s2dverification (version 2.10.3)

Consist_Trend: Computes Trends Using Only Model Data For Which Observations Are Available

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. Provides also the detrended observational and modeled data. 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 Mean1Dim() or elsewhise (see the example).

Usage

Consist_Trend(var_exp, var_obs, interval = 1)

Arguments

var_exp

Ensemble mean of model hindcasts with dimensions: c(nmod/nexp, nsdates, nltime) up to c(nmod/nexp, nsdates, nltime, nlevel, nlat, nlon)

var_obs

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

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

$trend

Trend coefficients of model and observational data with dimensions: c(nmod/nexp + nobs, 3, nltime) up to c(nmod/nexp + nobs, 3, nltime, nlevel, nlat, nlon) 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.

$detrendedmod

Same dimensions as var_exp with linearly detrended values of var_exp along the second = start date dimension.

$detrendedobs

Same dimensions as var_exp with linearly detrended values of var_obs along the second = start date dimension.

Examples

# NOT RUN {
#'# 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)

 
# }
# NOT RUN {
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')
 
# }
# NOT RUN {
# }