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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AMV.R
\name{AMV}
\alias{AMV}
\title{Compute the Atlantic Multidecadal Variability (AMV) index}
\usage{
AMV(data, data_lats, data_lons, type, lat_dim = "lat", lon_dim = "lon",
mask = NULL, monini = 11, fmonth_dim = "fmonth", sdate_dim = "sdate",
indices_for_clim = NULL, year_dim = "year", month_dim = "month",
member_dim = "member")
}
\arguments{
\item{data}{A numerical array indicating the data to be used for the index
computation with latitude, longitude, start date, forecast month, and
member dimensions (in case of decadal predictions), with latitude,
longitude, year, month and member dimensions (in case of historical
simulations), or with latitude, longitude, year and month (in case of
observations or reanalyses). This data has to be provided, at least, over
the whole region needed to compute the index.}
\item{data_lats}{A numeric vector indicating the latitudes of the data.}
\item{data_lons}{A numeric vector indicating the longitudes of the data.}
\item{type}{A character string indicating the type of data ('dcpp' for
decadal predictions, 'hist' for historical simulations, or 'obs' for
observations or reanalyses).}
\item{lat_dim}{A character string of the name of the latitude dimension. The
default value is 'lat'.}
\item{lon_dim}{A character string of the name of the longitude dimension. The
default value is 'lon'.}
\item{mask}{An array of a mask (with 0's in the grid points that have to be
masked) or NULL (i.e., no mask is used). This parameter allows to remove
the values over land in case the dataset is a combination of surface air
temperature over land and sea surface temperature over the ocean. Also, it
can be used to mask those grid points that are missing in the observational
dataset for a fair comparison between the forecast system and the reference
dataset. The default value is NULL.}
\item{monini}{An integer indicating the month in which the forecast system is
initialized. Only used when parameter 'type' is 'dcpp'. The default value
is 11, i.e., initialized in November.}
\item{fmonth_dim}{A character string indicating the name of the forecast
month dimension. Only used if parameter 'type' is 'dcpp'. The default value
is 'fmonth'.}
\item{sdate_dim}{A character string indicating the name of the start date
dimension. Only used if parameter 'type' is 'dcpp'. The default value is
'sdate'.}
\item{indices_for_clim}{A numeric vector of the indices of the years to
compute the climatology for calculating the anomalies, or NULL so the
climatology is calculated over the whole period. If the data are already
anomalies, set it to FALSE. The default value is NULL.\cr
In case of parameter 'type' is 'dcpp', 'indices_for_clim' must be relative
to the first forecast year, and the climatology is automatically computed
over the actual common period for the different forecast years.}
\item{year_dim}{A character string indicating the name of the year dimension
The default value is 'year'. Only used if parameter 'type' is 'hist' or
'obs'.}
\item{month_dim}{A character string indicating the name of the month
dimension. The default value is 'month'. Only used if parameter 'type' is
'hist' or 'obs'.}
\item{member_dim}{A character string indicating the name of the member
dimension. The default value is 'member'. Only used if parameter 'type' is
'dcpp' or 'hist'.}
}
\value{
An numerical array of the AMV index with the dimensions of:
1) sdate, forecast year, and member (in case of decadal predictions);
2) year and the member (in case of historical simulations); or
3) year (in case of observations or reanalyses).
}
\description{
The Atlantic Multidecadal Variability (AMV), also known as Atlantic
Multidecadal Oscillation (AMO), is a mode of natural variability of the sea
surface temperatures (SST) over the North Atlantic Ocean on multi-decadal
time scales. The AMV index is computed as the difference of weighted-averaged
SST anomalies over the North Atlantic region (0ºN-60ºN, 280ºE-360ºE) and the
weighted-averaged SST anomalies over 60ºS-60ºN, 0ºE-360ºE (Trenberth &
Dennis, 2005; Doblas-Reyes et al., 2013).
}
\examples{
## Observations or reanalyses
obs <- array(1:100, dim = c(year = 5, lat = 19, lon = 37, month = 12))
lat <- seq(-90, 90, 10)
lon <- seq(0, 360, 10)
index_obs <- AMV(data = obs, data_lats = lat, data_lons = lon, type = 'obs')
## Historical simulations
hist <- array(1:100, dim = c(year = 5, lat = 19, lon = 37, month = 12, member = 5))
lat <- seq(-90, 90, 10)
lon <- seq(0, 360, 10)
index_hist <- AMV(data = hist, data_lats = lat, data_lons = lon, type = 'hist')
## Decadal predictions
dcpp <- array(1:100, dim = c(sdate = 5, lat = 19, lon = 37, fmonth = 24, member = 5))
lat <- seq(-90, 90, 10)
lon <- seq(0, 360, 10)
index_dcpp <- AMV(data = dcpp, data_lats = lat, data_lons = lon, type = 'dcpp', monini = 1)
}
\author{
Carlos Delgado-Torres, \email{carlos.delgado@bsc.es}
Roberto Bilbao, \email{roberto.bilbao@bsc.es}
Núria Pérez-Zanón, \email{nuria.perez@bsc.es}
}