Sensitivity Analysis of Cirrus Cloud Properties from High-Resolution Infrared Spectra. Part I Methodology and Synthetic Cirrus

• Published in: Journal of climate Vol. 17; no. 24; pp. 4856 - 4870
• Main Authors: Kahn, Brian H., Eldering, Annmarie, Ghil, Michael, Bordoni, Simona, Clough, Shepard A.
• Format: Journal Article
• Language: English
• Published: Boston, MA American Meteorological Society 15.12.2004
• Subjects: Atmospherics; Cirrus clouds; Climate change; Climatology. Bioclimatology. Climate change; Clouds; Cylinders; Earth, ocean, space; Exact sciences and technology; External geophysics; Infrared radiation; Infrared spectrum; Meteorology; Principal components analysis; Radiance; Simulation; Spectral resolution; Spectroscopic analysis; Spectrum analysis; Temperature; Water cloud; Climatology; Particle size; sensitivity analysis; thickness; Empirical orthogonal function; ice; principal components analysis; Variance; Cloud cover; Effective radius; Cirrus; water vapor; infrared spectra; Height; emission spectra; Particle shape; Mixing ratio; high resolution; radiance
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/JCLI-3220.1

A set of simulated high-resolution infrared (IR) emission spectra of synthetic cirrus clouds is used to perform a sensitivity analysis of top-of-atmosphere (TOA) radiance to cloud parameters. Principal component analysis (PCA) is applied to assess the variability of radiance across the spectrum with respect to microphysical and bulk cloud quantities. These quantities include particle shape, effective radius (r eff), ice water path (IWP), cloud heightZ cldand thickness ΔZ cld, and vertical profiles of temperatureT(z) and water vapor mixing ratiow(z). It is shown that IWP variations in simulated cloud cover dominate TOA radiance variability. Cloud height and thickness, as well asT(z) variations, also contribute to considerable TOA radiance variability. The empirical orthogonal functions (EOFs) of radiance variability show both similarities and differences in spectral shape and magnitude of variability when one physical quantity or another is being modified. In certain cases, it is possible to identify the EOF that represents variability with respect to one or more physical quantities. In other instances, similar EOFs result from different sets of physical quantities, emphasizing the need for multiple, independent data sources to retrieve cloud parameters. When analyzing a set of simulated spectra that include joint variations of IWP,r eff, andw(z) across a realistic range of values, the first two EOFs capture approximately 92%–97% and 2%–6% of the total variance, respectively; they reflect the combined effect of IWP andr eff. The third EOF accounts for only 1%–2% of the variance and resembles the EOF from analysis of spectra where onlyw(z) changes. Sensitivity with respect to particle size increases significantly forr effseveral tens of microns or less. For small-particler eff, the sensitivity with respect to the joint variation of IWP,r eff, andw(z) is well approximated by the sum of the sensitivities with respect to variations in each of three quantities separately.