Weather and seasonal climate prediction for South America using a multi-model superensemble

被引:4
|
作者
Chaves, RR [1 ]
Ross, RS [1 ]
Krishnamurti, TN [1 ]
机构
[1] Florida State Univ, Dept Meteorol, Tallahassee, FL 32306 USA
关键词
South America; climate; weather; prediction; superensemble; multi model;
D O I
10.1002/joc.1230
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
This work examines the feasibility of weather and seasonal climate predictions for South America using the multi-model synthetic superensemble approach for climate, and the multi-model conventional superensemble approach for numerical weather prediction, both developed at Florida State University (FSU). The effect on seasonal climate forecasts of the number of models used in the synthetic superensemble is investigated. It is shown that the synthetic superensemble approach for climate and the conventional superensemble approach for numerical weather prediction can reduce the errors over South America in seasonal climate prediction and numerical weather prediction. For climate prediction, a suite of 13 models is used. The forecast lead-time is 1 month for the climate forecasts, which consist of precipitation and surface temperature forecasts. The multi-model ensemble is comprised of four versions of the FSU-Coupled Ocean-Atmosphere Model, seven models from the Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER), a version of the Community Climate Model (CCM3), and a version of the predictive Ocean Atmosphere Model for Australia (POAMA). The results show that conditions over South America are appropriately simulated by the Florida State University Synthetic Superensemble (FSUSSE) in comparison to observations and that the skill of this approach increases with the use of additional models in the ensemble. When compared to observations, the forecasts are generally better than those from both a single climate model and the multi-model ensemble mean, for the variables tested in this study. For numerical weather prediction, the conventional Florida State University Superensemble (FSUSE) is used to predict the mass and motion fields over South America. Predictions of mean sea level pressure, 500 hPa geopotential height, and 850 hPa wind are made with a multi-model superensemble comprised of six global models for the period January, February, and December of 2000. The six global models are from the following forecast centers: FSU, Bureau of Meteorology Research Center (BMRC), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), Naval Research Laboratory (NRL), and Recherche en Prevision Numerique (RPN). Predictions of precipitation are made for the period January, February, and December of 2001 with a 'multi-analysis-multi-model' superensemble where, in addition to the six forecast models just mentioned, five additional versions of the FSU model are used in the ensemble, each with a different initialization (analysis) based on different physical initialization procedures. On the basis of observations, the results show that the FSUSE provides the best forecasts of the mass and motion field variables to forecast day 5, when compared to both the models comprising the ensemble and the multi-model ensemble mean during the wet season of December-February over South America. Individual case studies show that the FSUSE provides excellent predictions of rainfall for particular synoptic events to forecast day 3. Copyright (c) 2005 Royal Meteorological Society.
引用
收藏
页码:1881 / 1914
页数:34
相关论文
共 50 条
  • [21] Statistical downscaling methods based on APCC multi-model ensemble for seasonal prediction over South Korea
    Kang, Suchul
    Hur, Jina
    Ahn, Joong-Bae
    [J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2014, 34 (14) : 3801 - 3810
  • [22] Global crop yield forecasting using seasonal climate information from a multi-model ensemble
    Iizumi, Toshichika
    Shin, Yonghee
    Kim, Wonsik
    Kim, Moosup
    Choi, Jaewon
    [J]. CLIMATE SERVICES, 2018, 11 : 13 - 23
  • [23] Rainfall anomaly prediction using statistical downscaling in a multimodel superensemble over tropical South America
    Bradford Johnson
    Vinay Kumar
    T. N. Krishnamurti
    [J]. Climate Dynamics, 2014, 43 : 1731 - 1752
  • [24] Rainfall anomaly prediction using statistical downscaling in a multimodel superensemble over tropical South America
    Johnson, Bradford
    Kumar, Vinay
    Krishnamurti, T. N.
    [J]. CLIMATE DYNAMICS, 2014, 43 (7-8) : 1731 - 1752
  • [25] Forecast assimilation: a unified framework for the combination of multi-model weather and climate predictions
    Stephenson, DB
    Coelho, CAS
    Doblas-Reyes, FJ
    Balmaseda, M
    [J]. TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY, 2005, 57 (03) : 253 - 264
  • [26] Improving Arctic Weather and Seasonal Climate Prediction
    Ortega, Pablo
    Blockley, Edward W.
    Køltzow, Morten
    Massonnet, François
    Sandu, Irina
    Svensson, Gunilla
    Acosta Navarro, Juan C.
    Arduini, Gabriele
    Batté, Lauriane
    Bazile, Eric
    Chevallier, Matthieu
    Cruz-García, Rubén
    Day, Jonathan J.
    Fichefet, Thierry
    Flocco, Daniela
    Gupta, Mukesh
    Hartung, Kerstin
    Hawkins, Ed
    Hinrichs, Claudia
    Magnusson, Linus
    Moreno-Chamarro, Eduardo
    Pérez-Montero, Sergio
    Ponsoni, Leandro
    Semmler, Tido
    Smith, Doug
    Sterlin, Jean
    Tjernström, Michael
    Välisuo, Ilona
    Jung, Thomas
    [J]. Bulletin of the American Meteorological Society, 2022, 103 (10):
  • [27] Investigation of seasonal prediction of the South American regional climate using the nested model system
    De Sales, Fernando
    Xue, Y.
    [J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2006, 111 (D20)
  • [28] Sweat loss prediction using a multi-model approach
    Xiaojiang Xu
    William R. Santee
    [J]. International Journal of Biometeorology, 2011, 55 : 501 - 508
  • [29] Sweat loss prediction using a multi-model approach
    Xu, Xiaojiang
    Santee, William R.
    [J]. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2011, 55 (04) : 501 - 508
  • [30] Seasonal and Monthly Climate Variability in South Korea's River Basins: Insights from a Multi-Model Ensemble Approach
    Ghafouri-Azar, Mona
    Lee, Sang-Il
    [J]. WATER, 2024, 16 (04)