Decadal Prediction of the Sahelian Precipitation in CMIP5 SimulationsSource: Journal of Climate:;2013:;volume( 026 ):;issue: 019::page 7708DOI: 10.1175/JCLI-D-12-00635.1Publisher: American Meteorological Society
Abstract: n this study the capability of eight state-of-the-art ocean?atmosphere coupled models in predicting the monsoonal precipitation in the Sahel on a decadal time scale is assessed. To estimate the importance of the initialization, the predictive skills of two different CMIP5 experiments are compared, a set of 10 decadal hindcasts initialized every 5 years in the period 1961?2009 and the historical simulations in the period 1961?2005. Results indicate that predictive skills are highly model dependent: the Fourth Generation Canadian Coupled Global Climate Model (CanCM4), Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5 (CNRM-CM5), and Max Planck Institute Earth System Model, low resolution (MPI-ESM-LR) models show improved skill in the decadal hindcasts, while the Model for Interdisciplinary Research on Climate, version 5 (MIROC5) is skillful in both the decadal and historical experiments. The Beijing Climate Center, Climate System Model, version 1.1 (BCC-CSM1.1), Hadley Centre Coupled Model, version 3 (HadCM3), L'Institut Pierre-Simon Laplace Coupled Model, version 5, coupled with NEMO, low resolution (IPSL-CM5A-LR), and Meteorological Research Institute Coupled Atmosphere?Ocean General Circulation Model, version 3 (MRI-CGCM3) models show insignificant or no skill in predicting the Sahelian precipitation. Skillful predictions are produced by models properly describing the SST multidecadal variability and the initialization appears to play an important role in this respect.
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contributor author | Gaetani, Marco | |
contributor author | Mohino, Elsa | |
date accessioned | 2017-06-09T17:07:34Z | |
date available | 2017-06-09T17:07:34Z | |
date copyright | 2013/10/01 | |
date issued | 2013 | |
identifier issn | 0894-8755 | |
identifier other | ams-79772.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4222589 | |
description abstract | n this study the capability of eight state-of-the-art ocean?atmosphere coupled models in predicting the monsoonal precipitation in the Sahel on a decadal time scale is assessed. To estimate the importance of the initialization, the predictive skills of two different CMIP5 experiments are compared, a set of 10 decadal hindcasts initialized every 5 years in the period 1961?2009 and the historical simulations in the period 1961?2005. Results indicate that predictive skills are highly model dependent: the Fourth Generation Canadian Coupled Global Climate Model (CanCM4), Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5 (CNRM-CM5), and Max Planck Institute Earth System Model, low resolution (MPI-ESM-LR) models show improved skill in the decadal hindcasts, while the Model for Interdisciplinary Research on Climate, version 5 (MIROC5) is skillful in both the decadal and historical experiments. The Beijing Climate Center, Climate System Model, version 1.1 (BCC-CSM1.1), Hadley Centre Coupled Model, version 3 (HadCM3), L'Institut Pierre-Simon Laplace Coupled Model, version 5, coupled with NEMO, low resolution (IPSL-CM5A-LR), and Meteorological Research Institute Coupled Atmosphere?Ocean General Circulation Model, version 3 (MRI-CGCM3) models show insignificant or no skill in predicting the Sahelian precipitation. Skillful predictions are produced by models properly describing the SST multidecadal variability and the initialization appears to play an important role in this respect. | |
publisher | American Meteorological Society | |
title | Decadal Prediction of the Sahelian Precipitation in CMIP5 Simulations | |
type | Journal Paper | |
journal volume | 26 | |
journal issue | 19 | |
journal title | Journal of Climate | |
identifier doi | 10.1175/JCLI-D-12-00635.1 | |
journal fristpage | 7708 | |
journal lastpage | 7719 | |
tree | Journal of Climate:;2013:;volume( 026 ):;issue: 019 | |
contenttype | Fulltext |