Overview The Slab Ocean El Nino How good are the CMIP models Nonlinearity Seasonality spring barrier CP vs EP Teleconnections delayed feedback Climate Change Overview The Slab Ocean El Nino ID: 571369
Download Presentation The PPT/PDF document "Dommenget et al." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Dommenget et al.Slide2
Overview
The Slab Ocean El Nino
How good are the CMIP models?
Non-linearity
Seasonality (spring barrier)CP vs. EP
Teleconnections delayed feedback
Climate ChangeSlide3
Overview
The Slab Ocean El Nino
How good are the CMIP models?
Non-linearity
Seasonality (spring barrier)CP vs. EP
Teleconnections delayed feedback
Climate ChangeSlide4
The Odd ENSO
Atmospheric GCM / Slab ocean
Dommenget [2010]
Slab Ocean
No Ocean Dynamics
(
Rossby
/Kelvin waves)
No Thermocline variability
Only heat capacity
All lateral dynamics are from AtmosphereSlide5
20 slab ocean models
SST standard deviation
4 slab ocean models
Dommenget [2010]
The Slab Ocean El NinoSlide6
Dommenget [2010]
The Slab Ocean El NinoSlide7
Neutral mean state
Initial phase
Mature phase
Decay phase
Dommenget [2010]
The Slab Ocean El Nino DynamicsSlide8
SST mean state dependence
El Nino
OFF:
20 modelsEl Nino ON: 4 models
Dommenget [2010]
El Nino ON: mean difference
The Slab Ocean El NinoSlide9
Mean SST RMSE relative to ensemble mean
ECHAM5-slab
CMIP3 slabs (13 models)
Mean SST of runs with
stdv
NINO3 >0.5K
Ratio SST
stdv
NINO3 [cold NINO3]/[all]
CMIP3 AGCM-slab-oceansSlide10
CGCM3.1(T63)
GISS−AOM
ACCESS1
CCSM4
GFDL−ESM2MNorESM1−ME
Negative cloud/sensible feedback
Weak/normal cold tongue
Positive cloud/sensible feedback
Strong cold tongue
East-2-West propagation
BCCR
−BCM2.0
CNRM−CM3
GISS−EH
INM−CM3.0
BCC
−CSM1−1
CSIRO−Mk3.6
INMCM4
CMIP3
CMIP5
Obs.
Slab Ocean
[
D
ommenget et al. 2014]
CMIP Models: Simulated
heat flux feedbacks Slide11
El Nino SST variability can exist in models without ocean dynamics
This exist in many, if not all AGCMs coupled to slab oceans
These feedbacks exist in many CGCMs
It exist if the eq. cold tongue is very cold These feedbacks exist in observations
The El Nino in CGCMs follows different dynamics, some are atmospherically driven (at least partly)
C
loud and turbulent flux feedbacks can create an
atmospheric
El Nino
Summary: The Slab Ocean El NinoSlide12
Overview
The Slab Ocean El Nino
How good are the CMIP models?
Non-linearity
Seasonality (spring barrier)CP vs. EP
Teleconnections delayed feedback
Climate ChangeSlide13
El Nino Delayed Negative Feedback
Textbook knowledge:
ENSO Delayed negative feedback is due to ocean dynamics
(Rossby/Kelvin waves) ENSO teleconnections influence remote regions
Remote regions do not influence ENSO dynamics
+
-
-
+
[
Kug
and Kang 2006]
[Dommenget et al. 2006]
[Jansen et al. 2009]
[Ham et al. 2013]
… many others
Recent findings: Slide14
Simulation with decoupled regions
ACCESS-
ReOsc
-Slab with decoupled regions (500yrs)
Delayed Negative
Feedback for
TSlide15
NINO3 SST lag-lead cross-
corelationSlide16
NINO3 SST auto-correlationSlide17
Remote regions influence ENSO dynamics, variability and pattern.
The remote regions are a delayed negative feedback.
About 40% of ENSO delayed negative feedback is from the coupling to remote regions.
Summary: Teleconnections delayed feedbackSlide18
Overview
The Slab Ocean El Nino
How good are the CMIP models?
Non-linearity
Seasonality (spring barrier)CP vs. EP
Teleconnections delayed feedback
Climate ChangeSlide19
El Nino non-linearity
El
Ninos
are stronger than La Ninas
SST has positive skewnessEl NinoLa NinaSlide20
A
Strong El Niño
B
Strong La Niña
C Weak El Niño
D
Weak La Niña
Diff. Strong
A
-
B
Diff. Weak
D
-
C
Diff. La Niña
D
-
B
Diff. El Niño
C
-
A
EOF-2
Composites are normalized by the mean NINO3.4 SST
[K/K]
Pattern non-linearitySlide21
Strong El Niño
Weak El Niño
Weak La Niña
Strong La Niña
PC-2
[
Takahashi et al. 2011]
[
Dommenget et al. 2013]
Pattern non-linearitySlide22
El Niño
La Niña
Idealized ENSO patternsSlide23
difference
Strong El Niño
Strong La Niña
Composites are normalized by the mean NINO3.4 SST at lag 0
[K/K]
Time Evolution non-linearitySlide24
t
ime evolution difference
Pattern difference
CMIP model non-linearity: pattern vs. time evolutionSlide25
Wind response
Wind-SST non-linearity
Thermocline depth evolution
dashed: linear
solid: non-linear
Model simulation with linear oceanSlide26
100 perfect model forecast
Anomaly correlation skill
Jan.
Dec.
RECHOZ Model ForecastsWind-SST non-linearitySlide27
Pattern non-linearity:
strong El
Ninos
are to the east strong La Ninas are further westVice versa for weak events
Time evolution non-linearity: strong El Ninos are followed by La
Ninas
strong La
Ninas
are preceded by El
Ninos
Vice versa
for weak
events
Wind Feedback non-linearity:
strong El
Ninos
are forced by stronger zonal winds
Strong La
Ninas
are forced by stronger thermocline depth anomalies
The stronger thermocline depth is caused by the non-linear zonal wind
Predictability non-linearity:
strong La
Ninas
are better predictable than strong El
Ninos
Summary: non-linearitySlide28
Overview
The Slab Ocean El Nino
How good are the CMIP models?
Non-linearity
Seasonality (spring barrier)CP vs. EP
Teleconnections delayed feedback
Climate ChangeSlide29
Observed STDV NINO3 SST
Calendar month
Standard deviation [C]
Seasonality (spring barrier)Slide30
SST lags time [
mon
] SST leads
Observed seasonal cross-
correl
: NINO3 SST vs. tendenciesSlide31
SST lags time [
mon
] SST leads
Observed seasonal cross-correl: NINO3 SST vs. tendenciesSlide32
standard deviation NINO3 SST [
o
C
]Model STDV NINO3 SSTSlide33
State dependent Cloud feedback
Cloud cover (ISCCP) [%]
Eq.
Pacific seasonal
mean state & cloud feedbackSlide34
SST standard deviations of toy models
(NINO3)
Simple cloud-short-wave feedback modelSlide35
Seasonally changing cloud feedbacks are likely to contribute to the seasonal phase locking of
ENSO.
The
warmer mean SST supports stronger negative cloud feedbacks.Slab ocean and ENSO-recharge oscillator both have similar seasonal phase locking.Both are similar to observed.Both contribute to the total eq. SST variability.
Summary: Seasonality (spring barrier)Slide36
Overview
The Slab Ocean El Nino
How good are the CMIP models?
Non-linearity
Seasonality (spring barrier)CP vs. EP
Teleconnections delayed feedback
Climate ChangeSlide37
ENSO diversity: CP vs. EP
East Pacific (EP) El Nino
Central Pacific (EP) El NinoSlide38
El Nino
Modoki
[Ashok et al. 2007]
Observations
ENSO diversitySlide39
El Nino
Modoki
[Ashok et al. 2007]
Observations
ENSO diversity
A Cautionary Note
Don’t trust Google!
EOF-
mode = statistical mode
≠
physical mode
An EOF-mode is a superposition of many physical
modes
EOF-modes are not independent of each other
El Nino
Modoki
(EOF-2)
is
not
a physical modeSlide40
CP El Nino / El
Nino
Modoki
:
Our Hypothesis:ENSO diversitySlide41
Strong El Niño
Weak El Niño
Weak La Niña
Strong La Niña
PC-2
[
Takahashi et al. 2011]
[
Dommenget et al. 2013]
ENSO diversity: non-linearitySlide42
Observations
Slab
(red noise)
ReOsc
-Slab
ReOsc
1
st
EOF 100%
[
Y
u et al. 2016]
ENSO diversity: Red NoiseSlide43
ReOsc
-Slab
CMIP models
[
Y
u et al. 2014, submitted]
1
st
DEOF 14.6%
obs
5.3% model
ENSO diversity simulations: missing patternSlide44
CP El Nino / El
Nino
Modoki
:
Red Noise: A single mode (ReOsc) interacting with Slab Ocean
Non-Linearity
:
El
Ninos
and La
Ninas
have different patterns due to wind-
sst
interaction.
Dynamics
:
Some eq. ocean dynamics of smaller scales; GCMs can not simulate well.
Summary: ENSO diversitySlide45
Overview
The Slab Ocean El Nino
How good are the CMIP models?
Non-linearity
Seasonality (spring barrier)CP vs. EP
Teleconnections delayed feedback
Climate ChangeSlide46
ENSO patternSlide47
EOF-modes: Models vs. Obs. error
Model Errors In EOF-modes
RMSE
EOF
(short time scales; <5yrs)
[% of eigenvalues]
RMSE
EOF
(long time scales; >5yrs)Slide48
ENSO processes
T
damping
h
damping
c
oupling
T
to
h
c
oupling
h
to
T
noise forcing
T
noise forcing
h
T
damping (ocean)
w
ind response
net heat responseSlide49
CMIP model process errors
m
ost important
l
east important
h
damping
c
oupling
T
to
h
c
oupling
h
to
T
noise forcing
T
noise forcing
h
T
damping (ocean)
heat response
w
ind response
SST
stdv
observed
modelsSlide50
ENSO pattern is still biased
ENSO processes are mostly too weak.
Models look tuned to fit observed.
Models are improving a little bit.Summary: How good are the CMIP models?Slide51
Overview
The Slab Ocean El Nino
How good are the CMIP models?
Non-linearity
Seasonality (spring barrier)CP vs. EP
Teleconnections delayed feedback
Climate Change