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Climate model OSSE: Evolution of OLR spectrum and attribution of the change

Climate model OSSE: Evolution of OLR spectrum and attribution of the change. Yi Huang, Stephen Leroy, James Anderson, John Dykema Harvard University Jon Gero University of Wisconsin V. Ramaswamy NOAA/GFDL CLARREO workshop May 13, 2009. Outline.

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Climate model OSSE: Evolution of OLR spectrum and attribution of the change

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  1. Climate model OSSE:Evolution of OLR spectrum and attribution of the change Yi Huang, Stephen Leroy, James Anderson, John Dykema Harvard University Jon Gero University of Wisconsin V. Ramaswamy NOAA/GFDL CLARREO workshop May 13, 2009

  2. 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)

  3. 1980-2004 evolution of atmosphere and surface conditions T_sfc T_atm H2O OLR Cld OLR_c Blue lines: anomaly time series; red lines: 3σunforced variability. Black dots: significant changes (> 3σ).

  4. CO2 Window CH4 H2O vib.-rot. CO2 H2O rot. O3 Interannual variability (Model compared to AIRS) Global ocean annual mean radiance changes relative to 1980 Black dots: significant changes (> 3 σ)

  5. H2O rot Window H2O vib-rot. O3 CO2 CO2 CH4 Global Mean Pre-industrial to Present Change 2K 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. -7K

  6. 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]

  7. Anticipated Spatial-Average Trends for CLARREO Science analysis uses simple spatial average of SI-traceable spectra; uncertainty of spectra is frequently tested by direct on-orbit measurement 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 Optimal Detection Method Provides Simple Relationship between SI Traceable Observable and Science Product

  8. δOLRXi (PRP) – OLR changes due to different physical causes in 2xCO2 experiment all-sky; unit: [W m-2]

  9. 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]

  10. 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!

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

  12. 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

  13. Thank you!Questions?Comments?

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