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 2xCO 2 experiment + MODTRAN –All-sky optimal detection (OD)

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 ).

4 Global ocean annual mean radiance changes relative to 1980 Interannual variability (Model compared to AIRS) H 2 O rot. CO 2 WindowCH 4 H 2 O vib.-rot.CO 2 O3O3 Black dots: significant changes (> 3 σ)

5 Pre-industrial to Present Change Red: climate change signal; minus 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

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 2xCO 2 experiment + MODTRAN –Optimal Detection (OD) [Leroy et al., 2008]

7 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

8 δOLR Xi (PRP) – OLR changes due to different physical causes in 2xCO2 experiment all-sky; unit: [W m-2] CO 2 TsTs T trop T strat q trop q strat C low C mid C high

9 δOLR Xi (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 δOLR Xi 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 Point wise 10 ° x 20 ° 30 ° x 60 ° Global CO TsTs T trop T strat q trop q strat C low C mid C high 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 ].

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?