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Robert E. Livezey and Jae-Kyung E. Schemm

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Edward A. O'Lenic and Robert E. Livezey

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The relationship between the existence of low-frequency 700 mb height anomalies in the initial conditions of NMC's MRF global spectral model and subsequent 5-, 7-, and 10-day forecasts of 700 mb height from 1982 to 1988 is explored. Low-frequency 700 mb flow regimes are specified in each of four two-month seasons by performing a rotated principal component analysis (RPCA) on 38 or 39 year time series of daily, low-pass filtered 700 mb height analyses. In a given season, the amplitude time series (ATS) for each mode is used to decide which MRF forecast error maps should be used in forming a composite map corresponding to either the “+” phase or the “−” phase of the given mode. Several methods, including Monte Carlo simulations, are used to evaluate the statistical significance of the composite maps.

Many modes, including the Pacific North American (PNA) mode in winter and the leading summer mode, are found to be related to either unusually strong or unusually weak systematic error signature. Two different modes, one in spring and one in autumn, corresponding to quasi-stationary patterns over the United States and the North Atlantic, respectively, are related to unusually strong forecast error signatures. A statistically significant number of such modes is found in each of the four seasons, with the number of such results being smallest in autumn, and 1argest in spring. The results also indicate that the MRF model response to the presence of low-frequency regimes in the initial conditions is such that composite error signatures have a component with opposite phase and amplitude for opposite phases of a given mode (linear response). The overall results demonstrate the feasibility of using this technique to identify mode-linked forecast error signatures, and provides a potential opportunity to correct forecasts in the MRF, and possibly in other models, by removing the appropriate systematic error signatures.

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Robert E. Livezey and Sherwin W. Jamison

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Operational long-range weather prediction in the Soviet Union is reviewed. Methods for producing forecasts at the 5- and 10-day, monthly and seasonal range are described in terms of the synoptic, statistical and hydrodynamic tools available to Soviet forecasters. Skill scores for these forecasts published by the Soviets are summarized and examined.

Skill scores for Soviet operational forecasts of mean seasonal (about two months) temperature anomaly and precipitation category are computed separately for regions, seasons and years and compared to persistence skill scores. In addition, forecast-observation sets for the sign of the mean temperature anomaly are tested for “no skill.” The forecasts for the sign of the mean temperature anomaly are found to be best by region for the Arctic and by season for March through April, but generally do not outperform persistence, exhibit demonstrable skill, or show an improvement trend over the verification period. Forecasts of the mean precipitation category are shown to be consistently better than persistence, but to have quite modest skill scores.

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Edward A. O'Lenic and Robert E. Livezey

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Rotated principal component analysis (RPCA) is a powerful tool for studying upper air height data because of its ability to distill information about the variance existing in a large number of maps to a much smaller set of physically meaningful maps which together explain a large fraction of the variance of she input dataset. However, in order to achieve this, one faces the problem of deciding how many eigenmodes to rotate. A discussion of the dangers of incorrectly choosing the rotation point and a quasi-objective technique that leads to a good compromise between over- and underrotation are presented. Finally, the use of RPCA for detecting errors and inconsistencies in upper air data along with two examples is discussed.

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Anthony G. Barnston and Robert E. Livezey

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Orthogonally rotated principle component analysis (RPCA) of Northern Hemisphere 1-month mean 700 mb heights is used to identify and describe the seasonality and persistence of the major modes of interannual variability. The analysis is detailed and comprehensive, in that 1) a high resolution, approximately equal-area 358-point grid is used for the virtually maximum possible 35-year period of record, 2) a positive bias in the NMC data base in the early 1950s in the subtropics is largely eliminated for the first time, and 3) homogeneous, separate analyses of each month of the year are carried out, detailing the mouth-to-month changes in the dominant circulation patterns.

Winter results are similar to those of other recent RPCA and teleconnection studies except that some less obvious patterns are identified and further detail of the better-known patterns is provided. Two north-south dipole patterns are found over the Pacific Ocean (West Pacific Oscillation and East Pacific pattern) and over the Atlantic Ocean (North Atlantic Oscillation and East Atlantic pattern); two uncorrelated phases of 3-center, approximately east–west wave trains are found over the Eurasian continent (Eurasian Type 1 and Eurasian Type 2 patterns) and North American continent (Pacific/North American and Tropical/Northern Hemisphere patterns) and a Siberian north–south dipole emerges (Northern Asian pattern).

The strongest summer pattern is also the strongest winter pattern—the North Atlantic Oscillation, which systematically contracts northward in summer and expands southward in winter, being the only pattern found for every month of the year. Another strong summer pattern, named Subtropical Zonal, is a north–south dipole of great zonal extent at low latitudes. A single-center Asian summer pattern is also found. Two other regular patterns are found during transition seasons.

An evaluation of the intermonthly and interseasonal persistence of the patterns shows that many of the strong winter patterns have statistically significant persistence in the middle of their active periods, and the Subtropical Zonal summer pattern shows considerable interannual, as well as intermonthly and interseasonal persistence.

The robustness of the RPCA results is examined through consistency with results of other studies and of adjacent month solutions within this study, and by replicating the results using 3-month and 10-day means of 700 mb height. (Results using 10-day means point the way to use of a larger sample without noticeably obscuring the low-frequency signal.) Moreover, the analyses are repeatedly rerun withholding different sets of years from the record, and results are objectively compared with those using the full 35-year record. The conclusion from all considerations is that the RPCA method provides a physically meaningful, as well as statistically stable product with the simplicity of teleconnection patterns but with pattern choice and depiction superior to those of the teleconnection method.

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Robert E. Livezey and W. Y. Chen

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The effects of number and interdependence in evaluating the collective significance of finite sets of statistics are frequently non-trivial, especially for spatial networks of time-averaged meteorological data. These effects can be taken into account in two steps: By first prescreening for significance assuming data independence and then, if necessary, by taking into consideration dependence through the use of estimated effective degrees of freedom and the binomial distribution or, failing that, Monte Carlo simulation. Seasonal averages of 700 mb height data are used to illustrate the problem and to demonstrate how the data set properties are taken into account. Papers by Hancock and Yarger (1979), Nastrom and Belmont (1980) and Williams (1980) are critically examined in light of these considerations and Monte Carlo strategies for clarification of ambiguities suggested.

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Anthony G. Barnston and Robert E. Livezey

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A recently discovered association between the 11-year solar cycle and the Northern Hemispheric low-frequency atmospheric circulation structure, which is most easily delectable when the two phases of the Quasi-biennial Oscillation (QBO) are considered individually, is described and subjected to global statistical significance tests.

Highly significant relationships are found during the January–February period. This is especially true for the west QBO phase, in which the solar flux is positively correlated with 700 mb heights and surface temperatures over central and northern Canada, and negatively correlated with heights in the western Atlantic along 30°N and with temperature in the southern and much of the eastern portions of the United States. The pattern of the flux-height correlation field resembles primarily the Tropical/Northern Hemisphere (TNH) long-wave circulation pattern and secondarily the North Atlantic Oscillation (NAO) pattern. For east QBO phase years a different structure is found, and for all years pooled a weaker but quite Characterizable pattern emerges.

January–February correlations are studied for sensitivity to lead time in the QBO phase definition and for shorter period means for the west QBO phase. The latter inquiry reveals a concentration of the west phase relationship during the latter half of January.

The climate of the October–November period also appears to participate, to a lesser but significant degree, in a solar–QBO relationship for west phase QBO years.

For the west QBO phase, the January–February solar flux versus 700 mb height (and United States–Canada surface temperature) correlation pattern contains sufficient amplitude and field significance to be exploited for operational forecasting purposes at the Climate Analysis Center. However, in the absence of a verifiable physical basis of the solar–QBO–atmosphere association, and because the 45 mb stratospheric winds were selected to characterize the QBO in an a posteriors manner, the relationships are accepted with caution and will be regularly reevaluated.

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Robert E. Livezey and Anthony G. Barnston

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The objective prediction technique presented by Preisendorfer and Mobley called the Probable Markover (or contingency method) is studied in some detail with a much longer dataset than that available for the previous work. It is pointed out and then clearly demonstrated how the scheme as originally reported enjoys two artificial advantages over other objective and “humon” approaches. These advantages lead to considerable overestimation of its realizable skill relative to the other methods. In fact when placed in the position of other forecasters, namely with no prior knowledge of the future, it fails to produce sets of forecasts distinguishable from random ones. The reasons for this are discussed through example and through its theoretical relationship to simple persistence forecasts.

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Thomas M. Smith and Robert E. Livezey

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Specifications of 1- and 3-month mean Pacific–North America region 700-hPa heights and U.S. surface temperatures and precipitation, from global sea surface temperatures (SSTs) and the ensemble average output of multiple runs of a general circulation model with the same SSTs prescribed, were explored with canonical correlation analysis. In addition to considerable specification skill, the authors found that 1) systematic errors in SST-forced model variability had substantial linear parts, 2) use of both predictor fields usually enhanced specification performance for the U.S. fields over that for just one of the predictor fields, and 3) skillful specification and model correction of the heights and temperatures were also possible for nonactive or transitional El Niño–Southern Oscillation situations.

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Robert E. Livezey, Michiko Masutani, and Ming Ji

The feasibility of using a two-tier approach to provide guidance to operational long-lead seasonal prediction is explored. The approach includes first a forecast of global sea surface temperatures (SSTs) using a coupled general circulation model, followed by an atmospheric forecast using an atmospheric general circulation model (AGCM). For this exploration, ensembles of decade-long integrations of the AGCM driven by observed SSTs and ensembles of integrations of select cases driven by forecast SSTs have been conducted. The ability of the model in these sets of runs to reproduce observed atmospheric conditions has been evaluated with a multiparameter performance analysis.

Results have identified performance and skill levels in the specified SST runs, for winters and springs over the Pacific/North America region, that are sufficient to impact operational seasonal predictions in years with major El Niño–Southern Oscillation (ENSO) episodes. Further, these levels were substantially reproduced in the forecast SST runs for 1-month leads and in many instances for up to one-season leads. In fact, overall the 0- and 1-month-lead forecasts of seasonal temperature over the United States for three falls and winters with major ENSO episodes were substantially better than corresponding official forecasts. Thus, there is considerable reason to develop a dynamical component for the official seasonal forecast process.

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