R client for Climate Change, Agriculture, and Food Security (CCAFS) General Circulation Models (GCM) data.
The CCAFS-Climate data portal (http://ccafs-climate.org/) 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.
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.
Check out https://aws.amazon.com/s3/ for more info.
ccafs is a client to work with the data CCAFS provides via Amazon Web Services S3 data.
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.
Cite CCAFS data following their guidelines at http://ccafs-climate.org/about/
Get a citation for this package like
citation(package = 'ccafs') after installing the package.
The main useful output are
raster package objects of class
RasterBrick - so in general have
raster loaded in your session to maximize happiness.
You can search by the numbers representing each possible value for each parameter. See the
?'ccafs-search' for help on that.
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`)) #>  "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"
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/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
Can load in a single file (gives
RasterLayer), or many (gives
cc_data_read(res) #> 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