vignettes/NEXGDDP-CMIP6.Rmd
NEXGDDP-CMIP6.RmdNASAaccess has a handy tool to access, extract, and reformat climate change data of rainfall and air temperature from NASA Earth Exchange Global Daily Downscaled Projections NEX-GDDP-CMIP6 AMES servers for grids within a specified watershed.
NEX-GDDP-CMIP6 dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model GCM runs conducted under the Coupled Model Intercomparison Project Phase 6 CMIP6 (Eyring et al. 2016) and across two of the four “Tier 1” greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs) (O’Neill et al. 2016; Meinshausen et al. 2020). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change IPCC AR6. This dataset includes downscaled projections from the 35 models and scenarios for which daily scenarios were produced and distributed under CMIP6.
The Bias-Correction Spatial Disaggregation BCSD method used in generating the NEX-GDDP-CMIP6 dataset is a statistical downscaling algorithm specifically developed to address the current limitations of the global GCM outputs (Andrew W. Wood et al. 2002; A. W. Wood et al. 2004; Maurer and Hidalgo 2008; Thrasher et al. 2012). The NEX-GDDP-CMIP6 climate projections is downscaled at a spatial resolution of 0.25 degrees x 0.25 degrees (approximately 25 km x 25 km). The NEX_GDDP_CMIP6 downscales the NEX-GDDP-CMIP6 data to grid points of 0.1 degrees x 0.1 degrees following nearest point methods described by Mohammed et al. (2018).
Let’s use the example watersheds that we introduced with
GPMswat and GPMpolyCentroid. Please visit
NASAaccess GPM functions for more
information.
#Reading input data
dem_path <- system.file("extdata",
"DEM_TX.tif",
package = "NASAaccess")
shape_path <- system.file("extdata",
"basin.shp",
package = "NASAaccess")
#CMIP6 example for air temperature
library(NASAaccess)
NEX_GDDP_CMIP6(
Dir = "./NEX_GDDP_CMIP6/",
watershed = shape_path,
DEM = dem_path,
start = "2060-12-1",
end = "2060-12-3",
model = 'ACCESS-CM2',
type = 'tas',
slice = 'ssp245')Let’s examine the air temperature station file
NEX_GDDP_CMIP6.temperature.Master <- system.file('extdata/NEX_GDDP_CMIP6',
'tasGrid_Master.txt',
package = 'NASAaccess')
NEX_GDDP_CMIP6.table<-read.csv(NEX_GDDP_CMIP6.temperature.Master)
head(NEX_GDDP_CMIP6.table)
#> ID NAME LAT LONG ELEVATION
#> 1 2160842 tasclimate2160842 29.93337 -95.82337 50.20436
#> 2 2160843 tasclimate2160843 29.93337 -95.72340 46.65100
#> 3 2160844 tasclimate2160844 29.93337 -95.62343 39.71060
#> 4 2160845 tasclimate2160845 29.93337 -95.52346 35.15914
#> 5 2164442 tasclimate2164442 29.83343 -95.82337 47.60787
#> 6 2164443 tasclimate2164443 29.83343 -95.72340 40.46636
dim(NEX_GDDP_CMIP6.table)
#> [1] 11 5
sessionInfo()
#> R version 4.3.0 (2023-04-21)
#> Platform: x86_64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.6.3
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#> BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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#> time zone: America/New_York
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> [1] vctrs_0.6.5 cli_3.6.2 knitr_1.45 rlang_1.1.3
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