Predictability during active break phases of Indian summer monsoon in an ensemble prediction system using climate forecast system

2013 ◽  
Vol 100-101 ◽  
pp. 13-23 ◽  
Author(s):  
S Abhilash ◽  
A. K Sahai ◽  
S. Pattnaik ◽  
S. De
2013 ◽  
Vol 34 (5) ◽  
pp. 1628-1641 ◽  
Author(s):  
Subodh K. Saha ◽  
Samir Pokhrel ◽  
Hemantkumar S. Chaudhari ◽  
Ashish Dhakate ◽  
Swati Shewale ◽  
...  

2015 ◽  
Vol 45 (9-10) ◽  
pp. 2485-2498 ◽  
Author(s):  
Rodrigo J. Bombardi ◽  
Edwin K. Schneider ◽  
Lawrence Marx ◽  
Subhadeep Halder ◽  
Bohar Singh ◽  
...  

2012 ◽  
Vol 39 (9-10) ◽  
pp. 2143-2165 ◽  
Author(s):  
Samir Pokhrel ◽  
H. S. Chaudhari ◽  
Subodh K. Saha ◽  
Ashish Dhakate ◽  
R. K. Yadav ◽  
...  

2012 ◽  
Vol 25 (7) ◽  
pp. 2490-2508 ◽  
Author(s):  
Deepthi Achuthavarier ◽  
V. Krishnamurthy ◽  
Ben P. Kirtman ◽  
Bohua Huang

Abstract The observed negative correlation between El Niño–Southern Oscillation (ENSO) and the Indian summer monsoon is not simulated by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) coupled model. The correlation is partially restored in the simulations where the Indian Ocean (IO) sea surface temperature (SST) is prescribed with the daily mean or climatology. Comparison among the simulations suggests that ENSO-induced SST anomalies form a strong dipole pattern oriented along the zonal direction in the IO in the coupled model, preventing the ENSO signals from reaching the Indian monsoon region. In the model, the dipole develops early in the monsoon season and extends to the central equatorial IO while it is formed at the end of the season in observations. The dipole modifies low-level winds and surface pressure, and grows in a positive feedback loop involving winds, surface pressure, and SST. Examination of the mean state in the model reveals that the thermocline is relatively shallow in the eastern IO. This preconditions the ocean such that the atmospheric fluxes can easily impart fluctuations in the subsurface temperature and thereby in the SST. These results suggest that biases in the IO can adversely affect the ENSO–monsoon teleconnection in a coupled model.


2015 ◽  
Vol 28 (22) ◽  
pp. 8988-9012 ◽  
Author(s):  
Bidyut B. Goswami ◽  
R. P. M. Krishna ◽  
P. Mukhopadhyay ◽  
Marat Khairoutdinov ◽  
B. N. Goswami

Abstract An analysis of a 5-yr (from 1 January 2009 to 31 December 2013) free run of the superparameterized (SP) Climate Forecast System (CFS) version 2 (CFSv2) (SP-CFS), implemented for the first time at a spectral triangular truncation at wavenumber 62 (T62) atmospheric horizontal resolution, is presented. The SP-CFS simulations are evaluated against observations and traditional convection parameterized CFSv2 simulations at T62 resolution as well as at some higher resolutions. The metrics for evaluating the model performance are chosen in order to mainly address the improvement in systematic biases observed in the CFSv2 documented in earlier studies. While the primary focus of this work is on evaluating the improvement of the simulation of the Indian summer monsoon (ISM) by the SP-CFS model, some results are also presented within the context of the global climate. The SP-CFS significantly reduces the dry bias of precipitation over the Indian subcontinent and better captures the monsoon intraseasonal oscillation (MISO) modes. SP-CFS also improves the northward and eastward propagation of high- and low-frequency modes of ISM. Compared to CFSv2, the SP-CFS model simulates improved convectively coupled equatorial waves; better temperature structures both spatially and vertically, leading to a significantly improved relative distribution of variance for the synoptic disturbances and low-frequency tropical intraseasonal oscillations (ISOs). This analysis of the development of SP-CFS is particularly important as it shows promise for improving the cloud process representation through an SP framework and is able to improve the mean as well as intraseasonal characteristics of CFSv2 within the context of the ISM.


2015 ◽  
Vol 124 (2) ◽  
pp. 321-333 ◽  
Author(s):  
Kailas Sonawane ◽  
O P Sreejith ◽  
D R Pattanaik ◽  
Mahendra Benke ◽  
Nitin Patil ◽  
...  

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