Bayesian Consideration of GCM Simulations for Rainfall Quantile Estimation: Uncertainty from GCMs and RCP Scenarios

Yoo, Chulsang; Na, Wooyoung

doi:10.1061/(asce)he.1943-5584.0001649

Cited by 2 publications

(3 citation statements)

References 37 publications

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“…The probability of the data was determined by different distributions (parametric and nonparametric), and they were used in the Bayesian network to derive the posterior probability. In the Bayesian method, the posterior probability in discreet form is given by Equation () (Yoo & Na, 2018)

f^{''} (italicθi) ∆ θ = \frac{L (italicθi) f^{'} (italicθi) ∆ θ}{\sum_{i = 1}^{n} L (italicθi) f^{'} (italicθi) ∆ θ}

where

f^{''}

= posterior probability of the discrete parameter θi ,

L (italicθi)

is the likelihood function and

f^{'} (θ)

is the prior probability.…”

Section: Methodsmentioning

confidence: 99%

Section: Bayesian Model Averagingmentioning

confidence: 99%

“…The consideration of the distribution becomes pertinent since the posterior derived in the Bayesian framework is a direct function of the degree of accuracy with which the priors and likelihoods are stochastically modelled. In most cases, the Normal distribution has been used to model temperature (Raftery et al, 2005), and Gumbel/Gamma distribution has been used to model precipitation (Watterson & Dix, 2003; Yoo & Na, 2018), all of which are parametric distributions. Parametric distributions are widely used; however, the degree to which they can efficiently model the randomness of climate phenomena is bound by the accuracy of the parameter estimation procedures (Cunnane, 1985; Dooge, 1986).…”

Section: Introductionmentioning

confidence: 99%

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A novel framework for a multimodel ensemble of GCMs and its application in the analysis of projected extremes

Ganguly,

Arya

2023

Intl Journal of Climatology

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Methodologies for evaluating global climate models (GCMs) and generating multimodel ensembles have branched to meet the diverse needs of impact assessment studies. However, there has been limited focus on their intercomparison. In this context, a comprehensive framework comprising the major strands of GCM ranking and ensemble data generation is proposed in the current study. The framework incorporates error index‐based rankings and Bayesian method‐based weight distribution. Within the Bayesian analysis, a nonparametric (orthonormal) distribution is introduced in this study with the hypothesis that the efficiency of nonparametric distribution in modelling natural random phenomena will accentuate the efficiency of the ranking and ensemble process. Precipitation and temperature data of 21 GCMs from Coupled Model Intercomparison Project Phase 5 covering India is used to validate the proposed framework. Finally, the ensemble data generated from the framework is used for the analysis of projected extremes and their attribution. Results show that Bayesian framework‐based rankings outperform other methods in 87.5% of instances. In the case of precipitation, the orthonormal distribution‐based Bayesian ranking produces better results for 85.2% of India, while it produces the lowest error ensemble for 79% of the country. The data from the framework are compared to the widely used mathematical average of GCMs. It is found that for precipitation and maximum temperature, the weighted ensemble has closer proximity to the distributive properties of the observed data for the entire study area. Furthermore, uncertainty analysis shows that the ensemble data have minimum uncertainty for the entire area in the case of precipitation. Finally, the assessment of projected extremes shows low to medium confidence (~27%) in the attribution of precipitation extremes to anthropogenic causes under representative concentration pathway (RCP) 4.5, while under RCP 8.5, the confidence is around 80%. For temperature, it is greater than 90% in both scenarios.

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