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

1. 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

2. 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

3. Manabe and Wetherald 1998 Bony et al. 2006 Surface T Change Planck Damping TOA Radiative Forcing Feedbacks Water vapor (WV), clouds (C), lapse rate (LR), albedo (A) Feedback uncertainties in climate models Conventional Methods: 1) Partial Radiative Perturbation (PRP) method [Wetherald and Manabe 1988] DRXi = R(X1,…,Xi+dXi,…) – R(X1,…,Xi,…) 2) Radiative sensitivity kernel [Soden and Held 2005] Pre-computed dR/dXi ? Observation

4. 1 Feedback analysis requires partitioning the total change signal into individual contributions [W m-2] Total change in OLR observable wanted CO2 T-surf T-atmos W.V. Cloud Synthesized, 2xCO2 experiment

5. 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) IR spectral fingerprints fi: fingerprint ofthe i’th forcing or feedback Ri: characteristic radiance spectrum Fi: partial OLR change (forcing or feedback magnitude) <…>: global average

6. f Radiance spectral change - observable CO2 Ts Ttrop Tstrat Optimal Detection (Multi-pattern linear regression) qtrop qstrat Clow y: overall spectral radiance changes f: fingerprints a: feedback magnitudes r: residual signals Cmid Chigh y

7. y f CO2 Spectral changes across the globe Ts Ttrop [cm-1] Tstrat CO2 qtrop T-surf T-atmos qstrat Clow W.V. Cloud Cmid Chigh

8. 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

9. IR-only OD results Similarity between some fingerprints leads to compensating errors.[Huang et al 2010 JGR]

10. RO log-dry pressure profile IR IR and RO fingerprints fi: fingerprint ofthe i’th forcing or feedback Ri: characteristic radiance spectrum and log-dry pressure profile Fi: partial OLR change (forcing or feedback magnitude) <…>: global average

11. Joint OD results Unit: W m-2

12. 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.

13. 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