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R client for Climate Change, Agriculture, and Food Security (CCAFS) General Circulation Models (GCM) data.


The CCAFS-Climate data portal ( provides global and regional future high-resolution climate datasets that serve as a basis for assessing the climate change impacts and adaptation in a variety of fields including biodiversity, agricultural and livestock production, and ecosystem services and hydrology. CCAFS data can be used by anyone from scientists studying climate change, and how climate impacts various aspects of the earth, to companies projecting crop yields in the future. Search google scholar with "CCAFS" "GCM" to see example uses.

These open-access datasets are hosted by Amazon Web Services. CCAFS GCM data for this package comes from Amazon S3 root path.

As far as I can tell, CCAFS GCM data comes from IPCC data.

About Amazon S3

Amazon S3 stands for "Simple Storage Service" - it's like a file system, and they give you links to the files and metadata around those links.

S3 is split up into buckets, essentially folder. All CCAFS data is in one bucket. Within the CCAFS bucket on S3 are a series of nested folders. To get to various files we need to navigate down the tree of folders. Keys are file paths with all their parent folders, e.g., "/foo/bar/1/2". Unfortunately, there's no meaningful search of the CCCAFS data as they have on their website However, you can set a prefix for a search of these keys, e.g., "/foo/bar" for the key above.

Check out for more info.

About the package

ccafs is a client to work with the data CCAFS provides via Amazon Web Services S3 data.

The ccafs data has access to is the "Spatial Downscaling" data that you see on the page. The other data sets are not open.


Cite CCAFS data following their guidelines at

Get a citation for this package like citation(package = 'ccafs') after installing the package.


The main useful output are raster package objects of class RasterLayer or RasterBrick - so in general have raster loaded in your session to maximize happiness.


CRAN version


Development version



You can search by the numbers representing each possible value for each parameter. See the ?'ccafs-search' for help on that.

(res <- cc_search(file_set = 4, scenario = 6, model = 2, extent = "global",
  format = "ascii", period = 5, variable = 2, resolution = 3))
#> [1] ""

Alternatively, you can use the helper list where you can reference options by name; the downside is that this leads to very verbose code.

(res <- cc_search(file_set = cc_params$file_set$`Delta method IPCC AR4`,
                  scenario = cc_params$scenario$`SRES B1`,
                  model = cc_params$model$bccr_bcm2_0,
                  extent = cc_params$extent$global,
                  format = cc_params$format$ascii,
                  period = cc_params$period$`2040s`,
                  variable = cc_params$variable$Precipitation,
                  resolution = cc_params$resolution$`5 minutes`))
#> [1] ""

Fetch files

Note, files are not loaded as they can be very large

key <- "ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1_m/10min/"
(res <- cc_data_fetch(key = key, progress = FALSE))
#> <CCAFS GCM files>
#>    12 files
#>    Base dir: /bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc
#>    File types (count):
#>      - .asc: 12

Load data

Can load in a single file (gives RasterLayer), or many (gives RasterBrick)

#> class       : RasterLayer
#> dimensions  : 900, 2160, 1944000  (nrow, ncol, ncell)
#> resolution  : 0.1666667, 0.1666667  (x, y)
#> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : NA
#> data source : /Users/sacmac/Library/Caches/ccafs/bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc/prec_1.asc
#> names       : prec_1
#> values      : -2147483648, 2147483647  (min, max)
#> class       : RasterStack
#> dimensions  : 900, 2160, 1944000, 2  (nrow, ncol, ncell, nlayers)
#> resolution  : 0.1666667, 0.1666667  (x, y)
#> extent      : -180, 180, -60, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : NA
#> names       :      prec_1,     prec_10
#> min values  : -2147483648, -2147483648
#> max values  :  2147483647,  2147483647



plot of chunk unnamed-chunk-10



ropenscilabs/ccafs documentation built on Jan. 6, 2018, 8:32 p.m.