scholarly journals Prediction of Eastern and Central Pacific ENSO Events and Their Impacts on East Asian Climate by the NCEP Climate Forecast System

2014 ◽  
Vol 27 (12) ◽  
pp. 4451-4472 ◽  
Author(s):  
Song Yang ◽  
Xingwen Jiang
Keyword(s):  
East Asian ◽  
Climate Impacts ◽  
Eastern Pacific ◽  
Climate Forecast ◽  
Forecast System ◽  
Central Pacific ◽  
Climate Forecast System ◽  
Enso Events ◽  
East Asian Climate ◽  
Ncep Climate Forecast System

Abstract The eastern Pacific (EP) El Niño–Southern Oscillation (ENSO) and the central Pacific (CP) ENSO exert different influences on climate. In this study, the authors analyze the hindcasts of the NCEP Climate Forecast System, version 2 (CFSv2), and assess the skills of predicting the two types of ENSO and their impacts on East Asian climate. The possible causes of different prediction skills for different types of ENSO are also discussed. CFSv2 captures the spatial patterns of sea surface temperature (SST) related to the two types of ENSO and their different climate impacts several months in advance. The dynamical prediction of the two types of ENSO by the model, whose skill is season dependent, is better than the prediction based on the persistency of observed ENSO-related SST, especially for summer and fall. CFSv2 performs well in predicting EP ENSO and its impacts on the East Asian winter monsoon and on the Southeast Asian monsoon during its decaying summer. However, for both EP ENSO and CP ENSO, the model overestimates the extent of the anomalous anticyclone over the western North Pacific Ocean from the developing autumn to the next spring but underestimates the magnitude of climate anomalies in general. It fails to simulate the SST pattern and climate impact of CP ENSO during its developing summer. The model’s deficiency in predicting CP ENSO may be linked to a warm bias in the eastern Pacific. However, errors in simulating the climate impacts of the two types of ENSO should not be solely ascribed to the bias in SST simulation.

Dynamical prediction of the East Asian winter monsoon by the NCEP Climate Forecast System

2013 ◽  
Vol 118 (3) ◽  
pp. 1312-1328 ◽  
Author(s):  
Xingwen Jiang ◽  
Song Yang ◽  
Yueqing Li ◽  
Arun Kumar ◽  
Wanqiu Wang ◽  
...  
Keyword(s):  
East Asian Winter Monsoon ◽  
East Asian ◽  
Winter Monsoon ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Asian Winter Monsoon ◽  
Ncep Climate Forecast System

scholarly journals Variations of the East Asian Mei-Yu and Simulation and Prediction by the NCEP Climate Forecast System

2011 ◽  
Vol 24 (1) ◽  
pp. 94-108 ◽  
Author(s):  
Hui Gao ◽  
Song Yang ◽  
Arun Kumar ◽  
Zeng-Zhen Hu ◽  
Bohua Huang ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Function Analysis ◽  
East Asian ◽  
Eastern China ◽  
Monsoon Circulation ◽  
The Tibetan Plateau ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

Abstract The East Asian mei-yu (EAMY), which includes the mei-yu over eastern China, baiu over Japan, and changma over Korea, is an important component of the Asia summer monsoon system. The EAMY rain belt jumps northward to the Yangtze and Huaihe River valleys (in China), Japan, and Korea from mid-June to mid-July, with remarkable interannual variability. In this study, the variability and predictability of EAMY are investigated using the retrospective ensemble predictions of the NCEP Climate Forecast System (CFS). The CFS reasonably captures the centers, magnitude, northward jump, and other features of EAMY over most regions. It also reasonably simulates the interannual variations of EAMY and its main influencing factors such as the western Pacific subtropical high, the East Asian monsoon circulation, and El Niño–Southern Oscillation (ENSO). The CFS is skillful in predicting EAMY and related circulation patterns with a lead time of one month. An empirical orthogonal function analysis with maximized signal-to-noise ratio is applied to determine the most predictable patterns of EAMY. Furthermore, experiments in which the CFS is forced by observed sea surface temperature (SST) exhibit lower skill in EAMY simulation, suggesting the importance of ocean–atmosphere coupling in predicting EAMY. The CFS, which exaggerates the precipitation over the southern–southeastern hills of the Tibetan Plateau, overestimates the relationship between EAMY and tropical–subtropical atmospheric circulation due to the overly strong ENSO signals in the model, whereas the experiments forced by observed SST produce a weaker relationship. On the contrary, the CFS underestimates the link of EAMY to higher-latitude processes. An increase in the horizontal resolution of the CFS is expected to reduce some of these errors.

Summer monsoon circulation and precipitation over the tropical Indian Ocean during ENSO in the NCEP climate forecast system

2013 ◽  
Vol 42 (7-8) ◽  
pp. 1925-1947 ◽  
Author(s):  
J. S. Chowdary ◽  
H. S. Chaudhari ◽  
C. Gnanaseelan ◽  
Anant Parekh ◽  
A. Suryachandra Rao ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Tropical Indian Ocean ◽  
Monsoon Circulation ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

scholarly journals A Hybrid Global Ocean Data Assimilation System at NCEP

2015 ◽  
Vol 143 (11) ◽  
pp. 4660-4677 ◽  
Author(s):  
Stephen G. Penny ◽  
David W. Behringer ◽  
James A. Carton ◽  
Eugenia Kalnay
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Global Ocean ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ocean Data Assimilation ◽  
Data Assimilation System ◽  
Ncep Climate Forecast System ◽  
Assimilation System

Abstract Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

Predicting US summer precipitation using NCEP Climate Forecast System version 2 initialized by multiple ocean analyses

2013 ◽  
Vol 41 (7-8) ◽  
pp. 1941-1954 ◽  
Author(s):  
Jieshun Zhu ◽  
Bohua Huang ◽  
Zeng-Zhen Hu ◽  
James L. Kinter ◽  
Lawrence Marx
Keyword(s):  
Summer Precipitation ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System
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