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Detecting Changes in Large-Scale Metrics of Climate in Short Integrations of a Global Storm-Resolving Model of the Atmosphere
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  • Ilai Guendelman,
  • Timothy M. Merlis,
  • Kai-Yuan Cheng,
  • Lucas Harris,
  • Christopher S. Bretherton,
  • Maximilien Bolot,
  • Linjiong Zhou,
  • Alex David Kaltenbaugh,
  • Spencer Koncius Clark,
  • Stephan Fueglistaler
Ilai Guendelman
Princeton University

Corresponding Author:[email protected]

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Timothy M. Merlis
Princeton University
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Kai-Yuan Cheng
Princeton University
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Lucas Harris
GFDL
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Christopher S. Bretherton
Allen Institute for Artificial Intelligence
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Maximilien Bolot
Princeton University
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Linjiong Zhou
Princeton University
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Alex David Kaltenbaugh
UCAR
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Spencer Koncius Clark
Allen Institute for Artificial Intelligence / NOAA-GFDL
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Stephan Fueglistaler
Princeton University
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Abstract

Recent advances have allowed for integration of global storm resolving models (GSRMs) to a timescale of several years. These short simulations are sufficient for studying characteristics and statistics of short- and small-scale phenomena; however, it is questionable what we can learn from these integrations about the large-scale climate response to perturbations. To address this question, we use the response of X-SHiELD (a GSRM) to uniform SST warming and CO$_2$ increase in a two-year integration and compare it to similar CMIP6 experiments. Specifically, we assess the statistical meaning of having two years in one model outside the spread of another model or model ensemble. This is of particular interest because X-SHiELD shows a distinct response of the global mean precipitation to uniform warming, and the northern hemisphere jet shift response to isolated CO$_2$ increase. We use the CMIP6 models to estimate the probability of two years in one model being more than one standard deviation away from another model (ensemble) mean, knowing the mean of two models. For example, if two years in one model are more than one standard deviation away from the other model’s mean, we find that the chances for these models’ means to be within one standard deviation are $\sim 25\%$. We find that for some large-scale metrics, there is an important base-state dependence that, when taken into account, can qualitatively change the interpretation of the results. We note that a year-to-year comparison is physically meaningful due to the use of prescribed sea-surface-temperature simulations.