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Andrew R. Solow

Abstract

An approach to reconstructing a partially observed annual time series of tropical cyclone counts is presented. The approach is based on a simple model of the time series of true counts and on a simple model of the way in which this time series is observed through time. The approach is applied to a record of observed tropical cyclone counts for the Australian region for the period 1910–88. Some diagnostics are presented that indicate the model performs fairly well at reproducing the behavior of the observed counts.

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Andrew R. Solow

Abstract

There is considerable interest in detecting a long-term trend in hurricane intensity possibly related to large-scale ocean warming. This effort is complicated by the paucity of wind speed measurements for hurricanes occurring in the early part of the observational record. Here, results are presented regarding the maximum observed wind speed in a sparsely randomly sampled hurricane based on a model of the evolution of wind speed over the lifetime of a hurricane.

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Andrew R. Solow

Abstract

A Bayesian approach to statistical inference about climate change based on the two-phase regression model is presented. This approach is useful when nonobservational information is available about possible climate change. This information may refer to the timing or the nature of the possible change. The approach is applied to a historic temperature record.

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Andrew R. Solow

Abstract

Historic records of global or hemispheric temperature are an important source of information about climate change. In order to analyze such records statistically, it is necessary to have some knowledge about the behavior of their variances through time. A modified version of robust locally weighted regression, a nonparametric regression procedure, is used to study the behavior of the variance for a record of Southern Hemisphere temperature deviations. Sampling considerations suggest that the variance should decrease through time, as new recording stations are added to the sampling network. Surprisingly, the variance is found to remain virtually constant through time.

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Andrew R. Solow

Abstract

A statistical model is presented of a recently compiled record of monthly extratropical storm counts for the mid-Atlantic coast of the United States for the period 1942–83. The counts are modeled as a Poisson process with nonstationary mean function. The mean function is decomposed into a secular component and a seasonal cycle. Because the form of the secular component is unknown, a nonparametric regression approach suitable for Poisson data is used to estimate it. The estimated secular component is generally constant through the 1950s, then declines through the 1970s. The estimate is found to be statistically significant. A Fourier series involving two harmonics is fit to the seasonal cycle. A preliminary check indicates that the seasonal cycle remains stable through time. Some diagnostics based on suitably defined residuals are presented that generally confirm the goodness-of-fit and distributional assumptions underlying the model.

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Andrew R. Solow

Abstract

A simple, informal method is presented for discriminating between competing models of trend in a climate record. The method is applied to a tide gauge record of relative see level at Brest for the period 1807–1970. Although relative sea level at Brest appears to have accelerated over this period, it is impossible to distinguish between a smooth acceleration and one that occurred over a relatively short period of time.

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Andrew R. Solow

Abstract

Climate records sometimes have the form of point processes (i.e., observations of the times of occurrence of a specified type of event). A central problem in the analysis of point process data is the estimation of the rate function, defined as the expected number of events occurring in a time interval of unit length. This paper describes some simple nonparametric methods for estimating the rate function and for assessing its statistical significance. The methods are applied to the freezing history of Lake Konstanz.

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Andrew R. Solow

Abstract

This note describes and applies a test for trend in the frequency of El Niño events over the period 1525–1987. Although there appears to have been a significant increase in frequency over this period, this result is consistent with an overall increase in the completeness of the historical record. When the analysis is repeated for the later part of the period and for strong events alone, no significant trends are found.

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Andrew R. Solow

Abstract

A statistical test for detecting a change in the behavior of an annual temperature series is presented. The test is based on the two-phase regression model. By trading the hypothesized time of change as an unknown parameter, the approach allows an inference to be made about the time of change. The approach also avoids a serious problem, called data-dredging, that can arise in testing for change occurring at a specified time. The test is applied to a series of Southern Hemisphere temperatures, and the hypothesis of no change cannot be rejected.

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Andrew R. Solow and Laura Moore

Abstract

The record of annual counts of basinwide North Atlantic hurricanes is incomplete prior to 1946. This has restricted efforts to identify a long-term trend in hurricane activity to the postwar period. In contrast, the complete record of U.S. landfalling hurricanes extends back to 1930 or earlier. Under the assumption that the proportion of basinwide hurricanes that make landfall is constant over time, it is possible to use the record of landfalling hurricanes to extend a test for trend in basinwide hurricane activity beyond the postwar period. This note describes and illustrates a method for doing this. The results suggest that there has been a significant reduction in basinwide hurricane activity over the period 1930–98.

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