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  • Author or Editor: Robert E. Livezey x
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Robert R. Dickson and Robert E. Livezey


It is known that the Southern Oscillation Index (SOI) and the mean sea surface temperature off the Peru Coast are highly coherent and that variations of the latter are dominated by infrequent warming episodes. The present study examines the relative contribution of these warming episodes to the covariance of statistically significant correlations between the fall SOI and winter mean 700 mb heights in the Northern Hemisphere. The degree of dominance of the warming episode years in this context is evaluated by Monte Carlo methods.

It was found that, for the 30-year period studied, data pairs following tropical east Pacific warming events contributed disproportionately to major correlation maxima in much of the Northern Hemisphere. Such covariance concentrations, however, were found to be fairly likely outcomes (probability > 9%) if groups of years are chosen at random from the appropriate covariance arrays. Thus, we conclude that the influence of the fall SOI upon the subsequent winter mean 700 mb height distribution is a rather pervasive one, not limited to tropical east Pacific warming situations.

In contrast to other areas, correlation maxima in the North American sector received disproportionately small covariance contributions from the warming episode years. In northwest Canada, the contribution of those years was small and opposite in sign to the total covariance.

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Daniel S. Wilks and Robert E. Livezey


Eleven alternatives to the annually updated 30-yr average for specifying climate “normals” are considered for the purpose of projecting nonstationarity in the mean U.S. temperature climate during 2006–12. Comparisons are made for homogenized U.S. Historical Climatology Network station data, corresponding nonhomogenized station data, and spatially aggregated (“megadivision”) data. The use of homogenized station data shows clear improvement over nonhomogenized station data and spatially aggregated data in terms of mean-squared specification errors on independent data. The best single method overall was the most recent 15-yr average as implemented by the Climate Prediction Center (CPC15), consistent with previous work using nonhomogenized and spatially aggregated data, although “hinge” functions with the change point fixed at 1975 performed well for the spring and summer seasons. A hybrid normals-specification method, using one of these piecewise continuous functions when the regressions are sufficiently strong and the CPC15 otherwise, exhibits a favorable trade-off between squared error and bias that may make it an optimal choice for some users.

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Robert E. Livezey, Konstantin Y. Vinnikov, Marina M. Timofeyeva, Richard Tinker, and Huug M. van den Dool


WMO-recommended 30-yr normals are no longer generally useful for the design, planning, and decision-making purposes for which they were intended. They not only have little relevance to the future climate, but are often unrepresentative of the current climate. The reason for this is rapid global climate change over the last 30 yr that is likely to continue into the future. It is demonstrated that simple empirical alternatives already are available that not only produce reasonably accurate normals for the current climate but also often justify their extrapolation to several years into the future. This result is tied to the condition that recent trends in the climate are approximately linear or have a substantial linear component. This condition is generally satisfied for the U.S. climate-division data. One alternative [the optimal climate normal (OCN)] is multiyear averages that are not fixed at 30 yr like WMO normals are but rather are adapted climate record by climate record based on easily estimated characteristics of the records. The OCN works well except with very strong trends or longer extrapolations with more moderate trends. In these cases least squares linear trend fits to the period since the mid-1970s are viable alternatives. An even better alternative is the use of “hinge fit” normals, based on modeling the time dependence of large-scale climate change. Here, longer records can be exploited to stabilize estimates of modern trends. Related issues are the need to avoid arbitrary trend fitting and to account for trends in studies of ENSO impacts. Given these results, the authors recommend that (a) the WMO and national climate services address new policies for changing climate normals using the results here as a starting point and (b) NOAA initiate a program for improved estimates and forecasts of official U.S. normals, including operational implementation of a simple hybrid system that combines the advantages of both the OCN and the hinge fit.

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