Current ENSO Information
Summary of ENSO Model Forecasts
20 May 2004
Note on interpreting model forecasts
The following graph and table show forecasts made by dynamical and statistical
models for SST in the Nino 3.4 region for nine overlapping 3-month periods.
Note that the expected skills of the models, based on historical performance,
are not equal to one another. The skills also generally decrease as the
lead time increases. Thirdly, forecasts made at some times of the year generally
have higher skill than forecasts made at other times of the year--namely,
they are better when made between June and December than when they are made
between February and May. Differences among the forecasts of the models reflect
both differences in model design, and actual uncertainty in the forecast of
the possible future SST scenario.
Discussion of current forecasts
The set of dynamical and statistical model forecasts issued during late
April and early May 2004 shows a range of possible sea surface temperature
conditions for the coming 2 to 10 months (May - June - July 2004 through January
- February - March 2005). Most models are indicating near-neutral conditions
over the coming several seasons. However, many of them indicate some degree
of positive anomaly, and several of these are strong enough to be considered
at least a weak El Nino.
Table 1. Forecast SST Anomalies (deg
C) in the Nino 3.4 Region
| |
Seasons (2004-05) |
|
Model
|
MJJ
|
JJA
|
JAS
|
ASO
|
SON
|
OND
|
NDJ
|
DJF
|
JFM |
| Dynamical
models |
|
NASA/NSIPP
model
|
0.3
|
0.3
|
0.3
|
0.4
|
0.5
|
0.6
|
0.7
|
0.8
|
0.8
|
|
NCEP
Coupled model
|
0.3
|
0.4
|
0.5
|
0.6
|
0.6
|
0.8
|
0.9
|
|
|
NCEP Coupled Fcst Sys
|
-0.1
|
-0.2
|
-0.3
|
-0.2
|
-0.1
|
-0.0
|
-0.0
|
|
|
Japan
Met. Agency model
|
0.6
|
0.8
|
0.9
|
1.0
|
1.1
|
|
|
|
|
Scripps
Inst. HCM
|
0.1
|
0.2
|
0.3
|
0.4 |
0.5 |
0.6 |
0.7 |
0.7 |
0.7 |
Lamont-Doherty
model
|
0.4
|
0.6
|
0.6
|
0.8
|
0.9
|
1.1
|
1.0
|
|
|
|
POAMA
(Austr) model
|
0.2
|
0.5
|
0.7
|
0.8
|
0.7
|
0.7
|
0.6
|
|
|
ECMWF
model
|
0.4
|
0.5
|
0.5
|
|
|
|
|
|
|
|
SNU
(Korea) model
|
-0.2
|
-0.1
|
0.0
|
0.1
|
0.1
|
0.0
|
-0.0
|
-0.1 |
-0.1 |
ZHANG
ICM model
|
0.1
|
0.1 |
0.1 |
0.1
|
0.1
|
0.1
|
0.1
|
0.1 |
0.0 |
|
ECHAM/MOM
|
-0.1
|
0.2
|
0.3
|
|
|
|
|
|
|
|
COLA
Anomaly model
|
-0.4
|
-0.4
|
-0.5
|
-0.5
|
-0.5
|
-0.5
|
-0.4
|
-0.2
|
0.1
|
|
Average, dynamical models
|
0.1
|
0.2
|
0.3
|
0.4
|
0.4
|
0.4
|
0.4
|
|
|
| Statistical
models |
|
NCEP/CPC
Markov model
|
0.0
|
0.1
|
0.1
|
0.2
|
0.3
|
0.4
|
0.5
|
0.6
|
0.5
|
|
NOAA/CDC
Linear Inverse
|
0.1
|
0.1
|
0.0
|
0.0
|
-0.0
|
-0.1
|
-0.1
|
-0.1
|
-0.1
|
|
Dool
Constructed Analog
|
0.2
|
0.5
|
0.5
|
0.5
|
0.5
|
0.6
|
0.7
|
0.8
|
0.7
|
|
NCEP/CPC
Can Cor Anal
|
0.5
|
0.6
|
0.7
|
0.7
|
0.9
|
0.0
|
1.1
|
1.0
|
0.9
|
|
Landsea/Knaff
CLIPER
|
0.0
|
0.0
|
-0.0
|
-0.0
|
0.0
|
0.1
|
0.1
|
0.1
|
0.0
|
|
Univ.
BC nonlinear CCA
|
-0.0
|
0.0
|
0.1
|
0.2
|
0.3
|
0.4
|
0.5
|
0.6
|
0.7
|
|
FSU
Regression
|
0.3
|
0.3
|
0.4
|
0.5
|
0.6
|
0.6
|
0.7
|
0.6
|
0.5
|
|
TDC
- UCLA
|
0.2
|
0.3
|
0.4
|
0.4
|
0.5
|
0.5
|
0.6
|
0.4
|
|
|
Average, statistical models
|
0.2
|
0.2
|
0.3
|
0.3
|
0.4
|
0.4
|
0.5
|
0.5
|
0.5
|
|
Average, all models
|
0.2
|
0.2
|
0.3
|
0.3
|
0.4
|
0.4
|
0.5
|
0.4
|
0.4
|
Notes on the data
Only models producing forecasts on a monthly basis are included. This means
that some models whose forecasts appear in the Experimental Long-Lead Forecast
Bulletin (produced by COLA) do not appear in the table.
The SST anomaly forecasts are for the 3-month periods shown,
and are for the Nino 3.4 region (120-170W, 5N-5S). Often, the anomalies are
provided directly in a graph or a table by the respective forecasting centers
for the Nino 3.4 region. In some cases, however, they are given for 1-month
periods, for 3-month periods that skip some of the periods in the above
table, and/or only for a region (or regions) other than Nino 3.4. In these
cases, the following means are used to obtain the needed anomalies for the
table:
- Temporal averaging,
- Linear temporal interpolation
- Visual averaging of values on a contoured map
- Regional SST anomaly adjustment using the climatological
variances of one region versus that of another
As an example of the last case, suppose only the Nino 3 anomaly is provided.
The Nino 3.4 anomaly is then obtained by decreasing the Nino 3 anomaly by
the factor defined by the ratio of the year-to-year variabilities of Nino
3.4 to the year-to-year variance of Nino 3 SST, for the 3-month season in
question.
The anomalies shown are those with respect to the base period
used to define the normals, which vary among the groups producing model forecasts.
They have not been adjusted to anomalies with respect to a common base period.
Discrepancies among the climatological SST resulting from differing base periods
may be as high as a quarter of a degree C in the worst cases. Forecasters
are encouraged to use the standard 1971-2000 period as the base period, or
a period not very different from it.
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