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Longwave benchmark simulation from data

Longwave benchmark simulation from data. Seiji Kato, Fred G. Rose, Xu Liu, Dave P. Kratz, and Bruce A. Wielicki NASA Langley Research Center Robert O. Knuteson, S. Dutcher, H. Revercomb, D. Tobin, F. Best University of Wisconsin-Madison, Space Science and Engineering Center.

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Longwave benchmark simulation from data

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  1. Longwave benchmark simulation from data Seiji Kato, Fred G. Rose, Xu Liu, Dave P. Kratz, and Bruce A. Wielicki NASA Langley Research Center Robert O. Knuteson,S. Dutcher, H. Revercomb, D. Tobin, F. Best University of Wisconsin-Madison, Space Science and Engineering Center

  2. FOV, spectral coverage, Temporal sampling * Spectral range used in the study ** ~20% reduction of surface temperature change signal going from nadir to 53 degree but no loss of cloud information *** needs clear-sky scenes to calibrate CLARREO to better than 10 mK/year (clear-sky scenes occur less than 15% with 10 km FOV)

  3. Mean radiance variability Blue: computed from data, Black: inferred from other temporal resolution standard deviation * Standard deviation does not depend on FOV size (13.5 to 100 km) ** Variability in global decadal mean = Daily variability 0.55 / sqrt(30 × 120) ≈ 0.01 K Global monthly mean CERES window deseasonalized radiance variability (nadir view only) is 0.24% of the mean value. The autocorrelation of CERES window radiances with a one month lag is 0.27.

  4. GLOBAL Annual Means Deviation from 2003 Inter-annual 

  5. Spectral signal • Probability of detecting trend in window region measured over Arctic (lat. 66.5 – 85) and N.H. midlatitude (lat. 23.5 – 66.5) during AIRS observation period of (5 years) exceeds 95% and close to 95% over Tropics (lat -23.5 to 23.5) [UW]. • Spectral signal (global mean nadir-view spectral radiance change) from Low-level cloud height change is different (different enough that a matrix inversion can separate) from surface temperature change [Larc]. • Spectral signals from high-level cloud height change, high-level cloud fraction change, high-level optical thickness change are different from spectral signal from upper tropospheric humidity change [Larc]. • The smallest signal among perturbations simulating changes over a decade is from low-level cloud fraction change (-0.025), which gives window radiance TOA LW flux change of 0.05 W m-2,and the maximum of 0.031 K brightness temperature change at 1128 cm-1 [Larc]. • The largest signal among perturbations is from high-level cloud fraction change (-0.025), which gives window radiance TOA LW flux change of 0.98 W m-2,and the maximum of 0.324 K brightness temperature change at 821 cm-1 [Larc].

  6. Perturbations High-level clouds, top > 6.5 km, low-level clouds, top < 3.5 km Estimates from World Climate Research Programme’s Coupled Model Intercomparison Project Stage 3 (CMIP-3, Sanderson et al. 2009) × 0.2 = 0.11

  7. Linearity of the signal Radiance change with all perturbations equal to the sum of radiance change, i.e. When the perturbation is doubled, the radiance change is also doubled for all perturbations we tested, i.e.

  8. Summary of requirements • Instrument calibration uncertainty 0.1 K (3sigma). Understanding the spectral shape of calibration uncertainty is necessary. • 100 km FOV nadir view is sufficient for the longwave benchmark to determine most of atmospheric changes (low-level cloud fraction change is difficult to detect). • 0.6 cm-1 or better spectral resolution. • Spectral range (200 – 2760 cm-1) • Need to establish methods to transfer the SI traceable calibration on the ground to CLARREO LW instrument after the launch.

  9. Backups slides for details

  10. AIRS-Proxy Analysis Simulate nadir viewing CLARREO IR radiances at 100 km and 13.5 km: Inter-annual and Monthly Variability R. Knuteson, S. Dutcher, H. Revercomb, D. Tobin, F. Best Space Science and Engineering Center University of Wisconsin-Madison 11 May 2009

  11. AIRS-Proxy Inter-annual Variability 2003-2007

  12. GLOBAL Annual Means Deviation from 2003 Inter-annual 

  13. ARCTIC Annual Means Deviation from 2003 Inter-annual 

  14. NH Mid-Lat Annual Means Deviation from 2003 Inter-annual 

  15. TROPICAL Annual Means Deviation from 2003 Inter-annual 

  16. SH Mid-Lat Annual Means Deviation from 2003 Inter-annual 

  17. ANTARCTIC Annual Means Deviation from 2003 Inter-annual 

  18. AIRS-Proxy Inter-annual Variability Summary 100 km FOV * Std Deviation after detrending

  19. Cloud cover along the ground track of CALIPSO and CloudSat Clear-sky: less than 5% cloud fraction

  20. Spectral difference Temperature perturbation – low-level cloud height perturbation Spectral signal from clouds between 800 to 1000 cm-1 is less wavelength dependent because clouds are not completely opaque and cloud are compose of ~10 micron particles

  21. Spectral signal

  22. Spectral signal

  23. Spectral signal

  24. Standard deviation of spectral Radiance from 10 days of global mean Radiance simulation

  25. Linearity TOA spectral signal changes nearly linearly to atmospheric property changes

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