Package 'QUALYPSO'

Title: Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections
Description: These functions apply an analysis of variance to incomplete ensembles of climate projections. It provides estimates of climate change responses of all simulation chains and of all uncertainty variables. It has been applied to different ensembles of projections simulated to study the impact of climate change: for climate indicators in Evin et al. (2019) <doi:10.1175/JCLI-D-18-0606.1>; seasonal precipitation and temperature in Evin, Somot and Hingray (2021) <doi:10.5194/esd-12-1543-2021>; hydrological variables in Evin et al. (2026) <doi:10.5194/hess-30-1023-2026>; photovoltaic energy in Bichet et al. (2019) <doi:10.1088/1748-9326/ab500a>.
Authors: Guillaume Evin [aut, cre]
Maintainer: Guillaume Evin <[email protected]>
License: GPL-3
Version: 3.2
Built: 2026-06-03 21:59:10 UTC
Source: https://github.com/guillaumeevin/qualypso

Help Index

Bayesian.ANOVA

Description

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

Usage

Bayesian.ANOVA(phiStar, scen, 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
scen

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

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>.

Bayesian.ANOVA.i

Description

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

Usage

Bayesian.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

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>.

checkTypeandDimension

Description

Check type and dimensions if objects passed as arguments

Usage

checkTypeandDimension(Y, scen, X)

Arguments

Y

matrix nS x nY
scen

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.

Author(s)

Guillaume Evin

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,
  typeClimateResponse,
  parClimateResponse,
  Xmat,
  Xfut,
  typeChangeVariable
)

Arguments

Y

matrix of simulation chains: nS x nY
typeClimateResponse

type of climate response fitted to the projections.
parClimateResponse

parameter of the model applied for the extraction of the climate response.
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

  • varInterVariability: nF, internal variability component of the MME

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. J. Climate, 32, 2423–2440. <doi:10.1175/JCLI-D-18-0606.1>.

get.Qmat

Description

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

Q=Q∗(Q∗TQ∗)−1/2Q = 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

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. J. Climate, 32, 2423–2440. <doi:10.1175/JCLI-D-18-0606.1>.

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>.

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. J. Climate, 32, 2423–2440. <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, scen, 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
scen

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

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,
  iS = NULL,
  lim = NULL,
  xlab = "X",
  ylab = "Y",
  ...
)

Arguments

QUALYPSOOUT

output from QUALYPSO
iS

index of the projection (integer). If NULL, one scenario is chosen randomly
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

plotQUALYPSOinternalvar

Description

Plot eta* as a function of X as well as eta* +/- 1.645*sqrt(INTERNALVAR), where INTERNALVAR is a function of Xfut.

Usage

plotQUALYPSOinternalvar(
  QUALYPSOOUT,
  lim = NULL,
  xlab = "X",
  ylab = expression(paste(eta, "*")),
  ...
)

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

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

QUALYPSO

Description

Partition uncertainty in climate responses using an ANOVA applied to climate change responses. The main function of the package needs at least two arguments: Y is an ensemble of climate projections, i.e. a matrix nS x nY where nS projections are provided for nY years or future time steps; scen is a data.frame which provides nEff characteristics for each projection.

Usage

QUALYPSO(Y, scen, X = NULL, Xfut = NULL, listOption = NULL)

Arguments

Y

matrix nS x nY of climate projections.
scen

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. The first value defines the reference period or warming level.
listOption

(optional) list of options

  • typeClimateResponse: type of climate response fitted to the projections. Can be either "smooth.spline" (smooth.spline) where the parameter df in (1,nY] is the equivalent number of degrees of freedom, "poly" (polynomial model fitted with lm) where the parameter degree is an integer indicating the degree of the polynomial (poly), "lowess" (lowess) where the parameter f in (0,1) is the proportion of points which influence the smooth at each value, or "tweedie" (tweedie), a Generalized Linear Model with the Tweedie distribution where the parameter var.power is the index of power variance function (positive value). The tweedie model is the only model that handles zeros in the climate projections, typically with var.power in (1,2).

  • parClimateResponse: parameter of the model applied for the extraction of the climate response (i.e. df, degree, f, or var.power). Default values are df=4, degree=1, f=0.4 or var.power=1.5 for the smoothing spline, polynomial, lowess, or tweedie models, respectively.

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

  • ANOVAmethod: ANOVA method: "lm" (default) applies a linear model (lm) to estimate the main effects. "Bayesian" applies the method described in Evin et al. (2020).

  • nBurn: if ANOVAmethod=="Bayesian", number of burn-in samples (default: 1000). If nBurn is too small, the convergence of MCMC chains might not be obtained.

  • nKeep: if ANOVAmethod=="Bayesian", 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 [nF].

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.

    • CI: matrix n x 2 of credible intervals of probability probCI given in listOption

    • QUANT: matrix n x nQ of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="Bayesian".

  • 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.

    • CI: array n x 2 x nTypeEff of credible intervals of probability probCI given in listOption

    • QUANT: array n x nQ x nTypeEff of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="Bayesian".

  • 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=="Bayesian".

    • CI: array n x 2 x nTypeEff of credible intervals of probability probCI given in listOption if ANOVAmethod=="Bayesian".

    • QUANT: array n x nQ x nTypeEff of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="Bayesian".

  • 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=="Bayesian".

    • CI: matrix n x 2 of credible intervals of probability probCI given in listOption if ANOVAmethod=="Bayesian".

    • QUANT: matrix n x nQ of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="Bayesian".

  • INTERNALVAR: Internal variability

  • 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

  • namesEff: names of the main effects

  • Y: matrix of available combinations given as inputs

  • listOption: list of options used to obtained these results (output of QUALYPSO.check.option)

  • listScenarioInput: list of scenario characteristics (output of 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

#____________________________________________________________
# CALLING QUALYPSO
#____________________________________________________________
# call main QUALYPSO function: two arguments are mandatory:
# - Y: Climate projections for nS scenarios and nY time steps. Y is a matrix nS x nY, we
# run QUALYPSO nY times, for each time step.
# - scen: 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".

#____________________________________________________________
# Example 1: Synthetic scenarios
#____________________________________________________________
# create nS=3 fictive climate scenarios with 2 GCMs and 2 RCMs, for a period of nY=20 years
n=20
t=0:n/n

# GCM effects (sums to 0 for each t)
effGCM1 = t*2
effGCM2 = t*1
effGCM3 = t*-3

# 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+1,sd=0.5)
scenGCM1RCM2 = effGCM1 + effRCM2 + rnorm(n=n+1,sd=0.5)
scenGCM2RCM1 = effGCM2 + effRCM1 + rnorm(n=n+1,sd=0.5)
scenGCM2RCM2 = effGCM2 + effRCM2 + rnorm(n=n+1,sd=0.5)
scenGCM3RCM1 = effGCM3 + effRCM1 + rnorm(n=n+1,sd=0.5)
Y_synth = rbind(scenGCM1RCM1,scenGCM1RCM2,scenGCM2RCM1,scenGCM2RCM2,scenGCM3RCM1)

# Here, scen 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".
scen_synth = data.frame(GCM=c('GCM1','GCM1','GCM2','GCM2','GCM3'),
RCM=c('RCM1','RCM2','RCM1','RCM2','RCM1'))


# run QUALYPSO
QUALYPSO_synth = QUALYPSO(Y=Y_synth, scen=scen_synth, X=2000:2020,
listOption = list(typeClimateResponse="poly"))

# plot main GCM effects
plotQUALYPSOeffect(QUALYPSO_synth,nameEff="GCM",xlab="Years")
lines(2000:2020, effGCM1,lty=2,lwd=2,col="black")
lines(2000:2020, effGCM2,lty=2,lwd=2,col="red")
lines(2000:2020, effGCM3,lty=2,lwd=2,col="green")

# plot main RCM effects
plotQUALYPSOeffect(QUALYPSO_synth,nameEff="RCM",xlab="Years")
lines(2000:2020, effRCM1,lty=2,lwd=2,col="black")
lines(2000:2020, effRCM2,lty=2,lwd=2,col="red")

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

#____________________________________________________________
# Example 2: climate projections of mean winter (DJF) temperature
# with time as the predictor
#____________________________________________________________

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

# call QUALYPSO
QUALYPSO_time = QUALYPSO(Y=Y_DJFTas,scen=scen_DJFTas,X=1971:2099,
                         Xfut=1990:2099,listOption=listOption)

# diagnostic of the climate responses
plotQUALYPSOclimateResponse(QUALYPSO_time)

# diagnostic of the internal variability
plotQUALYPSOinternalvar(QUALYPSO_time)

# 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")

# variance decomposition
plotQUALYPSOTotalVarianceDecomposition(QUALYPSO_time,xlab="Years")

#____________________________________________________________
# Example 3: climate projections of mean winter (DJF) temperature
# with global temperature as the predictor
#____________________________________________________________

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

# call QUALYPSO
QUALYPSO_wl = QUALYPSO(Y=Y_DJFTas,scen=scen_DJFTas,X=X_DJFTas_WL,
                              Xfut=seq(from=0.5,to=3,by=0.1),listOption=listOption)

# diagnostic of the climate responses
plotQUALYPSOclimateResponse(QUALYPSO_wl)

# diagnostic of the internal variability
plotQUALYPSOinternalvar(QUALYPSO_wl)

# grand mean effect
plotQUALYPSOgrandmean(QUALYPSO_wl,xlab="Global warming (Celsius)")

# main GCM effects
plotQUALYPSOeffect(QUALYPSO_wl,nameEff="GCM",xlab="Global warming (Celsius)")

# main RCM effects
plotQUALYPSOeffect(QUALYPSO_wl,nameEff="RCM",xlab="Global warming (Celsius)")

# variance decomposition
plotQUALYPSOTotalVarianceDecomposition(QUALYPSO_wl,xlab="Global warming (Celsius)")

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(scen)

Arguments

scen

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, scen, scenComp, nEff, nTypeEff, nComp, isMissing, nMissing, iMatchScen, indexEffInCompScen, Qmat)

Author(s)

Guillaume Evin

data.frame indicating which GCMs and RCMs have been used for the 20 climate projections of mean winter temperature over CEU

Description

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

Usage

data(scen_DJFTas)

Format

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

Author(s)

Guillaume Evin [email protected]

data.frame indicating which GCM, RCM and RCP scenarios have been used to produce the 18 projections of SWE maxima.

Description

SWE maxima are provided for 18 projections obtained with 9 different combinations of GCMs and RCMs and two emission scenarios (RCP4.5 and RCP8.5).

Usage

data(scen_SWE)

Format

matrix 18 scenarios x 149 years

Author(s)

Guillaume Evin [email protected]

References

Evin, G., E. Le Roux, E. Kamir, and S. Morin. « Estimating changes in extreme snow load in Europe as a function of global warming levels ». Cold Regions Science and Technology 231 (2025): 104424. https://doi.org/10.1016/j.coldregions.2025.104424.

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:

  1. 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.

  2. 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).

  3. 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_DJFTas_WL)

Format

matrix 20 scenarios x 129 years

Author(s)

Guillaume Evin [email protected]

Annual warming levels simulated by different CMIP5 GCMs corresponding to the 18 projections Y_SWE

Description

Annual warming levels at the planetary scales simulated by different CMIP5 GCMs for the period 1951-2099. Warming levels are obtained with respect to the year 1860 (common starting year of the CMIP5 simulations). See Evin et al. (2025) for further details.

Usage

data(X_SWE_WL)

Format

matrix 18 scenarios x 149 years

Author(s)

Guillaume Evin [email protected]

References

Evin, G., E. Le Roux, E. Kamir, and S. Morin. « Estimating changes in extreme snow load in Europe as a function of global warming levels ». Cold Regions Science and Technology 231 (2025): 104424. https://doi.org/10.1016/j.coldregions.2025.104424.

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_DJFTas)

Format

matrix 20 scenarios x 129 years

Author(s)

Guillaume Evin [email protected]

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

Annual maxima of snow water equivalent for Loire-Atlantique, France, a NUTS-3 region located at a low mean elevation (0 m) and has a suboceanic climate with quite mild and rainy winters. SWE maxima are provided for 18 projections obtained with 9 different combinations of GCMs and RCMs and two emission scenarios (RCP4.5 and RCP8.5).

Description

Annual maxima of snow water equivalent for Loire-Atlantique, France, a NUTS-3 region located at a low mean elevation (0 m) and has a suboceanic climate with quite mild and rainy winters. SWE maxima are provided for 18 projections obtained with 9 different combinations of GCMs and RCMs and two emission scenarios (RCP4.5 and RCP8.5).

Usage

data(Y_SWE)

Format

matrix 18 scenarios x 149 years

Author(s)

Guillaume Evin [email protected]

References

Evin, G., E. Le Roux, E. Kamir, and S. Morin. « Estimating changes in extreme snow load in Europe as a function of global warming levels ». Cold Regions Science and Technology 231 (2025): 104424. https://doi.org/10.1016/j.coldregions.2025.104424.