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Using the Earth׳s reflected solar spectrum for climate change fingerprinting is an emerging research area. The spectral fingerprinting approach directly retrieves the changes in climate variables from the mean spectral data averaged across large space and time scales. To investigate this fingerprinting concept, we use ten years of satellite data to simulate the monthly and annual mean reflected solar spectra and the associated spectral fingerprints for different regions over the ocean. The interannual variations in the spectral data are derived and attributed to the interannual variations in the relevant climate variables. The fingerprinting retrieved changes in climate variables are then compared with the actual underlying variable changes from the observational data to evaluate the fingerprinting retrieval accuracy. Two important errors related to the fingerprinting approach, the nonlinearity error and the averaging error in the mean fingerprints, and their impact on the retrieval accuracy, are investigated. It is found that the averaging error increases but the nonlinearity error decreases as the region size increases. The averaging error has minimal effect on the fingerprinting retrieval accuracy in small regions but has more of an impact in large regions. In comparison, the effect of nonlinearity error on the retrieval accuracy decreases as the region size increases. It is also found that the fingerprinting retrieval accuracy is more sensitive to the nonlinearity error than to the averaging error. In addition, we compare the fingerprinting accuracy between using the monthly mean data and the annual mean data. The results show that on average higher retrieval accuracy is achieved when the annual mean data are used for the fingerprinting retrieval.

A successor version 3 of DISORT (DISORT3) is presented with important upgrades that improve the accuracy, efficiency, and stability of the algorithm. Compared with version 2 (DISORT2 released in 2000) these upgrades include (a) a redesigned BRDF computation that improves both speed and accuracy, (b) a revised treatment of the single scattering correction, and (c) additional efficiency and stability upgrades for beam sources. In DISORT3 the BRDF computation is improved in the following three ways: (i) the Fourier decomposition is prepared "off-line", thus avoiding the repeated internal computations done in DISORT2; (ii) a large enough number of terms in the Fourier expansion of the BRDF is employed to guarantee accurate values of the expansion coefficients (default is 200 instead of 50 in DISORT2); (iii) in the post processing step the reflection of the direct attenuated beam from the lower boundary is included resulting in a more accurate single scattering correction. These improvements in the treatment of the BRDF have led to improved accuracy and a several-fold increase in speed. In addition, the stability of beam sources has been improved by removing a singularity occurring when the cosine of the incident beam angle is too close to the reciprocal of any of the eigenvalues. The efficiency for beam sources has been further improved from reducing by a factor of 2 (compared to DISORT2) the dimension of the linear system of equations that must be solved to obtain the particular solutions, and by replacing the LINPAK routines used in DISORT2 by LAPACK 3.5 in DISORT3. These beam source stability and efficiency upgrades bring enhanced stability and an additional 5-7% improvement in speed. Numerical results are provided to demonstrate and quantify the improvements in accuracy and efficiency of DISORT3 compared to DISORT2.

The CALIPSO satellite mission consistently measures volume (including molecule and particulate) light depolarization ratio of ∼2% for smoke, compared to ∼1% for marine aerosols and ∼15% for dust. The observed ∼2% smoke depolarization ratio comes primarily from the nonspherical habits of particles in the smoke at certain particle sizes. In this study, the depolarization of linearly polarized light by small sphere aggregates and irregular Gaussian-shaped particles is studied, to reveal the physics between the depolarization of linearly polarized light and smoke aerosol shape and size. It is found that the depolarization ratio curves of Gaussian-deformed spheres are very similar to sphere aggregates in terms of scattering-angle dependence and particle size parameters when particle size parameter is smaller than 1.0 π . This demonstrates that small randomly oriented nonspherical particles have some common depolarization properties as functions of scattering angle and size parameter. This may be very useful information for characterization and active remote sensing of smoke particles using polarized light. We also show that the depolarization ratio from the CALIPSO measurements could be used to derive smoke aerosol particle size. From the calculation results for light depolarization ratio by Gaussian-shaped smoke particles and the CALIPSO-measured light depolarization ratio of ∼2% for smoke, the mean particle size of South-African smoke is estimated to be about half of the 532 nm wavelength of the CALIPSO lidar.

A computationally efficient method is developed to simulate the radiances in a scattering and absorbing atmosphere along an arbitrary path in the spectral region ranging from visible to far-infrared with a spectral resolution of 1 cm ! 1 . For a given spectral region, the method is based on fitting radiances pre-calculated from the discrete ordinate radiative transfer (DISORT) at several wavenumbers. Radiances at other wavenumbers are interpolated based on the pre-computed total absorption and scattering optical thicknesses and the surface albedo. The computational efficiency and accuracy of the method are tested in comparison with rigorous simulations for various scenarios under the same conditions. For both clear-sky and cloud atmospheres, the present method is at least 140 times faster than the direct application of DISORT. Across the spectral range, the standard relative differences between the new method and the DISORT are less than 2% for clear-sky conditions. Root-mean-square (RMS) differences of the top of the atmosphere (TOA) brightness temperatures between the new method and DISORT, for atmospheric infrared sounder (AIRS) channels over clear-sky, ice cloudy and water cloudy skies, are within the noise equivalent differential temperature (NEDT) of the AIRS sensor. The fast method is also applied to simulations of the spectral downwelling radiance measured by the Fourier transform infrared (FTIR) interferometer, and to the simulations of the AIRS upwelling radiances under clear-sky and cloudy conditions.