizontal resolution to improve
bjerknes feedback • Improved atmosphere convective physics (value of αSHF-)• Improved low clouds feedbacks (value of αSHF+)• Reduced cold tongue bias (spatial extent of αSHF+)• Validate ENSO feedbacks during model development phases
• Analysis method clearly attributes model systematic biases to errors in ENSO atmosphere feedbacks
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How can we improve ENSO in models ?
• Improve the quality and utility of historical records
• Maintain present ENSO observing system into the future
• Continue promoting intercomparison studies (ENSO metrics)
• Isolate the main sources of model error, guided by theory (BJ), observations, and rigourous evaluation of the models, including tests in seasonal forecast mode (talk by Benoît Vannière)
A few challenges lying ahead:
CLIVAR ENSO workshop, Paris, Nov. 2010 23
Amplitude error in Niño 3 and Niño 4 ENSO Frequency RMS
Zonal wind RMS error in Equatorial Pacific SST annual cycle amplitude error in Niño 3
ENSO performance metricsDevised by CLIVAR Pacific Panel WG for CMIP5
a few examples...
CLIVAR ENSO workshop, Paris, Nov. 2010 24
How can we improve ENSO in models ?
• Improve the quality and utility of historical records
• Maintain present ENSO observing system into the future
• Continue promoting intercomparison studies (ENSO metrics)
• Isolate the main sources of model error, guided by theory (BJ), observations, and rigourous evaluation of the models, including tests in seasonal forecast mode (talk by Benoît Vannière)
A few challenges lying ahead:
• Should we revisit the role of atmosphere processes in ENSO theory (non-linear feedbacks, WWB, ...) ?
• Are observational records long/precise enough ?
Thank you !
Other challenges:
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ENSO atmosphere feedbacks: what about
12 -18 0.9
3.9 -4.9 0.8
4.4 -5.1 0.95
6.2 -5.7 1.15
7.5 -4.3 1.13
7.8 -5 1.18
El Niño
Amplitude
Varying the horizontal atmosphere resolution in IPSL-CM4
Obs
10-3 N/m2/C W/m2/C oC
R97E (3.75 x 2.5)
R99A (3.75 x 1.8)
R149A (2.5 x 1.8)
R1414A (2.5 x 1.2)
R1914E (1.8 x 1.2)
AGCM resolution affects (but not ):• Atmosphere grid can “see” ocean equatorial wave guide
• Added non-linearities in AGCM: better circulation (on/off convection behavior reduced,...)
(Zonal x Merid.)
• SW (blue bars) and LH (green bars) components dominate.• The SW feedback shows the greatest model diversity – linked to cloud
uncertainties in East Pacific region (e.g. Sun et al. 2009).
• Split net feedback into four components: shortwave (SW), longwave (LW), latent heat (LH) and sensible heat (SH) flux feedbacks.
Lloyd et al. (2009)
Heat flux feedback components
CLIVAR ENSO workshop, Paris, Nov. 2010 28
SST threshold for ascent/convection
• Bin vertical velocity at 500 hPa in SST bins
• Ascent/Convective threshold when regime switches from subsidence to convection (Bony et al. 2004)
Subsidence
Convection
• AMIP KE threshold larger by 1oC / AMIP TI (same SST !)
• KE threshold unchanged
• TI threshold even lower: 2oC difference with KE !
TI physics triggers convection too easily which prevents ENSO from developping
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Bjerknes feedback μ
• Bjerknes feedback in AMIP KE and AMIP TI similar and within re-analysis estimates
• Bjerknes feedback in KE = 1/3rd of that of AMIP KE
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Heat flux feedback α
• Heat flux feedback α in AMIP TI is double that of AMIP KE (and closer to re-analysis estimates)
• Heat flux feedback in KE = half of that of AMIP KE
• Value unchanged in TI
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Evolution of El Nino composite Wind Stress
• Wind stress anomaly (shading)• SST anomaly (solid contours)• 3 year composite at equator
• Bjerknes feedback in AMIP KE and AMIP TI similar
• Bjerknes feedback in KE much weaker than in AMIP KE
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Evolution of El Nino composite Heat Flux
• Heat flux anomaly (shading)• SST anomaly (solid contours)• 3 year composite at equator
• Negative Heat Flux anomaly in AMIP TI much larger than in AMIP KE
• Negative Heat Flux anomaly during positive SST anomaly
• Negative Heat Flux anomaly in KE too far west and weak
• SHF main contributor to differences between AMIP TI and AMIP KE (LHF also contributes)
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Mean seasonal cycle at Eq.
• AMIP KE performs rather well• Convection in AMIP TI too strong
and triggered too soon
• Biases amplified in coupled mode
• Semi-annual cycle in TI
• Equinoctial Central American monsoon too strong in TI (Braconnot et al. 2007)
• Wind stress (shading)• SST (solid contours)• Precipitation (3 and 8 mm/day dashed)