Climate model OSSE:Evolution of OLR spectrum and

attribution of the change

Yi Huang, Stephen Leroy, James Anderson, John DykemaHarvard University

Jon GeroUniversity of Wisconsin

V. RamaswamyNOAA/GFDL

CLARREO workshopMay 13, 2009

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Outline

• Changes in the Outgoing Longwave Radiation (OLR) spectrum

– GFDL GCM + MODTRAN – Random overlapping clouds [Huang et al., 2008, GRL]– 25-year continuous evolution and pre-industrial-to-present change [Huang

and Ramaswamy, 2009, J. Climate]

• Attribution of the OLR changes– CFMIP 2xCO2 experiment + MODTRAN– All-sky optimal detection (OD)

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1980-2004 evolution of atmosphere and surface conditions

Blue lines: anomaly time series; red lines: 3σunforced variability.

T_sfc

OLR

OLR_c

T_atm

H2O

Cld

Black dots: significant changes (> 3σ).

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Global ocean annual mean radiance changes relative to 1980

Interannual variability(Model compared to AIRS)

H2O rot. CO2 Window CH4 H2O vib.-rot. CO2O3

Black dots: significant changes (> 3 σ)

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Pre-industrial to Present Change

Red: climate change signal; <2000-2004> minus <1861-1865>

Blue: variability among 3 ensemble members (3σ)

Green: natural variability measured (3σ)

• Detectability: forced change signal compared to variabilities is pronounced except in the water vapor bands.

• SI traceable measurements at 1 cm-1 spectral resolution and ~0.1 K accuracy.

H2O vib-rot.

CO2

Window

O3

CH4

H2O rot

CO2

Global Mean

2K

-7K

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Outline• Changes in Outgoing Longwave Radiation (OLR)

spectrum– GFDL GCM + MODTRAN – Random overlapping clouds [Huang et al., 2008, GRL]– 25-year continuous evolution and pre-industrial-to-present change [Huang

and Ramaswamy, 2009, J. Climate]

• Attribution of OLR changes– CFMIP 2xCO2 experiment + MODTRAN– Optimal Detection (OD) [Leroy et al., 2008]

y Sa r

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Optimal Detection Method Provides Simple Relationship

between SI Traceable Observable and Science Product

Analysis method utilizes model computation of spatially-averaged spectral signals; simple propagation of measured radiometric uncertainty of spectra allows direct evaluation of impact of sensor accuracy on information content

Science analysis uses simple spatial average of SI-traceable spectra; uncertainty of spectra is frequently tested by direct on-orbit measurement

Anticipated Spatial-Average Trends for CLARREO

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δOLRXi (PRP) – OLR changes due to different

physical causes in 2xCO2 experiment

all-sky; unit: [W m-2]

CO2 Ts

Ttrop Tstrat

qtrop qstrat

Clow Cmid

Chigh

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δOLRXi (optimal detection)

all-sky; computed with point wise (3.75x3.75 lat/lon grid box) fingerprints;keeping first 50 EOFs;unit: [W m-2]

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Errors in OD determined δOLRXi

all-sky; local (3.75x3.75 lat/lon grid box) fingerprints;unit: [W m-2] Bias = OD – PRP

Note correlated errors between some panels - degeneracy!

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Point wise 10°x 20° 30°x 60° Global

CO2 0.01 0.10 0.20 0.10

Ts 0.21 1.14 2.04 3.27

Ttrop 0.06 0.62 1.06 2.02

Tstrat 0.005 0.07 0.13 0.25

qtrop 0.08 0.38 0.64 1.32

qstrat 0.01 0.04 0.06 0.10

Clow 0.23 1.03 1.89 3.76

Cmid 0.15 0.97 1.68 1.36

Chigh 0.08 0.81 1.07 3.05

Limited to just one CFMIP model, inhibiting a strong estimation of signal shape uncertainty. Approximate signal shape uncertainty by looking at regional variation of the fingerprints.

Optimal detection errors increase as fingerprint shapes become more uncertain.

Global root-mean-square (RMS) error in optimally detected all-sky OLR changes. Unit: [W m-2].

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Concluding points and future work• Climate model OSSE

– demonstrates the advantage of longwave spectral measurements in monitoring climate change;– provides an estimate of the interesting change signals as well as internal variability (noise) in comparison– points to stringent demands on spectral resolution and accuracy (0.1 K at 1 cm -1 resolution).

• Attribution of the OLR change– Spectral fingerprinting of greenhouse gas forcing, temperature, water vapor and cloud feedbacks enables resolution of the longwave feedbacks;– Marginally distinctive fingerprints plus uncertainties in their shapes may result in compensating errors. Remaining ambiguities:

low-cloud and surface temperature, high-cloud and tropospheric temperature; to a lesser extent: clouds at adjacent levels, atmospheric water vapor and temperature

• Future investigations– auxiliary data to help disentangle the ambiguities, e.g., GNSS RO – atmospheric temperature– detection time in the case of transient climate change (relative roles of different noises are different from the equilibrium case)– spatial structure of the signals

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Thank you!Questions?Comments?