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\begin{document}

\title{The curious case of a strong relationship between ENSO and Indian summer
monsoon in CFSv2 model}



\author[1]{Priyanshi Singhai}%
\author[2]{Arindam Chakraborty}%
\author[3]{Kaushik Jana}%
\author[4]{Kavirajan Rajendran}%
\author[5]{Sajani Surendran}%
\affil[1]{Indian Institute of Science, Bengaluru, India}%
\affil[2]{Indian Institute of Science}%
\affil[3]{Ahmedabad University}%
\affil[4]{CSIR Fourth Paradigm Institute, Bangalore, India}%
\affil[5]{CSIR Fourth Paradigm Institute}%


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  \date{May 27, 2024}


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\begin{abstract}
An ensemble of forecasts is necessary to identify the uncertainty in
predicting a non-linear system like climate. While ensemble averages are
often used to represent the mean state and diagnose physical mechanisms,
they can lead to information loss and inaccurate assessment of the
model's characteristics. We highlight an intriguing case in the seasonal
hindcasts of the Climate Forecast System version-2. While all ensemble
members often agree on the sign of predicted El Nino Southern
Oscillation (ENSO) for a particular season, non-ENSO climate forcings,
although present in individual members, are disparate. As a result, an
ensemble mean retains ENSO anomalies while diminishing non-ENSO signals.
This difference between ENSO and non-ENSO predictions and a more
decisive impact of ENSO on seasonal climate increases the ensemble mean
ENSO-Indian Summer Monsoon Rainfall correlation. Thus, a model's
teleconnection skills, which often help interpret physical mechanisms,
should be studied using individual members rather than ensemble
averages.%
\end{abstract}%



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