Potential Predictability in the NCEP CPC Dynamical Seasonal Forecast System

Monthly and seasonal predictions of mean atmospheric states have traditionally been viewed as a boundary forcing problem, with little regard for the role of atmospheric initial conditions (IC). The potential predictability of these mean states is investigated using hindcasted monthly mean January (JAN) and seasonal mean January– February–March (JFM) 200-hPa geopotential heights from the National Centers for Environmental Prediction Climate Prediction Center (NCEP CPC) Dynamical Seasonal Prediction System along with the corresponding data from the NCEP–National Center for Atmospheric Research (NCAR) reanalysis for the period 1980–2000. With lead times ranging from 1 to 4 months, analyses of variance tests are employed to separate the total variability into an unpredictable internal component, due to atmospheric dynamics, and a potentially predictable external component, due to the boundary forcing. These components represent the noise and signal, respectively, and areas where the signal exceeds the noise designate where time averages could be potentially predicted with some degree of skill. Temporal anomaly correlations (ACs) between ensemble-averaged model height anomalies and reanalysis height anomalies also provide a measure of the model skill. Comparisons between the results of these tests for the different initialization times confirm that, for this model, the atmospheric initial conditions have little effect on the monthly and seasonal means for lead times of one month or more. The model proves to be highly skillful in the Tropics, as expected. Signal-to-noise ratios (SNRs) and ACs also show four areas in the extratropics displaying significant skill: the South Pacific Ocean, Southern Ocean, Southeast Asia, and the Pacific–North America (PNA) region. The skill found in the extratropics outside of the PNA region is highly encouraging. SNRs for JFM are approximately twice those for JAN, suggesting that seasonal forecasts are more reliable than monthly forecasts. Anomaly correlations for El Niño–Southern Oscillation (ENSO) warm and cold events are markedly higher than correlations for both the period 1980–2000 and the subset of ENSO neutral events. The model's ability to accurately capture changes in the atmosphere in response to changes in sea surface temperatures (SSTs) suggests that accurate forecasting of SSTs in the ocean could lead to more accurate forecasts of atmospheric conditions associated with ENSO warm and cold events.