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Summary of ENSO Model Forecasts

21 October 2004

> Note on interpreting model forecasts
> Discussion of current forecasts
> Figure of Nino3.4 SST forecasts
> Table of Nino3.4 SST forecasts
> Notes on the data

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 Septembert and early October 2004 shows a  range of possible sea surface temperature conditions for the coming 2 to 10 months (October - November - December 2004 through June - July -August 2005). Many models are indicating weak El Nino conditions over the coming several season. A few indicate moderate El Nino (e.g. more than 1 degree C anomaly), and a few indicate conditions only 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 at about three-quarters of a degree above average.  

Table 1. Forecast SST Anomalies (deg C) in the Nino 3.4 Region


 
  Seasons (2004-05)
Model
OND
NDJ
DJF
JFM
FMA
MAM
AMJ
 MJJ JJA
          Dynamical models
NASA/NSIPP model
 0.8
0.7
0.7
0.7
0.8
1.0
1.3
1.6
1.8
NCEP Coupled Fcst Sys
0.6
0.7
0.8
1.1
1.1
0.9
0.7
0.7

Japan Met. Agency model
0.6
0.7
0.7
0.8
0.8



 
Scripps Inst. HCM
0.9
0.9
0.8
0.8 0.8 0.7 0.6 0.5
 0.4
Lamont-Doherty model
1.0
1.0
1.0
0.8
0.6
0.5
0.4
 0.3
0.2
POAMA (Austr) model
0.8
1.0
1.2
1.2
1.1
0.9
0.4


ECMWF model
0.8
0.8
0.7



     
SNU (Korea) model
0.4
0.4
0.4
0.4
0.4
0.4
0.4
 0.4 0.3
ZHANG ICM model
 0.7 0.8 0.7
0.5 
0.4
0.3
0.3
 0.2 0.2
ECHAM/MOM
0.6
0.6
0.7
0.7
0.5




COLA ANOM 1.2
 1.3
 1.1
 0,8
 0.6
 0.5
 0.4
 0.3
 0.2
Average, dynamical models
0.8
0.8
0.8
0.8
0.7
0.6
0.6
0.6
 0.5
          Statistical models
NCEP/CPC Markov model
0.8
0.9
1.0
1.0
0.9
0.9
0.9
0.8
0.8
NOAA/CDC Linear Inverse 
0.4
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.0
Dool Constructed Analog
0.8
0.9
0.8
0.6
0.4
0.3
0.2
0.1
-0.0
NCEP/CPC Can Cor Anal
0.6
0.7
0.7
0.7
0.7
0.7
0.8
0.8
0.8
Landsea/Knaff CLIPER
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.1
-0.0
Univ. BC Neural Network
0.2
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.3
FSU Regression
1.2
1.3
1.2
1.1
0.8
0.6
0.4
0.3
0.2
TDC - UCLA
1.1
1.1
1.0
0.9
0.8
0.6
0.6
0.5
0.5
Average, statistical models
0.8
0.8
0.7
0.7
0.6
0.5
0.4
0.4
0.3
Average, all models
0.8
0.8
0.8
0.7
0.6
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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