Predictability of Coupled GCM Forecasts: NCEP CFS, CliPAS, and DEMETER

Emilia K. Jin

The SST predictability of CGCM hindcasts is investigated by analyzing structure of systematic error and estimating the growth of forecast error from small initial perturbations. Focusing on the NCEP Coupled Forecast System (CFS) having 9-month integrations for all 12 calendar months, the CGCM datasets that come from the CliPAS (Climate Prediction and its Application to Society) and DEMETER (European Multimodel Ensemble system for seasonal to inTERannual prediction) projects are used. The 12 models used are fully coupled ocean-land-atmosphere dynamical seasonal prediction systems with 5- to 9-month integrations for 3 to 15 different initial conditions for summer and winter seasons in the common 23 years from 1981 to 2003.

For up to two forecast months, the SST systematic error more than doubles over the whole global ocean. Beyond two months lead time, the subsequent increase shows a clear seasonal and regional dependence irrespective of lead time. After removing the systematic error, the root-mean-square error of SST anomaly also shows a clear seasonality distinguished from that of the systematic error. From the initial state, the growth of forecast error and the lower limit of error in the forecast system are investigated with respect to lead time. In coupled GCMs, initial error growth is saturated within two months and then error growth is following the identical model error for all initial cases. Therefore, Lorenz curve of ensemble mean is not growing. While, Lorenz curve of individual member in CFS grows as fast as forecast error because it has large ensemble spread due to instability of coupled system.

Overall forecast skills of the state-of-the-art coupled GCMs are assessed. Focusing on tropical Pacific region, forecasted annual mean, annual cycle, and its influence on forecast skill is analyzed with respect to lead month. The predictability with respect to ENSO phase shows that the phase locking of the ENSO to the mean annual cycle has an influence on the seasonal dependence of skill. Growth phase of both warm and cold events is more predictable than decay phase and normal events are far less predictable than warm and cold events. As a reference, dynamic-statistical SST forecast skill for tier-two forecast system is also compared.

The behavior of multiple CGCMs in long simulations is also investigated as the cause of forecast error in short-term forecasts with respect to lead time. The main analysis focuses on the CFS and the SINTEX, SNU and UKMO models are analyzed also, since they provide both more than 50-year control simulations and 23-year forecasts. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related with model properties having a long life cycle with a summer peak that differs from observations, as shown in the long run case. For other models, the systematic errors in the long run - for example, mean bias, phase shift, weak amplitude, and wrong seasonal cycle - are reflected in the forecast skill as a major factor limiting predictability. Accordingly, the influence of coupled model errors on real forecasts is an important factor degrading the predictability after the influence of initial conditions fades out with respect to lead time.