Seasonal Forecasting Using the Climate Predictability Tool Combining Predictors and Predictands in CPT Simon Mason simon@iri.columbia.edu Seasonal Forecasting Using the Climate Predictability Tool

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.

Index Data xmlns:cpt=http://iri.columbia.edu/CPT/v10/ cpt:field=ENSO, cpt:nrow=9, cpt:ncol=4, cpt:col=index, cpt:row=T NINO12 NINO3 NINO3.4 NINO4 1991-01 -7.04E-02 0.2694498 0.5850401 0.9240733 1992-01 0.9675602 1.567671 1.78421 0.9166834 1993-01 0.2538059 0.142204 0.3976894 0.8017575 1995-01 1.319356 0.9692747 1.051067 1.088881 1996-01 -0.169409 -0.5436825 -0.7101253 -0.1563722 1997-01 -0.2674479 -0.6353493 -0.325502 0.3376727 1998-01 3.716633 3.442826 2.581284 1.036142 1999-01 -0.3437423 -0.8765103 -1.365756 -1.278136 2000-01 -0.4427198 -1.439994 -1.668904 -0.9916633 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).

Index Data xmlns:cpt=http://iri.columbia.edu/CPT/v10/ …, cpt:nrow=10, cpt:ncol=4, cpt:missing=-999 NINO12 NINO3 NINO3.4 NINO4 1991-01 -7.04E-02 0.2694498 0.5850401 0.9240733 1992-01 0.9675602 1.567671 1.78421 0.9166834 1993-01 0.2538059 0.142204 0.3976894 0.8017575 1994-01 -999 -999 -999 -999 1995-01 1.319356 0.9692747 1.051067 1.088881 1996-01 -0.169409 -0.5436825 -0.7101253 -0.1563722 1997-01 -0.2674479 -0.6353493 -0.325502 0.3376727 1998-01 3.716633 3.442826 2.581284 1.036142 1999-01 -0.3437423 -0.8765103 -1.365756 -1.278136 2000-01 -0.4427198 -1.439994 -1.668904 -0.9916633 With the missing included cpt:nrow should now be 10.

Date Formats YYYY 2012 YYYY-MM 2012-05 YYYY-MM-DD 2012-05-10 YYYY-MM-DDTHH:MM 2012-05-10T09:00 Start-date/End-date YYYY/YYYY 2011/2020 YYYY-MM/YYYY-MM 2011-08/2012-07 YYYY-MM/MM 2012-04/06 YYYY-MM-DD/DD 2012-05-06/12 YYYY-MM-DD/MM-DD 2012-04-30/05-12 YYYY-MM-DD/YYYY-MM-DD 2011-05-11/2012-05-10 The date formats follow the ISO 8601 standard, although not all the standard is currently implemented (e.g., the week format is not implemented).

Station Data xmlns:cpt=http://iri.columbia.edu/CPT/v10/ 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 -75.80000 -75.50000 -75.60000 -76.00000 cpt:Y 39.40000 38.70000 38.20000 38.60000 1981-01/03 1.78333 2.09000 1.97667 2.00000 1982-01/03 3.34000 3.59667 3.60333 3.55333 1983-01/03 4.52667 4.05333 4.62000 3.58000 1984-01/03 3.89667 4.84000 5.36667 4.53000 1985-01/03 2.04667 2.68667 3.07000 2.66333 1986-01/03 3.16000 2.88667 2.45333 3.19333 1987-01/03 2.68000 3.91667 4.78333 3.77667 1988-01/03 2.92333 3.59000 3.72667 3.61000

Gridded Data xmlns:cpt=http://iri.columbia.edu/CPT/v10/ cpt:nfields=1 cpt:field=ssta, cpt:units=C, cpt:T=1979-01, cpt:nrow=3, cpt:ncol=4, cpt:row=Y, cpt:col=X, cpt:missing=-9999 -18.75 -16.25 -13.75 -11.25 -58.75 -2.40977 -1.40228 -1.01278 -1.03197 -61.25 -3.65581 -3.18779 -2.28418 -1.45435 -63.75 -3.54459 -3.04635 -2.07969 -1.32451 cpt:T=1980-01 -58.75 -2.22299 -0.68736 0.27909 -0.42638 -61.25 -1.13313 -0.56869 -1.02204 -1.09111 -63.75 -1.40550 -1.10838 -0.37565 0.88614

Gridded Data xmlns:cpt=http://iri.columbia.edu/CPT/v10/ cpt:nfields=2 cpt:field=ssta, cpt:units=C, cpt:T=1979-01, cpt:nrow=3, cpt:ncol=4, cpt:row=Y, cpt:col=X, cpt:missing=-9999 -18.75 -16.25 -13.75 -11.25 -58.75 -2.40977 -1.40228 -1.01278 -1.03197 -61.25 -3.65581 -3.18779 -2.28418 -1.45435 -63.75 -3.54459 -3.04635 -2.07969 -1.32451 cpt:field=mslp, cpt:units=mb, cpt:T=1979-02, cpt:nrow=2, cpt:ncol=2, cpt:row=Y, cpt:col=X, cpt:missing=-10 -70.0 -65.0 15.0 1001.2 1000.6 20.0 1002.8 1003.2 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.

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)?

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