Predictability of Coupled GCM Forecasts:
NCEP CFS, CliPAS, and DEMETER
Emilia K. Jin
COLA
Abstract:
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.