QUALYPSO.ANOVA {QUALYPSO}

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

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>.