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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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Anthony G. Barnston and Robert E. Livezey

Abstract

An expanded version of the multifield analog prediction system developed by Barnett and Preisendorfer (1978) was described and applied to the winter season in Part I of this two-part series (Livezey and Barnston 1988). This second part reviews briefly the major design features detailed in Part I, and then describes the predictive skills in spring, summer, fall and all other intermediate 3-month seasons.

In none of the 11 nonwinter seasons is the United States surface temperature predicted with as much skill as in winter. The major winter skill peak (16%) extends partially into the following two seasons (January–February–March and February–March–April), and a secondary maximum in summer (13%) similarly includes the two following seasons (July–August–September and August–September–October). In both skillful periods of the year the skill tends to be greatest over the eastern third of the United States and the immediate Pacific Coast and lowest over the Rockies and Plateau. More predictor variables are used as criteria for analog selection during the skillful times of the year, while fewer are found to contribute to skill levels at other times. The annual cycle of skill of simple persistence forecasts has its primary maximum in August–September–October, when it slightly exceeds the skill of the analog method, and a secondary maximum in winter when it is outperformed by the analog method by a substantial margin. The analog-forecasted temperature patterns are found to be statistically largely independent of the temperature patterns of persistence forecasts at all times of the year.

In an exercise aimed at determining which part of the predicted period within the season is most skilfully forecast, it is found that from fall through winter the later month(s) of the season are better predicted than the earlier months, suggesting a potential for useful long-lead forecasting of subseasonal periods in much of the coldest part of the year.

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Robert E. Livezey and Marina M. Timofeyeva

The first 10 yr (issued starting in mid-December 1994) of official, long-lead (out to 1 yr) U.S. 3-month mean temperature and precipitation forecasts are verified using a categorical skill score. Through aggregation of forecasts over overlapping 3-month target periods and/or multiple leads, we obtain informative results about skill improvements, skill variability (by lead, season, location, variable, and situation), skill sources, and potential forecast utility. The forecasts clearly represent advances over zero-lead forecasts issued prior to 1995. But our most important result is that skill hardly varies by lead time all the way out to 1 yr, except for cold-season forecasts under strong El Niño or La Niña (ENSO) conditions. The inescapable conclusion is that this lead-independent skill comes from use of long-term trends to make the forecasts and we show that these trends are almost entirely associated with climate change. However, we also argue that climate change is not yet being optimally taken into account, so there is scope for improving the quality of the forecasts. Practically all other skill in the forecasts comes from exploitation of strong and predictable ENSO episodes for winter forecasts, out to a 6.5-month lead for precipitation and beyond 8.5 months for temperature. Apparently other sources of skill supported by existing research, including predictability inherent in weaker ENSO episodes and interactive feedbacks between the extratropical atmosphere and underlying surfaces, do not materially contribute to positive forecast performance. Compared to strong ENSO and climate change signals, other sources are too weak, unreliable, or poorly understood to detect an impact. Another consequence of the clear attribution of skill is that often-observed high regional/seasonal skills imply that the forecasts can be unambiguously valuable to a wide range of users. With these findings, steps (some immediate) can be taken to improve both the skill and usability of official long-lead forecasts.

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Anthony G. Barnston and Robert E. Livezey

Abstract

The association between the 11-year solar cycle and the tropospheric Northern Hemisphere climate in January-February for the 21 west QBO phase years in the 1951–88 period failed strongly in 1989. This failure is explained in part by the high Southern Oscillation (SO) episode of 1988/89, whose influence on the climate conflicted with that hypothesized from the solar flux/QBO in much of North America. The occurrence of high flux during the west QBO phase along with a high SO (i.e., mid tropical Pacific SST event) was unprecedented before 1989. Bivariate multiple linear regression is used to predict Northern Hemisphere 700 mb heights and United States surface temperatures on the basis of the solar flux and the SO for each QBO phase. Exploratory analyses are carried out to describe more generally the associations among flux, QBO, SO, and the climate.

Major findings are: 1) Interactions among the four phenomena include primarily the SO-climate and solar flux-climate relationships, with the QBO phase as a required stratifier for the latter. Within this stratified framework the solar flux and the SO are essentially independent influences on the tropospheric climate, but act with moderate strength in overlapping regions such that their effects may offset or enhance one another; and 2) the true strength and nature of the solar flux-QBO-climate association still has large uncertainty because of the small samples resulting from QBO stratification and sharing of predictive power with the SO.

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Robert E. Livezey and Thomas M. Smith

Abstract

No abstract available.

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Kingtse C. Mo and Robert E. Livezey

Abstract

Simultaneous and lagged correlation statistics have been calculated between time series of seasonal height anomalies at selected stations and extratropical grid-point anomalies in both hemispheres. The tropical stations in two major tropical precipitation zones, the Indo-China maritime continent and Africa, are well correlated with each other. These stations are also correlated with stations in the North Pacific and Australia, but the coefficients are smaller. The correlations between height anomalies at any of these stations and Northern Hemisphere height anomalies show a well-defined global pattern. Depending upon the location of the stations, the pattern is either a Pacific North American (PNA), a Tropical Northern Hemisphere (TNH) pattern or a mixed pattern having both elements. All three patterns, PNA, TNH and WPO (Western Pacific Oscillation), have been linked to tropical variations. The correlations between height anomalies at these well-correlated stations and the Southern Hemisphere height anomalies at the 500 mb level give the summer teleconnection pattern of Mo and White (1985). The vertical structure of patterns indicate that they are approximately equivalent barotropic.

The TNH pattern tends to be associated more with tropical variability for time scales longer than one season, while the PNA pattern is present in both high- and low-pass filtered analyses, although weakly in the former. Moreover, its low frequency connection to the tropics appears to be confined to ENSO years.

During ENSO years both patterns appear in both simultaneous and lagged maps, but in non-ENSO years, the TNH is weak in simultaneous charts.

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Robert E. Livezey and W. Y. Chen

Abstract

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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Daniel S. Wilks and Robert E. Livezey

Abstract

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, Jonathan D. Hoopingarner, and Jin Huang

Abstract

Quality analyses have been performed on a 21-yr record of monthly mean Northern Hemisphere extratropical 700-hPa height anomaly forecasts issued by the National Weather Service. A positive trend in skill noted a decade ago is shown to have continued to recent years. This trend is present in terms of overall reduction in squared error as well as individually in reduction of both phase and amplitude errors for all three subdomain sectors examined. The higher skill in the last decade principally is concentrated in forecasts for winter months and particularly over the oceans and at high latitudes and is attributed to advances in global numerical weather prediction. Prior to the 1980s, average forecast bias varied from region to region and overall was not large. Since then it has tended to be negative for all subsectors, mainly as a result of negatively biased height anomalies in midlatitudes for forecast months in the winter and spring. This bias is, perhaps, a reflection of the anomalous observed warmth during the period.

An attempt to improve the quality of the forecasts with a principal component filter had modest success in the sense that reductions in squared error were achieved aside from those that would have been expected from simple smoothing.

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Thomas M. Smith, Robert E. Livezey, and Samuel S. Shen

Abstract

An improved method for interpolating sparsely sampled climatological data onto a regular grid is shown. The method uses the spatial and temporal covariance of the field, along with the sparse data, to fill the full grid. This improves on similar methods that have recently been developed by eliminating the development of features that are not sufficiently supported by the data (i.e., overfitting). Statistical tests are used to tune the method to represent as much variability as the spatial–temporal information will support without overfitting. The method is further improved by a data-checking procedure that detects and removes suspect data. The method is developed and evaluated by interpolating tropical Pacific sea surface temperature (SST) monthly anomalies to a regular grid for the 1856–1995 period. Ship data averaged to 5° squares are used as input and are interpolated to a complete 1° grid. Comparing the results to interpolations using other methods shows this method’s quantitative improvements where satellite data are available for validation. Comparisons in the presatellite era show sharper and stronger anomaly patterns with this method, compared to another method developed for use with sparse data. Also shown are several periods when data are so sparse that only very weak SST anomalies may be reliably reconstructed in the tropical Pacific (i.e., before 1870 and 1915–25). In future research, the global SST and possibly other climatological fields will be gridded using improved methods.

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