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\begin{document}

\title{A New GFSv15 based Climate Model Large Ensemble and Its Application to
Understanding Climate Variability, and Predictability}



\author[1]{Tao Zhang}%
\author[2]{Weiyu Yang}%
\author[3]{Xiaowei Quan}%
\author[4]{Jieshun Zhu}%
\author[5]{Bhaskar Jha}%
\author[6]{Arun Kumar}%
\author[7]{Martin P. Hoerling}%
\author[8]{Joseph Barsugli}%
\author[4]{Wanqiu Wang}%
\affil[1]{NOAA/National Centers for Environmental Prediction, ESSIC}%
\affil[2]{NCEP/NOAA, EMC/NCEP/WWB}%
\affil[3]{NOAA-CIRES CDC}%
\affil[4]{NOAA/NCEP/CPC}%
\affil[5]{NCEP/CPC}%
\affil[6]{NOAA CPC}%
\affil[7]{National Oceanic and Atmospheric Administration (NOAA)}%
\affil[8]{University of Colorado Boulder}%


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  \date{May 20, 2024}


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\begin{abstract}
NOAA Climate Prediction Center (CPC) has generated a 100-member ensemble
of Atmospheric Model Intercomparison Project (AMIP) simulations from
1979 to present using the GFSv15 with FV3 dynamical core. The intent of
this study is to document a development in an infrastructure capability
with a focus to demonstrate the quality of these new simulations is on
par with the previous GFSv2 AMIP simulations. These simulations are part
of CPC's efforts to attribute observed seasonal climate variability to
SST forcings and get updated once a month by available observed SST.\\

The performance of these simulations in replicating observed climate
variability and trends, together with an assessment of climate
predictability and the attribution of some climate events is documented.
A particular focus of the analysis is on the US climate trend, Northern
Hemisphere winter height variability, US climate response to three
strong El Ni\selectlanguage{ngerman}ño events, the analysis of signal to noise ratio (SNR), the
anomaly correlation for seasonal climate anomalies, and the South Asian
flooding of 2022 summer, and thereby samples wide aspects that are
important for attributing climate variability. Results indicate that the
new model can realistically reproduce observed climate variability and
trends as well as extreme events, better capturing the US climate
response to extreme El Niño events and the 2022 summer South Asian
record-breaking flooding than GFSv2. The new model also shows an
improvement in the wintertime simulation skill of US surface climate,
mainly confined in the Northern and Southeastern US for precipitation
and in the east for temperature.%
\end{abstract}\selectlanguage{ngerman}%



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