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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

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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

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)


Time scales of climate responses, climate sensitivity,

and the recalcitrant component of global warming

Isaac Held

Beijing, 2011

Importance of Ocean Heat Uptake Efficacy to Transient Climate Change

Winton, Takahashi, Held, J. Clim, 2010

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


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



Time scales of climate response

Ultra-fast

Fast

Slow

Ultra-slow

Months

(Atmosphere)

a few years

(mixed layer)

Multiple centuries

(deep ocean)


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

Typical setup – increase till doubling – then hold constant

CO2 forcing

T response

W/m2

t

Heat uptake by deep ocean

After CO2 stabilized, warming of near surface

can be thought of as due to reduction in heat uptake

11


CMIP3/AR4 models

2.5

2

Transient

response

1.5

1

2

3

4

5

Equilibrium sensitivity

Not well correlated across models – equilibrium response brings into play

feedbacks/dynamics (especially in subpolar oceans)

that are suppressed in transient response

19


Histogram of TCR/TEQ

for AR4 models

Increase CO2 by 1%/yr ; global mean warming at the time of doubling

= Transient Climate Response (TCR)


Response of global mean temperature in GFDL’s CM2.1

to instantaneous doubling of CO2

Equilibrium sensitivity 3.4K

Transient response 1.5K

Slow response

evident only

after 80 yrs

Fast response

20


forcing

Mixed layer

Heat capacity

Heat exchange

between mixed layer

and deep ocean

Deep ocean

heat capacity

in equilibrium



Forcing varies on time scales longer than

and time scales shorter than

“Intermediate regime”


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

total

OLR

SW down

51

SW up



Fit with model

53


Forcing (with no damping) fits the trend well, if you use model

transient climate sensitivity,

which takes into account

magnitude/efficacy of heat uptake

Forcing with no damping

54


GFDL’s CM2.1 with well-mixed greenhouse gases only model

Global mean

temperature

change

Observations

(GISS)

46


GFDL’s CM2.1 with well-mixed greenhouse gases only model

Global mean

temperature

change

Observations

(GISS)

“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).

46


A1B-CM2.1 model


Return instantaneously to pre-industrial forcing model

( F = 0)

the “Recalcitrant” warming


Relaxation to recalcitrant warming model

5 years

3 years



Normalized to unity over the globe model

Fast

Slow

“Recalcitrant”


Sea level response due to thermal expansion model

Control drift

Sea level response mostly recalcitrant


The simplest linear model model

If correct, evolution should be along the diagonal

N/F

T/TEQ

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 eB (using CO2 as a standard)


Efficacy else) 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 that

are 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 = bT + H or bT = F + H

with bT = F + eH H with eH > 1

Equivalently,

T = TF + TH = F/b - H/bH

With bH = b/eH


Heat uptake = efficacygT ; g = efficiency of heat uptake

Cooling due to heat uptake = egT ; e = efficacy of heat uptake

Efficiency

CM 2.1

CM 2.0

Efficacy


Assorted estimates of equilibrium sensitivity efficacy

Knutti+Hegerl, 2008

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 anthropogenic aerosols,

  • 20th century warming

  • 1000yr record

  • Ice ages – LGM

  • Deep time

  • Volcanoes

  • Solar cycle

  • Internal Fluctuations

  • Seasonal cycle etc

36



Camp and Tung, 2007 => cycle (~ 0.2% peak-to-peak)

0.2K peak to peak

(other studies yield ~0.1K)

Seems to imply large

sensitivity

4 yr damping time

Only gives

0.05 peak to peak

1.8K (transient) sensitivity

43


Global mean cooling due to Pinatubo volcanic eruption cycle (~ 0.2% peak-to-peak)

Observations

with

El Nino

removed

Range of

~10 Model

Simulations

GFDL CM2.1

Courtesy of G Stenchikov

40

Relaxation time after abrupt cooling contains information on climate sensitivity


Low sensitivity model cycle (~ 0.2% peak-to-peak)

Pinatubo simulation

High sensitivity model

Yokohata, et al, 2005

41


Response to pulse of forcing (volcano), F(t): cycle (~ 0.2% peak-to-peak)

2-box model:


Stenchikov, et al 2009 cycle (~ 0.2% peak-to-peak)


Near surface air temperature response (20 member ensemble) cycle (~ 0.2% peak-to-peak)

Courtesy of Stenchikov, et al


Integrated forcing and response cycle (~ 0.2% peak-to-peak)

Wm-2yr

Response

with exponential fit

TOA flux

Forcing


CM2.1 Pinatubo summary cycle (~ 0.2% peak-to-peak)

-- fast response --


Radiative restoring cycle (~ 0.2% peak-to-peak)

(W/m2)yr

2.8

Forcing (W/m2)yr

5.0

Heat uptake (W/m2)yr

2.2

CM2.1 Pinatubo summary

-- fast response --


Pinatubo cycle (~ 0.2% peak-to-peak) =>

b ~ 1.0 (W/m2)/K

g ~ 0.8 (W/m2)/K

1%/yr CO2 increase =>

b ~ 1.7 (W/m2)/K

g ~ 0.7 (W/m2)/K


Can we use interannual variability to determine cycle (~ 0.2% peak-to-peak)

the strength of the radiative restoring?

Model results (CM2.1) raise some roadblocks


Longwave regression across ensemble cycle (~ 0.2% peak-to-peak)

(collaboration with K. Swanson)

All-forcing

20th century

bLW

Wm-2K-1

year

61

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 cycle (~ 0.2% peak-to-peak),

collaboration with K. Swanson

All-forcing

20th century

A1B scenario

bLW

Wm-2K-1

62


Longwave regression across ensemble cycle (~ 0.2% peak-to-peak),

collaboration with K. Swanson

bLW

Wm-2K-1

63

Estimate of noise in this statistic from 2000yr control run


Longwave regression across ensemble cycle (~ 0.2% peak-to-peak),

collaboration with K. Swanson

bLW

Wm-2K-1

Well-mixed

greenhouse gases only

64


Longwave regression across ensemble cycle (~ 0.2% peak-to-peak),

collaboration with K. Swanson

bLW

Wm-2K-1

Independent set of 10

A1B runs

65


Longwave regression across ensemble cycle (~ 0.2% peak-to-peak),

collaboration with K. Swanson

But we can fit the models 20th century simulations

without time-dependence in OLR-temperature relationship!

bLW

Wm-2K-1

Independent set of 10

A1B runs

65

May be telling us that ENSO is changing,

but with no obvious connection to global sensitivity


I suspect that: cycle (~ 0.2% peak-to-peak)

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


21 st century emissions commitment ar4 ipcc
21 cycle (~ 0.2% peak-to-peak)st century emissions commitmentAR4/IPCC


Shortwave regression across ensemble, cycle (~ 0.2% peak-to-peak)

following K. Swanson 2008

All-forcing

20th century

Wm-2K-1

56

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


Shortwave regression across ensemble, cycle (~ 0.2% peak-to-peak)

following K. Swanson 2008

A1B scenario

All-forcing

20th century

Wm-2K-1

57

Is this a sign of non-linearity? What is this?


Shortwave regression across ensemble, cycle (~ 0.2% peak-to-peak)

following K. Swanson 2008

A1B scenario

All-forcing

20th century

Wm-2K-1

90%

58

Estimate of noise in this statistic from 2000yr control run


Shortwave regression across ensemble, cycle (~ 0.2% peak-to-peak)

following K. Swanson 2008

Well-mixed

greenhouse gases only

Wm-2K-1

59


Shortwave regression across ensemble, cycle (~ 0.2% peak-to-peak)

following K. Swanson 2008

Independent set of 10

A1B runs

Wm-2K-1

60


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