Sensitivity of Single-Column Model Solutions to Convective Parameterizations and Initial Conditions
Two sets of single-column model (SCM) simulations are performed to determine whether the SCM solutions are more sensitive to model parameterization schemes than to initial perturbations in temperature and moisture profiles. The first set of simulations (S3) used the Zhang and McFarlane scheme for th...
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Veröffentlicht in: | Journal of climate 2001-06, Vol.14 (12), p.2563-2582 |
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description | Two sets of single-column model (SCM) simulations are performed to determine whether the SCM solutions are more sensitive to model parameterization schemes than to initial perturbations in temperature and moisture profiles. The first set of simulations (S3) used the Zhang and McFarlane scheme for the deep convection and the Hack scheme for the shallow convection, while the second set (S2) used the Hack scheme for all types of convection. The same random perturbation used by Hack and Pedretti is applied in S2 and S3. The observed total (horizontal and vertical) advections of temperature and moisture during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment are used to force all simulations. A major difference in temperature and moisture biases occurs between the ensemble means of the two sets of simulations, and is much larger than the standard deviation of each set. Differences are also evident in cloud and radiative properties. This demonstrates that SCM solutions can be more sensitive to the model physics than to the initial perturbations. In other words, the deterministic aspects of SCM solutions dominate the nondeterministic aspects, which is important for their continued use in developing parameterization schemes of convection and clouds in large-scale models. This point is also supported by the SCM simulations using several available longer observational datasets over different regions. |
doi_str_mv | 10.1175/1520-0442(2001)014<2563:SOSCMS>2.0.CO;2 |
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The first set of simulations (S3) used the Zhang and McFarlane scheme for the deep convection and the Hack scheme for the shallow convection, while the second set (S2) used the Hack scheme for all types of convection. The same random perturbation used by Hack and Pedretti is applied in S2 and S3. The observed total (horizontal and vertical) advections of temperature and moisture during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment are used to force all simulations. A major difference in temperature and moisture biases occurs between the ensemble means of the two sets of simulations, and is much larger than the standard deviation of each set. Differences are also evident in cloud and radiative properties. This demonstrates that SCM solutions can be more sensitive to the model physics than to the initial perturbations. In other words, the deterministic aspects of SCM solutions dominate the nondeterministic aspects, which is important for their continued use in developing parameterization schemes of convection and clouds in large-scale models. This point is also supported by the SCM simulations using several available longer observational datasets over different regions.</description><identifier>ISSN: 0894-8755</identifier><identifier>EISSN: 1520-0442</identifier><identifier>DOI: 10.1175/1520-0442(2001)014<2563:SOSCMS>2.0.CO;2</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Atmosphere ; Atmospheric moisture ; Climate ; Convection ; Convection clouds ; Earth, ocean, space ; Exact sciences and technology ; External geophysics ; Geophysics. Techniques, methods, instrumentation and models ; Liquids ; Meteorology ; Mixing ratios ; Moisture profiles ; Parameterization ; Parametric models ; Precipitation ; Simulation ; Standard deviation ; Temperature gradients</subject><ispartof>Journal of climate, 2001-06, Vol.14 (12), p.2563-2582</ispartof><rights>2001 American Meteorological Society</rights><rights>2001 INIST-CNRS</rights><rights>Copyright American Meteorological Society Jun 15, 2001</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c422t-a317da3729a4d8d602a3d0bd8430518bbf3ec4d2f3d6ed24b230073deae257023</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26247448$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26247448$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,3668,27901,27902,57992,58225</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1080960$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Xiaoqing</creatorcontrib><creatorcontrib>Moncrieff, Mitchell W.</creatorcontrib><title>Sensitivity of Single-Column Model Solutions to Convective Parameterizations and Initial Conditions</title><title>Journal of climate</title><description>Two sets of single-column model (SCM) simulations are performed to determine whether the SCM solutions are more sensitive to model parameterization schemes than to initial perturbations in temperature and moisture profiles. The first set of simulations (S3) used the Zhang and McFarlane scheme for the deep convection and the Hack scheme for the shallow convection, while the second set (S2) used the Hack scheme for all types of convection. The same random perturbation used by Hack and Pedretti is applied in S2 and S3. The observed total (horizontal and vertical) advections of temperature and moisture during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment are used to force all simulations. A major difference in temperature and moisture biases occurs between the ensemble means of the two sets of simulations, and is much larger than the standard deviation of each set. Differences are also evident in cloud and radiative properties. This demonstrates that SCM solutions can be more sensitive to the model physics than to the initial perturbations. In other words, the deterministic aspects of SCM solutions dominate the nondeterministic aspects, which is important for their continued use in developing parameterization schemes of convection and clouds in large-scale models. This point is also supported by the SCM simulations using several available longer observational datasets over different regions.</description><subject>Atmosphere</subject><subject>Atmospheric moisture</subject><subject>Climate</subject><subject>Convection</subject><subject>Convection clouds</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Geophysics. Techniques, methods, instrumentation and models</subject><subject>Liquids</subject><subject>Meteorology</subject><subject>Mixing ratios</subject><subject>Moisture profiles</subject><subject>Parameterization</subject><subject>Parametric models</subject><subject>Precipitation</subject><subject>Simulation</subject><subject>Standard deviation</subject><subject>Temperature gradients</subject><issn>0894-8755</issn><issn>1520-0442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkVFrFDEQx4MoeLb9CMIiIvqw18kk2WRVBFm0LbScsPoccpus5NhL2mSvUD-9u91Sik8-DZn5zT8MP0JOKawpleKUCoQSOMf3CEA_AOWfUVTsY7tpm6v2C65h3Ww-4TOyeiSfkxWompdKCvGSvMp5N21iBbAiXetC9qO_9eNdEfui9eH34MomDod9KK6idUPRTo_Rx5CLMRZNDLeumxZc8cMks3ejS_6PWeYm2OIiTHFmmEHr79vH5EVvhuxOHuoR-fX928_mvLzcnF00Xy_LjiOOpWFUWsMk1oZbZStAwyxsreIMBFXbbc9cxy32zFbOIt8iA5DMOuNQSEB2RN4tudcp3hxcHvXe584NgwkuHrKmSoAAXv8HSJWsGJ3AN_-Au3hIYTpCI2JNa3mfdrZAXYo5J9fr6-T3Jt1pCnpWpmcRehahZ2V6UqZnZXpRplGDbjZ6PuDtw3cmd2bokwmdz0_iFNQVTNjrBdvlMabHMVbIJeeK_QWja6Pb</recordid><startdate>20010615</startdate><enddate>20010615</enddate><creator>Wu, Xiaoqing</creator><creator>Moncrieff, Mitchell W.</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>AEUYN</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></search><sort><creationdate>20010615</creationdate><title>Sensitivity of Single-Column Model Solutions to Convective Parameterizations and Initial Conditions</title><author>Wu, Xiaoqing ; Moncrieff, Mitchell W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c422t-a317da3729a4d8d602a3d0bd8430518bbf3ec4d2f3d6ed24b230073deae257023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Atmosphere</topic><topic>Atmospheric moisture</topic><topic>Climate</topic><topic>Convection</topic><topic>Convection clouds</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Geophysics. Techniques, methods, instrumentation and models</topic><topic>Liquids</topic><topic>Meteorology</topic><topic>Mixing ratios</topic><topic>Moisture profiles</topic><topic>Parameterization</topic><topic>Parametric models</topic><topic>Precipitation</topic><topic>Simulation</topic><topic>Standard deviation</topic><topic>Temperature gradients</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Xiaoqing</creatorcontrib><creatorcontrib>Moncrieff, Mitchell W.</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 One Sustainability</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 - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Agricultural Science Database</collection><collection>Military Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic 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>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Journal of climate</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Xiaoqing</au><au>Moncrieff, Mitchell W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sensitivity of Single-Column Model Solutions to Convective Parameterizations and Initial Conditions</atitle><jtitle>Journal of climate</jtitle><date>2001-06-15</date><risdate>2001</risdate><volume>14</volume><issue>12</issue><spage>2563</spage><epage>2582</epage><pages>2563-2582</pages><issn>0894-8755</issn><eissn>1520-0442</eissn><abstract>Two sets of single-column model (SCM) simulations are performed to determine whether the SCM solutions are more sensitive to model parameterization schemes than to initial perturbations in temperature and moisture profiles. The first set of simulations (S3) used the Zhang and McFarlane scheme for the deep convection and the Hack scheme for the shallow convection, while the second set (S2) used the Hack scheme for all types of convection. The same random perturbation used by Hack and Pedretti is applied in S2 and S3. The observed total (horizontal and vertical) advections of temperature and moisture during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment are used to force all simulations. A major difference in temperature and moisture biases occurs between the ensemble means of the two sets of simulations, and is much larger than the standard deviation of each set. Differences are also evident in cloud and radiative properties. This demonstrates that SCM solutions can be more sensitive to the model physics than to the initial perturbations. In other words, the deterministic aspects of SCM solutions dominate the nondeterministic aspects, which is important for their continued use in developing parameterization schemes of convection and clouds in large-scale models. This point is also supported by the SCM simulations using several available longer observational datasets over different regions.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/1520-0442(2001)014<2563:SOSCMS>2.0.CO;2</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Atmosphere Atmospheric moisture Climate Convection Convection clouds Earth, ocean, space Exact sciences and technology External geophysics Geophysics. Techniques, methods, instrumentation and models Liquids Meteorology Mixing ratios Moisture profiles Parameterization Parametric models Precipitation Simulation Standard deviation Temperature gradients |
title | Sensitivity of Single-Column Model Solutions to Convective Parameterizations and Initial Conditions |
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