The Geographical Distribution and Seasonality of Persistence in Monthly Mean Air Temperatures over the United States

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Abstract
Eighty years of monthly mean station temperatures are used to evaluate the persistence of monthly air temperature anomalies over the United States. The geographical and seasonal dependence of the monthly persistence are described in term of the day-to-day persistence of temperature anomalies, the influence of the large-scale atmospheric circulation, and inferred associations with the slowly varying properties of the earth's surface. The monthly persistence is generally smallest in the continental interior and largest in coastal regions. The seasonality of this spatial pattern is quite small, although the continental interior is characterized by a summer maximum. For the country as a whole, persistence is highest (0.30) in winter and summer and least (0.15) in fall and spring. For both raw and detrended data, the anomaly pattern correlations at lags of two and three months are much larger than would be expected from a first-order Markov process. The pattern of persistences computed using day-to-da... Abstract Eighty years of monthly mean station temperatures are used to evaluate the persistence of monthly air temperature anomalies over the United States. The geographical and seasonal dependence of the monthly persistence are described in term of the day-to-day persistence of temperature anomalies, the influence of the large-scale atmospheric circulation, and inferred associations with the slowly varying properties of the earth's surface. The monthly persistence is generally smallest in the continental interior and largest in coastal regions. The seasonality of this spatial pattern is quite small, although the continental interior is characterized by a summer maximum. For the country as a whole, persistence is highest (0.30) in winter and summer and least (0.15) in fall and spring. For both raw and detrended data, the anomaly pattern correlations at lags of two and three months are much larger than would be expected from a first-order Markov process. The pattern of persistences computed using day-to-da...

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