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Robert E. Livezey

The widely held belief that monthly and seasonal predictions, while containing some information, are not skillful enough to be of economic benefit, is reexamined through an extended example of winter seasonal temperature skill as it relates to the natural gas industry. A case is made that forecasts of mean conditions for periods as long as a season can be made with appreciable reliability for certain parameters, places, seasons, and situations, thereby making them potentially beneficial to certain users.

Several opportunities to improve the reliability of these forecasts over the next several years are described in the context of operational systems currently used to make predictions for the United States. Finally, the levels and types of research necessary to realize the potential benefits to users of skillful long-range forecasts are outlined. It is argued that it makes little sense, from a scientific or societal point of view, to neglect research on prediction of intraseasonal to interannual time scales (the long-range problem) in the face of growing concern and interest in climate fluctuations (such as global warming) on longer time scales.

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Robert E. Livezey and Richard Tinker

Various aspects of the severe heat wave that affected the midwestern and eastern United States in mid-July 1995 and led to hundreds of heat-related deaths are examined. First, the event is placed in historical context through examination of relatively long records at several affected sites. Next, the origins of both the strong high pressure cell and the unusually large moisture content of the air mass are traced. This is followed by a brief summary that concludes with the suggestion that longer-term processes played minor roles at best in the event. Finally, microclimatic factors in the Chicago metropolitan area are considered for their role in exacerbating conditions in the city most severely affected by the heat wave.

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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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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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Thomas R. Karl, Robert E. Livezey, and Edward S. Epstein

A long-time series (1895–1984) of mean areally averaged winter temperatures in the contiguous United States depicts an unprecedented spell of abnormal winters beginning with the winter of 1975–76. Three winters during the eight-year period, 1975–76 through 1982–83, are defined as much warmer than normal (abnormal), and the three consecutive winters, 1976–77 through 1978–79, much colder than normal (abnormal). Abnormal is defined here by the least abnormal of these six winters based on their normalized departures from the mean. When combined, these two abnormal categories have an expected frequency close to 21%. Assuming that the past 89 winters (1895–1984) are a large enough sample to estimate the true interannual temperature variability between winters, we find, using Monte Carlo simulations, that the return period of a series of six winters out of eight being either much above or much below normal is more than 1000 years. This event exceeds the calculated return period of the three consecutive much colder than normal winters (1976–77 through 1978–79) all falling into a much below normal category, i.e., one that is expected to contain approximately 10% of the data. The more moderate winters of 1981–82 and 1983–84 can also be considered abnormal by relaxing the limits necessary for an abnormal classification, but this gives a return period of 467 years for the spell of eight abnormal winters in the nine consecutive winters 1975–76 through 1983–84.

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Richard J. Reed, Robert M. White, Edward S. Epstein, Richard A. Craig, Harry Hamilton, Robert E. Livezey, David Houghton, and Frederick Carr
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Anthony G. Barnston, Ants Leetmaa, Vernon E. Kousky, Robert E. Livezey, Edward A. O'Lenic, Huug Van den Dool, A. James Wagner, and David A. Unger

The strong El Niño of 1997–98 provided a unique opportunity for National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center (CPC) forecasters to apply several years of accumulated new knowledge of the U.S. impacts of El Niño to their long-lead seasonal forecasts with more clarity and confidence than ever previously. This paper examines the performance of CPC's official forecasts, and its individual component forecast tools, during this event. Heavy winter precipitation across California and the southern plains–Gulf coast region was accurately forecast with at least six months of lead time. Dryness was also correctly forecast in Montana and in the southwestern Ohio Valley. The warmth across the northern half of the country was correctly forecast, but extended farther south and east than predicted. As the winter approached, forecaster confidence in the forecast pattern increased, and the probability anomalies that were assigned reached unprecedented levels in the months immediately preceding the winter. Verification scores for winter 1997/98 forecasts set a new record at CPC for precipitation.

Forecasts for the autumn preceding the El Niño winter were less skillful than those of winter, but skill for temperature was still higher than the average expected for autumn. The precipitation forecasts for autumn showed little skill. Forecasts for the spring following the El Niño were poor, as an unexpected circulation pattern emerged, giving the southern and southeastern United States a significant drought. This pattern, which differed from the historical El Niño pattern for spring, may have been related to a large pool of anomalously warm water that remained in the far eastern tropical Pacific through summer 1998 while the waters in the central Pacific cooled as the El Niño was replaced by a La Niña by the first week of June.

It is suggested that in addition to the obvious effects of the 1997–98 El Niño on 3-month mean climate in the United States, the El Niño (indeed, any strong El Niño or La Niña) may have provided a positive influence on the skill of medium-range forecasts of 5-day mean climate anomalies. This would reflect first the connection between the mean seasonal conditions and the individual contributing synoptic events, but also the possibly unexpected effect of the tropical boundary forcing unique to a given synoptic event. Circumstantial evidence suggests that the skill of medium-range forecasts is increased during lead times (and averaging periods) long enough that the boundary conditions have a noticeable effect, but not so long that the skill associated with the initial conditions disappears. Firmer evidence of a beneficial influence of ENSO on subclimate-scale forecast skill is needed, as the higher skill may be associated just with the higher amplitude of the forecasts, regardless of the reason for that amplitude.

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Anthony G. Barnston, Huug M. van den Dool, Stephen E. Zebiak, Tim P. Barnett, Ming Ji, David R. Rodenhuis, Mark A. Cane, Ants Leetmaa, Nicholas E. Graham, Chester R. Ropelewski, Vernon E. Kousky, Edward A. O'Lenic, and Robert E. Livezey

The National Weather Service intends to begin routinely issuing long-lead forecasts of 3-month mean U. S. temperature and precipitation by the beginning of 1995. The ability to produce useful forecasts for certain seasons and regions at projection times of up to 1 yr is attributed to advances in data observing and processing, computer capability, and physical understanding—particularly, for tropical ocean-atmosphere phenomena. Because much of the skill of the forecasts comes from anomalies of tropical SST related to ENSO, we highlight here long-lead forecasts of the tropical Pacific SST itself, which have higher skill than the U.S forecasts that are made largely on their basis.

The performance of five ENSO prediction systems is examined: Two are dynamical [the Cane-Zebiak simple coupled model of Lamont-Doherty Earth Observatory and the nonsimple coupled model of the National Centers for Environmental Prediction (NCEP)]; one is a hybrid coupled model (the Scripps Institution for Oceanography-Max Planck Institute for Meteorology system with a full ocean general circulation model and a statistical atmosphere); and two are statistical (canonical correlation analysis and constructed analogs, used at the Climate Prediction Center of NCEP). With increasing physical understanding, dynamically based forecasts have the potential to become more skillful than purely statistical ones. Currently, however, the two approaches deliver roughly equally skillful forecasts, and the simplest model performs about as well as the more comprehensive models. At a lead time of 6 months (defined here as the time between the end of the latest observed period and the beginning of the predict and period), the SST forecasts have an overall correlation skill in the 0.60s for 1982–93, which easily outperforms persistence and is regarded as useful. Skill for extra-tropical surface climate is this high only in limited regions for certain seasons. Both types of forecasts are not much better than local higher-order autoregressive controls. However, continual progress is being made in understanding relations among global oceanic and atmospheric climate-scale anomaly fields.

It is important that more real-time forecasts be made before we rush to judgement. Performance in the real-time setting is the ultimate test of the utility of a long-lead forecast. The National Weather Service's plan to implement new operational long-lead seasonal forecast products demonstrates its effectiveness in identifying and transferring “cutting edge” technologies from theory to applications. This could not have been accomplished without close ties with, and the active cooperation of, the academic and research communities.

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