Sampling/Variability of CLARREO Solar Spectral Benchmark

1. Sampling/Variability of CLARREO Solar Spectral Benchmark Zhonghai Jin Costy Loukachine Bruce Wielicki NASA Langley research Center / SSAI, Inc. March 16, 2009

2. Objectives: • Understand the interannual variability and sampling error expected in the CLARREO solar spectral benchmark to • evaluate the ability of CLARREO to detect various climate trends; • clarify the sampling requirement for CLARREO mission design. How much is the natural interannual variability of solar spectral benchmark over different spatial and temporal scales (global and regional, yearly and monthly)? How large is the decadal solar spectral climate signal? How this signal compare to the natural interannual variation? What’s the time frame for the climate signal to be large enough to be detected by CLARREO solar benchmark? What’s the optimal wavelengths and spectral resolution for different climate signal detection? How large is the nadir sampling error? How the sampling error compare to the climate signal and the natural interannual variation? ……

3. Interannual Variability of Solar Refectance Input parameters (monthly mean): Aerosol, PW, Surface properties, Cloud properties (τ, RE, amount, phase) from CERES ARBAVG; Ozone from SMOBA. • Five regions: • Polar north (>60N) (NP) • Mid-latitude north (30N-60N) (NML) • Tropic (30N – 30S) (TRO) • Mid-latitude south (30S–60S) (SML) • Polar south (> 60S) (SP) Input • 6 Modtran calculations in each region each month: • Clear open ocean • Clear land (include sea ice) • Water cloud ocean • Water cloud land • Ice cloud ocean • Ice cloud land Spectral reflectance for 68 months (Mar 2000 to Oct 2005) in 5 regions and global for ocean and land for clear/cloudy/all skies for 0.2-5.0μm in every 5cm-1 Out

4. An Example of TOA Spectral Reflectance (all sky) H2O H2O+CO2 Major absorption bands labeled O3 O2

5. Examples of Monthly Mean Regional Reflectance Anomaly (2000 - 2005) 2001 has high surface albedo but also low cloud Fc O3 H2O +Ice cloud Reflectance Anomaly High water cloud Fc Wavelength (μm) Low water cloud Fc H2O + Ice cloud Interannual variations are related to changes of clouds, aerosol, snow, water vapor, ozone etc., depending significantly on region.

6. An Example of Monthly Mean Global Reflectance Anomaly (2000 - 2005) Reflectance Anomaly Note: interannual variation for global monthly mean reflectance is within ±0.005 or about ±2% of the reflectance.

7. Annual Mean Global Reflectance Anomaly (2000 - 2005)

8. 2σ of monthly mean reflectance anomaly

9. Note: the 2σ variation for annual mean reflectance is much smaller, indicating that the natural variability is dropped.

10. Model and Schiamachy anomalies match better over ocean. Note: Schiamachy orbit is different from Terra, and footprint size is much larger than CERES.

11. Same as last slide, but for April.

12. Same as last slide, but for July.

13. Same as last slide, but for Oct. Anomaly in Oct. is much smaller.

15. 2. Solar Spectral Signal of Climate Change • Results above showed that the modeled interannual solar reflectance variation from CERES data is realistic. • How this interannual variation compare to the decadal change of climate spectrum based on IPCC climate change scenario? • What is the optimal wavelengths and spectral resolution to detect different climate signals? • What’s the difference of the climate signals for different regions? Using the six year average of CERES data as input and plus the decadal perturbation of the input parameters based on IPCC climate change scenario, we have calculated the climate change spectra (regional and global) for various climate parameters.

16. Decadal reflectance change due to CO2 change (+17ppm/decade). (This example is for global annual mean)

23. Inter-comparison of the interannual variation of solar reflectance benchmark and the one decadal climate change.

24. 3. Sampling Effect: Nadir vs Full Swath To investigate the sampling effect and the nadir sampling error, we calculated and compared the reflectance spectra between nadir only sampling and full swath sampling. The input properties for these calculations are from CERES SFC data. For full swath, data from all view angles are used, while for nadir view, only data within 5 degree of nadir (about an area of 100km diameter) are used.

25. Full swath Nadir view An example of monthly mean regional reflectance anomaly comparison between full swath and nadir sampling.

26. Full swath Reflectance Anomaly Nadir view Wavelength (μm) An example of monthly mean global reflectance anomaly comparison between full swath and nadir sampling (January).

27. Full swath Reflectance Anomaly Nadir view Wavelength (μm) Annual mean global reflectance anomaly comparison between full swath and nadir sampling.

28. Nadir Sampling Error for Monthly Mean Reflectance Ndir-All Reflectance Anomaly Difference Wavelength (μm) Examples of nadir sampling error for monthly mean reflectance. The nadir sampling error is defined as the reflectance anomaly difference between nadir views and all views (full swath).

29. Nadir Sampling Error for Annual Mean Reflectance

30. Relative Nadir Sampling Error for Annual Mean Reflectance (%)

33. Inter-comparison of the interannual variation of solar reflectance benchmark, the decadal climate change and the nadir sampling error. (This example is for global annual mean reflectance).

34. Same as last slide, but for global ocean.

35. Same as last slide, but for global land.

36. 3. Conclusion • We calculated nearly 6 years of monthly mean high resolution solar reflectance spectra and 5 decades of climate change spectra for 5 regions and global, for ocean and land, and for nadir views only and full swath. • The inter-annual variability and nadir sampling error for various regions are investigated and are shown being comparable with Schimachy and CERES observations. • Monthly mean global solar reflectance anomaly is within ±0.005 in most spectra (i.e., ±2% of reflectance), but it is larger for polar and land regions, where have more surface variations. • Annual mean global solar reflectance anomaly is within ±0.002 (< ±1% of reflectance); the 2σ anomaly is similar to one decade of climate change signal estimated from IPCC AR4 climate change scenario in the visible, but larger than that in most of NIR spectra.

37. For annual mean and most monthly mean global reflectances, the year to year variation trends are consistent between full swath and nadir sampling over most spectra, indicating that the nadir sampling can catch most of the interannual variations over global scale. • The 2σ nadir sampling error for annual mean global reflectance in the visible and transparent bands is less than 0.4%; lower over ocean, higher over land. But it could be much higher for regional/seasonal spectrum. • The interannual variability, climate change spectrum and sampling error are all dependent on wavelength and spatial/temporal scale for averaging. Solar benchmark observation over 2 decades or more is required to detect the global climate signal over most solar spectra. • For many regional climate trend detection (e.g., aerosol effect, surface albedo), the sampling error of 100km nadir view (assuming one orbit) appears to be too large, indicating the requirement for a separate instrument for solar benchmark (from calibration) or an additional orbit.

38. Acknowledgement: We thank the Sciamachy science team for the solar radiance data, NASA Langley DAAC for CERES data, and Dr. Sky Yang for SMOBA ozone data.