a climate osse study of fingerprinting longwave forcing and feedback with ir and ro data
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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