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CO 2 concentration data in the atmosphere are widely known to possess a seasonal cycle, largely due to plant photosynthesis and respiration, superimposed upon an upward trend that is largely due to increasing fossil fueI use. In this paper we assess the information contained in the seasonal component of atmospheric CO 2 data by applying modern techniques of time series decomposition to monthly average CO 2 observations at three locations. At Mauna Loa and South Pole, which have the longest time series, the amplitudes of the seasonal components are found to be increasing with time, from • 5.6 ppm in 1958 to •6.2 ppm in 1978 at Mauna Loa and from • 1.0 ppm in 1965 to ~ 1.3 ppm in 1978 at the South Pole. We consider four possible causes of the CO 2 seasonal behavior--changes in the seasonal pattern of fossil fuel use, increasing vegetation, increasing global photosynthetic activity, and changes in ocean temperature--and conclude that it is most likely that the CO 2 seasonal behavior reflects an increase in global photosynthetic activity. 1. INTRODUCTION Many geophysical time series contain seasonal variation. One example is CO2 concentration data in the atmosphere, which are widely known to possess a seasonal cycle, largely due to plant photosynthesis and respiration, superimposed upon an upward trend that is largely due to increasing fossil fuel use [Keeling et al., 1976a, b]. The upward trend has received great publicity because of predictions that further increases in CO2 may have the potential to produce changes in global climate [Hansen et al., 1981; Kukla and Gavin, 1981]. Although the seasonal behavior of the data has been studied as well [Machta, 1972; Hall et al., 1975; Bacastow et al., 1981a, b; Pearman and Hyson, 1980, 1981], it has received somewhat less attention than the upward trend since the seasonal component has been regarded as a relatively stable periodic fluctuation of the concentrations. However, because the seasonal oscillations reflect, at least in part, the effect of vegetation on atmospheric CO2, they provide important information about the properties of plant processes on the earth.During the past several decades, substantial effort has been devoted to developing statistical procedures for describing seasonal variation in time series. Thus far the major area of application has been economic time series [Zellner, 1978], but the methodology is not tied to economic applications and has potential widespread applicability in many other disciplines, including geophysics.In this paper we will describe SABL [Cleveland and Terpenning, 1981; Cleveland et al., 1982], a recently developed set of procedures for decomposing time series into three components: trend, seasonal, and the remaining variation, which is called the irregular. We will use the SABL methodology to decompose atmospheric CO2 in order to assess the seasonal variation.

This article discusses through three examples several new methods to aid in the analysis of large contingency tables. The general goal is to give better understanding of specific contingency tables, both by comparing how various log-linear/logistic models fit and through clearer interpretations of the resulting fits. For model selection, we show how to focus on a subset of simple, good-fitting models, beginning with a plot of a goodness-of-fit statistic versus residual degrees of freedom for all of the fitted models. To assess whether a particular model is adequate, we demonstrate that certain plots of residuals can reveal interesting effects that are often otherwise hidden. For model summarization and interpretation, we plot odds-ratio factors with confidence intervals to show the effects of explanatory variables in a concise and appealing way. The first example involves the relationship of job satisfaction to demographic variables for craft employees of a large corporation. The data presented consist of a fiveway contingency table with about 10,000 counts. Job satisfaction for such employees increased with age and was higher in the Southwest and West than in the Northeast. Of four race-by-sex groups, the most satisfied was nonwhite males; the least satisfied was nonwhite females. Another example gives a six-way table with about 1,200 counts concerning whether or not high-school students think they will need mathematics in their future work. Among other results, for students planning to take a job right after graduation, those from suburban schools had odds about 2.6 times those from urban schools of thinking that mathematics will be useful. Moreover, among urban students, males had odds of finding mathematics useful about 2.1 times those for females, but there was little difference between the odds for males and females among suburban students. The third example, drawn from the literature, relates knowledge about cancer to four dichotomous variables. We compare our analysis with earlier ones.

This paper contains a statistical summary of the 14,000,000 measurements taken during 27 rainfalls in a six‐month period in 1967 from a 96‐station, rapid‐response rain guage network spread over a rectangular area 13 by 14 kilometers centered near Crawford Hill, New Jersey. The analysis emphasizes rain rates greater than 50 millimeters per hour, which interfere with radio transmission in the 10 to 30 GHz frequency range. Heavy rain rates are relatively rare events, come in irregular bursts, and do not appear amenable to description by simple analytic distributions. This paper presents statistics concerning the behavior of rain rates at a point in space, the relationship of rain rates separated in space or time, and the relationship of average rain rates on pairs of paths in various configurations.

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