1. Seasonal Forecasting Using the Climate Predictability Tool
Combining Predictors and Predictands in CPT
Simon Mason
Seasonal Forecasting Using the Climate Predictability Tool

2. CPT Formats
Predictors and / or predictands can be combined in version CPT 10+, but the new CPT formats must be used. CPT can read three different types of data:
Index data : indices have no single specific geographical location, e.g. the SOI;
Station data : stations have specific geographical locations, but are not typically distributed in a regular pattern
Gridded data : the latitudes and longitudes do not have to be evenly spaced; the grid points can be representative of area-averages or be point specific.

3. Index Data
xmlns:cpt= cpt:field=ENSO, cpt:nrow=9, cpt:ncol=4, cpt:col=index, cpt:row=T NINO12 NINO3 NINO3.4 NINO E
The cpt:col=index, and cpt:row=T tags identify the dataset as an index file. Data can be missed out completely (e.g., 1994 in the example), although doing so is not recommended. It would be better to use include the missing year, and then provide missing values, identified by the cpt:missing=n tag. The number of rows should indicate the number of rows available (in this case 9), rather than the number of years spanned by the data (10).

4. Index Data
xmlns:cpt= …, cpt:nrow=10, cpt:ncol=4, cpt:missing=-999 NINO12 NINO3 NINO3.4 NINO E
With the missing included cpt:nrow should now be 10.

5. Date Formats
YYYY 2012 YYYY-MM YYYY-MM-DD YYYY-MM-DDTHH:MM T09:00 Start-date/End-date YYYY/YYYY 2011/2020 YYYY-MM/YYYY-MM / YYYY-MM/MM /06 YYYY-MM-DD/DD /12 YYYY-MM-DD/MM-DD /05-12 YYYY-MM-DD/YYYY-MM-DD /
The date formats follow the ISO 8601 standard, although not all the standard is currently implemented (e.g., the week format is not implemented).

6. Station Data
xmlns:cpt= cpt:field=prcp, cpt:nrow=8, cpt:ncol=4, cpt:col=station, cpt:row=T Station_A Station_B Station_C Station_D cpt:X cpt:Y / / / / / / / /

7. Gridded Data
xmlns:cpt= cpt:nfields=1 cpt:field=ssta, cpt:units=C, cpt:T= , cpt:nrow=3, cpt:ncol=4, cpt:row=Y, cpt:col=X, cpt:missing= cpt:T=

8. Gridded Data
xmlns:cpt= cpt:nfields=2 cpt:field=ssta, cpt:units=C, cpt:T= , cpt:nrow=3, cpt:ncol=4, cpt:row=Y, cpt:col=X, cpt:missing= cpt:field=mslp, cpt:units=mb, cpt:T= , cpt:nrow=2, cpt:ncol=2, cpt:row=Y, cpt:col=X, cpt:missing=
The simplest way of setting up a multi-field file is to copy the second set of data beneath the first set (deleting the cpt header lines), and setting the cpt:nfields=n tag appropriately. Note that the fields do not have to be the same resolution, or even the same date. However, the first set of data for field-1 will be paired with the first set for fields-2, so if one set of data starts in a different year, make sure that you only include only the data for the matching periods in the combined file.

9. Exercises
Using the ERSST3 data as predictors use data for November and December to make one model for predicting MAM rainfall for Thailand. Compare these models with the separate models for these two models.
Try combining the ECHAM GCM predictors with the ERSST3 data for December. How does the forecast compare with that for the SSTs and the GCM output as predictors separately?
Combine the ECHAM GCM data with CFS2 data. Using CCA, does there appear to be a preference for using either of the two models (hint – look at the CCA mode maps, and the climatology correlations)?

10. CPT Help Desk web: iri.columbia.edu/cpt/ @climatesociety
@climatesociety
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