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

About CCAFS

CCAFS GCM data for this package comes from Amazon S3 root path. 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. More about Amazon S3 below.

CCAFS data can be used for studying climate change, and how climate impacts various aspects of the earth. Search google scholar with "CCAFS" "GCM" to see example uses.

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 http://ccafs-climate.org/. However, you can set a prefix for a search of these keys, e.g., "/foo/bar" for the key above.

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 http://ccafs-climate.org/data/ page. The other data sets are not open.

Citations

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

raster

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.

Install

install.packages("ccafs")

devtools::install_github("ropensci/ccafs")

library("ccafs")

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] "http://gisweb.ciat.cgiar.org/ccafs_climate/files/data/ipcc_4ar_ciat/sres_b1/2040s/bccr_bcm2_0/5min/bccr_bcm2_0_sres_b1_2040s_prec_5min_no_tile_asc.zip"

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] "http://gisweb.ciat.cgiar.org/ccafs_climate/files/data/ipcc_4ar_ciat/sres_b1/2040s/bccr_bcm2_0/5min/bccr_bcm2_0_sres_b1_2040s_prec_5min_no_tile_asc.zip"

Fetch files

key <- "ccafs/ccafs-climate/data/ipcc_5ar_ciat_downscaled/rcp2_6/2030s/bcc_csm1_1_m/10min/bcc_csm1_1_m_rcp2_6_2030s_prec_10min_r1i1p1_no_tile_asc.zip"
(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

cc_data_read(res[1])
#> 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)

cc_data_read(res[1:2])
#> 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

library("raster")
plot(cc_data_read(res[1:3]))

ccafs