Science Innovation Fund: Quantifying the Variability of Hyperspectral

Shortwave 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 Langley2CU-Boulder/LASP

3UC-Berkeley/LBNL

• 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?

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How well do climate models reproduce observed the variability in Earth’s climate system and why?

SIF 2013 Project Goals

Importance of continuous spectral sampling for climate benchmarking

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SCIAMACHY

POLDER - 9

AVHRR - 3

MODIS - 19

APS - 8

VIIRS - 11

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Linking physical processes to observed variability using spectral information

Spectrally resolved reflectance exhibits annual and seasonal variability

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Quantitative Comparison of SubspacesSCIA Reflectances OSSE Reflectances

SCIA Eigenvectors Calculate Intersection

Spectrally Decompose Intersection

The relationship between each pair of transformed eigenvectors. Range = [0,Subspace Dimension]

OSSE Eigenvectors

PCA

SCIA Transformed Eigenvectors

OSSE Transformed Eigenvectors

1 2 3

SVD

Roberts et al. 2013 (ACP)

Quantitative Comparison of Subspaces

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Quantitative Comparison of Subspaces

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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.

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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.

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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

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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?

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Lon: -170.000 -135.000 Lat: 73.0000 85.0000

Quantifying Difference in Information

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Lon: -170.000 -135.000 Lat: 73.0000 85.0000

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

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

Locations where the trend is significant at 1σ

Decadal spectral reflectance trends

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Trends significant at 1σ

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

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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

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Intersection Database Method

SCIA PCA Scores

SCIA Shared Intersection Scores

Database Shared Intersection Scores

1. For each PC, find the SCIA spectra corresponding to scores more than 3 standard deviations from the mean.

2. Using the spectra found in (1.), calculate the Euclidean distance between the corresponding Shared Intersection SCIA Scores and all Database Intersection Scores.

4. Examine Database inputs used to simulate reflectances to understand which model inputs drive measured variance.

3. Find the minimum Euclidean distance for each spectrum.

This finds Database spectrum with closest spectral shape to SCIA spectrum of interest.

SCIA Reflectances Database Reflectances

Database Physical Inputs

PCA Space

Intersection Space

Measurement Space

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

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