Consist_Trend: Computes Trends Using Only Model Data For Which Observations...

In s2dverification: Set of Common Tools for Forecast Verification

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.

Author(s)

History:
0.1 - 2011-11 (V. Guemas) - Original code
1.0 - 2013-09 (N. Manubens) - Formatting to R CRAN

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')
 

s2dverification documentation built on April 20, 2022, 9:06 a.m.