Yolanda Roberts 1 Constantine Lukashin 1 , Patrick Taylor 1

1. Science Innovation Fund: Quantifying the Variability of HyperspectralShortwave Radiation for Climate Model Validation Yolanda Roberts1 Constantine Lukashin1, Patrick Taylor1 Collaborators: Peter Pilewskie2, Daniel Feldman3, William Collins3, Zhonghai Jin1, Xu Liu1, Hui Li1 1NASA Langley 2CU-Boulder/LASP 3UC-Berkeley/LBNL

2. How welldo climate models reproduce observed the variability in Earth’s climate system and why? • Demonstrate using the information in highly accurate, hyperspectral shortwave reflectance measurements for climate model validation • Why use direct measurements of reflectance? • Why hyperspectral sampling? • Does shortwave hyperspectral model validation tell the modelers something new about model performance? SIF 2013 Project Goals

3. Importance of continuous spectral sampling for climate benchmarking SCIAMACHY POLDER - 9 AVHRR - 3 MODIS - 19 APS - 8 VIIRS - 11

4. Linking physical processes to observed variability using spectral information

5. Spectrally resolved reflectance exhibits annual and seasonal variability

6. Quantitative Comparison of Subspaces OSSE Reflectances SCIA Reflectances PCA SCIA Eigenvectors OSSE Eigenvectors CalculateIntersection SVD Spectrally Decompose Intersection 2 1 3 The relationship between each pair of transformed eigenvectors. Range = [0,Subspace Dimension] SCIA Transformed Eigenvectors OSSE Transformed Eigenvectors Roberts et al. 2013 (ACP)

7. Quantitative Comparison of Subspaces

8. Quantitative Comparison of Subspaces

9. Spectral Variability of Hyperspectral Shortwave Radiation: What have we learned? • Importance of continuous spectral sampling for climate benchmarking • Contains spectral information needed to link physical processes to observed variability • Spectrally resolved reflectance exhibits regional, annual, and seasonal variability • Quantitative comparison using spectral information in shortwave hyperspectral reflectance • At the beginning of the 21st century, OSSE and SCIAMACHY reflectance have similar variability.

10. SCIAMACHY validation product • SCIAMACHY CLARREO-like validation product • Spectral Resolution: 8 nm FWHM • Spectral Sampling Resolution: 4 nm • Spatial Sampling: 5.625 degrees (T85 * 4) • Temporal Sampling: Monthly averages • Output Format: netCDF • Variables Included: Clear sky and All Sky reflectance and radiance, surface scene type IDs using IGBP database, cloud optical properties, etc.

11. Compare to OSSE output • Generating 2003-2010 OSSE output to correspond with ENVISAT orbital info (10AM descending node) • MODIS monthly average surface products instead of climatology • SORCE Total Solar Irradiance

12. Comparing decadal trends • What secular trends are there in the observed decadal temporal variability and what physical processes drive these secular trends? • Regional Changes: e.g. Arctic Ocean, Eastern US, sub-Saharan Africa, Greenland, Amazon • What are the differences among broadband, multispectral, and hyperspectral data sets in detecting and attributing those changes? • How do these trends compare between observations and model simulations?

13. Lon: -170.000 -135.000 Lat: 73.0000 85.0000

14. Quantifying Difference in Information Lon: -170.000 -135.000 Lat: 73.0000 85.0000

15. CERES EBAF(Energy Balanced and Filled) Level 4 Data Product. TOA SW flux change/decade

16. CERES EBAF(Energy Balanced and Filled) Level 4 Data Product. TOA SW flux change/decade Locations where the trend is significant at 1σ

17. Decadal spectral reflectance trends Trends significant at 1σ

18. Validating Climate Model Output • Compare SCIA and OSSE spectral decadal trends • Compare spectral variability using PC spectral shapes • Quantify data set differences and similarities • Utilize distance metric from intersection comparison method

19. Attribution: If models and observations differ, why? • Radiative Response Using Shortwave Spectral Kernels • PCRTM spectral fitting • Intersection Database Method • Use intersection to match the spectral shape of observations to simulated spectra efficiently • Quickly matching the spectral shapes provides link between model physical inputs to observed data variance drivers

20. Intersection Database Method PCA Space 1. For each PC, find the SCIA spectra corresponding to scores more than 3 standard deviations from the mean. SCIA PCA Scores Intersection Space 2. Using the spectra found in (1.), calculate the Euclidean distance between the corresponding Shared Intersection SCIA Scores and all Database Intersection Scores. Database Shared Intersection Scores SCIA Shared Intersection Scores This finds Database spectrum with closest spectral shape to SCIA spectrum of interest. 3. Find the minimum Euclidean distance for each spectrum. Measurement Space 4. Examine Database inputs used to simulate reflectances to understand which model inputs drive measured variance. SCIA Reflectances Database Reflectances Database Physical Inputs

21. What’s Next? Beyond the 2013 SIF • What does delivery look like for modeling groups? • We will have tested our methods using the CCSM3 model. How do other models compare? • No CLARREO SW instrument yet, we can convince modelers of importance of using available data for model validation – MODIS/SCIAMACHY radiance/reflectance • Continued attribution efforts • Publish initial results • Explore further funding options to expand upon project results