A Climate OSSE Study of Fingerprinting Longwave Forcing and Feedback with IR

and RO Data

Yi Huang, Stephen Leroy and James Anderson

School of Engineering and Applied Sciences

Harvard University

CLARREO Science Meeting, LaRC

July 7, 2010

Outline• Introduction

– Climate feedback uncertainty in AR4 models

– Feedback analysis methods

• PRP

• Sensitivity kernel

– Observing the feedbacks

• Fingerprinting with InfraRed (IR) and Radio Occultation (RO) data– Observation System Simulation Experiemnt (OSSE): CCCMA

2xCO2 experiment + MODTRAN

– IR vs. IR+RO

• Remaining challenges and future works– Signal detection

– Signal attribution

Feedback uncertainties in climate models

Bony et al. 2006

Water vapor (WV), clouds (C), lapse rate (LR), albedo (A)

Surface TChange

TOA RadiativeForcing

Planck Damping

Feedbacks

1)(R

Ts

Xi

Xi

RTs

Manabe and Wetherald 1998

Conventional Methods:

1) Partial Radiative Perturbation (PRP) method [Wetherald and Manabe 1988]

RXi = R(X1,…,Xi+dXi,…) – R(X1,…,Xi,…)

2) Radiative sensitivity kernel [Soden and Held 2005]

Pre-computed dR/dXi

? Observation

observable

Total change in OLR1

[W m-2]

wanted

CO2 T-surf T-atmos

W.V. Cloud

R =R

XiXi

Feedback analysis requires partitioning the total change signal into individual contributions

Synthesized, 2xCO2 experiment

IR spectral fingerprints

CO2

Surface temp.(Ts)

Tropospheric temp.(Ttrop)

Stratospheric temp.(Tstrat)

Tropospheric w.v.(qtrop)

Stratospheric w.v.(qstrat)

Low-cloud(Clow)

Mid-cloud(Cmid)

High-cloud(Chigh)

ii

i

Rf

F

fi: fingerprint of the i’th forcing or feedback

Ri: characteristic radiance spectrum

Fi: partial OLR change (forcing or feedback magnitude)

<…>: global average

CO2

Ts

Ttrop

Tstrat

qtrop

qstrat

Clow

Cmid

Chigh

f

y fa r

Optimal Detection

(Multi-pattern linear regression)

y: overall spectral radiance changesf: fingerprintsa: feedback magnitudesr: residual signals

yRadiance spectral change - observable

CO2

Ts

Ttrop

Tstrat

qtrop

qstrat

Clow

Cmid

Chigh

fSpectral changes across the globe

[cm-1]

y

CO2 T-surf T-atmos

W.V. Cloud

OSSE Setup• 2xCO2 experiment

– CFMIP archived 3-D atmospheric and cloud profiles of CCCMA– Climate change: difference between post- and pre-doubling steady

states

• Simulation of observation data– IR: MODTRAN– RO:

• Assessment– Truth: PRP method– IR vs. IR+RO

2refractivity: ( )

dry pressure: ( ) ( ') '

W

dd h

P Pn h a b

T Tg

P h n h dhaR

Similarity between some fingerprints leads to compensating errors.[Huang et al 2010 JGR]

IR-only OD results

CO2

Ts

Ttrop

Tstrat

qtrop

qstrat

Clow

Cmid

Chgh

“Truth” (PRP) OD OD-Truth

IR and RO fingerprints

ii

i

Rf

F

fi: fingerprint of the i’th forcing or feedback

Ri: characteristic radiance spectrum and log-dry pressure profile

Fi: partial OLR change (forcing or feedback magnitude)

<…>: global average

CO2

Ts

Ttrop

Tstrat

qtrop

qstrat

Clow

Cmid

Chigh

IR

RO

log-dry pressure profile

P

d(h)

dg

aRn(h ')dh '

h

Joint OD results

CO2

Ts

Ttrop

Tstrat

qtrop

qstrat

Clow

Cmid

Chgh Unit: W m-2

RMS (IR)

RMS (IR+RO)

G.M.

CO20.10 0.11 -2.73

Ts3.27 3.88 3.35

Ttrop2.02 0.98 9.78

Tstrat0.25 0.09 -0.47

qtrop1.32 0.73 -4.99

qstrat0.10 0.12 -0.27

Clow3.76 3.66 0.18

Cmid1.36 1.25 0.07

Chigh 3.05 1.07 -1.18

“Truth” IR-only IR+RO

Summary• We use a climate OSSE based on a 2xCO2 experiment to investigate the

determination of longwave forcing and feedbacks in the all-sky condition from IR spectral and GNSS RO measurements by using an optimal detection method.

• Combining RO measurement with IR measurement substantially reduces the uncertainty in the feedbacks of Ttrop, Tstrat, qtrop, and Chigh, with their global mean errors generally being 50% smaller compared to the IR-only case.

• The radiative forcing of CO2 and the feedbacks of Ttrop, Tstrat and qtrop can be accurately quantified from combined IR and RO data types, with relative errors in their global mean values being less than 4%, 10%, 20% and 15% respectively.

Lessons learned and future work

• Signal attribution

– Ambiguity issue with the fingerprinting method.

– OD allows effective integration of complementary data types.

=> Additional data type to solve the cloud ambiguity?

• Signal detection

– Detectability: signal vs. noise (natural variability, sampling, and instrumentation)

– 2xCO2 vs. real climate change

=> A more sophisticated OSSE; theoretical and practical