The Continuum of Hydroclimate Variability in Western North America during the Last Millennium
The distribution of climatic variance across the frequency spectrum has substantial importance for anticipating how climate will evolve in the future. Here power spectra and power laws (β) are estimated from instrumental, proxy, and climate model data to characterize the hydroclimate continuum in we...
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Veröffentlicht in: | Journal of climate 2013-08, Vol.26 (16), p.5863-5878 |
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description | The distribution of climatic variance across the frequency spectrum has substantial importance for anticipating how climate will evolve in the future. Here power spectra and power laws (β) are estimated from instrumental, proxy, and climate model data to characterize the hydroclimate continuum in western North America (WNA). The significance of the estimates of spectral densities andβare tested against the null hypothesis that they reflect solely the effects of local (nonclimate) sources of autocorrelation at the monthly time scale. Although tree-ring-based hydroclimate reconstructions are generally consistent with this null hypothesis, values ofβcalculated from long moisture-sensitive chronologies (as opposed to reconstructions) and other types of hydroclimate proxies exceed null expectations. Therefore it may be argued that there is more low-frequency variability in hydroclimate than monthly autocorrelation alone can generate. Coupled model results archived as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are consistent with the null hypothesis and appear unable to generate variance in hydroclimate commensurate with paleoclimate records. Consequently, at decadal-to-multidecadal time scales there is more variability in instrumental and proxy data than in the models, suggesting that the risk of prolonged droughts under climate change may be underestimated by CMIP5 simulations of the future. |
doi_str_mv | 10.1175/jcli-d-11-00732.1 |
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Here power spectra and power laws (β) are estimated from instrumental, proxy, and climate model data to characterize the hydroclimate continuum in western North America (WNA). The significance of the estimates of spectral densities andβare tested against the null hypothesis that they reflect solely the effects of local (nonclimate) sources of autocorrelation at the monthly time scale. Although tree-ring-based hydroclimate reconstructions are generally consistent with this null hypothesis, values ofβcalculated from long moisture-sensitive chronologies (as opposed to reconstructions) and other types of hydroclimate proxies exceed null expectations. Therefore it may be argued that there is more low-frequency variability in hydroclimate than monthly autocorrelation alone can generate. Coupled model results archived as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are consistent with the null hypothesis and appear unable to generate variance in hydroclimate commensurate with paleoclimate records. Consequently, at decadal-to-multidecadal time scales there is more variability in instrumental and proxy data than in the models, suggesting that the risk of prolonged droughts under climate change may be underestimated by CMIP5 simulations of the future.</description><identifier>ISSN: 0894-8755</identifier><identifier>EISSN: 1520-0442</identifier><identifier>DOI: 10.1175/jcli-d-11-00732.1</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>20th century ; Americas ; Archives & records ; Autocorrelation ; Climate ; Climate change ; Climate models ; Climatology. Bioclimatology. Climate change ; Continuums ; Drought ; Earth, ocean, space ; Energy spectra ; Exact sciences and technology ; External geophysics ; Frequency spectra ; Frequency spectrum ; General circulation models ; Growth rings ; Hydroclimate ; Hypotheses ; Intercomparison ; Meteorology ; Modeling ; Modelling ; Moisture effects ; Null hypothesis ; Paleoclimate ; Paleoclimatology ; Power spectra ; Precipitation ; Proxy client servers ; Reconstruction ; Soil water ; Spectral index ; Statistical variance ; Time series ; Tree rings ; Variability ; Variables ; Variance ; Water in the atmosphere (humidity, clouds, evaporation, precipitation)</subject><ispartof>Journal of climate, 2013-08, Vol.26 (16), p.5863-5878</ispartof><rights>2013 American Meteorological Society</rights><rights>2015 INIST-CNRS</rights><rights>Copyright American Meteorological Society Aug 15, 2013</rights><rights>Copyright American Meteorological Society 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c429t-4c19be7b2970aba0ecf475109186114fa503fe7eda0b139f2c9d7a59abce39fe3</citedby><cites>FETCH-LOGICAL-c429t-4c19be7b2970aba0ecf475109186114fa503fe7eda0b139f2c9d7a59abce39fe3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26192731$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26192731$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,3681,27924,27925,58017,58250</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27643352$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Ault, Toby R.</creatorcontrib><creatorcontrib>Cole, Julia E.</creatorcontrib><creatorcontrib>Overpeck, Jonathan T.</creatorcontrib><creatorcontrib>Pederson, Gregory T.</creatorcontrib><creatorcontrib>St. George, Scott</creatorcontrib><creatorcontrib>Otto-Bliesner, Bette</creatorcontrib><creatorcontrib>Woodhouse, Connie A.</creatorcontrib><creatorcontrib>Deser, Clara</creatorcontrib><title>The Continuum of Hydroclimate Variability in Western North America during the Last Millennium</title><title>Journal of climate</title><description>The distribution of climatic variance across the frequency spectrum has substantial importance for anticipating how climate will evolve in the future. Here power spectra and power laws (β) are estimated from instrumental, proxy, and climate model data to characterize the hydroclimate continuum in western North America (WNA). The significance of the estimates of spectral densities andβare tested against the null hypothesis that they reflect solely the effects of local (nonclimate) sources of autocorrelation at the monthly time scale. Although tree-ring-based hydroclimate reconstructions are generally consistent with this null hypothesis, values ofβcalculated from long moisture-sensitive chronologies (as opposed to reconstructions) and other types of hydroclimate proxies exceed null expectations. Therefore it may be argued that there is more low-frequency variability in hydroclimate than monthly autocorrelation alone can generate. Coupled model results archived as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are consistent with the null hypothesis and appear unable to generate variance in hydroclimate commensurate with paleoclimate records. Consequently, at decadal-to-multidecadal time scales there is more variability in instrumental and proxy data than in the models, suggesting that the risk of prolonged droughts under climate change may be underestimated by CMIP5 simulations of the future.</description><subject>20th century</subject><subject>Americas</subject><subject>Archives & records</subject><subject>Autocorrelation</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climatology. Bioclimatology. Climate change</subject><subject>Continuums</subject><subject>Drought</subject><subject>Earth, ocean, space</subject><subject>Energy spectra</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Frequency spectra</subject><subject>Frequency spectrum</subject><subject>General circulation models</subject><subject>Growth rings</subject><subject>Hydroclimate</subject><subject>Hypotheses</subject><subject>Intercomparison</subject><subject>Meteorology</subject><subject>Modeling</subject><subject>Modelling</subject><subject>Moisture effects</subject><subject>Null hypothesis</subject><subject>Paleoclimate</subject><subject>Paleoclimatology</subject><subject>Power spectra</subject><subject>Precipitation</subject><subject>Proxy client servers</subject><subject>Reconstruction</subject><subject>Soil water</subject><subject>Spectral index</subject><subject>Statistical variance</subject><subject>Time series</subject><subject>Tree rings</subject><subject>Variability</subject><subject>Variables</subject><subject>Variance</subject><subject>Water in the atmosphere (humidity, clouds, evaporation, precipitation)</subject><issn>0894-8755</issn><issn>1520-0442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kU1v1DAQhi0EEkvhB3BAsoSQuKTM-CNOjtUCbdEClwInZDmOQ71K7NZ2Dvvv67IVSBw4jUd-5tGrGUJeIpwiKvlub2ffjA1iA6A4O8VHZIOSQQNCsMdkA10vmk5J-ZQ8y3kPgKwF2JCfV9eObmMoPqzrQuNELw5jitW2mOLod5O8Gfzsy4H6QH-4XFwK9EtM5ZqeLS55a-i4Jh9-0VJNO5ML_ezn2YXg1-U5eTKZObsXD_WEfPv44Wp70ey-nl9uz3aNFawvjbDYD04NrFdgBgPOTkJJhB67FlFMRgKfnHKjgQF5PzHbj8rI3gzW1dbxE_L26L1J8XatIfXis3XzbIKLa9YouAAGSvKKvv4H3cc1hZpOs67KleCy_R-FQsB9rrarFB4pm2LOyU36JtW9pYNG0Pdn0Z-2u0v9vr7177NorDNvHswmWzNPyQTr859BplrBuWSVe3Xk9rnE9Pe_xZ4pjvwOeM2WnA</recordid><startdate>20130815</startdate><enddate>20130815</enddate><creator>Ault, Toby R.</creator><creator>Cole, Julia E.</creator><creator>Overpeck, Jonathan T.</creator><creator>Pederson, Gregory T.</creator><creator>St. George, Scott</creator><creator>Otto-Bliesner, Bette</creator><creator>Woodhouse, Connie A.</creator><creator>Deser, Clara</creator><general>American Meteorological Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>7X2</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8AF</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M0K</scope><scope>M1Q</scope><scope>M2O</scope><scope>M2P</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20130815</creationdate><title>The Continuum of Hydroclimate Variability in Western North America during the Last Millennium</title><author>Ault, Toby R. ; Cole, Julia E. ; Overpeck, Jonathan T. ; Pederson, Gregory T. ; St. George, Scott ; Otto-Bliesner, Bette ; Woodhouse, Connie A. ; Deser, Clara</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c429t-4c19be7b2970aba0ecf475109186114fa503fe7eda0b139f2c9d7a59abce39fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>20th century</topic><topic>Americas</topic><topic>Archives & records</topic><topic>Autocorrelation</topic><topic>Climate</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Climatology. Bioclimatology. Climate change</topic><topic>Continuums</topic><topic>Drought</topic><topic>Earth, ocean, space</topic><topic>Energy spectra</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Frequency spectra</topic><topic>Frequency spectrum</topic><topic>General circulation models</topic><topic>Growth rings</topic><topic>Hydroclimate</topic><topic>Hypotheses</topic><topic>Intercomparison</topic><topic>Meteorology</topic><topic>Modeling</topic><topic>Modelling</topic><topic>Moisture effects</topic><topic>Null hypothesis</topic><topic>Paleoclimate</topic><topic>Paleoclimatology</topic><topic>Power spectra</topic><topic>Precipitation</topic><topic>Proxy client servers</topic><topic>Reconstruction</topic><topic>Soil water</topic><topic>Spectral index</topic><topic>Statistical variance</topic><topic>Time series</topic><topic>Tree rings</topic><topic>Variability</topic><topic>Variables</topic><topic>Variance</topic><topic>Water in the atmosphere (humidity, clouds, evaporation, precipitation)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ault, Toby R.</creatorcontrib><creatorcontrib>Cole, Julia E.</creatorcontrib><creatorcontrib>Overpeck, Jonathan T.</creatorcontrib><creatorcontrib>Pederson, Gregory T.</creatorcontrib><creatorcontrib>St. George, Scott</creatorcontrib><creatorcontrib>Otto-Bliesner, Bette</creatorcontrib><creatorcontrib>Woodhouse, Connie A.</creatorcontrib><creatorcontrib>Deser, Clara</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>eLibrary</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Here power spectra and power laws (β) are estimated from instrumental, proxy, and climate model data to characterize the hydroclimate continuum in western North America (WNA). The significance of the estimates of spectral densities andβare tested against the null hypothesis that they reflect solely the effects of local (nonclimate) sources of autocorrelation at the monthly time scale. Although tree-ring-based hydroclimate reconstructions are generally consistent with this null hypothesis, values ofβcalculated from long moisture-sensitive chronologies (as opposed to reconstructions) and other types of hydroclimate proxies exceed null expectations. Therefore it may be argued that there is more low-frequency variability in hydroclimate than monthly autocorrelation alone can generate. Coupled model results archived as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are consistent with the null hypothesis and appear unable to generate variance in hydroclimate commensurate with paleoclimate records. Consequently, at decadal-to-multidecadal time scales there is more variability in instrumental and proxy data than in the models, suggesting that the risk of prolonged droughts under climate change may be underestimated by CMIP5 simulations of the future.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/jcli-d-11-00732.1</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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subjects | 20th century Americas Archives & records Autocorrelation Climate Climate change Climate models Climatology. Bioclimatology. Climate change Continuums Drought Earth, ocean, space Energy spectra Exact sciences and technology External geophysics Frequency spectra Frequency spectrum General circulation models Growth rings Hydroclimate Hypotheses Intercomparison Meteorology Modeling Modelling Moisture effects Null hypothesis Paleoclimate Paleoclimatology Power spectra Precipitation Proxy client servers Reconstruction Soil water Spectral index Statistical variance Time series Tree rings Variability Variables Variance Water in the atmosphere (humidity, clouds, evaporation, precipitation) |
title | The Continuum of Hydroclimate Variability in Western North America during the Last Millennium |
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