Help for package QUALYPSO

Type:

Package

Date:

2023-10-24

License:

GPL-3

Title:

Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections

Version:

2.3

Author:

Guillaume Evin [aut, cre]

Maintainer:

Guillaume Evin <guillaume.evin@inrae.fr>

Imports:

MASS, expm, Rfast, stats, graphics, grDevices

Description:

These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.

Depends:

R (≥ 2.10)

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.2.3

NeedsCompilation:

Packaged:

2023-10-24 07:13:30 UTC; eving

Repository:

CRAN

Date/Publication:

2023-10-24 07:30:02 UTC

QUALYPSO

Description

Partition uncertainty in climate responses using an ANOVA applied to climate change responses.

Usage

QUALYPSO(Y, scenAvail, X = NULL, Xfut = NULL, iFut = NULL, listOption = NULL)

Arguments

Y

matrix nS x nY or array nG x nS x nY of climate projections.

scenAvail

data.frame nS x nEff with the nEff characteristics (e.g. type of GCM) for each of the nS scenarios. The number of characteristics nEff corresponds to the number of main effects that will be included in the ANOVA model.

X

(optional) predictors corresponding to the projections, e.g. time or global temperature. It can be a vector if the predictor is the same for all scenarios (e.g. X=2001:2100) or a matrix of the same size as Y if these predictors are different for the scenarios. By default, a vector 1:nY is created.

Xfut

(optional) nF values of the predictor over which the ANOVA will be applied. It must be a vector of values within the range of values of X. By default, it corresponds to X if X is a vector, 1:nY if X is NULL or a vector of 10 values equally spaced between the minimum and maximum values of X if X is a matrix.

iFut

index in 1:nF corresponding to a future predictor value . This index is necessary when Y is an array nG x nS x nY available for nG grid points. Indeed, in this case, we run QUALYPSO only for one future predictor. The first value defines the reference period or warming level.

listOption

(optional) list of options

args.smooth.spline: list of arguments to be passed to smooth.spline. The names attribute of args.smooth.spline gives the argument names (see do.call). The default option runs smooth.spline with spar=1.
typeChangeVariable: type of change variable: "abs" (absolute, value by default) or "rel" (relative).
ANOVAmethod: ANOVA method: "QUALYPSO" applies the method described in Evin et al. (2020), "lm" applies a simple linear model to estimate the main effects.
nBurn: if ANOVAmethod=="QUALYPSO", number of burn-in samples (default: 1000). If nBurn is too small, the convergence of MCMC chains might not be obtained.
nKeep: if ANOVAmethod=="QUALYPSO", number of kept samples (default: 2000). If nKeep is too small, MCMC samples might not represent correctly the posterior distributions of inferred parameters.
probCI: probability (in [0,1]) for the confidence intervals, probCI = 0.9 by default.
quantilePosterior: vector of probabilities (in [0,1]) for which we compute the quantiles from the posterior distributions quantilePosterior = c(0.005,0.025,0.05,0.1,0.25,0.33,0.5,0.66,0.75,0.9,0.95,0.975,0.995) by default.
climResponse: NULL by default. If it is provided, it must correspond to the outputs of fit.climate.response, i.e. a list with YStar [nS x nY], phiStar [nS x nF], etaStar [nS x nY], phi [nS x nF] and varInterVariability [scalar] if Y is a matrix [nS x nY], or a list with phiStar [nG x nS x nF], etaStar [nG x nS x nY], phi [nG x nS x nF] and varInterVariability vector of length nG if Y is an array [nG x nS x nY].

Value

List providing the results for each of the n values of Xfut if Y is a matrix or for each grid point if Y is an array, with the following fields:

CLIMATERESPONSE: list of climate change responses and corresponding internal variability. Contains phiStar (climate change responses), etaStar (deviation from the climate change responses as a result of internal variability), Ystar (change variable from the projections),and phi (fitted climate responses).
GRANDMEAN: List of estimates for the grand mean:
- MEAN: vector of length n of means.
- SD: vector of length n of standard dev. if ANOVAmethod=="QUALYPSO".
- CI: matrix n x 2 of credible intervals of probability probCI given in listOption if ANOVAmethod=="QUALYPSO".
- QUANT: matrix n x nQ of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="QUALYPSO".
MAINEFFECT: List of estimates for the main effects. For each main effect (GCM, RCM,..), each element of the list contains a list with:
- MEAN: matrix n x nTypeEff
- SD: matrix n x nTypeEff of standard dev. if ANOVAmethod=="QUALYPSO".
- CI: array n x 2 x nTypeEff of credible intervals of probability probCI given in listOption if ANOVAmethod=="QUALYPSO".
- QUANT: array n x nQ x nTypeEff of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="QUALYPSO".
CHANGEBYEFFECT: For each main effect, list of estimates for the mean change by main effect, i.e. mean change by scenario. For each main effect (GCM, RCM,..), each element of the list contains a list with:
- MEAN: matrix n x nTypeEff
- SD: matrix n x nTypeEff of standard dev. if ANOVAmethod=="QUALYPSO".
- CI: array n x 2 x nTypeEff of credible intervals of probability probCI given in listOption if ANOVAmethod=="QUALYPSO".
- QUANT: array n x nQ x nTypeEff of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="QUALYPSO".
EFFECTVAR: Matrix n x nTypeEff giving, for each time variability related to the main effects (i.e. variability between the different RCMs, GCMs,..).
CONTRIB_EACH_EFFECT: Contribution of each individual effect to its component (percentage), e.g. what is the contribution of GCM1 to the variability related to GCMs. For each main effect (GCM, RCM,..), each element of the list contains a matrix n x nTypeEff
RESIDUALVAR: List of estimates for the variance of the residual errors:
- MEAN: vector of length n.
- SD: vector of length n of standard dev. if ANOVAmethod=="QUALYPSO".
- CI: matrix n x 2 of credible intervals of probability probCI given in listOption if ANOVAmethod=="QUALYPSO".
- QUANT: matrix n x nQ of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="QUALYPSO".
INTERNALVAR: Internal variability (constant over time)
TOTALVAR: total variability, i.e. the sum of internal variability, residual variability and variability related to the main effects
DECOMPVAR: Decomposition of the total variability for each component
RESERR: differences between the climate change responses and the additive anova formula (grand mean + main effects)
Xmat: matrix of predictors
Xfut: future predictor values
paralType: type of parallelisation (Time or Grid)
namesEff: names of the main effects
Y: matrix of available combinations given as inputs
listOption: list of options used to obtained these results (obtained from QUALYPSO.check.option)
listScenarioInput: list of scenario characteristics (obtained from QUALYPSO.process.scenario)

Author(s)

Guillaume Evin

References

Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie (2020) Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections Using Data Augmentation. Journal of Climate. <doi:10.1175/JCLI-D-18-0606.1>.

Examples

##########################################################################
# SYNTHETIC SCENARIOS
##########################################################################
# create nS=3 fictive climate scenarios with 2 GCMs and 2 RCMs, for a period of nY=20 years
n=20
t=1:n/n

# GCM effects (sums to 0 for each t)
effGCM1 = t*2
effGCM2 = t*-2

# RCM effects (sums to 0 for each t)
effRCM1 = t*1
effRCM2 = t*-1

# These climate scenarios are a sum of effects and a random gaussian noise
scenGCM1RCM1 = effGCM1 + effRCM1 + rnorm(n=n,sd=0.5)
scenGCM1RCM2 = effGCM1 + effRCM2 + rnorm(n=n,sd=0.5)
scenGCM2RCM1 = effGCM2 + effRCM1 + rnorm(n=n,sd=0.5)
Y.synth = rbind(scenGCM1RCM1,scenGCM1RCM2,scenGCM2RCM1)

# Here, scenAvail indicates that the first scenario is obtained with the combination of the
# GCM "GCM1" and RCM "RCM1", the second scenario is obtained with the combination of
# the GCM "GCM1" and RCM "RCM2" and the third scenario is obtained with the combination
# of the GCM "GCM2" and RCM "RCM1".
scenAvail.synth = data.frame(GCM=c('GCM1','GCM1','GCM2'),RCM=c('RCM1','RCM2','RCM1'))

##########################################################################
# RUN QUALYPSO
##########################################################################
# call main QUALYPSO function: two arguments are mandatory:
# - Y: Climate projections for nS scenarios and nY time steps. if Y is a matrix nS x nY, we
# run QUALYPSO nY times, for each time step. If Y is an array nG x nS x nY, for nG grid points,
# we run QUALYPSO nG times, for each grid point, for one time step specified using the argument
# iFut
# - scenAvail: matrix or data.frame of available combinations nS x nEff. The number of
# characteristics nEff corresponds to the number of main effects that will be included in the
# ANOVA model. In the following example, we have nEff=2 main effects corresponding to the GCMs
# and RCMs.

# Many options can be specified in the argument "listOption". When ANOVAmethod=="QUALYPSO"
# a Bayesian inference is performed. Here, we change the default values for nBurn and nKeep
# in order to speed up computation time for this small example. However, it must be noticed
# that convergence and sampling of the posterior distributions often require higher values
#  for these two arguments.
listOption = list(nBurn=100,nKeep=100,ANOVAmethod="QUALYPSO",quantilePosterior=c(0.025,0.5,0.975))

# run QUALYPSO
QUALYPSO.synth = QUALYPSO(Y=Y.synth, scenAvail=scenAvail.synth, X=2001:2020, listOption=listOption)

##########################################################################
# SOME PLOTS
##########################################################################
# plot grand mean
plotQUALYPSOgrandmean(QUALYPSO.synth,xlab="Years")

# plot main GCM effects
plotQUALYPSOeffect(QUALYPSO.synth,nameEff="GCM",xlab="Years")

# plot main RCM effects
plotQUALYPSOeffect(QUALYPSO.synth,nameEff="RCM",xlab="Years")

# plot fraction of total variance for the differences sources of uncertainty
plotQUALYPSOTotalVarianceDecomposition(QUALYPSO.synth,xlab="Years")

# plot mean prediction and total variance with the differences sources of uncertainty
plotQUALYPSOMeanChangeAndUncertainties(QUALYPSO.synth,xlab="Years")

#____________________________________________________________
# EXAMPLE OF QUALYPSO WHEN THE PREDICTOR IS TIME
#____________________________________________________________

# list of options
listOption = list(typeChangeVariable='abs')

# call QUALYPSO
QUALYPSO.time = QUALYPSO(Y=Y,scenAvail=scenAvail,X=X_time_vec,
                         Xfut=Xfut_time,listOption=listOption)

# grand mean effect
plotQUALYPSOgrandmean(QUALYPSO.time,xlab="Years")

# main GCM effects
plotQUALYPSOeffect(QUALYPSO.time,nameEff="GCM",xlab="Years")

# main RCM effects
plotQUALYPSOeffect(QUALYPSO.time,nameEff="RCM",xlab="Years")

# mean change and associated uncertainties
plotQUALYPSOMeanChangeAndUncertainties(QUALYPSO.time,xlab="Years")

# variance decomposition
plotQUALYPSOTotalVarianceDecomposition(QUALYPSO.time,xlab="Years")

#____________________________________________________________
# EXAMPLE OF QUALYPSO WHEN THE PREDICTOR IS THE GLOBAL TEMPERATURE
#____________________________________________________________

# list of options
listOption = list(typeChangeVariable='abs')

# call QUALYPSO
QUALYPSO.globaltas = QUALYPSO(Y=Y,scenAvail=scenAvail,X=X_globaltas,
                              Xfut=Xfut_globaltas,listOption=listOption)

# grand mean effect
plotQUALYPSOgrandmean(QUALYPSO.globaltas,xlab="Global warming (Celsius)")

# main GCM effects
plotQUALYPSOeffect(QUALYPSO.globaltas,nameEff="GCM",xlab="Global warming (Celsius)")

# main RCM effects
plotQUALYPSOeffect(QUALYPSO.globaltas,nameEff="RCM",xlab="Global warming (Celsius)")

# mean change and associated uncertainties
plotQUALYPSOMeanChangeAndUncertainties(QUALYPSO.globaltas,xlab="Global warming (Celsius)")

# variance decomposition
plotQUALYPSOTotalVarianceDecomposition(QUALYPSO.globaltas,xlab="Global warming (Celsius)")

QUALYPSO.ANOVA

Description

Partition uncertainty in climate responses using an ANOVA inferred with a Bayesian approach.

Usage

QUALYPSO.ANOVA(phiStar, scenAvail, listOption = NULL, namesEff)

Arguments

phiStar

matrix of climate change responses (absolute or relative changes): nS x n. n can be the number of time steps or the number of grid points

scenAvail

data.frame nS x nEff with the nEff characteristics (e.g. type of GCM) for each of the nS x nS scenarios

listOption

list of options (see QUALYPSO)

namesEff

names of the main effects

Value

list with the following fields:

GRANDMEAN: List of estimates for the grand mean:
- strong: MEAN: vector of length n of posterior means
- strong: SD: vector of length n of posterior standard dev.
- strong: CI: matrix n x 2 of credible intervals of probability probCI given in listOption.
- strong: QUANT: matrix n x nQ of quantiles related to the probabilities quantilePosterior given in listOption
RESIDUALVAR: List of estimates for the variance of the residual errors:
- strong: MEAN: vector of length n of posterior means
- strong: SD: vector of length n of posterior standard dev.
- strong: CI: matrix n x 2 of credible intervals of probability probCI given in listOption.
- strong: QUANT: matrix n x nQ of quantiles related to the probabilities quantilePosterior given in listOption
MAINEFFECT: List of estimates for the main effects. For each main effect (GCM, RCM,..), each element of the list contains a list with:
- strong: MEAN: matrix n x nTypeEff of posterior means
- strong: SD: matrix n x nTypeEff of posterior standard dev.
- strong: CI: array n x 2 x nTypeEff of credible intervals of probability probCI given in listOption.
- strong: QUANT: array n x nQ x nTypeEff of quantiles related to the probabilities quantilePosterior given in listOption
CHANGEBYEFFECT: For each main effect, list of estimates for the mean change by main effect, i.e. mean change by scenario (RCP4.5). For each main effect (GCM, RCM,..), each element of the list contains a list with:
- strong: MEAN: matrix n x nTypeEff of posterior means
- strong: SD: matrix n x nTypeEff of posterior standard dev.
- strong: CI: array n x 2 x nTypeEff of credible intervals of probability probCI given in listOption.
- strong: QUANT: array n x nQ x nTypeEff of quantiles related to the probabilities quantilePosterior given in listOption
EFFECTVAR: variability related to the main effects (i.e. variability between the different RCMs, GCMs,..). Matrix n x nTypeEff
CONTRIB_EACH_EFFECT: Contribution of each individual effect to its component (percentage), e.g. what is the contribution of GCM1 to the variability related to GCMs. For each main effect (GCM, RCM,..), each element of the list contains a matrix n x nTypeEff
listOption: list of options used to obtained these results (obtained from QUALYPSO.check.option)
listScenarioInput: list of scenario characteristics (obtained from QUALYPSO.process.scenario)

Author(s)

Guillaume Evin

References

QUALYPSO.ANOVA.i

Description

Partition sources of uncertainty in climate change responses for one lead time or one grid point.

Usage

QUALYPSO.ANOVA.i(phiStar.i, nMCMC, listScenarioInput)

Arguments

phiStar.i

vector of nS climate change response for one lead time or for one grid point: nS x 1

nMCMC

number of MCMC simulation required

listScenarioInput

list containing specifications, provided by QUALYPSO.process.scenario

Value

list with the following fields:

mu: vector of length nMCMC, mean climate change response
sigma2: vector of length nMCMC, variance of the residual terms
effect: list with nTypeEff elements, where each element corresponds to a different type of effect (e.g. alpha, beta, gamma in Eq. 7) Each element is a matrix nMCMC x nMaineff, and nMaineff is the number of main effects (e.g. number of GCMs, RCMs, etc.)

Author(s)

Guillaume Evin

References

QUALYPSO.check.option

Description

Check if input options provided in QUALYPSO are valid and assigned default values if missing.

Usage

QUALYPSO.check.option(listOption)

Arguments

listOption

list of options

Value

List containing the complete set of options.

Author(s)

Guillaume Evin

QUALYPSO.process.scenario

Description

Process input scenarios.

Usage

QUALYPSO.process.scenario(scenAvail)

Arguments

scenAvail

data.frame nS x nEff with the nEff characteristics (e.g. type of GCM) for each of the nS x nS scenarios

Value

list of preprocessed objects (listEff, scenAvail, scenComp, nEff, nTypeEff, nComp, isMissing, nMissing, iMatchScen, indexEffInCompScen, Qmat)

Author(s)

Guillaume Evin

Annual warming levels simulated by different CMIP5 GCMs

Description

Annual warming levels at the planetary scales simulated by different CMIP5 GCMs for the period 1971-2099. Warming levels are obtained with respect to the year 1860 (common starting year of the CMIP5 simulations). These warming levels have been obtained with the following steps:

Annual tas averages simulated by different CMIP5 have first been smoothed using a smoothing spline. Let us denote these smoothed values by tas_GCM(y) for a year y.
Large discrepancies can be observed for tas_GCM_smooth(y) even in the past due to large first-order biases in the GCM simulations. In order to obtain a common reference, we also consider observed tas estimates at the global scale. HadCRUT5 (Morice et al., 2021, 10.1029/2019JD032361) provides anomalies with respect to the period 1961-1990. An estimate of absolute average temperature for this period is 14°C (Jones et al., 1999, 10.1029/1999RG900002). Smoothed estimates of absolute tas averages are obtained using a smoothing spline and is denoted by tas_obs(y).
Warming levels are obtained as anomalies with respect to the period 1860 and considering a reference year, here 1990, where the warming levels WL are in agreement: WL(y) = tas_GCM(y)-tas_GCM(1990)+tas_obs(1990)-tas_obs(1860)

Usage

data(X_globaltas)

Format

matrix 20 scenarios x 129 years

Author(s)

Guillaume Evin guillaume.evin@inrae.fr

Years 1971-2099 repeated for the 20 scenarios

Description

Years 1971-2099 repeated for the 20 scenarios

Usage

data(X_time_mat)

Format

matrix 20 scenarios x 129 years

Author(s)

Guillaume Evin guillaume.evin@inrae.fr

X_time_vec gives the years corr. to Y, i.e. from 1971 to 2099

Description

X_time_vec gives the years corr. to Y, i.e. from 1971 to 2099

Usage

data(X_time_vec)

Format

vector of length 129

Author(s)

Guillaume Evin guillaume.evin@inrae.fr

Vector of of future warming levels

Description

Equally spaced vector of of future warming levels

Usage

data(Xfut_globaltas)

Format

vector of length 13

Author(s)

Guillaume Evin guillaume.evin@inrae.fr

Xfut_time is a vector of 11 years equally spaced from 1999 to 2099

Description

Xfut_time is a vector of 11 years equally spaced from 1999 to 2099

Usage

data(Xfut_time)

Format

vectors of length 11

Author(s)

Guillaume Evin guillaume.evin@inrae.fr

Mean winter temperature over CEU with 20 GCM/RCM combinations for 1971-2099

Description

climate projections of mean winter (DJF) temperature over the SREX region CEU simulated by 20 combinations of CMIP5 GCMs and RCMs for the period 1971-2099

Usage

data(Y)

Format

matrix 20 scenarios x 129 years

Author(s)

Guillaume Evin guillaume.evin@inrae.fr

References

Seneviratne, S. I. et al. Changes in Climate Extremes and their Impacts on the Natural Physical Environment, in: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, edited by: Field, C., Barros, V., Stocker, T., and Dahe, Q., Cambridge University Press, Cambridge, 109-230, https://doi.org/10.1017/CBO9781139177245.006, 2012

fit.climate.response

Description

Fit trends for each simulation chain of an ensemble of nS projections. Each simulation chain is a time series of nY time steps (e.g. number of years).

Usage

fit.climate.response(Y, args.smooth.spline, Xmat, Xfut, typeChangeVariable)

Arguments

Y

matrix of simulation chains: nS x nY

args.smooth.spline

list of arguments to be passed to smooth.spline. The names attribute of args.smooth.spline gives the argument names (see do.call).

Xmat

matrix of predictors corresponding to the projections, e.g. time or global temperature.

Xfut

values of the predictor over which the ANOVA will be applied.

typeChangeVariable

type of change variable: "abs" (absolute, value by default) or "rel" (relative)

Details

See QUALYPSO for further information on arguments indexReferenceYear and typeChangeVariable.

Value

list with the following fields for each simulation chain:

YStar: nS x nY, change variable
phiStar: nS x nF, climate change responses
etaStar: nS x nY, deviation from the climate change response due to the internal variability, for Xmat
phi: nS x nF, raw trends obtained using smooth.spline
climateResponse: output from smooth.spline
varInterVariability: scalar, internal variability component of the MME

Author(s)

Guillaume Evin

References

get.Qmat

Description

Provide matrix Q derived from a matrix Q* of Helmert contrasts:

Q = Q^* (Q^{*T} Q^*)^{-1/2}

See Eq. A6 in Evin et al., 2019.

Usage

get.Qmat(p)

Arguments

p

integer

Value

matrix

p x p matrix

Author(s)

Guillaume Evin

References

get.Qstar.mat

Description

Provide matrix containing Helmert contrasts (see Eq. A7 in Evin et al., 2019).

Usage

get.Qstar.mat(p)

Arguments

p

integer

Value

matrix

p x (p-1) matrix containing Helmert contrasts

Author(s)

Guillaume Evin

References

Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie (2020) <doi:10.1175/JCLI-D-18-0606.1>.

lm.ANOVA

Description

Partition uncertainty in climate responses using an ANOVA inferred with a Bayesian approach.

Usage

lm.ANOVA(phiStar, scenAvail, listOption = NULL, namesEff)

Arguments

phiStar

matrix of climate change responses (absolute or relative changes): nS x n. n can be the number of time steps or the number of grid points

scenAvail

data.frame nS x nEff with the nEff characteristics (e.g. type of GCM) for each of the nS x nS scenarios

listOption

list of options (see QUALYPSO)

namesEff

names of the main effects

Value

list with the following fields:

GRANDMEAN: List of estimates for the grand mean:
- strong: MEAN: vector of length n of means
- strong: SD: vector of length n of standard dev.
- strong: CI: matrix n x 2 of credible intervals of probability probCI given in listOption.
RESIDUALVAR: List of estimates for the variance of the residual errors:
- strong: MEAN: vector of length n
MAINEFFECT: List of estimates for the main effects. For each main effect (GCM, RCM,..), each element of the list contains a list with:
- strong: MEAN: matrix n x nTypeEff
CHANGEBYEFFECT: For each main effect, list of estimates for the mean change by main effect, i.e. mean change by scenario (RCP4.5). For each main effect (GCM, RCM,..), each element of the list contains a list with:
- strong: MEAN: matrix n x nTypeEff
EFFECTVAR: variability related to the main effects (i.e. variability between the different RCMs, GCMs,..). Matrix n x nTypeEff
CONTRIB_EACH_EFFECT: Contribution of each individual effect to its component (percentage), e.g. what is the contribution of GCM1 to the variability related to GCMs. For each main effect (GCM, RCM,..), each element of the list contains a matrix n x nTypeEff
listOption: list of options used to obtained these results (obtained from QUALYPSO.check.option)
listScenarioInput: list of scenario characteristics (obtained from QUALYPSO.process.scenario)

Author(s)

Guillaume Evin

plotQUALYPSOMeanChangeAndUncertainties

Description

Plot fraction of total variance explained by each source of uncertainty.

Usage

plotQUALYPSOMeanChangeAndUncertainties(
  QUALYPSOOUT,
  col = NULL,
  ylim = NULL,
  xlab = "",
  ylab = "Change variable",
  addLegend = TRUE,
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO

col

colors for each source of uncertainty, the first two colors corresponding to internal variability and residual variability, respectively

ylim

y-axis limits

xlab

x-axis label

ylab

y-axis label

addLegend

if TRUE, a legend is added

...

additional arguments to be passed to plot

Author(s)

Guillaume Evin

plotQUALYPSOMeanChangeAndUncertaintiesBetatest

Description

Plot fraction of total variance explained by each source of uncertainty.

Usage

plotQUALYPSOMeanChangeAndUncertaintiesBetatest(
  QUALYPSOOUT,
  col = NULL,
  ylim = NULL,
  xlab = "",
  ylab = "Change variable",
  addLegend = TRUE,
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO

col

colors for each source of uncertainty, the first two colors corresponding to internal variability and residual variability, respectively

ylim

y-axis limits

xlab

x-axis label

ylab

y-axis label

addLegend

if TRUE, a legend is added

...

additional arguments to be passed to plot

Author(s)

Guillaume Evin

plotQUALYPSOTotalVarianceByScenario

Description

Plot fraction of total variance explained by each source of uncertainty.

Usage

plotQUALYPSOTotalVarianceByScenario(
  QUALYPSOOUT,
  nameEff,
  nameScenario,
  col = NULL,
  ylim = NULL,
  xlab = "",
  ylab = "Change variable",
  addLegend = TRUE,
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO

nameEff

name of the main effect to be plotted in QUALYPSOOUT$namesEff

nameScenario

name of the scenario to be plotted (as provided in scenAvail)

col

colors for each source of uncertainty, the first two colors corresponding to internal variability and residual variability, respectively

ylim

y-axis limits

xlab

x-axis label

ylab

y-axis label

addLegend

if TRUE, a legend is added

...

additional arguments to be passed to plot

Author(s)

Guillaume Evin

plotQUALYPSOTotalVarianceDecomposition

Description

Plot fraction of total variance explained by each source of uncertainty.

Usage

plotQUALYPSOTotalVarianceDecomposition(
  QUALYPSOOUT,
  vecEff = NULL,
  col = c("orange", "yellow", "cadetblue1", "blue1", "darkgreen", "darkgoldenrod4",
    "darkorchid1"),
  xlab = "",
  ylab = "% Total Variance",
  addLegend = TRUE,
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO

vecEff

vector of indices corresponding to the main effects (NULL by default), so that the order of appearance in the plot can be modified

col

colors for each source of uncertainty, the first two colors corresponding to internal variability and residual variability, respectively

xlab

x-axis label

ylab

y-axis label

addLegend

if TRUE, a legend is added

...

additional arguments to be passed to plot

Author(s)

Guillaume Evin

plotQUALYPSOclimateChangeResponse

Description

Plot climate change responses.

Usage

plotQUALYPSOclimateChangeResponse(
  QUALYPSOOUT,
  lim = NULL,
  xlab = "",
  ylab = "Climate change response",
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO

lim

y-axis limits (default is NULL)

xlab

x-axis label

ylab

y-axis label

...

additional arguments to be passed to plot

Author(s)

Guillaume Evin

plotQUALYPSOclimateResponse

Description

Plot the climate responses.

Usage

plotQUALYPSOclimateResponse(
  QUALYPSOOUT,
  lim = NULL,
  xlab = "X",
  ylab = "Y",
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO

lim

y-axis limits (default is NULL)

xlab

x-axis label

ylab

y-axis label

...

additional arguments to be passed to plot

Author(s)

Guillaume Evin

plotQUALYPSOeffect

Description

Plot prediction of ANOVA effects for one main effect. By default, we plot we plot the credible intervals corresponding to a probability 0.95.

Usage

plotQUALYPSOeffect(
  QUALYPSOOUT,
  nameEff,
  includeMean = FALSE,
  lim = NULL,
  col = 1:20,
  xlab = "",
  ylab = "Effect",
  addLegend = TRUE,
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO

nameEff

name of the main effect to be plotted in QUALYPSOOUT$namesEff

includeMean

if TRUE, the grand mean is added to the main effect in the plot

lim

y-axis limits (default is NULL)

col

colors for each effect

xlab

x-axis label

ylab

y-axis label

addLegend

if TRUE, a legend is added

...

additional arguments to be passed to plot

Author(s)

Guillaume Evin

plotQUALYPSOgrandmean

Description

Plot prediction of grand mean ensemble.

Usage

plotQUALYPSOgrandmean(
  QUALYPSOOUT,
  lim = NULL,
  col = "black",
  xlab = "",
  ylab = "Grand mean",
  addLegend = T,
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO

lim

y-axis limits (default is NULL)

col

color for the overall mean and the credible interval

xlab

x-axis label

ylab

y-axis label

addLegend

if TRUE, a legend is added

...

additional arguments to be passed to plot

Author(s)

Guillaume Evin

List of GCM and RCM which have been used for the 20 climate projections

Description

scenAvail gives the GCM and RCM which have been used for the 20 climate projections (obtained with the RCP8.5)

Usage

data(scenAvail)

Format

data.frame with 20 rows and two columns: GCM and RCM

Author(s)

Guillaume Evin guillaume.evin@inrae.fr