[{"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."}],"page":"1923-1937","volume":33,"OA_type":"closed access","date_updated":"2026-08-06T07:47:25Z","intvolume":"        33","extern":"1","type":"journal_article","publisher":"Springer Nature","language":[{"iso":"eng"}],"year":"2019","fulldoi":"https://doi.org/10.1007/s00477-018-1621-2","oa_version":"None","publication_status":"published","article_processing_charge":"No","_id":"22544","title":"On the use of observations in assessment of multi-model climate ensemble","publication_identifier":{"issn":["1436-3240"],"eissn":["1436-3259"]},"date_created":"2026-07-27T12:30:24Z","citation":{"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>.","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>","short":"D. Xu, V.Y. Ivanov, J. Kim, S. Fatichi, Stochastic Environmental Research and Risk Assessment 33 (2019) 1923–1937.","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>.","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.","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>"},"das_tickbox":"1","scopus_import":"1","date_published":"2019-12-01T00:00:00Z","article_type":"original","day":"01","keyword":["Bayesian weighted averaging","Multi-model ensemble","Weighting skill","Model bias","Observations","Factor of change"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.1007/s00477-018-1621-2","publication":"Stochastic Environmental Research and Risk Assessment","quality_controlled":"1","author":[{"last_name":"Xu","full_name":"Xu, Donghui","first_name":"Donghui"},{"last_name":"Ivanov","full_name":"Ivanov, Valeriy Y.","first_name":"Valeriy Y."},{"last_name":"Kim","full_name":"Kim, Jongho","first_name":"Jongho"},{"last_name":"Fatichi","first_name":"Simone","id":"cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6","full_name":"Fatichi, Simone"}],"month":"12","status":"public"},{"das_tickbox":"1","scopus_import":"1","citation":{"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>.","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>.","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.","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>","short":"J. Kim, V.Y. Ivanov, S. Fatichi, Stochastic Environmental Research and Risk Assessment 30 (2015) 923–944.","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>","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."},"date_published":"2015-06-16T00:00:00Z","keyword":["Climate change","Weather generator","Stochastic downscaling","Uncertainty","CMIP3","Internal variability","Emission scenarios","Extreme indicators","The Great Lakes region"],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","doi":"10.1007/s00477-015-1097-2","day":"16","article_type":"original","status":"public","month":"06","author":[{"full_name":"Kim, Jongho","first_name":"Jongho","last_name":"Kim"},{"full_name":"Ivanov, Valeriy Y.","first_name":"Valeriy Y.","last_name":"Ivanov"},{"last_name":"Fatichi","first_name":"Simone","id":"cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6","full_name":"Fatichi, Simone"}],"issue":"3","publication":"Stochastic Environmental Research and Risk Assessment","quality_controlled":"1","OA_type":"closed access","date_updated":"2026-08-12T13:55:28Z","intvolume":"        30","extern":"1","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."}],"volume":30,"page":"923-944","publisher":"Springer Nature","type":"journal_article","year":"2015","language":[{"iso":"eng"}],"article_processing_charge":"No","oa_version":"None","fulldoi":"https://doi.org/10.1007/s00477-015-1097-2","publication_status":"published","publication_identifier":{"issn":["1436-3240"],"eissn":["1436-3259"]},"date_created":"2026-07-27T12:30:23Z","title":"Climate change and uncertainty assessment over a hydroclimatic transect of Michigan","_id":"22493"}]