A New GFSv15 With FV3 Dynamical Core Based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability

NOAA Climate Prediction Center (CPC) has generated a 100‐member ensemble of atmospheric model simulations from 1979 to present using the Global Forecast System version 15 (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 Atmospheric Model Intercomparison Project simulations. These simulations are part of CPC's efforts to attribute observed seasonal climate variability to sea surface temperature (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 Niño events, the analysis of signal to noise ratio, 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, trends, and 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 fidelity of US surface climate, mainly confined in the Northern and Southeastern US for precipitation and in the east for temperature. Plain Language Summary To correctly account for extreme weather and climate events such as heatwaves, floods, and droughts that have devastating effects on the US economy and human lives, climate model experiments have become a key tool to disentangle numerous responsible factors. A recent development of an updated modeling framework at the National Centers for Environmental Prediction to support the attribution of observed seasonal anomalies is reported in this study. We have generated 100‐member simulations where each member has identical sea surface temperature (SST) forcing but differs only by the initial atmospheric condition. These simulations are updated once a month when the observed SST data becomes available. We use the ensemble mean of these simulations to describe the responses to