2004: Beyond the means: Validating climate models with higher-order statistics
Large-scale climate models are validated by comparing the model’s mean and variability to observations. New applications are placing more demands on such models, which can be addressed by examining the models ’ distributions of daily quantities such as temperature and precipitation. What determines...
Main Author: | |
---|---|
Other Authors: | |
Format: | Text |
Language: | English |
Subjects: | |
Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.581 http://meteora.ucsd.edu/~pierce/docs/Pierce_2004_CiSE.pdf |
id |
ftciteseerx:oai:CiteSeerX.psu:10.1.1.77.581 |
---|---|
record_format |
openpolar |
spelling |
ftciteseerx:oai:CiteSeerX.psu:10.1.1.77.581 2023-05-15T18:18:32+02:00 2004: Beyond the means: Validating climate models with higher-order statistics David W. Pierce The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.581 http://meteora.ucsd.edu/~pierce/docs/Pierce_2004_CiSE.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.581 http://meteora.ucsd.edu/~pierce/docs/Pierce_2004_CiSE.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://meteora.ucsd.edu/~pierce/docs/Pierce_2004_CiSE.pdf Earth and atmospheric sciences model validation and analysis mathematics and statistics text ftciteseerx 2016-01-08T19:10:06Z Large-scale climate models are validated by comparing the model’s mean and variability to observations. New applications are placing more demands on such models, which can be addressed by examining the models ’ distributions of daily quantities such as temperature and precipitation. What determines the climate where you live? Why does it vary, so that some years have unusually cold winters, or particularly hot summers? What will next winter be like? What will the climate be in coming years, and is it affected by emissions of gasses such as CO2? These are just a few of the questions that are examined with coupled ocean-atmosphere general circulation models (O-A GCMs). Such models are complicated, incorporating the equations of motion for air and water masses, the properties of sea ice, parameterizations for cloud processes, schemes for river flow, and the effects of soil moisture and ground cover. The projections given by such models might influence decisions ranging from whether someone’s aging roof should be repaired before the coming winter to what future technologies the automobile industry should pursue. How are such models validated, so that we understand what confidence should be placed in their predictions? This is typically done by comparing the model’s behavior to that of the real world. The assumption Text Sea ice Unknown |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
ftciteseerx |
language |
English |
topic |
Earth and atmospheric sciences model validation and analysis mathematics and statistics |
spellingShingle |
Earth and atmospheric sciences model validation and analysis mathematics and statistics David W. Pierce 2004: Beyond the means: Validating climate models with higher-order statistics |
topic_facet |
Earth and atmospheric sciences model validation and analysis mathematics and statistics |
description |
Large-scale climate models are validated by comparing the model’s mean and variability to observations. New applications are placing more demands on such models, which can be addressed by examining the models ’ distributions of daily quantities such as temperature and precipitation. What determines the climate where you live? Why does it vary, so that some years have unusually cold winters, or particularly hot summers? What will next winter be like? What will the climate be in coming years, and is it affected by emissions of gasses such as CO2? These are just a few of the questions that are examined with coupled ocean-atmosphere general circulation models (O-A GCMs). Such models are complicated, incorporating the equations of motion for air and water masses, the properties of sea ice, parameterizations for cloud processes, schemes for river flow, and the effects of soil moisture and ground cover. The projections given by such models might influence decisions ranging from whether someone’s aging roof should be repaired before the coming winter to what future technologies the automobile industry should pursue. How are such models validated, so that we understand what confidence should be placed in their predictions? This is typically done by comparing the model’s behavior to that of the real world. The assumption |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
David W. Pierce |
author_facet |
David W. Pierce |
author_sort |
David W. Pierce |
title |
2004: Beyond the means: Validating climate models with higher-order statistics |
title_short |
2004: Beyond the means: Validating climate models with higher-order statistics |
title_full |
2004: Beyond the means: Validating climate models with higher-order statistics |
title_fullStr |
2004: Beyond the means: Validating climate models with higher-order statistics |
title_full_unstemmed |
2004: Beyond the means: Validating climate models with higher-order statistics |
title_sort |
2004: beyond the means: validating climate models with higher-order statistics |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.581 http://meteora.ucsd.edu/~pierce/docs/Pierce_2004_CiSE.pdf |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
http://meteora.ucsd.edu/~pierce/docs/Pierce_2004_CiSE.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.581 http://meteora.ucsd.edu/~pierce/docs/Pierce_2004_CiSE.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766195129933103104 |