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Seasonal prediction skill of Indian summer monsoon rainfall in NMME models and monsoon mission CFSv2
Author(s) -
Pillai Prasanth A.,
Rao Suryachandra A.,
Ramu Dandi A.,
Pradhan Maheswar,
George Gibies
Publication year - 2018
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5413
Subject(s) - teleconnection , climatology , environmental science , sea surface temperature , forcing (mathematics) , forecast skill , el niño southern oscillation , monsoon , climate forecast system , atmospheric sciences , meteorology , geology , geography , precipitation
Along with good prediction skill for major SST boundary forcings such as El Niño and IOD, their appropriate teleconnection spatial patterns also need to be captured well for the better prediction of the land precipitation like Indian summer monsoon rainfall. Here in the study, even though majority of the models has better skill for Nino3.4 index and IOD index, their spatial teleconnection pattern is higher for CFSv2‐T382 (pattern correlation of 0.8) and also has less bias in tropical region. Thus as seen in the figure, it has better Indian summer monsoon rainfall (ISMR)–SST relationship (PCC = 0.6) compared to all other models and hence CFSv2‐T382 has better skill (0.55) for ISMR, while skill is less than 0.1 for the models with PCC values very less. Spatial pattern of correlation between ISMR and seasonal SST anomalies from (a) observations, (b)–(p) individual model hindcasts and (o) MME of all models. Statistically significant (90% confidence level) correlations are stippled.