Current ENSO Information
Summary of ENSO Model Forecasts
16 September 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
August and early September 2004 shows a range of possible sea
surface
temperature conditions for the coming 2 to 10 months (September -
October - November 2004 through May - June - July 2005). Most models
are
indicating either weak El Nino conditions
over the coming several seasons, or conditions in the upper portion of
the neutral range. At the time of
preparing this, SST observations in the NINO3.4 region are indicative
of weak El Nino conditions.
Table 1. Forecast SST Anomalies
(deg C) in the Nino 3.4 Region
| |
Seasons (2004-05) |
|
Model
|
SON
|
OND
|
NDJ
|
DJF
|
JFM
|
FMA
|
MAM
|
AMJ |
MJJ |
|
Dynamical models |
|
NASA/NSIPP
model
|
1.2 |
0.9
|
0.7
|
0.5
|
0.4
|
0.4
|
0.5
|
0.6
|
0.6
|
|
NCEP
Coupled model
|
0.6
|
0.7
|
0.8
|
0.8
|
0.7
|
0.5
|
0.4
|
|
|
NCEP Coupled Fcst Sys
|
0.6
|
0.6
|
0.7
|
0.8
|
0.8
|
0.8
|
0.8
|
|
|
Japan
Met. Agency model
|
0.8
|
0.9
|
1.0
|
1.0
|
1.0
|
|
|
|
|
Scripps
Inst. HCM
|
0.8
|
0.8
|
0.8
|
0.8 |
0.8 |
0.8 |
0.7 |
0.6
|
0.5 |
Lamont-Doherty
model
|
1.0
|
1.2
|
1.3
|
1.2
|
0.9
|
0.6
|
0.3
|
|
|
|
POAMA
(Austr) model
|
0.5
|
0.5
|
0.6
|
0.6
|
0.7
|
0.7
|
0.6
|
|
|
ECMWF
model
|
0.9
|
0.9
|
0.9
|
|
|
|
|
|
|
|
SNU
(Korea) model
|
0.4
|
0.4
|
0.4
|
0.4
|
0.4
|
0.4
|
0.4
|
0.4 |
0.4 |
ZHANG
ICM model
|
0.7 |
0.8 |
0.9 |
0.8
|
0.7
|
0.5
|
0.5
|
0.5 |
0.5 |
|
ECHAM/MOM
|
1.5
|
1.6
|
1.5
|
1.4
|
1.2
|
|
|
|
|
|
Average, dynamical models
|
0.8
|
0.9
|
0.9
|
0.8
|
0.8
|
0.6
|
0.5
|
|
|
|
Statistical models |
|
NCEP/CPC
Markov model
|
0.6
|
0.8
|
0.9
|
1.0
|
1.0
|
0.9
|
0.9
|
0.9
|
0.9
|
|
NOAA/CDC
Linear Inverse
|
0.3
|
0.3
|
0.3
|
0.2
|
0.2
|
0.2
|
0.1
|
0.1
|
0.1
|
|
Dool
Constructed Analog
|
0.6
|
0.8
|
0.8
|
0.7
|
0.6
|
0.4
|
0.3
|
0.1
|
0.0
|
|
NCEP/CPC
Can Cor Anal
|
0.6
|
0.8
|
0.9
|
0.9
|
1.0
|
0.9
|
0.8
|
0.8
|
0.8
|
|
Landsea/Knaff
CLIPER
|
1.5
|
1.4
|
1.3
|
1.2
|
0.9
|
0.5
|
0.1
|
0.1
|
0.2
|
|
Univ.
BC Neural Network
|
0.3
|
0.3
|
0.3
|
0.3
|
0.3
|
0.2
|
0.2
|
0.2
|
0.2
|
|
FSU
Regression
|
0.9
|
1.1
|
1.1
|
1.1
|
0.9
|
0.7
|
0.5
|
0.4
|
0.3
|
|
TDC
- UCLA
|
0.8
|
1.0
|
1.1
|
1.2
|
1.2
|
1.1
|
1.0
|
0.9
|
0.9
|
|
Average, statistical models
|
0.7
|
0.8
|
0.8
|
0.8
|
0.7
|
0.6
|
0.5
|
0.4
|
0.4
|
|
Average, all models
|
0.8
|
0.8
|
0.9
|
0.8
|
0.8
|
0.6
|
0.5
|
0.5
|
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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