Title:	Set of Tools to Compute Various Climate Indices
Version:	0.3.4
Description:	Set of tools to compute metrics and indices for climate analysis. The package provides functions to compute extreme indices, evaluate the agreement between models and combine theses models into an ensemble. Multi-model time series of climate indices can be computed either after averaging the 2-D fields from different models provided they share a common grid or by combining time series computed on the model native grid. Indices can be assigned weights and/or combined to construct new indices. The package makes use of some of the methods described in: N. Manubens et al. (2018) <doi:10.1016/j.envsoft.2018.01.018>.
Depends:	R (≥ 3.2.0)
Imports:	graphics, methods, multiApply (≥ 2.0.0), stats
Suggests:	knitr, testthat, markdown, rmarkdown
License:	GPL-3
URL:	https://earth.bsc.es/gitlab/es/ClimProjDiags
BugReports:	https://earth.bsc.es/gitlab/es/ClimProjDiags/-/issues
Encoding:	UTF-8
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	yes
SystemRequirements:	C++11
Packaged:	2025-09-01 08:36:49 UTC; vagudets
Author:	BSC-CNS [aut, cph], Nuria Perez-Zanon [aut], An-Chi Ho [ctb], Victòria Agudetse [cre], Nicolau Manubens [ctb], Alasdair Hunter [aut], Louis-Philippe Caron [ctb], Eva Rifà [ctb], Ulrich Drepper [ctb], David Bronaugh [ctb], James Hiebert [ctb]
Maintainer:	Victòria Agudetse <victoria.agudetse@bsc.es>
Repository:	CRAN
Date/Publication:	2025-09-01 09:10:19 UTC

Percentage of anomalies which agrees with the sign of the mean anomaly for multidimensional arrays

Description

This function computes the mean and the percentage of agreement between anomalies.

Usage

AnoAgree(ano, membersdim, na.rm = TRUE, ncores = NULL)

Arguments

Value

An array of one dimension less than the ano object, except for one dimensional arrays or vectors, for which an array of dimension 1 called 'var' is returned.

Examples

# Example with random sample:
a <- NULL
for(i in 1:20) { a <- c(a, rnorm(6)) }
dim(a) <- c(lat = 2, lon = 3, var = 4, mod = 5)

agree <- AnoAgree(ano = a, membersdim = which(names(dim(a)) == 'mod'), 
                 na.rm = TRUE, ncores = NULL)
print(agree)

a <- rnorm(6)
agree <- AnoAgree(ano = a, membersdim = 1, na.rm = TRUE, ncores = NULL)
print(agree)

Split an array into list by a given array dimension

Description

This function splits an array into a list as required by PlotLayout function from package "s2dv" when parameter 'special_args' is used. See: N. Manubens et al. (2018) <<doi:10.1016/j.envsoft.2018.01.018>. The function ArrayToList allows to add names to the elements of the list in two different levels, the 'list' or the 'sublist'.

Usage

ArrayToList(data, dim, level = "list", names = NULL)

Arguments

Value

A list of arrays of the length of the dimension set in parameter 'dim'.

Examples

data <- array(1:240, c(month = 12, member = 5, time = 4))
# Create a list:
datalist <- ArrayToList(data, dim = 'month', level = 'list', names = month.name)
class(datalist)
class(datalist[[1]])
str(datalist)
# Create a sublist:
datalist <- ArrayToList(data, dim = 'month', level = 'sublist', names = 'dots')
class(datalist)
class(datalist[[1]])
class(datalist[[1]][[1]])
str(datalist)

Wrapper for applying the climdex routine ETCCDI climate change indices to n-dimensional arrays.

Description

This function computes the t90p, t10p, cdd or rx5day indices from n-dimensional arrays.

Usage

Climdex(
  data,
  metric,
  threshold = NULL,
  base.range = NULL,
  dates = NULL,
  timedim = NULL,
  calendar = NULL,
  ncores = NULL
)

Arguments

Value

A list of length 2:

• $result, an array with the same dimensions as the input array, except for the temporal dimension which is renamed to 'year', moved to the first dimension position and reduce to annual resolution.

• $years, a vector of the corresponding years.

References

David Bronaugh for the Pacific Climate Impacts Consortium (2015). climdex.pcic: PCIC Implementation of Climdex Routines. R package version 1.1-6. http://CRAN.R-project.org/package=climdex.pcic

Examples

##Example synthetic data:
data <- 1:(2 * 3 * 372 * 1)
dim(data) <- c(lon = 2, lat = 3, time = 372, model = 1)
time <- c(seq(ISOdate(1900, 1, 1), ISOdate(1900, 1, 31), "day"), 
         seq(ISOdate(1901, 1, 1), ISOdate(1901, 1, 31), "day"),
         seq(ISOdate(1902, 1, 1), ISOdate(1902, 1, 31), "day"),
         seq(ISOdate(1903, 1, 1), ISOdate(1903, 1, 31), "day"),
         seq(ISOdate(1904, 1, 1), ISOdate(1904, 1, 31), "day"),
         seq(ISOdate(1905, 1, 1), ISOdate(1905, 1, 31), "day"),
         seq(ISOdate(1906, 1, 1), ISOdate(1906, 1, 31), "day"),
         seq(ISOdate(1907, 1, 1), ISOdate(1907, 1, 31), "day"),
         seq(ISOdate(1908, 1, 1), ISOdate(1908, 1, 31), "day"),
         seq(ISOdate(1909, 1, 1), ISOdate(1909, 1, 31), "day"),
         seq(ISOdate(1910, 1, 1), ISOdate(1910, 1, 31), "day"),
         seq(ISOdate(1911, 1, 1), ISOdate(1911, 1, 31), "day"))
metadata <- list(time = list(standard_name = 'time', long_name = 'time', 
                            calendar = 'gregorian', 
                            units = 'days since 1970-01-01 00:00:00', 
                            prec = 'double', 
                            dim = list(list(name = 'time', unlim = FALSE))))
attr(time, "variables") <- metadata
attr(data, 'Variables')$dat1$time <- time

thres <- rep(10, 31 * 2 * 3)
dim(thres) <- c(jdays = 31, lon = 2, lat = 3,  model = 1)
str(thres)

clim <- Climdex(data, metric = "t90p", threshold = thres)
str(clim)

Combine weighted indices of n-dimensional arrays

Description

Function to combine climate indices for multiple models through addition, subtraction, division or averaging, optionally applying weights to each index.

Usage

CombineIndices(indices, weights = NULL, operation = "mean")

Arguments

Value

An array of the same dimensions as one of the elements in the parameter indices.

Examples

a <- matrix(rnorm(6), 2, 3)
b <- matrix(rnorm(6), 2, 3)

comb_ind <- CombineIndices(indices = list(a, b), weights = c(2, 1), 
                          operation = "add")
print(comb_ind)

a <- rnorm(24)
dim(a) <- c(lon = 2, lat = 3, mod = 4)
b <- rnorm(24)
dim(b) <- c(lon = 2, lat = 3, mod = 4)
comb_ind <- CombineIndices(indices = list(a, b), weights = c(2, 1), 
                          operation = "add")
print(comb_ind)

Diurnal temperature range indicator (DTR) of multidimensional arrays

Description

This function computes the diurnal temperature indicator, defined as the number of days where the diurnal temperature variation exceeds the vulnerability threshold (defined as the mean(tmax -tmin) + 5 from the reference period).

Usage

DTRIndicator(
  tmax,
  tmin,
  ref,
  by.seasons = TRUE,
  dates = NULL,
  timedim = NULL,
  calendar = NULL,
  ncores = NULL
)

Arguments

Value

A list of length 3:

• $dtr.ref, an array with the same dimensions as the input data, but with the time dimension reduce from daily to monthly or seasonal resolution depending on the selected resolution in by.season.

• $year, a vector of the corresponding years.

• $season, a vector of the seasons or months corresponding to the resolution selected in by.season.

Examples

##Exmaple with synthetic data:
tmax <- 1 : (2 * 3 * 730 * 1)
dim(tmax) <- c(lon = 2, lat = 3, time = 730, model = 1)
tmin <- (1 : (2 * 3 * 730 * 1)) - 1
dim(tmin) <- c(lon = 2, lat = 3, time = 730, model = 1)
time <- seq(as.POSIXct("1900-01-01 12:00:00", tz = "", 
                      format = "%Y-%d-%m %H:%M:%S"), 
           as.POSIXct("1901-31-12 18:00:00", tz = "", 
                      format = "%Y-%d-%m %H:%M:%S"), "day")
time <- as.POSIXct(time, tz = "CET")
metadata <- list(time = list(standard_name = 'time', long_name = 'time', 
                            calendar = 'noleap',
                            units = 'days since 1970-01-01 00:00:00', 
                            prec = 'double', 
                            dim = list(list(name ='time', unlim = FALSE))))
attr(time, "variables") <- metadata
attr(tmax, 'Variables')$dat1$time <- time
attr(tmax, 'Variables')$common[[2]]$dim[[3]]$len = length(time)
attr(tmax, 'Variables')$common[[2]]$dim[[3]]$vals <- time
attr(tmin, 'Variables')$dat1$time <- time
attr(tmin, 'Variables')$common[[2]]$dim[[3]]$len = length(time)
attr(tmin, 'Variables')$common[[2]]$dim[[3]]$vals <- time
a <- DTRRef(tmax, tmin, by.seasons = FALSE, ncores = NULL)

aa <- DTRIndicator(tmax, tmin, ref = a, by.seasons = FALSE, ncores = NULL)
str(aa)
dim(aa$indicator)

Diurnal temperature range of multidimensional arrays

Description

This function computes the mean diurnal temperature range (tmax - tmin).

Usage

DTRRef(
  tmax,
  tmin,
  by.seasons = TRUE,
  dates = NULL,
  timedim = NULL,
  calendar = NULL,
  na.rm = TRUE,
  ncores = NULL
)

Arguments

Details

The function returns a reordered array with 'time' dimension in the first position in the dtr.ref label.

Value

A list of length 2:

• $dtr.ref, an array with the same dimensions as the input data, but with the time dimension reduce from daily to monthly or seasonal resolution depending on the selected resolution in by.season.

• $season, a vector of the season or months corresponding to the resolution selected in by.season.

Examples

##Exmaple with synthetic data:
tmax <- 1:(2 * 3 * 365 * 1)
dim(tmax) <- c(lon = 2, lat = 3, time = 365, model = 1)
tmin <- (1:(2 * 3 * 365 * 1))-1
dim(tmin) <- c(lon = 2, lat = 3, time = 365, model = 1)
time <- seq.Date(as.Date("1900-01-01", format = "%Y-%d-%m"), 
                as.Date("1900-31-12", format = "%Y-%d-%m"), 1)
time <- as.POSIXct(time, tz = "CET")
metadata <- list(time = list(standard_name = 'time', long_name = 'time', 
                            calendar = 'noleap',
                            units = 'days since 1970-01-01 00:00:00', 
                            prec = 'double', 
                            dim = list(list(name ='time', unlim = FALSE))))
attr(time, "variables") <- metadata
attr(tmax, 'Variables')$dat1$time <- time
attr(tmax, 'Variables')$common[[2]]$dim[[3]]$len = length(time)
attr(tmax, 'Variables')$common[[2]]$dim[[3]]$vals <- time
attr(tmin, 'Variables')$dat1$time <- time
attr(tmin, 'Variables')$common[[2]]$dim[[3]]$len = length(time)
attr(tmin, 'Variables')$common[[2]]$dim[[3]]$vals <- time

a <- DTRRef(tmax, tmin, by.seasons = FALSE, ncores = NULL)
str(a)

tmax <- 1:(2 * 3 * 365 * 1)
dim(tmax) <- c(2, 3, 365)
tmin <- (1:(2 * 3 * 365 * 1))-1
dim(tmin) <- c(2, 3, 365)

a <- DTRRef(tmax, tmin, by.seasons = FALSE, dates = time,  timedim = 3, 
           ncores = NULL)
str(a)

Daily anomalies

Description

This function computes daily anomalies from a vector containing the daily time series.

Usage

DailyAno(data, jdays = NULL, dates = NULL, calendar = NULL, na.rm = TRUE)

Arguments

Value

A vector of daily anomalies of the same length as parameter data.

Examples

# Time series in a vector example:
data <- 1:10
jdays <- c(rep(1, 5), rep(2, 5))
daily_anomaly <- DailyAno(data = data, jdays = jdays, na.rm = TRUE)
print(daily_anomaly)

Sum of spell lengths exceeding daily threshold for n-dimensional arrays

Description

This function returns the number of spells of more than min.length days which exceed or are below the given threshold from daily data.

Usage

Extremes(
  data,
  threshold,
  op = ">",
  min.length = 6,
  spells.can.span.years = TRUE,
  max.missing.days = 5,
  dates = NULL,
  timedim = NULL,
  calendar = NULL,
  ncores = NULL
)

Arguments

Details

This routine compares data to the thresholds using the given operator, generating a series of TRUE or FALSE values; these values are then filtered to remove any sequences of less than min.length days of TRUE values. It then computes the lengths of the remaining sequences of TRUE values (spells) and sums their lengths. The spells.can.spa .years option controls whether spells must always terminate at the end of a period, or whether they may continue until the criteria ceases to be met or the end of the data is reached. The default for fclimdex is FALSE.

Value

A list of length 2:

• $output1, an array with the same dimensions as the original data, except the time dimension which is reduced to annual resolution given a timeseries of maximum spell lengths for each year.

• $year, a vector indicating the corresponding years.

Examples

##Example synthetic data:
data <- 1:(2 * 3 * 310 * 1)
dim(data) <- c(time = 310, lon = 2, lat = 3, model = 1)
time <- as.POSIXct(paste(sort(rep(1902:1911, 31)), 1, 1:31, sep = "-"), tz = "CET")
metadata <- list(time = list(standard_name = 'time', long_name = 'time', 
                            calendar = 'noleap', 
                            units = 'days since 1970-01-01 00:00:00', 
                            prec = 'double', 
                            dim = list(list(name = 'time', unlim = FALSE))))
attr(time, "variables") <- metadata
attr(data, 'Variables')$dat1$time <- time
threshold <- Threshold(data, dates = NULL, base.range = NULL, qtiles = 0.9, 
                      ncores = NULL)
res <- Extremes(data, threshold = threshold, op = ">", min.length = 6, 
               spells.can.span.years = TRUE, max.missing.days = 5, 
               ncores = NULL)
str(res)

Obtain the index of positions for a region in longitudes

Description

This auxiliary function returns the index of position of a region of longitudes in a given vector of longitudes.

Usage

Lon2Index(lon, lonmin, lonmax)

Arguments

Value

the index of positions of all values inside the region in the vector lon.

Examples

lon <- 1 : 360
pos <- Lon2Index(lon, lonmin = -20, lonmax = 20)
lon[pos]
pos <- Lon2Index(lon, lonmin = 340, lonmax = 20)
lon[pos]
lon <- -180 : 180
pos <- Lon2Index(lon, lonmin = -20, lonmax = 20)
lon[pos]
pos <- Lon2Index(lon, lonmin = 340, lonmax = 20)
lon[pos]

Selects a season from daily data for multidimensional arrays

Description

This function selects the daily data corresponding to the specified season.

Usage

SeasonSelect(data, season, dates = NULL, timedim = NULL, calendar = NULL)

Arguments

Value

A list of length 2:

• $data, a vector or array containing the daily values for the selected season, with the same dimensions as data input but the 'time' dimension reduce to the number of days corresponding to the selected season.

• $dates, a vector of dates reduce to the number of days corresponding to the selected season.

Examples

## Example with synthetic data:
data <- 1:(2 * 3 * (366 + 365) * 2)
dim(data) <- c(lon = 2, lat = 3, time = 366 + 365, model = 2)
time <- seq(ISOdate(1903,1,1), ISOdate(1904,12,31), "days")
time <- as.POSIXct(time, tz = "CET")
metadata <- list(time = list(standard_name = 'time', long_name = 'time', 
                            calendar = 'noleap',
                            units = 'days since 1970-01-01 00:00:00', 
                            prec = 'double', 
                            dim = list(list(name ='time', unlim = FALSE))))
attr(time, "variables") <- metadata
attr(data, 'Variables')$dat1$time <- time
attr(data, 'Variables')$dat2$time <- time
attr(data, 'Variables')$common[[2]]$dim[[3]]$len = length(time)
attr(data, 'Variables')$common[[2]]$dim[[3]]$vals <- time

a <- SeasonSelect(data = data, season = 'JJA')
str(a)

Select spatial region from multidimensional arrays

Description

Subset a spatial region from spatial data giving a vector with the maximum and minimum of latitudes and longitudes of the selected region.

Usage

SelBox(data, lon, lat, region, londim = "lon", latdim = "lat", mask = NULL)

Arguments

Value

A list of length 4:

• $data, an array with the same dimensions as the input data array, but with spatial dimension reduced to the selected region.

• $lat, a vector with the new corresponding latitudes for the selected region.

• $lon, a vector with the new corresponding longitudes for the selected region.

• $mask, if parameter mask is supplied, an array with reduced length of the dimensions to the selected region. Otherwise, a NULL element is returned.

Examples

# Example with synthetic data:
data <- 1:(20 * 3 * 2 * 4)
dim(data) <- c(lon = 20, lat = 3, time = 2, model = 4)
lon <- seq(2, 40, 2)
lat <- c(1, 5, 10)

a <- SelBox(data = data, lon = lon, lat = lat, region = c(2, 20, 1, 5), 
           londim = "lon", latdim = "lat", mask = NULL)

Shift longitudes of a data array

Description

Shift the longitudes of a data array. Only reasonable for global longitude shifting. It is useful for map plotting or aligning datasets.

Usage

ShiftLon(data, lon, westB, lon_dim = "lon", ncores = NULL)

Arguments

Value

A list of 2:

Examples

data <- array(data = 1:50, dim = c(lon = 360, lat = 181))
lon <- array(data = 0:359, dim = c(lon = 360))
lat <- -90:90 ## lat does not change
shifted <- ShiftLon(data = data, lon = lon, westB = -180, ncores = 1)

 ## Not run: 
s2dv::PlotEquiMap(var = data, lon = lon, lat = lat, filled.continents = FALSE)
s2dv::PlotEquiMap(var = shifted$data, lon = shifted$lon, lat = lat, filled.continents = FALSE)
 
## End(Not run)
 

Subset a Data Array

Description

This function allows to subset (i.e. slice, take a chunk of) an array, in a similar way as done in the function take() in the package plyr. There are two main snprovements:

First, the input array can have dimension names, either in names(dim(x)) or in the attribute 'dimensions'. If both exist, names(dim(x)) is prioritized. The dimensions to subset along can be specified via the parameter along either with integer indices or either by their name.

Second, there are additional ways to adjust which dimensions are dropped in the resulting array: either to drop all, to drop none, to drop only the ones that have been sliced or to drop only the ones that have not been sliced.

Usage

Subset(x, along, indices, drop = FALSE)

Arguments

Value

An array with similar dimensions as the x input, but with trimmed or dropped dimensions.

Examples

#Example synthetic data:
# Dimension has name already
data <- 1:(2 * 3 * 372 * 1)
dim(data) <- c(time = 372, lon = 2, lat = 3, model = 1)
data_subset <- Subset(data, c('time', 'model'), 
                     list(1:10, TRUE), drop = 'selected')
dim(data_subset)
# Use attributes 'dimensions'
data <- array(1:(2 * 3 * 372 * 1), dim = c(2, 3, 372, 1))
attributes(data)[['dimensions']] <- c('lat', 'lon', 'time', 'model')
data_subset <- Subset(data, c('lon', 'lat'), list(1, 1), drop = TRUE)
dim(data_subset)

Daily thresholds based on quantiles for n-dimensional arrays

Description

This function computes the threshold based on a quantile value for each day of the year of the daily data input.

Usage

Threshold(
  data,
  dates = NULL,
  calendar = NULL,
  base.range = NULL,
  qtiles = 0.9,
  ncores = NULL,
  na.rm = FALSE
)

Arguments

Value

An array with similar dimensions as the data input, but without 'time' dimension, and a new 'jdays' dimension.

Examples

##Example synthetic data:
data <- 1:(2 * 3 * 372 * 1)
dim(data) <- c(time = 372, lon = 2, lat = 3, model = 1)
time <- as.POSIXct(paste(sort(rep(1900:1911, 31)), 1, 1:31, sep = "-"), 
                  tz = "CET")
metadata <- list(time = list(standard_name = 'time', long_name = 'time',  
                calendar = 'noleap', 
                units = 'days since 1970-01-01 00:00:00', prec = 'double', 
                dim = list(list(name = 'time', unlim = FALSE))))
attr(time, "variables") <- metadata
attr(data, 'Variables')$dat1$time <- time

a <- Threshold(data, dates = NULL, base.range = NULL, qtiles = 0.9, 
              ncores = NULL)
str(a)

Heat and cold waves duration for n-dimensional arrays

Description

This function computes the duration of a heat/cold wave as the number of consecutive days for which the maximum/minimum temperature is exceeding/below a threshold over a minimum number of days in month or seasonal resolution.

Usage

WaveDuration(
  data,
  threshold,
  op = ">",
  spell.length = 6,
  by.seasons = TRUE,
  dates = NULL,
  calendar = NULL,
  ncores = NULL
)

Arguments

Value

A list of length 2:

• $result, an array with the same dimensions as the input data, but with the time dimension reduce from daily to monthly or seasonal resolution depending on the selected resolution in by.season.

• $years, a vector of the years and season/months corresponding to the resolution selected in by.season and temporal length of the input data.

Examples

##Example synthetic data:
data <- 1:(2 * 3 * 31 * 5)
dim(data) <- c(lon = 2, lat = 3, time = 31, model = 5)
time <- as.POSIXct(paste(paste(1900, 1, 1:31, sep = "-"), paste(12, 0, 0.0, 
                  sep = ":")), tz = "CET")
metadata <- list(time = list(standard_name = 'time', long_name = 'time', 
                calendar = 'standard', 
                units = 'days since 1970-01-01 00:00:00', prec = 'double', 
                dim = list(list(name ='time', unlim = FALSE))))
attr(time, "variables") <- metadata                 
attr(data, 'Variables')$dat1$time <- time
threshold <- rep(40, 31)

a <- WaveDuration(data, threshold, op = ">", spell.length = 6, 
                 by.seasons = TRUE, ncores = NULL)
str(a)

Compute the square-root of the cosine of the latitude weighting on the given array.

Description

This function performs square-root of the cosine of the latitude weighting on the given array.

Usage

WeightedCells(data, lat, lat_dim = "lat", method = "cos", ncores = NULL)

Arguments

Value

An array containing the latitude weighted data with same dimensions as parameter 'data'.

Examples

exp <- array(rnorm(1:30), dim = c(lat = 3, lon = 5, sdate = 2))
lat <- c(10, 15, 20)
res <- WeightedCells(data = exp, lat = lat)

Calculate spatial area-weighted average of multidimensional arrays

Description

This function computes a spatial area-weighted average of n-dimensional arrays being possible to select a region and to add a mask to be applied when computing the average.

Usage

WeightedMean(
  data,
  lon,
  lat,
  region = NULL,
  mask = NULL,
  londim = "lon",
  latdim = "lat",
  na.rm = TRUE,
  ncores = NULL
)

Arguments

Value

An array, matrix or vector containig the area-weighted average with the same dimensions as data, except for the spatial longitude and latitude dimensions, which disappear.

Examples

# Example 1:
data <- 1:(2 * 3 * 4 * 5)
dim(data) <- c(lon = 2, lat = 3, time = 4, model = 5)
lat <- c(1, 10, 20)
lon <- c(1, 10)
a <- WeightedMean(data = data, lon = lon, lat = lat, region = NULL)

mask <- c(0, 1, 0, 1, 0, 1)
dim(mask) <- c(lon = 2, lat = 3)
a <- WeightedMean(data = data, lon = lon, lat = lat, mask = mask)

region <- c(1, 10, 1, 10)
a <- WeightedMean(data = data, lon = lon, lat = lat, region = region, 
                 mask = mask)

# Example 2:
data <- 1:(2 * 3 * 4)
dim(data) <- c(lon = 2, lat = 3, time = 4)
lat <- c(1, 10, 20)
lon <- c(1, 10)
a <- WeightedMean(data = data, lon = lon, lat = lat)

PCICt

Description

Functions from the PCICt package. These functions convert between PCICt objects and other types of data.

Usage

as.PCICt(x, cal, ...)

Arguments

Value

For as.PCICt and .PCICt, a PCICt object with the given calendar type. For as.POSIXct.PCICt and as.POSIXlt.PCICt, a POSIXct or POSIXlt object, respectively.