Observational evidence
for the cloud and water vapor
feedbacks
A. E. Dessler
Department of Atmospheric Sciences
Texas A&M University
feedbacks
CO2 only
2
∆Tf = ∆Ti + g∆Ti + g2∆Ti + g3∆Ti + g4∆Ti + …
CO2
increase
Ts
increase
Atmospheric
humidity
increases
3
Water vapor feedback
2003
2004
2005
Year for q
2006
2007
2008
2009
20
0.4
10
0.2
Ts
0.0
0
-0.2
-10
-0.4
-0.6
-0.8
-1.0
-20
Tropical average (30N-30S)
Surface Temperature
AIRS q at 350 hPa
ERA-interim q
2003
2004
2005
-30
2006
2007
Year for Ts
2008
2009
q (g/kg)
Tropical Anomaly (°C)
2010
• Determine the strength of the climate’s
(fast) feedbacks from observations over the
last 10 years
• Can models reproduce this?
• What does this tell us about future
warming?
5
6
CERES top-of-atmosphere (TOA) net flux
SSF, 1-deg monthly avg., Ed. 2.5
1.5
(a)
2
∆Rall-sky (W/m )
1.0
0.5
0.0
-0.5
-1.0
2000
2002
2004
2006
2008
2010
Year
all fluxes in this analysis are downward positive
7
∆Rall-sky = ∆RT +∆Rq +∆Ralbedo +∆Rcloud+∆F
1.5
(a)
2
∆Rall-sky (W/m )
1.0
0.5
0.0
-0.5
-1.0
2000
2002
2004
2006
2008
2010
Year
8
To calculate feedbacks
• Soden et al. kernels
• CERES all-sky fluxes (for cloud feedbacks)
• Reanalysis (ERA-Interim, MERRA) for
other feedbacks
• these are “the observations”
9
water vapor anomaly
Use pre-computed kernels from Soden
et al., 2008, see also Shell et al. [2008]
“the observations”
10
to determine ∆Rcloud
•
start with cloud radiative forcing (∆CRF);
∆CRF = (∆Rclear-sky - ∆Rall-sky)
•
∆CRF can also be affected by changes in T,
q, albedo, radiative forcing
•
Soden et al. [2008] adjustment to get
∆Rcloud from ∆CRF
∆Rcloud =
1.5
1.5
Temperature
2
W/m /K
0.5
0.0
-0.5
1.5
-1.5
2000
1.5
2002
0.0
-0.5
-1.0
∆RcloudLW
-1.5
2000
1.5
1.0
2
2004
1.0
2006
2008
-0.5
2010
2000
1.5
1.0
0.5
2
W/m /K
2
W/m /K
0.5
0.0
2002
0.0
2004
2006
-1.0
2006
2000
2008
2010
2002
2000
2004
2002
2006
2004
2006
2008
2010
12
-1.5
2004
2010
∆RcloudSW
∆Ralbedo
2002
2008
0.0
-0.5
2000
2010
SW clouds
0.0
-1.0
2008
0.5
-1.0
albedo
0.5
2002
-0.5
-0.5
2004
∆Rq
-1.5
LW clouds
1.0
0.5
-1.0
∆RT
Water vapor
2
-1.0
W/m /K
1.0
W/m /K
2
W/m /K
1.0
2006
2008
2010
1.0
0.8
2
W/m /K
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
2000
∆Rq
2002
2004
2006
2008
2010
2008
2010
0.2
Feedback is defined
as change in ∆R per unit
change in ∆Ts
0.1
K
0.0
-0.1
-0.2
-0.3
-0.4
2000
Surface temperature
2002
2004
2006
13
∆Rq
Regress energy trapped by e.g., q
vs. surface temperature
Slope = feedback
∆Ts
downward fluxes are positive
positive flux = positive feedback
Temperature
Water vapor
Clouds
Ice-albedo
∆Ts
CERES + ERA-Interim
∆Ts
15
3
Feedback (W/m2/K)
2
1
0
-1
-2
-3
ERA
MERRA
-4
-5
T
Σ
WV
albedo
cloud
LW cloud
f = -1.1 ± 0.9 W/m2/K
16
SW clo
3
Feedback (W/m2/K)
2
1
0
-1
-2
-3
ERA
MERRA
-4
-5
T
Σ
WV
albedo
cloud
LW cloud
SW cloud
f = -1.1 ± 0.9 W/m2/K
ΔT = ∆F / (Σ f)
17
3
Feedback (W/m2/K)
2
1
0
-1
-2
-3
ERA
MERRA
-4
-5
T
Σ
WV
albedo
cloud
LW cloud
SW cloud
f = -1.1 ± 0.9 W/m2/K
ΔT2x = 3.7 W/m2 / (Σ f) = 1.9 K to ∞
Best estimate of 3.4 K
18
What I’m going to cover
• Determine the strength of the climate’s
(fast) feedbacks from observations over the
last 10 years
• Can models reproduce this?
• What does this tell us about future
warming?
19
To test models ...
• Observations are dominated by ENSO
• Compare to pre-industrial control
simulations
• Runs hundreds of years long
• Compare observations to average of
ensemble of 14 models
20
Comparison of global avg. feedbacks
3
Feedback (W/m2/K)
2
1
0
-1
-2
ERA
MERRA
PI control
-3
-4
-5
T
WV
albedo
PI control = ensemble avg. of 14 models
cloud
LW cloud
21
SW clo
water vapor feedback
Temperature feedback
22
ΔR
ΔTS
4.5
(a)
4.0
3.5
3.0
2.5
2.0
1.5
B
C
D
E
F
G
H
Model
AMIP models
I
J
K
L
Dessler 2008
A
ERA40
MERRA
λ=
q feedback (W/m^2/K)
5.0
23
Dessler and Wong, 2009
Water vapor feedback is primarily a “tropical” phenomenon
* ∆R determined by tropical UT ∆q
* tropical q controlled by tropical
surface temperatures
e.g., Minschwaner and Dessler, 2004
* ∆R (and the WV feedback) is
controlled by tropical surface T
Change in R per unit change in q(x,y,z): ∆R/∆q(x,y,z)
Fig. 2 of Soden et al., 2008
24
ΔR
ΔTS
q feedback (W/m^2/K)
5.0
4.5
(a)
4.0
3.5
3.0
2.5
2.0
1.5
A
€
B
C
D
E
!Rq vs. !TT (W/m^2/K)
ΔR
ΔTT
G
H
I
J
K
L
Model
5.0
4.5
F
(b)
4.0
3.5
3.0
2.5
2.0
€
B
C
D
E
F
G
Model
AMIP models
H
I
J
K
L
ERA40
MERRA
A
Dessler 2008
1.5
Spatial distribution of T + WV feedbacks
obs.
PI avg.
Temperature
Water vapor
26
obs.
PI avg.
Cloud feedback
27
solid line = observations
dotted line = PI models
28
obs.
PI avg.
29
30
How do models compare?
• Models do an excellent job reproducing the
global avg. feedbacks in obs.
• Patterns are pretty good, except for the net
cloud feedback
31
What I’m going to cover
• Determine the strength of the climate’s
(fast) feedbacks from observations over the
last 10 years
• Can models reproduce this?
• What does this tell us about future
warming?
32
ENSO vs. long-term warming
• Patterns of warming are different
• Long-term warming is forced, ENSO is
unforced
• While we hope the feedbacks are the
same ...
• ... they may be quite different
33
Comparison of global avg. feedbacks
3
Feedback (W/m2/K)
2
1
0
-1
-2
ERA
MERRA
PI control
A1B
-3
-4
-5
T
WV
albedo
PI control, A1B = ensemble avg. of 14 models
cloud
LW cloud
34
SW clo
ΔRq
λ=
ΔTS
+1
0
+1
+1
+1
0
€
smaller feedback
larger feedback
∆Rq for these two worlds is the same
∆Ts is different
Dessler and Wong, 2009
Spatial distribution of T + WV feedbacks
PI avg.
A1B avg.
Temperature
Water vapor
36
Each dot is a
particular climate
model
37
Conclusions
• Feedbacks from obs. suggest a climate sensitivity of
> 1.9°C, with a central value of 3.4°C
• Compared to pre-industrial model runs
– global avg. feedbacks agree well
– differences in the spatial pattern of the net cloud feedback
• Compared to A1B model runs
– global avg. feedbacks agree well
– differences in pattern
• No correlation between PI and A1B feedbacks
38
Clouds
• Cloud height
• Cloud amount
• Cloud optical depth
39
MODIS data (%/K)
Optical depth
40
Calculation of change in
TOA flux
• Use kernels calculated by Mark Zelinka
• ∆Rcloud=K(P,τ,α)*Δf(P,τ)
41
X
42
43
Flux
Month 3/00-2/10
44
Conclusions
• We are rapidly making good progress on
climate feedbacks
• Models do a good job of simulating the
feedbacks for ENSO variability
• Best we can do
• New work on cloud feedbacks should allow
us to take apart the feedback into its
constituent components
45