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  • Author or Editor: Tim P. Barnett x
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Alexander Gershunov
and
Tim P. Barnett

Seasonal climate anomalies over North America exhibit rather large variability between years characterized by the same ENSO phase. This lack of consistency reduces potential statistically based ENSO-related climate predictability. The authors show that the North Pacific oscillation (NPO) exerts a modulating effect on ENSO teleconnections. Sea level pressure (SLP) data over the North Pacific, North America, and the North Atlantic and daily rainfall records in the contiguous United States are used to demonstrate that typical ENSO signals tend to be stronger and more stable during preferred phases of the NPO. Typical El Niño patterns (e.g., low pressure over the northeastern Pacific, dry northwest, and wet southwest, etc.) are strong and consistent only during the high phase of the NPO, which is associated with an anomalously cold northwestern Pacific. The generally reversed SLP and precipitation patterns during La Niña winters are consistent only during the low NPO phase. Climatic anomalies tend to be weak and spatially incoherent during low NPO–E1 Niño and high NPO–La Niña winters. These results suggest that confidence in ENSO-based long-range climate forecasts for North America should reflect interdecadal climatic anomalies in the North Pacific.

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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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