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An analysis of multi-model ensemble ...

Yan, Xiaoqin.

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An analysis of multi-model ensemble for seasonal climate predictions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	An analysis of multi-model ensemble for seasonal climate predictions./
Author:	Yan, Xiaoqin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2011,
Description:	77 p.
Notes:	Source: Masters Abstracts International, Volume: 49-06, page: 3767.
Contained By:	Masters Abstracts International49-06.
Subject:	Environmental studies. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR75187
ISBN:	9780494751879

An analysis of multi-model ensemble for seasonal climate predictions.
Yan, Xiaoqin.

An analysis of multi-model ensemble for seasonal climate predictions. - Ann Arbor : ProQuest Dissertations & Theses, 2011 - 77 p.

Source: Masters Abstracts International, Volume: 49-06, page: 3767.

Thesis (M.Sc.)--University of Northern British Columbia (Canada), 2011.

In this study, the superiorities of the super ensemble for seasonal climate prediction are investigated based on the 500mb geopotential height (GPH500) hindcasts produced by four Canadian seasonal climate prediction models. The investigations are carried out mainly in two aspects: i) a comprehensive evaluation of predictions for each grid over the global domain by the deterministic, probabilistic and potential prediction skill measures; 2) the Empirical Orthogonal Function (EOF) and Maximum Signal-to-Noise (MSN) EOF analyses in the northern hemisphere (NH). It is found that improvements of the super ensemble are mainly due to the increase of ensemble size in the mid-high latitudes, and the offsets of model uncertainties in the tropical regions. Measures of temporal correlation coefficient (CORR), the relative root mean square error (RRMSE), reliability (REL) are more affected by the ensemble size; whereas resolution (RES) is sensitive to the offsets of model uncertainties. In addition, the super ensemble shows advantages in both EOF and MSN EOF analyses. The contributions of the sea surface temperature anomaly (SSTA) to the seasonal mean climate predictability are closely related to El Nino-Southern Oscillation (ENSO) forcing.

ISBN: 9780494751879Subjects--Topical Terms:

2122803
Environmental studies.

An analysis of multi-model ensemble for seasonal climate predictions.
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245 1 3 $a An analysis of multi-model ensemble for seasonal climate predictions.
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300 $a 77 p.
500 $a Source: Masters Abstracts International, Volume: 49-06, page: 3767.
502 $a Thesis (M.Sc.)--University of Northern British Columbia (Canada), 2011.
520 $a In this study, the superiorities of the super ensemble for seasonal climate prediction are investigated based on the 500mb geopotential height (GPH500) hindcasts produced by four Canadian seasonal climate prediction models. The investigations are carried out mainly in two aspects: i) a comprehensive evaluation of predictions for each grid over the global domain by the deterministic, probabilistic and potential prediction skill measures; 2) the Empirical Orthogonal Function (EOF) and Maximum Signal-to-Noise (MSN) EOF analyses in the northern hemisphere (NH). It is found that improvements of the super ensemble are mainly due to the increase of ensemble size in the mid-high latitudes, and the offsets of model uncertainties in the tropical regions. Measures of temporal correlation coefficient (CORR), the relative root mean square error (RRMSE), reliability (REL) are more affected by the ensemble size; whereas resolution (RES) is sensitive to the offsets of model uncertainties. In addition, the super ensemble shows advantages in both EOF and MSN EOF analyses. The contributions of the sea surface temperature anomaly (SSTA) to the seasonal mean climate predictability are closely related to El Nino-Southern Oscillation (ENSO) forcing.
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