---
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. Stochastic Environmental Research and Risk
Assessment. 2019;33:1923-1937. doi:10.1007/s00477-018-1621-2
apa: Xu, D., Ivanov, V. Y., Kim, J., & Fatichi, S. (2019). On the use of observations
in assessment of multi-model climate ensemble. Stochastic Environmental Research
and Risk Assessment. Springer Nature. https://doi.org/10.1007/s00477-018-1621-2
chicago: Xu, Donghui, Valeriy Y. Ivanov, Jongho Kim, and Simone Fatichi. “On the
Use of Observations in Assessment of Multi-Model Climate Ensemble.” Stochastic
Environmental Research and Risk Assessment. Springer Nature, 2019. https://doi.org/10.1007/s00477-018-1621-2.
ieee: D. Xu, V. Y. Ivanov, J. Kim, and S. Fatichi, “On the use of observations in
assessment of multi-model climate ensemble,” Stochastic Environmental Research
and Risk Assessment, 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.” Stochastic Environmental Research and Risk Assessment,
vol. 33, Springer Nature, 2019, pp. 1923–37, doi:10.1007/s00477-018-1621-2.
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'
...
---
OA_type: closed access
_id: '22493'
abstract:
- lang: eng
text: Predictions of a warmer climate over the Great Lakes region due to global
change generally agree on the magnitude of temperature changes, but precipitation
projections exhibit dependence on which General Circulation Models and emission
scenarios are chosen. To minimize model- and scenario-specific biases, we combined
information provided by the 3rd phase of the Coupled Model Intercomparison Project
database. Specifically, the results of 12 GCMs for three emission scenarios B1,
A1B, and A2 were analyzed for mid- (2046–2065) and end-century (2081–2100) intervals,
for six locations of a hydroclimatic transect of Michigan. As a result of Bayesian
Weighted Averaging, total annual precipitation averaged over all locations and
the three emission scenarios increases by 7 % (mid-)–10 % (end-century), as compared
to the control period (1961–1990). The projected changes across seasons are non-uniform
and precipitation decreases by 3 % (mid-)–5 % (end-) for the months of August
and September are likely. Further, average temperature is very likely to increase
by 2.02–2.85 °C by the mid-century and 2.58–4.73 °C by the end-century. Three
types of non-additive uncertainty sources due to climate models, anthropogenic
forcings, and climate internal variability are addressed. When compared to the
emission uncertainty, the relative magnitudes of the uncertainty types for climate
model ensemble and internal variability are 149 and 225 % for mean monthly precipitation,
and they are respectively 127 and 123 % for mean monthly temperature. A decreasing
trend of the frost days and an increasing trend of the growing season length are
identified. Also, a significant increase in the magnitude and frequency of heavy
rainfall events is projected, with relatively more pronounced changes for heavy
hourly rainfall as compared to daily events. Quantifying the inherent natural
uncertainty and projecting hourly-based extremes, the study results deliver useful
information for water resource stakeholders interested in impacts of climate change
on hydro-morphological processes.
article_processing_charge: No
article_type: original
author:
- first_name: Jongho
full_name: Kim, Jongho
last_name: Kim
- first_name: Valeriy Y.
full_name: Ivanov, Valeriy Y.
last_name: Ivanov
- first_name: Simone
full_name: Fatichi, Simone
id: cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
last_name: Fatichi
citation:
ama: Kim J, Ivanov VY, Fatichi S. Climate change and uncertainty assessment over
a hydroclimatic transect of Michigan. Stochastic Environmental Research and
Risk Assessment. 2015;30(3):923-944. doi:10.1007/s00477-015-1097-2
apa: Kim, J., Ivanov, V. Y., & Fatichi, S. (2015). Climate change and uncertainty
assessment over a hydroclimatic transect of Michigan. Stochastic Environmental
Research and Risk Assessment. Springer Nature. https://doi.org/10.1007/s00477-015-1097-2
chicago: Kim, Jongho, Valeriy Y. Ivanov, and Simone Fatichi. “Climate Change and
Uncertainty Assessment over a Hydroclimatic Transect of Michigan.” Stochastic
Environmental Research and Risk Assessment. Springer Nature, 2015. https://doi.org/10.1007/s00477-015-1097-2.
ieee: J. Kim, V. Y. Ivanov, and S. Fatichi, “Climate change and uncertainty assessment
over a hydroclimatic transect of Michigan,” Stochastic Environmental Research
and Risk Assessment, vol. 30, no. 3. Springer Nature, pp. 923–944, 2015.
ista: Kim J, Ivanov VY, Fatichi S. 2015. Climate change and uncertainty assessment
over a hydroclimatic transect of Michigan. Stochastic Environmental Research and
Risk Assessment. 30(3), 923–944.
mla: Kim, Jongho, et al. “Climate Change and Uncertainty Assessment over a Hydroclimatic
Transect of Michigan.” Stochastic Environmental Research and Risk Assessment,
vol. 30, no. 3, Springer Nature, 2015, pp. 923–44, doi:10.1007/s00477-015-1097-2.
short: J. Kim, V.Y. Ivanov, S. Fatichi, Stochastic Environmental Research and Risk
Assessment 30 (2015) 923–944.
das_tickbox: '1'
date_created: 2026-07-27T12:30:23Z
date_published: 2015-06-16T00:00:00Z
date_updated: 2026-08-12T13:55:28Z
day: '16'
doi: 10.1007/s00477-015-1097-2
extern: '1'
fulldoi: https://doi.org/10.1007/s00477-015-1097-2
intvolume: ' 30'
issue: '3'
keyword:
- Climate change
- Weather generator
- Stochastic downscaling
- Uncertainty
- CMIP3
- Internal variability
- Emission scenarios
- Extreme indicators
- The Great Lakes region
language:
- iso: eng
month: '06'
oa_version: None
page: 923-944
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: Climate change and uncertainty assessment over a hydroclimatic transect of
Michigan
type: journal_article
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 30
year: '2015'
...