---
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. <i>Stochastic Environmental Research and
    Risk Assessment</i>. 2015;30(3):923-944. doi:<a href="https://doi.org/10.1007/s00477-015-1097-2">10.1007/s00477-015-1097-2</a>
  apa: Kim, J., Ivanov, V. Y., &#38; Fatichi, S. (2015). Climate change and uncertainty
    assessment over a hydroclimatic transect of Michigan. <i>Stochastic Environmental
    Research and Risk Assessment</i>. Springer Nature. <a href="https://doi.org/10.1007/s00477-015-1097-2">https://doi.org/10.1007/s00477-015-1097-2</a>
  chicago: Kim, Jongho, Valeriy Y. Ivanov, and Simone Fatichi. “Climate Change and
    Uncertainty Assessment over a Hydroclimatic Transect of Michigan.” <i>Stochastic
    Environmental Research and Risk Assessment</i>. Springer Nature, 2015. <a href="https://doi.org/10.1007/s00477-015-1097-2">https://doi.org/10.1007/s00477-015-1097-2</a>.
  ieee: J. Kim, V. Y. Ivanov, and S. Fatichi, “Climate change and uncertainty assessment
    over a hydroclimatic transect of Michigan,” <i>Stochastic Environmental Research
    and Risk Assessment</i>, 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.” <i>Stochastic Environmental Research and Risk Assessment</i>,
    vol. 30, no. 3, Springer Nature, 2015, pp. 923–44, doi:<a href="https://doi.org/10.1007/s00477-015-1097-2">10.1007/s00477-015-1097-2</a>.
  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'
...