Climate change and uncertainty assessment over a hydroclimatic transect of Michigan
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 b...
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description | 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. |
doi_str_mv | 10.1007/s00477-015-1097-2 |
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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. 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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.</description><subject>Anthropogenic factors</subject><subject>Aquatic Pollution</subject><subject>Chemistry and Earth Sciences</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Emission</subject><subject>Emissions</subject><subject>Environment</subject><subject>Environmental impact</subject><subject>frost</subject><subject>General Circulation Models</subject><subject>Growing season</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical models</subject><subject>Original Paper</subject><subject>Physics</subject><subject>prediction</subject><subject>Predictions</subject><subject>Probability Theory and Stochastic Processes</subject><subject>rain</subject><subject>Rainfall</subject><subject>Seasons</subject><subject>stakeholders</subject><subject>Statistics for Engineering</subject><subject>Temperature</subject><subject>Uncertainty</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Water resources</subject><subject>Weather</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkU9LwzAYh4MoOOY-gCcDXrxU3yRN0hxl-A8UD3PnkKXpVunSmbTCvr2pFREP4ikJeZ4X3t8PoVMClwRAXkWAXMoMCM8IKJnRAzQhORMZo1wdft9zOEazGOtVcjhTisAELeZNvTWdw3Zj_Nph40vce-tCZ2rf7bGJ0cW4db7D7bsL2ODNvgyt_bRqi7tgfHQ2_Vb4qbabem38CTqqTBPd7OucouXtzcv8Pnt8vnuYXz9mNpeiy7ijlikOTrFSuMIyWgpGLANDaM4ryipDBVe5YqaA9KJCKlquclGUK8mlY1N0Mc7dhfatd7HT2zpa1zTGu7aPmsgCOCsYwD9QKUTKkA_o-S_0te2DT4sMFAEBIieJIiNlQxtjcJXehRRJ2GsCeihFj6XoVIoeStE0OXR0YmJT2OHH5D-ks1GqTKvNOtRRLxcUiAAgjKQV2QfBqZYr</recordid><startdate>20160301</startdate><enddate>20160301</enddate><creator>Kim, Jongho</creator><creator>Ivanov, Valeriy Y</creator><creator>Fatichi, Simone</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><scope>7U6</scope><scope>7SU</scope><scope>7TA</scope><scope>JG9</scope></search><sort><creationdate>20160301</creationdate><title>Climate change and uncertainty assessment over a hydroclimatic transect of Michigan</title><author>Kim, Jongho ; Ivanov, Valeriy Y ; Fatichi, Simone</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-5e2c3950e93d6e8c32d631c30a1245f23fa2659493a8023f26792db468db757e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Anthropogenic factors</topic><topic>Aquatic Pollution</topic><topic>Chemistry and Earth Sciences</topic><topic>Climate</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Emission</topic><topic>Emissions</topic><topic>Environment</topic><topic>Environmental impact</topic><topic>frost</topic><topic>General Circulation Models</topic><topic>Growing season</topic><topic>Math. Appl. in Environmental Science</topic><topic>Mathematical models</topic><topic>Original Paper</topic><topic>Physics</topic><topic>prediction</topic><topic>Predictions</topic><topic>Probability Theory and Stochastic Processes</topic><topic>rain</topic><topic>Rainfall</topic><topic>Seasons</topic><topic>stakeholders</topic><topic>Statistics for Engineering</topic><topic>Temperature</topic><topic>Uncertainty</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Water resources</topic><topic>Weather</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Jongho</creatorcontrib><creatorcontrib>Ivanov, Valeriy Y</creatorcontrib><creatorcontrib>Fatichi, Simone</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Materials Business File</collection><collection>Materials Research Database</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Jongho</au><au>Ivanov, Valeriy Y</au><au>Fatichi, Simone</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Climate change and uncertainty assessment over a hydroclimatic transect of Michigan</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2016-03-01</date><risdate>2016</risdate><volume>30</volume><issue>3</issue><spage>923</spage><epage>944</epage><pages>923-944</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00477-015-1097-2</doi><tpages>22</tpages></addata></record> |
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subjects | Anthropogenic factors Aquatic Pollution Chemistry and Earth Sciences Climate Climate change Climate models Computational Intelligence Computer Science Earth and Environmental Science Earth Sciences Emission Emissions Environment Environmental impact frost General Circulation Models Growing season Math. Appl. in Environmental Science Mathematical models Original Paper Physics prediction Predictions Probability Theory and Stochastic Processes rain Rainfall Seasons stakeholders Statistics for Engineering Temperature Uncertainty Waste Water Technology Water Management Water Pollution Control Water resources Weather |
title | Climate change and uncertainty assessment over a hydroclimatic transect of Michigan |
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