isaac held, beijing, 2011 thank you for your invitation and kind hospitality ! seminars:
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Isaac Held, Beijing, 2011 Thank you for your invitation and kind hospitality ! Seminars: Monday: Time Scales of Global Warming Tuesday: Simulating the climatology, interannual variability, and trends of tropical cyclone genesis - PowerPoint PPT PresentationTRANSCRIPT
Isaac Held, Beijing, 2011 Thank you for your invitation and kind hospitality !
Seminars:
Monday: Time Scales of Global Warming
Tuesday: Simulating the climatology, interannual variability, and trends of tropical cyclone genesis
Wednesday: The hydrological cycle and global warming
Thursday: Shifting latitude of surface westerlies – a case study in utilizing a hierarchy of climate models (understanding climate by starting with comprehensive models and gradually removing layers of complexity)
Friday: Problems in quasi-geostrophic dynamics (understanding climate by starting with very idealized models and gradually adding layers of complexity)
Probing the fast and slow components of global warming by returning abruptly to pre-industrial forcing
Held, Winton, Takahashi, Delworth, Zeng, Vallis, J. Clim 2010
Importance of Ocean Heat Uptake Efficacy to Transient Climate Change
Winton, Takahashi, Held, J. Clim, 2010
Time scales of climate responses, climate sensitivity, and the recalcitrant component of global warming
Isaac HeldBeijing, 2011
Uncertainty in climate sensitivity has not been reduced appreciably in past 30 years
2 well-known assessments reach similar conclusions :“Charney report” (1979) IPCC/AR4 (2006)
Equilibrium global mean surface temperature warming due to doubling of CO2
is most probably in the range 1.5-4.5 K
Knutti+Hegerl, 2008
Assorted estimates of equilibrium sensitivity
23
Ultra-fast SlowFast Ultra-slow
Months(Atmosphere)
a few years(mixed layer)
Multiple centuries(deep ocean)
Time scales of climate response
Equilibrium climate sensitivity:Double the CO2 and wait for the system to equilibrate
Transient climate response:Increase CO2 1%/yr and examine climate at the time of doubling
t
CO2 forcing
Heat uptake by deep ocean
W/m2
Typical setup – increase till doubling – then hold constant
After CO2 stabilized, warming of near surface can be thought of as due to reduction in heat uptake
T response
11
2 43 51
2
1.5
2.5
Equilibrium sensitivity
CMIP3/AR4 models
Transientresponse
Not well correlated across models – equilibrium response brings into play feedbacks/dynamics (especially in subpolar oceans)
that are suppressed in transient response19
Increase CO2 by 1%/yr ; global mean warming at the time of doubling = Transient Climate Response (TCR)
Histogram of TCR/TEQ
for AR4 models
Response of global mean temperature in GFDL’s CM2.1 to instantaneous doubling of CO2
Equilibrium sensitivity 3.4KTransient response 1.5K
€
T = (1.5K)e−t /(4 yrs)
Fast response
Slow responseevident onlyafter 80 yrs
20
€
cF
dT
dt= −βT − γ(T − TD ) + F
cD
dTD
dt= γ(T − TD ) forcing
Heat exchangebetween mixed layerand deep ocean
Mixed layerHeat capacity
Deep ocean heat capacity
€
T = TD =F
βin equilibrium
€
cF
dT
dt= −βT − γ(T − TD ) + F
cD
dTD
dt= γ(T − TD )
€
τF =cF
β + γForcing varies on time scales longer than
€
⇒ T ≈F
β + γ+
γTD
β + γ
€
⇒ cD
dTD
dt= −
βγ
β + γTD +
γ
β + γF
€
⇒ TD ≈ 0 T ≈F
β + γ
TCR /TEQ ≈β
β + γ
€
τF =cF
β + γForcing varies on time scales longer than
€
τD =cD
β
β + γ
γand time scales shorter than
€
“Intermediate regime”
OLR
SW down
SW up
total
Forcing computed from differencing TOA fluxes in two runs of a model (B-A)B = fixed SSTs with varying forcing agents; A fixed SSTs and fixed forcing agents
51
Temperature change averaged over 5 realizations of coupled model
52
€
CdT
dt= F −αT; α =1.6 Wm−2 /K;
C
α= 4years
Fit with
53
Forcing with no damping
Forcing (with no damping) fits the trend well, if you use transient climate sensitivity, which takes into account magnitude/efficacy of heat uptake
54
Observations (GISS)
GFDL’s CM2.1 with well-mixed greenhouse gases only
Global mean temperature change
46
“It is likely that increases in greenhouse gas concentrations alone would have caused more warming than observed because volcanic and anthropogenic aerosols have offset some warming that would otherwise have taken place.” (AR4 WG1 SPM).
Observations (GISS)
GFDL’s CM2.1 with well-mixed greenhouse gases only
Global mean temperature change
46
A1B-CM2.1
€
T ≈F
β + γ+
γTD
β + γ
€
⇒ T ≈γTD
β + γ
Return instantaneously to pre-industrial forcing ( F = 0)
the “Recalcitrant” warming
Relaxation to recalcitrant warming
5 years
3 years
Normalized to unity over the globe
Normalized to unity over the globe
Fast
Slow“Recalcitrant”
Control drift
Sea level response due to thermal expansion
Sea level response mostly recalcitrant
€
CdT
dt= F − βT ≡ N
TEQ = F β
N /F =1− T /TEQ
N/F
T/TEQ
The simplest linear model
If correct, evolution should be along the diagonal
15
Suppose you have two forcing agents C02 and B (something else)
leading to radiative forcing FC02 and FB
.
But suppose the global mean temperature responses TC02 and TB
are not proportional to the the radiative forcing
Following Hansen, define efficacy B (using CO2 as a standard)
€
B =TB /FB
TC 02 /FC 02
Efficacy can orten be understood in terms of the spatial structure of the response, Coupling of surface with troposphere is weaker in high latitudes => harder to radiate away a perturbation
=> Radiative restoring strength is weaker for responses thatare larger in higher latitudes
=> Forcings with stronger high latitude responses have larger efficacy
Forcings with stronger high latitude responses have larger efficacy
Think of heat uptake as a forcing – ie
replace F = T + H or T = F + H
with T = F + H H with H > 1
Equivalently,
T = TF + TH = F/ - H/H
With H = /H
€
cF
dT
dt≈ 0 ≈ −βT − γ(T − TD ) + F = −βT − H + F
CM 2.0
CM 2.1
Efficacy
Eff
icie
ncy
Heat uptake = T ; = efficiency of heat uptake
Cooling due to heat uptake = T ; = efficacy of heat uptake
Knutti+Hegerl, 2008
Assorted estimates of equilibrium sensitivity
23
(GFDL CM2.1 -- Includes estimates of volcanic and anthropogenic aerosols, as well as estimates of variations in solar irradiance)
Models can produce very good fits by including aerosol effects, but models with
stronger aerosol forcing and higher climate sensitivity are also viable (and vice-versa) 45
Observational constraints
•20th century warming•1000yr record •Ice ages – LGM•Deep time
•Volcanoes•Solar cycle•Internal Fluctuations
•Seasonal cycle etc36
Observed total solar irradiance variations in 11yr solar cycle (~ 0.2% peak-to-peak)
42
Camp and Tung, 2007 => 0.2K peak to peak(other studies yield ~0.1K)
Seems to imply largesensitivity
4 yr damping time
1.8K (transient) sensitivity
Only gives 0.05 peak to peak
43
Global mean cooling due to Pinatubo volcanic eruption
Range of ~10 ModelSimulationsGFDL CM2.1
Courtesy of G Stenchikov
Observationswith El Ninoremoved
Relaxation time after abrupt cooling contains information on climate sensitivity
40
Yokohata, et al, 2005
Low sensitivity model
High sensitivity model
Pinatubo simulation
41
Response to pulse of forcing (volcano), F(t):
2-box model:
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Tdt =Fdt∫
β + γ0
fast
∫ Tdt =Fdt∫β0
∞
∫
Stenchikov, et al 2009
€
T0(1− e−t /τ )
T0 = 2.5K • yrs
τ = 4.2yrs
Near surface air temperature response (20 member ensemble)Courtesy of Stenchikov, et al
Forcing
TOA flux
Responsewith exponential fit
Wm-2yr
Integrated forcing and response
CM2.1 Pinatubo summary-- fast response --
€
Tdt =Fdt∫
β + γ0
fast
∫ €
Tdt = 2.8 Kyr0
fast
∫
5.02.8
2.2
CM2.1 Pinatubo summary-- fast response --
Forcing (W/m2)yr
€
Tdt =Fdt∫
β + γ0
fast
∫
€
Fdt∫
€
Tdt0
fast
∫
€
Tdt0
fast
∫
Heat uptake (W/m2)yr
Radiative restoring (W/m2)yr
€
Tdt = 2.8 Kyr0
fast
∫
Pinatubo =>
~ 1.0 (W/m2)/K
~ 0.8 (W/m2)/K
1%/yr CO2 increase =>
~ 1.7 (W/m2)/K
~ 0.7 (W/m2)/K
Can we use interannual variability to determine the strength of the radiative restoring?
Model results (CM2.1) raise some roadblocks
Longwave regression across ensemble (collaboration with K. Swanson)
LW
Wm-2K-1
All-forcing20th century
61
year
Following an idea of K. Swanson, take a set of realizations of the 20th century from one model, and correlate global mean TOA with surface temperature across the ensemble
Longwave regression across ensemble, collaboration with K. Swanson
All-forcing20th century
A1B scenario
62
LW
Wm-2K-1
Longwave regression across ensemble, collaboration with K. Swanson
63
LW
Wm-2K-1
Estimate of noise in this statistic from 2000yr control run
Longwave regression across ensemble, collaboration with K. Swanson
Well-mixedgreenhouse gases only
64
LW
Wm-2K-1
Longwave regression across ensemble, collaboration with K. Swanson
Independent set of 10 A1B runs
65
LW
Wm-2K-1
Longwave regression across ensemble, collaboration with K. Swanson
Independent set of 10 A1B runs
But we can fit the models 20th century simulations without time-dependence in OLR-temperature relationship!
May be telling us that ENSO is changing, but with no obvious connection to global sensitivity
65
LW
Wm-2K-1
I suspect that:
Transient climate sensitivity can be constrained more tightly that it currently is, despite the uncertainty in
aerosol forcing
Volcanic responses may play a central role in tightening this constraint, along with the observed
warming trend
Less hopeful about use of interannual variability
Solar cycle response has some mysteries
Thank you for listening
21st century emissions commitment
AR4/IPCC
Shortwave regression across ensemble, following K. Swanson 2008
Wm-2K-1
All-forcing20th century
Following an idea of K. Swanson, take a set of realizations of the 20th century from one model, and correlate global mean TOA with surface temperature across the ensemble
56
Shortwave regression across ensemble, following K. Swanson 2008
All-forcing20th century
A1B scenario
Wm-2K-1
Is this a sign of non-linearity? What is this?
57
Shortwave regression across ensemble, following K. Swanson 2008
All-forcing20th centuryWm-2K-1
A1B scenario
90%
Estimate of noise in this statistic from 2000yr control run58
Shortwave regression across ensemble, following K. Swanson 2008
Wm-2K-1
Well-mixedgreenhouse gases only
59
Shortwave regression across ensemble, following K. Swanson 2008
Wm-2K-1
Independent set of 10 A1B runs
60