Interannual Variability of Solar Reflectance From Data and Model
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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
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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. - PowerPoint PPT Presentation
Transcript of 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 CenterApril 10-13, 2012
Objectives:1) Understand the interannual variability expected in the CLARREO solar
benchmark spectra.2) 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.
NP (60N-90N)
NML (30N-60N)
TRO (30S-30N)
SML (30S-60S)
SP (60S-90S)
Five latitude regions
Data are first averaged to 10 deg latitude zones in each month. Reflectance is averaged over 5 large latitude regions and globe.
A sample of SCIAM measured solar irradiance over 7 years (2004-2010).(Each colored line is for a different year)
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.)
An example to show the interannual and seasonal variability of the monthly mean solar reflectance from SCIAM data.
Line thickness =
2σ of reflectance
across all years
LandOcean
Averaged reflectance over 2004-2010 from SCIAM.
An example of model-observation comparison of reflectance over the
SML ocean.(Each panel is for a different
month)
SCIAM measuredModeled with mean cloud τ
Reflectance diff.
Mean SZA (deg)
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.
Example of cloud PDF
τ
A model-observation comparison over global ocean.
SCIAM measuredModeled with mean cloud τModeled with τ PDF
Use mean tauUse tau PDF
The largest improvement is in the tropic.
SCIAM measuredModeled with mean cloud τModeled with τ PDF
Use mean tauUse tau PDF
The least improvement is in the polar regions.
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).
Dotted: SCIAMSolid: Model
One panel for one region, one color for one year.
Comparison of the global monthly mean reflectance anomalies between SCIAM measurements and model.
Dotted: SCIAMSolid: Model
One panel for one month, one color for one year.
Spectral ranges with large measurement uncertainty.
Comparison of the model-observation reflectance anomalies over the tropic region.
Dotted: SCIAMSolid: Model
Ocean Land
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.
The monthly mean reflectance σ from SCIAM and model.
Each panel shows a different region.
The annual mean reflectance σ from SCIAM and model.
Each panel shows a different region.
Summary1) 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).
2) 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.
3) 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.
4) 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.
Acknowledgement:
We thank the CERES team and DAAC at NASA Langley for the CERES SSF data, Sciamachy science team for the spectral solar radiance/irradiance data, and Dr. Sky Yang and Dr. Shuntai Zhou for the SMOBA data.
