Help for package QUALYPSO

Type:

Package

Date:

2026-11-03

License:

GPL-3

Title:

Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections

Version:

3.2

Author:

Guillaume Evin [aut, cre]

Maintainer:

Guillaume Evin <guillaume.evin@inrae.fr>

Imports:

MASS, expm, Rfast, stats, graphics, grDevices, gamlss, gamlss.dist, statmod

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. The current notations correspond to Evin et al. (2026) <doi:10.5194/hess-30-1023-2026>. QUALYPSO 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>.

Depends:

R (≥ 3.5.0)

Suggests:

knitr, rmarkdown, bookdown

VignetteBuilder:

knitr

URL:

https://github.com/guillaumeevin/QUALYPSO

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

8.0.0

NeedsCompilation:

Packaged:

2026-11-05 22:17:30 UTC; evigui1

Repository:

CRAN

Date/Publication:

2026-11-05 23:20:14 UTC

Perform ANOVA using a Bayesian approach

Description

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

Usage

Bayesian_ANOVA(CCR_tfut, scen, listOption = NULL, namesEff)

Arguments

CCR_tfut

matrix of climate change responses: nS x nFut.

scen

data.frame nS x nFactor for the nS scenarios and the nFactor factors (e.g. type of GCM)

listOption

list of options (see QUALYPSO)

namesEff

names of the main effects

Value

list with the following fields:

M_tfut: List of estimates for the grand mean:
- MEAN: vector of length nFut of posterior means
- SD: vector of length nFut of posterior standard dev.
- CI: matrix nFut x 2 of credible intervals of probability probCI given in listOption.
- QUANT: matrix nFut x nQ of quantiles related to the probabilities quantilePosterior given in listOption
RV_tfut: List of estimates for the variance of the residual errors:
- MEAN: vector of length nFut of posterior means
- SD: vector of length nFut of posterior standard dev.
- CI: matrix nFut x 2 of credible intervals of probability probCI given in listOption.
- QUANT: matrix nFut x nQ of quantiles related to the probabilities quantilePosterior given in listOption
E_tfut: List of estimates for the main effects. For each main effect (GCM, RCM,..), each element of the list contains a list with:
- MEAN: matrix nFut x nEffect of posterior means
- SD: matrix nFut x nEffect of posterior standard dev.
- CI: array nFut x 2 x nEffect of credible intervals of probability probCI given in listOption.
- QUANT: array nFut x nQ x nEffect of quantiles related to the probabilities quantilePosterior given in listOption
VARE_tfut: variability related to the main effects (i.e. variability between the different RCMs, GCMs,..). Matrix nFut x nEffect
CONTRIB_VARE_tfut: 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 nFut x nEffect
listOption: list of options used to obtained these results (obtained from check_option)
listScenario: list of scenario factors (obtained from process_scen)

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>.

Perform ANOVA using a Bayesian approach for a future time i

Description

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

Usage

Bayesian_ANOVA_i(CCR_tfut_i, nMCMC, listScenario)

Arguments

CCR_tfut_i

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

nMCMC

number of MCMC simulation required

listScenario

list containing specifications, provided by process_scen

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 nEffect elements, where each element corresponds to a different factor Each element is a matrix nMCMC x nMainFactor, and nMainFactor is the number of main effects (e.g. number of GCMs, RCMs, etc.)

Author(s)

Guillaume Evin

References

Main function

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 nFactor factors 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 nFactor with the nFactor factors (e.g. type of GCM) for each of the nS scenarios. The number of factors nFactor 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 different 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 (value by default), "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.
ClimateResponse: NULL by default. If it is provided, it must correspond to the outputs of fit_ClimateResponse, i.e. a list with CR_tfut [nS x nF], CCR_tfut [nS x nF], D_t [nS x nY], CHANGEVAR_t [nS x nY], and IV_tfut [nF].

Value

List providing the results for each of the nFut values of Xfut:

CR_tfut: Fitted climate responses [nS x nF],
CCR_tfut: Climate change responses [nS x nF],
D_t: Deviation from the climate change responses [nS x nY],
CHANGEVAR_t: Change variable from the projections [nS x nY],
M_tfut: List of estimates for the grand mean:
- MEAN: vector of length nFut of means.
- SD: vector of length nFut of standard dev.
- CI: matrix nFut x 2 of credible intervals of probability probCI given in listOption
- QUANT: matrix nFut x nQ of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="Bayesian".
E_tfut: List of estimates for the main effects. For each main effect (GCM, RCM,..), each element of the list contains a list with:
- MEAN: matrix nFut x nEffect
- SD: matrix nFut x nEffect of standard dev.
- CI: array nFut x 2 x nEffect of credible intervals of probability probCI given in listOption
- QUANT: array nFut x nQ x nEffect of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="Bayesian".
VARE_tfut: Matrix nFut x nEffect giving the variability related to the main effects (i.e. variability between the different RCMs, GCMs,..).
CONTRIB_VARE_tfut: 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 nFut x nEffect
RV_tfut: List of estimates for the variance of the residual errors:
- MEAN: vector of length nFut.
- SD: vector of length nFut of standard dev. if ANOVAmethod=="Bayesian".
- CI: matrix nFut x 2 of credible intervals of probability probCI given in listOption if ANOVAmethod=="Bayesian".
- QUANT: matrix nFut x nQ of quantiles of probability quantilePosterior given in listOption if ANOVAmethod=="Bayesian".
IV_tfut: Internal variability
CCRV_tfut: total uncertainty CCRV(t) i.e. the sum of variability related to the main effects and residual variability
FV_CCRV_tfut: Fraction of total uncertainty CCRV(t) (in variance) explained by the different sources of uncertainty
CCRV_IV_tfut: total uncertainty CCRV(t) + IV(t), i.e. the sum of variability related to the main effects residual variability and internal variability
FV_CCRV_IV_tfut: Fraction of total uncertainty CCRV(t) + IV(t) (in variance) explained by the different sources of uncertainty
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 check_option)
listScenario: list of scenario factors (output of process_scen)

Author(s)

Guillaume Evin

References

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 nFactor. The number of
# factors nFactor corresponds to the number of main effects that will be included in the
# ANOVA model. In the following example, we have nFactor=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
plotQUALYPSO_effect(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
plotQUALYPSO_effect(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
plotQUALYPSO_FV_CCRV_IV(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
plotQUALYPSO_CR(QUALYPSO_time)

# diagnostic of the internal variability
plotQUALYPSO_IV(QUALYPSO_time)

# grand mean effect
plotQUALYPSO_M(QUALYPSO_time,xlab="Years")

# main GCM effects
plotQUALYPSO_effect(QUALYPSO_time,nameEff="GCM",xlab="Years")

# main RCM effects
plotQUALYPSO_effect(QUALYPSO_time,nameEff="RCM",xlab="Years")

# variance decomposition
plotQUALYPSO_FV_CCRV_IV(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
plotQUALYPSO_CR(QUALYPSO_wl)

# diagnostic of the internal variability
plotQUALYPSO_IV(QUALYPSO_wl)

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

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

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

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

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_DJFTas_WL)

Format

matrix 20 scenarios x 129 years

Author(s)

Guillaume Evin guillaume.evin@inrae.fr

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 guillaume.evin@inrae.fr

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 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

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 guillaume.evin@inrae.fr

References

Check arguments provided to QUALYPSO

Description

Check type and dimensions of the arguments provided to QUALYPSO main function

Usage

check_arguments(Y, scen, X)

Arguments

Y

matrix nS x nY

scen

data.frame nS x nFactor with the nFactor factors for each of the nS scenarios. The number of factors nFactor corresponds to the number of main effects that will be included in the ANOVA model.

X

Author(s)

Guillaume Evin

Check validity of options provided in listOption

Description

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

Usage

check_option(listOption)

Arguments

listOption

list of options

Value

List containing the complete set of options.

Author(s)

Guillaume Evin

Fit the climate response for each simulation chain

Description

Fit the climate response 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_ClimateResponse(
  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:

CR_tfut: nS x nF, climate responses
CCR_tfut: nS x nF, climate change responses
D_t: nS x nY, deviation from the climate change response
CHANGEVAR_t: nS x nY, change variable
IV_tfut: nF, internal variability

Author(s)

Guillaume Evin

References

Provide the matrix Q derived from Qstar

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

Provide Qstar matrix containing Helmert contrasts

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

Perform ANOVA using a linear model

Description

Partition uncertainty in climate responses using an ANOVA inferred with a linear model.

Usage

lm_ANOVA(CCR_tfut, scen, listOption = NULL, namesEff)

Arguments

CCR_tfut

matrix of climate change responses: nS x nFut.

scen

data.frame nS x nFactor for each of the nS scenarios and the nFactor factors (e.g. type of GCM)

listOption

list of options (see QUALYPSO)

namesEff

names of the main effects

Value

list with the following fields:

M_tfut: List of estimates for the grand mean:
- MEAN: vector of length nFut of means
- SD: vector of length nFut of standard dev.
- CI: matrix nFut x 2 of credible intervals of probability probCI given in listOption.
RV_tfut: List of estimates for the variance of the residual errors:
- MEAN: vector of length nFut
E_tfut: List of estimates for the main effects. For each main effect (GCM, RCM,..), each element of the list contains a list with:
- MEAN: matrix nFut x nEffect
VARE_tfut: variability related to the main effects (i.e. variability between the different RCMs, GCMs,..). Matrix nFut x nEffect
CONTRIB_VARE_tfut: 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 nFut x nEffect
listOption: list of options used to obtained these results (obtained from check_option)
listScenario: list of scenario factors (obtained from process_scen)

Author(s)

Guillaume Evin

Plot climate change responses for all simulation chains.

Description

Plot the climate change responses CCR(t) for all simulation chains.

Usage

plotQUALYPSO_CCR(QUALYPSOOUT, lim = NULL, xlab = "", ylab = "CCR(t)", ...)

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

Plot the climate response for one simulation chain

Description

Plot the climate response CR(t) for one simulation chain and the corresponding raw projection. The choice of the simulation chain can be given or randomly sampled.

Usage

plotQUALYPSO_CR(
  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

plotQUALYPSO_FV_CCRV_IV

Description

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

Usage

plotQUALYPSO_FV_CCRV_IV(
  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

Plot deviations from the climate change response and an interval representing the magnitude of the estimated internal variability

Description

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

Usage

plotQUALYPSO_IV(QUALYPSOOUT, lim = NULL, xlab = "X", ylab = "D(t)", ...)

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

Plot the estimated grand mean

Description

Plot the estimated grand mean and the corresponding estimation uncertainty.

Usage

plotQUALYPSO_M(
  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

Plot estimated ANOVA effects for one factor

Description

Plot estimated ANOVA effects for one factor. By default, we plot the estimation uncertainty corresponding to a probability 0.95.

Usage

plotQUALYPSO_effect(
  QUALYPSOOUT,
  nameEff,
  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

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

Process scen

Description

Process factors describing each scenario (i.e each simulation chain)

Usage

process_scen(scen)

Arguments

scen

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

Value

list of preprocessed objects (listEff, scen, scenComp, nFactor, nEffect, 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 guillaume.evin@inrae.fr

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 guillaume.evin@inrae.fr

Package {QUALYPSO}

Perform ANOVA using a Bayesian approach

Description

Usage

Arguments

Value

Author(s)

References

Perform ANOVA using a Bayesian approach for a future time i

Description

Usage

Arguments

Value

Author(s)

References

Main function

Description

Usage

Arguments

Value

Author(s)

References

Examples

Annual warming levels simulated by different CMIP5 GCMs

Description

Usage

Format

Author(s)

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

Description

Usage

Format

Author(s)

References

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

Description

Usage

Format

Author(s)

References

Description

Usage

Format

Author(s)

References

Check arguments provided to QUALYPSO

Description

Usage

Arguments

Author(s)

Check validity of options provided in listOption

Description

Usage

Arguments

Value

Author(s)

Fit the climate response for each simulation chain

Description

Usage

Arguments

Details

Value

Author(s)

References

Provide the matrix Q derived from Qstar

Description

Usage

Arguments

Value

Author(s)

References

Provide Qstar matrix containing Helmert contrasts

Description

Usage

Arguments

Value

Author(s)

References

Perform ANOVA using a linear model

Description