Using Reflected Solar Spectra to Test the Concept of Climate Change Fingerprinting

Abstract:

The key process in the climate change fingerprinting is to attribute the averaged spectral variation in large space and time scales to different climate variables. Using ten years of satellite data, we generate a group of observation-based spectral kernels and a time series of monthly mean reflectance spectra in five large latitude regions and globe. Subsequently, these kernels and the interannual variation spectra are used to retrieve the interannual changes in the relevant climate parameters to test the concept of using fingerprinting approach for climate change attribution. Comparing the fingerprinting retrieval to the observational truth, the RMS differences between the two are less than 2σ of the variance for all variables in all regions. A large error usually corresponds to those variables with large nonlinear radiative response, such as the cloud optical depth and the ice cloud particle size. Taken into account the nonlinear radiative error in the kernels, the retrieval accuracy is significantly higher, so that the RMS errors are reduced to less than 1σ of the variance for nearly all parameters, indicating the profound impact of the nonlinear error on fingerprinting retrieval. Another important finding is that if the cloud fraction is known a priori, the retrieval accuracy in cloud optical depth would be improved substantially. The test results demonstrate that the concept of climate change fingerprinting based on the reflected solar benchmark spectra is viable.