ebm_bsamples.default: Generate a dataset of climate response to forcings

In thaos/FARallnat: FAR analysis on European Summer Temperature

Description

ebm_bsamples.default generates the climate responses to external forcings. No random scaling factors are included. The EBM takes the corresponding GCM parameters if available, otherwise it takes an available set of paramters chosent at random

ebm_bsamples.sf_gaussian generates the climate responses to external forcings with no random scaling factors. it takes the corresponding GCM parameters if available, otherwise it takes the average of the availables parameters

ebm_bsamples.default generates the climate responses to external forcings. Random gaussian scaling factors on the three categories of forcing (GHG, NAT, and OTHERS ) are included. The EBM takes the corresponding GCM parameters if available, otherwise it takes an available set of paramters chosent at random

Usage

ebm_bsamples.default(mdata, model, by = "year", R, gno2aan = TRUE)

ebm_bsamples.sf_gaussian(mdata, model, by = "year", R, gno2aan = TRUE)

ebm_bsamples.cm_avg.sf_default(mdata, model, by = "year", R, gno2aan = TRUE)

Arguments

mdata	the data to which the EBM simulations will be added
model,	the name of the GCM for which the climate responses are needed
by	the name of the time variable in mdata. Used to merge mdata with the EBM response.
R	the number of bootstrap simulations
gno2aan,	whether to express the climate response into the ALL, ANT and NAT responses instead of GHG, NAT, and OTHERS(anthropogenic) response

Value

a list of data.frame where the simulated EBM response are merge to the dataset mdata

Examples

#Simulate 5 EBM realisations and express the results in ALL, ANT and NAT
#responses. In this case, all the EBM simulations are the same since random
#scaling factor are not accounted for and because the EBM paramters for the
#CNRM are presents
data(cnrm)
ebm_bsamples.default(mdata=cnrm, R=5, model="cnrm", by="year", gno2aan=TRUE)
# Simulate 5 EBM realisations with no random scaling factors and express the
# results in GHG, NAT and OTHERS responses
data(bnu)
ebm_bsamples.sf_gaussian(mdata=bnu, R=5, model="bnu", by="year", gno2aan=FALSE)
# Simulate 5 EBM realisations with random scaling factors and express the
# results in GHG, NAT and OTHERS responses
data(cnrm)
ebm_bsamples.sf_gaussian(mdata=cnrm, R=5, model="cnrm", by="year", gno2aan=FALSE)

thaos/FARallnat documentation built on May 25, 2019, 8:18 a.m.