Curtis D. Mobley

Statistical Analysis of Historical Climate Data Sets

Abstract The problem of determining confidence intervals for climatic signals using data sets with spatial and temporal sampling inhomogeneities is solved by a four-step process. First, the actual data set is analysed to determine autoregressive models which are consistent with the actual data at daily, monthly and annual time scales. Second, these models are used to generate artificial, but realistic, data sets which reproduce selected statistical properties of the actual data. Third, these artificial data sets are sampled by Monte Carlo techniques to determine certain confidence interval coefficients appropriate to different fields, geographical regions, and averaging periods. Fourth, these confidence interval coefficients are used to place error bars on climatic signals derived from the actual data set. The technique is illustrated by the analysis of historical sea surface temperature and sea level pressure data in the eastern tropical Pacific Ocean.

Albedos And Glitter Patterns Of A Wind-Roughened Sea Surface

The downward albedo (irradiance reflectance) r_ and the upward albedo r+ of a random air-water surface, formed by capillary waves, are computed as a function of lighting conditions and wind speed by Monte Carlo means for incident unpolarized radiant flux. The possibility of multiple scattering of light rays and of ray-shielding of waves by other waves is included in the calculations. The Monte Carlo procedure is used to generate reflected and transmitted glitter patterns as functions of wind speed and sun position. Plots of the Monte Carlo r± are drawn as functions of wind speed and angle of incidence of light rays. The albedos r±; are also found for various continuous radiance distributions simulating overcast skies and upwelling submarine light fields just below the air-water surface.