Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Consist_Trend.R
\name{Consist_Trend}
\alias{Consist_Trend}
\title{Compute trend using only model data for which observations are available}
\usage{
Consist_Trend(
exp,
obs,
dat_dim = "dataset",
time_dim = "sdate",
interval = 1,
ncores = NULL
)
}
\arguments{
\item{exp}{A named numeric array of experimental data, with at least two
dimensions 'time_dim' and 'dat_dim'.}
\item{obs}{A named numeric array of observational data, same dimensions as
parameter 'exp' except along 'dat_dim'.}
\item{dat_dim}{A character string indicating the name of the dataset
dimensions. If data at some point of 'time_dim' are not complete along
'dat_dim' in both 'exp' and 'obs', this point in all 'dat_dim' will be
discarded. The default value is 'dataset'.}
\item{time_dim}{A character string indicating the name of dimension along
which the trend is computed. The default value is 'sdate'.}
\item{interval}{A positive numeric indicating the unit length between two
points along 'time_dim' dimension. The default value is 1.}
\item{ncores}{An integer indicating the number of cores to use for parallel
computation. The default value is NULL.}
}
\value{
A list containing:
\item{$trend}{
A numeric array of the trend coefficients of model and observational data
with dimensions c(stats = 2, nexp + nobs, the rest dimensions of 'exp' and
'obs' except time_dim), where 'nexp' is the length of 'dat_dim' in 'exp'
and 'nobs' is the length of 'dat_dim' in 'obs. The 'stats' dimension
contains the intercept and the slope.
}
\item{$conf.lower}{
A numeric array of the lower limit of 95\% confidence interval with
dimensions same as $trend. The 'stats' dimension contains the lower
confidence level of the intercept and the slope.
}
\item{$conf.upper}{
A numeric array of the upper limit of 95\% confidence interval with
dimensions same as $trend. The 'stats' dimension contains the upper
confidence level of the intercept and the slope.
}
\item{$detrended_exp}{
A numeric array of the detrended model data with the same dimensions as
'exp'.
}
\item{$detrended_obs}{
A numeric array of the detrended observational data with the same
dimensions as 'obs'.
}
}
\description{
Compute the linear trend for a time series by least square fitting together
with the associated error interval for both the observational and model data.
The 95\% confidence interval and detrended observational and model data are
also provided.\cr
The function doesn't do the ensemble mean, so if the input data have the
member dimension, ensemble mean needs to be computed beforehand.
}
\examples{
#'# Load sample data as in Load() example:
example(Load)
clim <- Clim(sampleData$mod, sampleData$obs)
ano_exp <- Ano(sampleData$mod, clim$clim_exp)
ano_obs <- Ano(sampleData$obs, clim$clim_obs)
runmean_months <- 12
smooth_ano_exp <- Smoothing(ano_exp, runmeanlen = runmean_months)
smooth_ano_obs <- Smoothing(ano_obs, runmeanlen = runmean_months)
dim_to_mean <- 'member' # average along members
years_between_startdates <- 5
trend <- Consist_Trend(MeanDims(smooth_ano_exp, dim_to_mean, na.rm = TRUE),
MeanDims(smooth_ano_obs, dim_to_mean, na.rm = TRUE),
interval = years_between_startdates)
#Bind data for plotting
trend_bind <- abind::abind(trend$conf.lower[2, , ], trend$trend[2, , ],
trend$conf.upper[2, , ], trend$trend[1, , ], along = 0)
trend_bind <- Reorder(trend_bind, c(2, 1, 3))
\donttest{
PlotVsLTime(trend_bind, 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))