Flow-dependent and dynamical systems analyses of predictability of the Pacific-North American summertime circulation

Abstract

Forecast skills of numerical weather prediction (NWP) models and intrinsic predictability can be flow-dependent, e.g., different amongweather regimes. Here, we have examined the predictability of distinct Pacific-North American weather regimes in June-September. Fourweather regimes are identified using a self-organizing map analysis of daily 500-hPa geopotential height anomalies, and are shown to havedistinct and coherent links to near-surface temperature and precipitation anomalies over the North American continent. The 4 to 14-dayforecast skills of these 4 regimes are quantified for the ECMWF and the NCEP models (from the TIGGE project) and the Global EnsembleForecast System (GEFS). Based on anomaly correlation coefficient, persistence, and transition frequency, the highest forecast skills areconsistently found for regime 3 (Arctic high). In general, the least skillful forecasts are for regime 1 (Pacific trough). The instantaneous localdimension and persistence of each regime are computed using a dynamical systems-based analysis. The local dimension and persistenceare indicators of intrinsic predictability. This analysis robustly shows that regime 3 has the highest intrinsic predictability. The analysisalso suggests that overall, regime 1 has the lowest intrinsic predictability. These findings are consistent with the high (low) forecast skillsof NWP models for regime 3 (regime 1). Weather regime 1 is associated with above-normal temperature and precipitation anomalies overwestern North America and around the Gulf of Mexico region, indicating potentially important implications for the poor predictability ofthis regime. The dynamical systems analysis suggests that better estimates of initial conditions might improve the forecasts of this regime.