---
OA_type: closed access
_id: '22544'
abstract:
- lang: eng
  text: 'The Bayesian weighted averaging (BWA) method is commonly used to integrate
    over multi-model ensembles of climate series. This method relies on two criteria
    to assign weights to individual outputs: model skill in reproducing historical
    observations, and inter-model agreement in simulating future period. Observations
    are generally thought to be relevant for correcting biases in model outputs in
    the BWA framework. However, they concurrently may introduce unpredictable impacts
    in the context of the downscaling process, in particular, when model output on
    precipitation is of interest. Specifically, the posterior distribution may excessively
    depend on few ‘outlier models’ being close to the observation, when all other
    models fail to capture observation of the historical period—a common situation
    for precipitation metrics. Another issue emerges for climates with very dry months:
    the inclusion of observation in BWA may result in a significant spread of the
    posterior distribution into the negative region. To address these problems, a
    modified version of the BWA method that removes observations in the initial phase
    of downscaling (computation of Factors of Change) and adds them in the estimation
    of posterior distributions is explored in this work. Comparisons of simulation
    results for the locations of Miami (FL), Fresno (CA), and Flint (MI) between the
    modified BWA and the traditional BWA demonstrate consistent outcomes with regards
    to the effect of observation in the Bayesian framework. Further, the modified
    BWA approach generally reduces uncertainty, as compared to ‘simple averaging’
    in the Bayesian context, which assigns equal weights to all model outputs.'
article_processing_charge: No
article_type: original
author:
- first_name: Donghui
  full_name: Xu, Donghui
  last_name: Xu
- first_name: Valeriy Y.
  full_name: Ivanov, Valeriy Y.
  last_name: Ivanov
- first_name: Jongho
  full_name: Kim, Jongho
  last_name: Kim
- first_name: Simone
  full_name: Fatichi, Simone
  id: cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  last_name: Fatichi
citation:
  ama: Xu D, Ivanov VY, Kim J, Fatichi S. On the use of observations in assessment
    of multi-model climate ensemble. <i>Stochastic Environmental Research and Risk
    Assessment</i>. 2019;33:1923-1937. doi:<a href="https://doi.org/10.1007/s00477-018-1621-2">10.1007/s00477-018-1621-2</a>
  apa: Xu, D., Ivanov, V. Y., Kim, J., &#38; Fatichi, S. (2019). On the use of observations
    in assessment of multi-model climate ensemble. <i>Stochastic Environmental Research
    and Risk Assessment</i>. Springer Nature. <a href="https://doi.org/10.1007/s00477-018-1621-2">https://doi.org/10.1007/s00477-018-1621-2</a>
  chicago: Xu, Donghui, Valeriy Y. Ivanov, Jongho Kim, and Simone Fatichi. “On the
    Use of Observations in Assessment of Multi-Model Climate Ensemble.” <i>Stochastic
    Environmental Research and Risk Assessment</i>. Springer Nature, 2019. <a href="https://doi.org/10.1007/s00477-018-1621-2">https://doi.org/10.1007/s00477-018-1621-2</a>.
  ieee: D. Xu, V. Y. Ivanov, J. Kim, and S. Fatichi, “On the use of observations in
    assessment of multi-model climate ensemble,” <i>Stochastic Environmental Research
    and Risk Assessment</i>, vol. 33. Springer Nature, pp. 1923–1937, 2019.
  ista: Xu D, Ivanov VY, Kim J, Fatichi S. 2019. On the use of observations in assessment
    of multi-model climate ensemble. Stochastic Environmental Research and Risk Assessment.
    33, 1923–1937.
  mla: Xu, Donghui, et al. “On the Use of Observations in Assessment of Multi-Model
    Climate Ensemble.” <i>Stochastic Environmental Research and Risk Assessment</i>,
    vol. 33, Springer Nature, 2019, pp. 1923–37, doi:<a href="https://doi.org/10.1007/s00477-018-1621-2">10.1007/s00477-018-1621-2</a>.
  short: D. Xu, V.Y. Ivanov, J. Kim, S. Fatichi, Stochastic Environmental Research
    and Risk Assessment 33 (2019) 1923–1937.
das_tickbox: '1'
date_created: 2026-07-27T12:30:24Z
date_published: 2019-12-01T00:00:00Z
date_updated: 2026-08-06T07:47:25Z
day: '01'
doi: 10.1007/s00477-018-1621-2
extern: '1'
fulldoi: https://doi.org/10.1007/s00477-018-1621-2
intvolume: '        33'
keyword:
- Bayesian weighted averaging
- Multi-model ensemble
- Weighting skill
- Model bias
- Observations
- Factor of change
language:
- iso: eng
month: '12'
oa_version: None
page: 1923-1937
publication: Stochastic Environmental Research and Risk Assessment
publication_identifier:
  eissn:
  - 1436-3259
  issn:
  - 1436-3240
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: On the use of observations in assessment of multi-model climate ensemble
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 33
year: '2019'
...