Temporal and Spatial Variability of Precipitation from Observations and Models
Principal component analysis (PCA) is utilized to explore the temporal and spatial variability of precipitation from GPCP and a CAM5 simulation from 1979 to 2010. In the tropical region, the interannual variability of tropical precipitation is characterized by two dominant modes (El Niño and El Niño...
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American Meteorological Society
2016
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ftcaltechauth:oai:authors.library.caltech.edu:66451 2023-05-15T13:57:16+02:00 Temporal and Spatial Variability of Precipitation from Observations and Models Trammell, James H. Jiang, Xun Li, Liming Kao, Angela Zhang, Guang J. Chang, Edmund K. M. Yung, Yuk 2016-04-01 application/pdf application/msword https://authors.library.caltech.edu/66451/ https://authors.library.caltech.edu/66451/1/jcli-d-15-0325%252E1.pdf https://authors.library.caltech.edu/66451/4/10%252E1175_jcli-d-15-0325%252Es1.doc https://resolver.caltech.edu/CaltechAUTHORS:20160425-115702237 en eng American Meteorological Society https://authors.library.caltech.edu/66451/1/jcli-d-15-0325%252E1.pdf https://authors.library.caltech.edu/66451/4/10%252E1175_jcli-d-15-0325%252Es1.doc Trammell, James H. and Jiang, Xun and Li, Liming and Kao, Angela and Zhang, Guang J. and Chang, Edmund K. M. and Yung, Yuk (2016) Temporal and Spatial Variability of Precipitation from Observations and Models. Journal of Climate, 29 (7). pp. 2543-2555. ISSN 0894-8755. doi:10.1175/JCLI-D-15-0325.1. https://resolver.caltech.edu/CaltechAUTHORS:20160425-115702237 <https://resolver.caltech.edu/CaltechAUTHORS:20160425-115702237> other Article PeerReviewed 2016 ftcaltechauth https://doi.org/10.1175/JCLI-D-15-0325.1 2021-11-11T19:09:09Z Principal component analysis (PCA) is utilized to explore the temporal and spatial variability of precipitation from GPCP and a CAM5 simulation from 1979 to 2010. In the tropical region, the interannual variability of tropical precipitation is characterized by two dominant modes (El Niño and El Niño Modoki). The first and second modes of tropical GPCP precipitation capture 31.9% and 15.6% of the total variance, respectively. The first mode has positive precipitation anomalies over the western Pacific and negative precipitation anomalies over the central and eastern Pacific. The second mode has positive precipitation anomalies over the central Pacific and negative precipitation anomalies over the western and eastern Pacific. Similar variations are seen in the first two modes of tropical precipitation from a CAM5 simulation, although the magnitudes are slightly weaker than in the observations. Over the Northern Hemisphere (NH) high latitudes, the first mode, capturing 8.3% of the total variance of NH GPCP precipitation, is related to the northern annular mode (NAM). During the positive phase of NAM, there are negative precipitation anomalies over the Arctic and positive precipitation anomalies over the midlatitudes. Over the Southern Hemisphere (SH) high latitudes, the first mode, capturing 13.2% of the total variance of SH GPCP precipitation, is related to the southern annular mode (SAM). During the positive phase of the SAM, there are negative precipitation anomalies over the Antarctic and positive precipitation anomalies over the midlatitudes. The CAM5 precipitation simulation demonstrates similar results to those of the observations. However, they do not capture both the high precipitation anomalies over the northern Pacific Ocean or the position of the positive precipitation anomalies in the SH. Article in Journal/Newspaper Antarc* Antarctic Arctic Caltech Authors (California Institute of Technology) Antarctic Arctic Pacific The Antarctic Journal of Climate 29 7 2543 2555 |
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Open Polar |
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Caltech Authors (California Institute of Technology) |
op_collection_id |
ftcaltechauth |
language |
English |
description |
Principal component analysis (PCA) is utilized to explore the temporal and spatial variability of precipitation from GPCP and a CAM5 simulation from 1979 to 2010. In the tropical region, the interannual variability of tropical precipitation is characterized by two dominant modes (El Niño and El Niño Modoki). The first and second modes of tropical GPCP precipitation capture 31.9% and 15.6% of the total variance, respectively. The first mode has positive precipitation anomalies over the western Pacific and negative precipitation anomalies over the central and eastern Pacific. The second mode has positive precipitation anomalies over the central Pacific and negative precipitation anomalies over the western and eastern Pacific. Similar variations are seen in the first two modes of tropical precipitation from a CAM5 simulation, although the magnitudes are slightly weaker than in the observations. Over the Northern Hemisphere (NH) high latitudes, the first mode, capturing 8.3% of the total variance of NH GPCP precipitation, is related to the northern annular mode (NAM). During the positive phase of NAM, there are negative precipitation anomalies over the Arctic and positive precipitation anomalies over the midlatitudes. Over the Southern Hemisphere (SH) high latitudes, the first mode, capturing 13.2% of the total variance of SH GPCP precipitation, is related to the southern annular mode (SAM). During the positive phase of the SAM, there are negative precipitation anomalies over the Antarctic and positive precipitation anomalies over the midlatitudes. The CAM5 precipitation simulation demonstrates similar results to those of the observations. However, they do not capture both the high precipitation anomalies over the northern Pacific Ocean or the position of the positive precipitation anomalies in the SH. |
format |
Article in Journal/Newspaper |
author |
Trammell, James H. Jiang, Xun Li, Liming Kao, Angela Zhang, Guang J. Chang, Edmund K. M. Yung, Yuk |
spellingShingle |
Trammell, James H. Jiang, Xun Li, Liming Kao, Angela Zhang, Guang J. Chang, Edmund K. M. Yung, Yuk Temporal and Spatial Variability of Precipitation from Observations and Models |
author_facet |
Trammell, James H. Jiang, Xun Li, Liming Kao, Angela Zhang, Guang J. Chang, Edmund K. M. Yung, Yuk |
author_sort |
Trammell, James H. |
title |
Temporal and Spatial Variability of Precipitation from Observations and Models |
title_short |
Temporal and Spatial Variability of Precipitation from Observations and Models |
title_full |
Temporal and Spatial Variability of Precipitation from Observations and Models |
title_fullStr |
Temporal and Spatial Variability of Precipitation from Observations and Models |
title_full_unstemmed |
Temporal and Spatial Variability of Precipitation from Observations and Models |
title_sort |
temporal and spatial variability of precipitation from observations and models |
publisher |
American Meteorological Society |
publishDate |
2016 |
url |
https://authors.library.caltech.edu/66451/ https://authors.library.caltech.edu/66451/1/jcli-d-15-0325%252E1.pdf https://authors.library.caltech.edu/66451/4/10%252E1175_jcli-d-15-0325%252Es1.doc https://resolver.caltech.edu/CaltechAUTHORS:20160425-115702237 |
geographic |
Antarctic Arctic Pacific The Antarctic |
geographic_facet |
Antarctic Arctic Pacific The Antarctic |
genre |
Antarc* Antarctic Arctic |
genre_facet |
Antarc* Antarctic Arctic |
op_relation |
https://authors.library.caltech.edu/66451/1/jcli-d-15-0325%252E1.pdf https://authors.library.caltech.edu/66451/4/10%252E1175_jcli-d-15-0325%252Es1.doc Trammell, James H. and Jiang, Xun and Li, Liming and Kao, Angela and Zhang, Guang J. and Chang, Edmund K. M. and Yung, Yuk (2016) Temporal and Spatial Variability of Precipitation from Observations and Models. Journal of Climate, 29 (7). pp. 2543-2555. ISSN 0894-8755. doi:10.1175/JCLI-D-15-0325.1. https://resolver.caltech.edu/CaltechAUTHORS:20160425-115702237 <https://resolver.caltech.edu/CaltechAUTHORS:20160425-115702237> |
op_rights |
other |
op_doi |
https://doi.org/10.1175/JCLI-D-15-0325.1 |
container_title |
Journal of Climate |
container_volume |
29 |
container_issue |
7 |
container_start_page |
2543 |
op_container_end_page |
2555 |
_version_ |
1766264864763805696 |