% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Consist_Trend.R
\name{Consist_Trend}
\alias{Consist_Trend}
\title{Computes Trends Using Only Model Data For Which Observations Are Available}
\usage{
Consist_Trend(var_exp, var_obs, interval = 1)
}
\arguments{
\item{var_exp}{Ensemble mean of model hindcasts with dimensions:\cr
c(nmod/nexp, nsdates, nltime) up to\cr
c(nmod/nexp, nsdates, nltime, nlevel, nlat, nlon)}

\item{var_obs}{Ensemble mean of observational data with dimensions:\cr
c(nobs, nsdates, nltime) up to\cr
c(nobs, nsdates, nltime, nlevel, nlat, nlon)\cr
Dimensions 2 to 6 should be the same as var_exp.}

\item{interval}{Number of months/years between 2 start dates. Default = 1. 
The trends will be provided respectively in field unit per month or per year.}
}
\value{
\item{$trend}{
 Trend coefficients of model and observational data with dimensions:\cr
 c(nmod/nexp + nobs, 3, nltime) up to\cr
 c(nmod/nexp + nobs, 3, nltime, nlevel, nlat, nlon)\cr
 The length 3 dimension corresponds to the lower limit of the 95\% 
 confidence interval, the slope of the trends and the upper limit of the 
 95\% confidence interval.
}
\item{$detrendedmod}{
 Same dimensions as var_exp with linearly detrended values of var_exp 
 along the second = start date dimension.
}
\item{$detrendedobs}{
 Same dimensions as var_exp with linearly detrended values of var_obs 
 along the second = start date dimension.
}
}
\description{
Computes the trend coefficients for a time series by least square fitting, 
together with the associated error interval for both the observational and 
model data.\cr
Provides also the detrended observational and modeled data.\cr
By default, the trend is computed along the second dimension of the input 
array, which is expected to be the start date dimension. For arrays 
containing multiple model members, the user will first have to calculate 
the ensemble average using \code{Mean1Dim()} or elsewhise (see the example).
}
\examples{
#'# Load sample data as in Load() example:
example(Load)
clim <- Clim(sampleData$mod, sampleData$obs)
ano_exp <- Ano(sampleData$mod, clim$clim_exp)
ano_obs <- Ano(sampleData$obs, clim$clim_obs)
runmean_months <- 12
dim_to_smooth <- 4  # Smooth along lead-times
smooth_ano_exp <- Smoothing(ano_exp, runmean_months, dim_to_smooth)
smooth_ano_obs <- Smoothing(ano_obs, runmean_months, dim_to_smooth)
dim_to_mean <- 2  # Mean along members
years_between_startdates <- 5
trend <- Consist_Trend(Mean1Dim(smooth_ano_exp, dim_to_mean), 
                      Mean1Dim(smooth_ano_obs, dim_to_mean), 
                      years_between_startdates)

PlotVsLTime(trend$trend, toptitle = "trend", ytitle = "K/(5 years)", 
           monini = 11, limits = c(-0.8, 0.8), listexp = c('CMIP5 IC3'), 
           listobs = c('ERSST'), biglab = FALSE, hlines = c(0), 
           fileout = 'tos_consist_trend.eps')
PlotAno(InsertDim(trend$detrendedmod,2,1), InsertDim(trend$detrendedobs,2,1), 
       startDates, "Detrended tos anomalies", ytitle = 'K', 
       legends = 'ERSST', biglab = FALSE, fileout = 'tos_detrended_ano.eps')

}
\author{
History:\cr
 0.1  -  2011-11  (V. Guemas, \email{vguemas@ic3.cat})  -  Original code\cr
 1.0  -  2013-09  (N. Manubens, \email{nicolau.manubens@ic3.cat})  -  Formatting to R CRAN
}
\keyword{datagen}

