loading page

A New GFSv15 based Climate Model Large Ensemble and Its Application to Understanding Climate Variability, and Predictability
  • +6
  • Tao Zhang,
  • Weiyu Yang,
  • Xiaowei Quan,
  • Jieshun Zhu,
  • Bhaskar Jha,
  • Arun Kumar,
  • Martin P. Hoerling,
  • Joseph Barsugli,
  • Wanqiu Wang
Tao Zhang
NOAA/National Centers for Environmental Prediction, ESSIC

Corresponding Author:[email protected]

Author Profile
Weiyu Yang
NCEP/NOAA, EMC/NCEP/WWB
Author Profile
Xiaowei Quan
NOAA-CIRES CDC
Author Profile
Jieshun Zhu
NOAA/NCEP/CPC
Author Profile
Bhaskar Jha
NCEP/CPC
Author Profile
Arun Kumar
NOAA CPC
Author Profile
Martin P. Hoerling
National Oceanic and Atmospheric Administration (NOAA)
Author Profile
Joseph Barsugli
University of Colorado Boulder
Author Profile
Wanqiu Wang
NOAA/NCEP/CPC
Author Profile

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 Niño 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.
14 Jun 2023Submitted to ESS Open Archive
14 Jun 2023Published in ESS Open Archive