eof.dc {clim.pact} | R Documentation

## Daily common EOF.

### Description

Common EOFs for daily December-February 2-meter temperature (T(2m)).

### Usage

    data(eof.dc)

### Format

EOF | EOF patterns.
W | Eigen values.
PC | Principal components of common PCA.
n.fld | Number of different predictors (see mixFields).
tot.var | Sum of all W squared.
id.t | Time labels for the fields (see catFields) - used in DS.
id.x | Spatial labels for the fields (see mixFields) - used in plotEOF.
id.lon | Spatial labels for the fields (see mixFields) - used in plotEOF.
id.lat | Spatial labels for the fields (see mixFields) - used in plotEOF.
region | Describes the region analysed.
tim | Time information (usually redundant).
lon | Longitudes associated with EOF patterns.
lat | Latitudes associated with EOF patterns.
var.eof | Fractional variances associated with EOF patterns.
yy | years.
mm | months.
dd | days.
v.name | Name of element.
c.mon | Month-season information.
f.name | File name of original data.

### Source

The common EOF was produced using `EOF`, with the combined December-February (DJF) 2-meter air temperature data field from European Centre for Medium-Range Weather Forecasts (ECMWF; UK) reanalysis ( see URL: http://www.ecmwf.int/) and HIRHAM dynamically dowwnscaled scenarios from the ECHAM4-GSDIO scenario (Max-Planck Institute for Meteorology, Hamburg, Germany; URL: http://www.mpimet.mpg.de/). The region is 5E - 25E, 58N - 65N.

### References

Reference to methodology: R.E. Benestad (2001), "A comparison between two empirical downscaling strategies", Int. J. Climatology, vol 210, pp.1645-1668. [DOI 10.1002/joc.703].

### Examples

    #The EOFs were produced using the following code:
    library(clim.pact)

    x.1.dm<-retrieve.nc("/data1/era15/ERA-15_t2m.nc",x.rng=c(5,25),y.rng=c(58,65))
    X.1.dm<-retrieve.nc("/data1/hirham/T2M_198001-199912.nc",x.rng=c(5,25),
                        y.rng=c(58,65))
    Y.1.dm<-retrieve.nc("/data1/hirham/T2M_203001-204912.nc",x.rng=c(5,25),
                        y.rng=c(58,65))
    Y.1.dm$yy <- Y.1.dm$yy + 50
    # It is important that demean=FALSE when concatinating the two time slices
    # from the model simulations, if a study of climate change is the objective.
    xX.1.dm <- catFields(X.1.dm,Y.1.dm,demean=FALSE)
    xX.1.dm <- catFields(x.1.dm,xX.1.dm)
    eof.dc <- eof(xX.1.dm,mon=1)

    # To read the data:
    data(eof.dc)

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