Uncertainties in the Climate Mean of Reanalyses, Observations, and the GFDL Climate Model

1. Uncertainties in the Climate Mean of Reanalyses, Observations, and the GFDL Climate Model Thomas Reichler and Junsu Kim Univ. of Utah, Salt Lake City, USA Supported by the Center for High Performance Computing, Univ. of Utah 3rd WCRP International Conference on Reanalysis, Tokyo, Japan, 28th January – 1st February 2008

2. Motivation Multi-variate model performance index Error worse avg. Models bb better • Some models outperform NCEP/NCAR reanalysis

3. Questions • Why are NCEP/NCAR reanalyses not better than freely evolving coupled model? • How do the other reanalyses do? • Can the reanalyses be improved? • How large are the observational uncertainties?

4. Data Observations Many global datasets; often multiple data for same quantity Reanalyses NCEP/NCAR NNR NCEP/DOE NDR ERA40 ERA JRA25 JRA Model GFDL CM2.1 GFD Base period ’79-’99 (in most cases)

5. Climate Quantities “Physics” (18) “Dynamics” (13) ERA-40 as reference

6. RSUT: Observations Annual mean outgoing shortwave radiation TOA (2000-2005) (1985-1989) (1984-1999) 2000- • <10 Wm-2 RMS error amongst different observations: • = Observational uncertainty • Mean of different observations: • = Best observational estimate

7. RSUT: Reanalysis vs. Observations RMS 24 22 19 15 12 Wm-2

8. RSUT Summary • Observational uncertainty (<10 Wm-2) is smaller than reanalysis error (20 Wm-2)  Observational uncertainty is acceptable • Reanalyses and model show similar error patterns, independent of observations  Errors are real  Common biases due to similar physics? • What about other quantities?

9. Common Reanalyses Biases

10. Error Analysis • Break-down by • product NNR, NDR, ERA, JRA, GFD • quantity physics - dynamics • region NH, TR, SH • season DJF, MAM, JJA, SON • observation 1-5 • Normalized RMS error:

11. “Physics” 3 2 2 3 5 5 2 2 1 2 3 4 4 4 3 3 4 4 OBS REA MOD small NRMS-error large • Large uncertainties for surface fluxes • Largest errors for “clouds and radiation” • Model sometimes as realistic as reanalyses Validated against multi-observational mean

12. “Dynamics” • Lack of global observations • Smaller differences than “physics” • Model clearly not as close • Except: meridional wind (MMC, VA) and specific humidity (HUS) REA GL ANN MOD large small NRMS-error Validated against ERA-40

13. Cumulative Errors Physics Dynamics • Physics • Largest errors over SH and during spring and summer • Model does quite well NNR NDR JRA ERA GFD • Dynamics • JRA closest to ERA, NNR most different • Large model errors, in particular Tropics

14. Conclusion • Mostly “physics” quantities • Tuning, model physics, forcings, data assimilation • Common biases • Overall, ERA closest to observations • Yes, see 1. • Large uncertainties in surface fluxes • Why do NNR not better than models? • What about other reanalyses? • Room for improvement? • Observational uncertainties?

15. Thanks

16. Global RMS errors

17. Observational Uncertainties Uncertainty Ratio: How tolerable are errors? <<1 tolerable