Abstract
The ratio of the probability of at least one extremely cold or warm month (standardized departures ≥ 1.282 or ≤ −1.282) in a season with near-normal mean temperatures (standardized departures ≤ 0.524 but ≥−0.524) to the probability of such an event in abnormal seasons has been calculated using statewide average monthly temperatures (1895–1983) across the United States. Values of this ratio, termed the “ratio of variability” (RV), near one reflect nearly an equal probability of one or more extreme months in both near-normal and abnormal seasons, while values near zero indicate little chance of an extreme month in a near-normal season. The values of RV vary with geographic location and the time of year in a systematic predictable manner. The magnitude of RV is greater during the transition seasons than during either summer or winter. The gradients of RV are relatively flat in the autumn, but comparatively sharp in spring with a maximum in the east and central United States and a minimum in the west. In the summer the largest values of RV are found along the northern tier of states and along the mid-Atlantic coastal states, but during winter the central portions of the United States and portions of the northeast have the largest values of RV.
The two factors responsible for the seasonal changes and spatial gradients of RV are the spatial and temporal changes of the month-to-month persistence (or lack of persistence) of unusually cold or warm weather and the unequal contribution of variances within a season by each of the months in the season. This is demonstrated using Monte Carlo simulations of an autoregressive model. Users of seasonal average temperatures, whether in a forecast or hindcast mode, whose operations are sensitive to persistent unusually cold or warm temperatures within a season with near-normal temperatures, should be cognizant of the spatial and temporal changes of RV.
