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CFtime is an R package that supports working with CF Metadata Conventions time coordinates, specifically geared to time-referencing data sets of climate projections such as those produced under the World Climate Research Programme and re-analysis data such as ERA5 from the European Centre for Medium-range Weather Forecasts (ECMWF).

The data sets include in their metadata a datum, a point in time from which other points in time are calculated. This datum takes the form of days since 1949-12-01, with each data source (Coupled Model Intercomparison Project (CMIP) generation, model, etc) having its own datum. The data itself has a dimension attribute of “time” with offset values such as 43289. To convert this offset to a date, using a specific calendar, is what this package does. Given that most calendars supported by the CF Metadata Conventions are not compatible with POSIXt, this conversion is not trivial. That it is important to account for these differences is easily demonstrated:


# POSIXt calculations on a standard calendar
as.Date("1949-12-01") + 43289
#> [1] "2068-06-08"

# CFtime calculation on a "360_day" calendar
as_timestamp(CFtime("days since 1949-12-01", "360_day", 43289))
#> [1] "2070-02-30"

That’s a difference of nearly 21 months! (And yes, 30 February is a valid date on a 360_day calendar.)

All defined calendars of the CF Metadata Conventions are supported:

Use of custom calendars is not supported. This package is also not suitable for paleo-calendars. Time periods prior to the introduction of the Gregorian calendar (1582-10-15) may be used but there are no special provisions for it. Finally, there is no specific consideration for the year 0 (which does not exist in any of the above calendars). Given that climate projections are typically made from 1850-01-01 onwards, these limitations should not be of any practical concern.

This package IS NOT intended to support the full date and time functionality of the CF Metadata Conventions. Instead, it facilitates use of a suite of models of climate projections that use different calendars in a consistent manner.

This package is particularly useful for working with climate projection data having a daily or higher resolution, but it will work equally well on data with a lower resolution.

Timestamps are generated using the ISO8601 standard.

Calendar-aware factors can be generated to support processing of data using tapply() and similar functions. Merging of multiple data sets and subsetting facilitate analysis while preserving computer resources.


Get the latest stable version on CRAN:


You can install the development version of CFtime from GitHub with:

# install.packages("devtools")

Basic operation

The package contains a class, CFtime, to describe the time coordinate system, including its calendar, - a datum - and which holds the time coordinate values that are offset from the datum to represent instants in time. This class operates on the data in the file of interest, here a Coordinated Regional Climate Downscaling Experiment (CORDEX) file of precipitation for the Central America domain:


# Opening a data file that is included with the package.
# Usually you would `list.files()` on a directory of your choice.
fn <- list.files(path = system.file("extdata", package = "CFtime"), full.names = TRUE)[1]
nc <- nc_open(fn)
attrs <- ncatt_get(nc, "")
#> [1] "NOAA GFDL GFDL-ESM4 model output prepared for CMIP6 update of RCP4.5 based on SSP2"
#> [1] "CMIP6 model data produced by NOAA-GFDL is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License ( Consult for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law."

# Create the CFtime instance
cf <- CFtime(nc$dim$time$units, 
#> CF datum of origin:
#>   Origin  : 1850-01-01 00:00:00
#>   Units   : days
#>   Calendar: noleap
#> CF time series:
#>   Elements: [2015-01-01 12:00:00 .. 2099-12-31 12:00:00] (average of 1.000000 days between 31025 elements)
#>   Bounds  : not set

# ... work with the data ...

Note that the information of interest (nc$dim$time$units, etc) is read out of the file “blindly”, without checking for available dimensions or attributes. This can be done because the time dimension and its attributes units and calendar are required by the CF Metadata Conventions. Should this fail, then your data set is not compliant. You could still use this package if the required information is contained in your file but using a different dimension name or different attribute names.

Using RNetCDF

If you are using the RNetCDF package rather than ncdf4, creating a CFtime instance goes like this:

nc <-
cf <- CFtime(, "time", "units"), 
   , "time", "calendar"), 
   , "time"))

Typical workflow

In a typical process, you would combine multiple data files into a single data set to analyze a feature of interest. To continue the previous example of precipitation in the Central America domain using CORDEX data, you can calculate the precipitation per month for the period 2041 - 2050 as follows:

# Open the files - one would typically do this in a loop
nc2041 <- nc_open("~/CC/CORDEX/CAM-22/RCP2.6/")
nc2046 <- nc_open("~/CC/CORDEX/CAM-22/RCP2.6/")

# Create the time object from the first file
# All files have an identical datum as per the CORDEX specifications
time <- CFtime(nc2041$dim$time$units, nc2041$dim$time$calendar, nc2041$dim$time$vals)

# Add the time values from the remaining files
time <- time + as.vector(nc2046$dim$time$vals)

# Grab the data from the files and merge the arrays into one, in the same order
# as the time values
pr <- abind(ncvar_get(nc2041, "pr"), ncvar_get(nc2046, "pr"))

# Optionally - Set the time dimension to the timestamps from the time object
dimnames(pr)[[3]] <- as_timestamp(time)

# Create the month factor from the time object
f_month <- CFfactor(time, "month")

# The result from applying this factor to a data set that it describes is a new
# data set with a different "time" dimension. The function result stores this
# new time object as an attribute.
pr_month_time <- attr(f_month, "CFtime")

# Now sum the daily data to monthly data
# Dimensions 1 and 2 are longitude and latitude, the third dimension is time
pr_month <- aperm(apply(pr, 1:2, tapply, f_month, sum), c(2, 3, 1))
dimnames(pr_month)[[3]] <- as_timestamp(pr_month_time)


This package has been tested with the following data sets:

The package also operates on geographical and/or temporal subsets of data sets so long as the subsetted data complies with the CF Metadata Conventions. This includes subsetting in the Climate Data Store. Subsetted data from Climate4Impact is not automatically supported because the dimension names are not compliant with the CF Metadata Conventions, use the corresponding dimension names instead.