GCM_control_run: GCM control run without any external forcing
In mapn/DAbayes: Bayesian Statistical Model for Climate-Change Detection and Attribution
Description
Outputs from general circulation models (GCMs) without any external forcing represent internal climate variability and GCM variability
Format
an n by L0 matrix, where n is the number of grid cells, and L0 is the number of control runs.
Examples
1
2
3
4
# plot GCM control runs
boxplot(GCM_control_run, xlab="number of runs without any external forcing scenario",
ylab="control runs", cex=0.5, cex.axis=0.6, cex.lab=0.8,
pars=list(boxwex=0.5), medcol="red", medlwd=1)
mapn/DAbayes documentation built on May 21, 2019, 11:26 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
Outputs from general circulation models (GCMs) without any external forcing represent internal climate variability and GCM variability
Format
an n by L0 matrix, where n is the number of grid cells, and L0 is the number of control runs.
Examples
1 2 3 4 | # plot GCM control runs
boxplot(GCM_control_run, xlab="number of runs without any external forcing scenario",
ylab="control runs", cex=0.5, cex.axis=0.6, cex.lab=0.8,
pars=list(boxwex=0.5), medcol="red", medlwd=1)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.