Dynamical simulation of Indian summer monsoon circulation, rainfall and its interannual variability using a high resolution atmospheric general circulation model

2010 ◽  
Vol 31 (13) ◽  
pp. 1927-1942 ◽  
Author(s):  
Satyaban Bishoyi Ratna ◽  
D. R. Sikka ◽  
Mohit Dalvi ◽  
J. Venkata Ratnam
Keyword(s):  
High Resolution ◽  
Interannual Variability ◽  
Indian Summer Monsoon ◽  
General Circulation ◽  
Atmospheric General Circulation Model ◽  
Circulation Model ◽  
Monsoon Circulation ◽  
Dynamical Simulation ◽  
Atmospheric General Circulation ◽  
Indian Summer Monsoon Circulation

scholarly journals An Assessment of Errors in the Simulation of Atmospheric Interannual Variability in Uncoupled AGCM Simulations

2008 ◽  
Vol 21 (10) ◽  
pp. 2204-2217 ◽  
Author(s):  
Arun Kumar ◽  
Qin Zhang ◽  
J-K. E. Schemm ◽  
Michelle L’Heureux ◽  
K-H. Seo
Keyword(s):  
Knowledge Base ◽  
Interannual Variability ◽  
General Circulation ◽  
Atmospheric General Circulation Model ◽  
Circulation Model ◽  
Variability Analysis ◽  
Atmospheric General Circulation ◽  
Ocean Atmosphere ◽  
Tropical Ssts ◽  
Proper Context

Abstract For the uncoupled atmospheric general circulation model (AGCM) simulations, the quantification of errors due to the lack of coupled ocean–atmospheric evolution on the characteristics of the atmospheric interannual variability is important for various reasons including the following: 1) AGCM simulations forced with specified SSTs continue to be used for understanding atmospheric interannual variability and 2) there is a vast knowledge base quantifying the global atmospheric influence of tropical SSTs that traditionally has relied on the analysis of AGCM-alone simulations. To put such results and analysis in a proper context, it is essential to document errors that may result from the lack of a coupled ocean–atmosphere evolution in the AGCM-alone integrations. Analysis is based on comparison of tier-two (or uncoupled) and coupled hindcasts for the 1982–2005 period, and interannual variability for the December–February (DJF) seasonal mean is analyzed. Results indicate that for the seasonal mean variability, and for the DJF seasonal mean, atmospheric interannual variability between coupled and uncoupled simulations is similar. This conclusion is drawn from the analysis of interannual variability of rainfall and 200-mb heights and includes analysis of SST-forced interannual variability, analysis of El Niño and La Niña composites, and a comparison of hindcast skill between tier-two and coupled hindcasts.

scholarly journals On improving the ability of a high-resolution atmospheric general circulation model for dynamical seasonal prediction of the extreme seasons of the Indian summer monsoon

MAUSAM ◽  
2021 ◽  
Vol 62 (3) ◽  
pp. 339-360
Author(s):  
D.R. SIKKA ◽  
SATYABANBISHOYI RATNA
Keyword(s):  
Boundary Condition ◽  
High Resolution ◽  
Summer Monsoon ◽  
General Circulation ◽  
Atmospheric General Circulation Model ◽  
Monsoon Season ◽  
Circulation Model ◽  
Seasonal Prediction ◽  
Atmospheric General Circulation ◽  
Dynamical Seasonal Prediction

The paper is devoted to examine the ability of a high-resolution National Center for Environmental Prediction (NCEP) T170/L42 Atmospheric General Circulation Model (AGCM), for exploring its utility for long-range dynamical prediction of seasonal Indian summer monsoon rainfall (ISMR) based on 5-members ensemble for the hindcast mode 20-year (1985-2004) period with observed global sea surface temperatures (SSTs) as boundary condition and 6-year (2005-2010) period in the forecast-mode with NCEP Coupled Forecast System (CFS) SSTs as boundary condition. ISMR simulations are examined on five day (pentad) rainfall average basis. It is shown that the model simulated ISMR, based on 5-members ensemble average basis had limited skill in simulating extreme ISMR seasons (drought/excess ISMR). However, if the ensemble averaging is restricted to similar ensemble members either in the overall run of pentad-wise below (B) and above (A) normal rainfall events, as determined by the departure for thethreshold value given by coefficient of variability (CV) for the respective pentads based on IMD observed climatology, or during the season as a whole on the basis of percentage anomaly of ISMR from the seasonal climatology, the foreshadowing of drought/excess monsoon seasons improved considerably. Our strategy of improving dynamical seasonal prediction of ISMR was based on the premise that the intra-seasonal variability (ISV) and intra-annual variability (IAV) are intimately connected and characterized by large scale perturbations westward moving (10-20 day) and northward moving (30-60 day) modes of monsoon ISV during the summer monsoon season. As such the cumulative excess of B events in the simulated season would correspond to drought season and vice-versa. The paper also examines El Niño-Monsoon connections of the simulated ISMR series and they appear to have improved considerably in the proposed methodology. This strategy was particularly found to improve for foreshadowing of droughts. Based on results of the study a strategy is proposed for using the matched signal for simulated ISMR based on excess B over A events and vice-versa for drought or excess ISMR category. The probability distribution for the forecast seasonal ISMR on category basis is also proposed to be based on the relative ratio of similar ensemble members and total ensembles on percentage basis. The paper also discusses that extreme monsoon season are produced by the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) modes in a combined manner and hence stresses to improve prediction of IOD mode in ocean-atmosphere coupled model just as it has happened for the prediction ENSO mode six to nine months in advance.

Future climate of the Caribbean from a super-high-resolution atmospheric general circulation model

2012 ◽  
Vol 113 (1-2) ◽  
pp. 271-287 ◽  
Author(s):  
Trevor C. Hall ◽  
Andrea M. Sealy ◽  
Tannecia S. Stephenson ◽  
Shoji Kusunoki ◽  
Michael A. Taylor ◽  
...  
Keyword(s):  
High Resolution ◽  
General Circulation Model ◽  
General Circulation ◽  
Atmospheric General Circulation Model ◽  
Circulation Model ◽  
Future Climate ◽  
Atmospheric General Circulation ◽  
The Caribbean
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