s2dverification: NAO – R documentation

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NAO

Computes the North Atlantic Oscillation (NAO) Index

Description

Compute the North Atlantic Oscillation (NAO) index based on the leading EOF of the sea level pressure (SLP) anomalies over the north Atlantic region (20N-80N, 80W-40E). The PCs are obtained by projecting the forecast and observed anomalies onto the observed EOF pattern (Pobs) or the forecast anomalies onto the EOF pattern of the other years of the forecast (Pmod). By default (ftime_average = 2:4) NAO() computes the NAO index for 1-month lead seasonal forecasts that can be plotted with BoxPlot(). Returns cross-validated PCs of the NAO index for forecast (ano_exp) and observations (ano_obs) based on the leading EOF pattern.

Usage

NAO(
  ano_exp = NULL,
  ano_obs = NULL,
  lon,
  lat,
  ftime_average = 2:4,
  obsproj = TRUE
)

Arguments

`ano_exp`	Array of North Atlantic SLP (20N-80N, 80W-40E) forecast anomalies from `Ano()` or `Ano_CrossValid()` with dimensions (n. of experimental data sets, n. of ensemble members, n. of start dates, n. of forecast time steps, n. of latitudes, n. of longitudes). If only NAO of observational data needs to be computed, this parameter can be left to NULL (default).
`ano_obs`	Array of North Atlantic SLP (20N-80N, 80W-40E) observed anomalies from `Ano()` or `Ano_CrossValid()` with dimensions (n. of observational data sets, n. of obs. ensemble members, n. of start dates, n. of forecast time steps, n. of latitudes, n. of longitudes). If only NAO of experimental data needs to be computed, this parameter can be left to NULL (default).
`lon`	Vector with the longitudes of `ano_exp` and `ano_obs`.
`lat`	Vector with the latitudes of `ano_exp` and `ano_obs`.
`ftime_average`	A vector with the forecast time steps to average across defining the target period. Takes by default 2:4, i.e. from 2nd to 4th forecast time steps.
`obsproj`	`obsproj = TRUE` will compute the NAO index by projecting the forecast anomalies onto the leading EOF of observational reference. `obsproj = FALSE` will compute the NAO by first computing the leading EOF of the forecast anomalies (in cross-validation mode, i.e. leaving the year you are evaluating out), and then projecting forecast anomalies onto this EOF.

Value

`NAO_exp`	Array of forecast NAO index in verification format (ensemble members, start dates).
`NAO_obs`	Array of observed NAO index in verification format (1, number of start dates).
`EOFs_obs`	EOFs of the observational references.

Author(s)

History:
0.1 - 2013-08 (F. Lienert, flienert@ic3.cat) - Original code
0.2 - 2014-03 (V. Guemas, virginie.guemas@bsc.es) - Removing the rotation
0.3 - 2014-05 (L. Batte, lauriane.batte@ic3.cat) - Changes to simplify function and add Pobs and Pmod options for NAO projection calculations
0.4 - 2015-03 (L. Batte, lauriane.batte@ic3.cat) - Polarity check and correction is wrong. Switched to have a negative NAO index when the anomaly pattern corresponds to NAO-. 1.0 - 2016-03 (N. Manubens, nicolau.manubens@bsc.es) - Formatted to CRAN

References

Doblas-Reyes, F.J., Pavan, V. and Stephenson, D. (2003). The skill of multi-model seasonal forecasts of the wintertime North Atlantic Oscillation. Climate Dynamics, 21, 501-514. DOI: 10.1007/s00382-003-0350-4

Examples

# See examples on Load() to understand the first lines in this example
 ## Not run: 
data_path <- system.file('sample_data', package = 's2dverification')
expA <- list(name = 'experiment', path = file.path(data_path,
            'model/$EXP_NAME$/$STORE_FREQ$_mean/$VAR_NAME$_3hourly',
            '$VAR_NAME$_$START_DATE$.nc'))
obsX <- list(name = 'observation', path = file.path(data_path,
            '$OBS_NAME$/$STORE_FREQ$_mean/$VAR_NAME$',
            '$VAR_NAME$_$YEAR$$MONTH$.nc'))

# Now we are ready to use Load().
startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
sampleData <- Load('tos', list(expA), list(obsX), startDates,
                  leadtimemin = 1, leadtimemax = 4, output = 'lonlat',
                  latmin = 20, latmax = 90, lonmin = -80, lonmax = 40)
 
## End(Not run)
 

# Now ready to compute the EOFs and project on, for example, the first 
# variability mode.
ano <- Ano_CrossValid(sampleData$mod, sampleData$obs)
# Note that computing the NAO over the region for which there is available 
# example data is not the full NAO area: NAO() will raise a warning.
nao <- NAO(ano$ano_exp, ano$ano_obs, sampleData$lon, sampleData$lat)
# Finally plot the NAO index
 
PlotBoxWhisker(nao$NAO_exp, nao$NAO_obs, "NAO index, DJF", "NAO index (PC1) TOS",
       monini = 12, yearini = 1985, freq = 1, "Exp. A", "Obs. X")

s2dverification

Set of Common Tools for Forecast Verification

v2.10.0

Apache License 2.0

Authors

BSC-CNS [aut, cph], Virginie Guemas [aut], Nicolau Manubens [aut], An-Chi Ho [ctb, cre], Nuria Perez-Zanon [ctb], Javier Garcia-Serrano [aut], Neven Fuckar [aut], Louis-Philippe Caron [aut], Omar Bellprat [aut], Luis Rodrigues [aut], Veronica Torralba [aut], Alasdair Hunter [aut], Chloe Prodhomme [aut], Martin Menegoz [aut], Domingo Manubens [ctb], Constantin Ardilouze [ctb], Lauriane Batte [ctb], Fabian Lienert [ctb], Julia Giner [ctb], Jean-Philippe Baudouin [ctb], Nube Gonzalez [ctb], Ludovic Auger [ctb], Nicola Cortesi [ctb], Eleftheria Exarchou [ctb], Ruben Cruz [ctb], Isabel Andreu-Burillo [ctb], Ramiro Saurral [ctb]

Initial release

NAO