Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU multiscale modeling framework
The Colorado State University (CSU) Multiscale Modeling Framework (MMF) is a new type of general circulation model (GCM) that replaces the conventional parameterizations of convection, clouds, and boundary layer with a cloud-resolving model (CRM) embedded into each grid column. The MMF has been used...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.452.6204 2023-05-15T18:18:31+02:00 Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU multiscale modeling framework Marat Khairoutdinov Charlotte Demott David Randall The Pennsylvania State University CiteSeerX Archives 2008 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.452.6204 http://kiwi.atmos.colostate.edu/pubs/i1520-0442-21-3-413.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.452.6204 http://kiwi.atmos.colostate.edu/pubs/i1520-0442-21-3-413.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://kiwi.atmos.colostate.edu/pubs/i1520-0442-21-3-413.pdf text 2008 ftciteseerx 2016-01-08T05:58:32Z The Colorado State University (CSU) Multiscale Modeling Framework (MMF) is a new type of general circulation model (GCM) that replaces the conventional parameterizations of convection, clouds, and boundary layer with a cloud-resolving model (CRM) embedded into each grid column. The MMF has been used to perform a 19-yr-long Atmospheric Model Intercomparison Project–style simulation using the 1985– 2004 sea surface temperature (SST) and sea ice distributions as prescribed boundary conditions. Particular focus has been given to the simulation of the interannual and subseasonal variability. The annual mean climatology is generally well simulated. Prominent biases include excessive precipita-tion associated with the Indian and Asian monsoon seasons, precipitation deficits west of the Maritime Continent and over Amazonia, shortwave cloud effect biases west of the subtropical continents due to insufficient stratocumulus clouds, and longwave cloud effect biases due to overestimation of high cloud amounts, especially in the tropics. The geographical pattern of the seasonal cycle of precipitation is well reproduced, although the seasonal variance is considerably overestimated mostly because of the excessive monsoon precipitation mentioned above. The MMF does a good job of reproducing the interannual vari-ability in terms of the spatial structure and magnitude of major anomalies associated with El Niño–Southern Text Sea ice Unknown Indian |
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English |
description |
The Colorado State University (CSU) Multiscale Modeling Framework (MMF) is a new type of general circulation model (GCM) that replaces the conventional parameterizations of convection, clouds, and boundary layer with a cloud-resolving model (CRM) embedded into each grid column. The MMF has been used to perform a 19-yr-long Atmospheric Model Intercomparison Project–style simulation using the 1985– 2004 sea surface temperature (SST) and sea ice distributions as prescribed boundary conditions. Particular focus has been given to the simulation of the interannual and subseasonal variability. The annual mean climatology is generally well simulated. Prominent biases include excessive precipita-tion associated with the Indian and Asian monsoon seasons, precipitation deficits west of the Maritime Continent and over Amazonia, shortwave cloud effect biases west of the subtropical continents due to insufficient stratocumulus clouds, and longwave cloud effect biases due to overestimation of high cloud amounts, especially in the tropics. The geographical pattern of the seasonal cycle of precipitation is well reproduced, although the seasonal variance is considerably overestimated mostly because of the excessive monsoon precipitation mentioned above. The MMF does a good job of reproducing the interannual vari-ability in terms of the spatial structure and magnitude of major anomalies associated with El Niño–Southern |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Marat Khairoutdinov Charlotte Demott David Randall |
spellingShingle |
Marat Khairoutdinov Charlotte Demott David Randall Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU multiscale modeling framework |
author_facet |
Marat Khairoutdinov Charlotte Demott David Randall |
author_sort |
Marat Khairoutdinov |
title |
Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU multiscale modeling framework |
title_short |
Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU multiscale modeling framework |
title_full |
Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU multiscale modeling framework |
title_fullStr |
Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU multiscale modeling framework |
title_full_unstemmed |
Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU multiscale modeling framework |
title_sort |
evaluation of the simulated interannual and subseasonal variability in an amip-style simulation using the csu multiscale modeling framework |
publishDate |
2008 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.452.6204 http://kiwi.atmos.colostate.edu/pubs/i1520-0442-21-3-413.pdf |
geographic |
Indian |
geographic_facet |
Indian |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
http://kiwi.atmos.colostate.edu/pubs/i1520-0442-21-3-413.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.452.6204 http://kiwi.atmos.colostate.edu/pubs/i1520-0442-21-3-413.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766195114131062784 |