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Interannual Variability of Solar Reflectance From Data and Model

Interannual Variability of Solar Reflectance From Data and Model. Z. Jin, C. Lukachin , B. Wielicki , and D. Young SSAI, Inc. / NASA Langley research Center April 10-13, 2012. Objectives: Understand the interannual variability expected in the CLARREO solar benchmark spectra.

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Interannual Variability of Solar Reflectance From Data and Model

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  1. Interannual Variability of Solar Reflectance From Data and Model Z. Jin, C. Lukachin, B. Wielicki, and D. Young SSAI, Inc. / NASA Langley research Center April 10-13, 2012

  2. Objectives: Understand the interannual variability expected in the CLARREO solar benchmark spectra. Evaluate the modeling ability to simulate the spectral solar reflectance and its variability. Data: SCIAMACHY radiance and solar irradiance (2003-2010). Spectral range: 300-1750 nm; resolution: 1 nm. Model: MODTRAN with input parameters from CERES SSF, MODIS and SMOBA.

  3. Five latitude regions NP (60N-90N) NML (30N-60N) TRO (30S-30N) SML (30S-60S) SP (60S-90S) Data are first averaged to 10 deg latitude zones in each month. Reflectance is averaged over 5 large latitude regions and globe.

  4. A sample of SCIAM measured solar irradiance over 7 years (2004-2010). (Each colored line is for a different year)

  5. An example of SCIAM measured solar reflectance averaged to the 5 latitude regions and globe. (Each panel is for a different region, each color is for a different year.)

  6. Ocean Land Averaged reflectance over 2004-2010 from SCIAM. Line thickness = 2σ of reflectance across all years An example to show the interannual and seasonal variability of the monthly mean solar reflectance from SCIAM data.

  7. An example of model-observation comparison of reflectance over the SML ocean. (Each panel is for a different month) SCIAM measured Modeled with mean cloud τ Mean SZA (deg) Reflectance diff.

  8. Example of cloud PDF τ The probability distribution function (PDF) of cloud τ from CERES MODIS in three 10 degree zones (20N-10S) in April months spanning 2000-2005, separated by cloud phase and by ocean and land. The cloud τ PDF is used in the RT modeling to account the large cloud variation from footprint to footprint.

  9. A model-observation comparison over global ocean. SCIAM measured Modeled with mean cloud τ Modeled with τ PDF Use mean tau Use tau PDF

  10. The largest improvement is in the tropic.

  11. The least improvement is in the polar regions. SCIAM measured Modeled with mean cloud τ Modeled with τ PDF Use mean tau Use tau PDF

  12. Dotted: SCIAM Solid: Model One panel for one region, one color for one year. An example of monthly mean reflectance anomaly from SCIAM and model. (Anomaly is defined as the reflectance difference from the average of the same months across all years).

  13. Comparison of the global monthly mean reflectance anomalies between SCIAM measurements and model. Dotted: SCIAM Solid: Model One panel for one month, one color for one year. Spectral ranges with large measurement uncertainty.

  14. Comparison of the model-observation reflectance anomalies over the tropic region. Dotted: SCIAM Solid: Model

  15. Ocean Land

  16. 1σ of the monthly mean global reflectance across all years from SCIAM and model. One panel for one month. Spectral ranges with large measurement uncertainty.

  17. The monthly mean reflectanceσfromSCIAMandmodel. Each panel shows a different region.

  18. The annual mean reflectanceσfromSCIAMand model. Each panel shows a different region.

  19. Summary Measurement data have shown large seasonal and regional variations in the solar benchmark spectra, but their interannual variability is much smaller (within ±0.005 for monthly global mean). When simple mean cloud τ is used, the modeled monthly mean solar reflectance over large climate regions could be biased from observation by 5-20% in magnitude, depending on season and region. When the cloud τ PDF is adopted, the bias can be reduced significantly. Modeled solar reflectance spectrum and its variability are consistent with observations, for example, both show an interannual variability (1σ) of 1-2% for monthly global mean reflectance and <1% for global annual mean. IF the PCRTM (X Liu) is fast enough, it will be used for footprint by footprint computation with CERES SSF for further comparison with SCIAM.

  20. Acknowledgement: We thank the CERES team and DAAC at NASA Langley for the CERES SSF data, Sciamachyscience team for the spectral solar radiance/irradiance data, and Dr. Sky Yang and Dr. Shuntai Zhou for the SMOBA data.

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