Estimating Uncertainty in Simulated ENSO Statistics
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- School of Earth Atmosphere and Environment Monash University Clayton VIC Australia, NOAA Pacific Marine Environmental Laboratory Seattle WA USA
- Lawrence Livermore National Laboratory Livermore CA USA
- NOAA Geophysical Fluid Dynamics Laboratory Princeton NJ USA
- NOAA Pacific Marine Environmental Laboratory Seattle WA USA
- LOCEAN‐IPSL CNRS‐IRD‐MNHN‐Sorbonne Université Paris France, NCAS‐Climate University of Reading Reading UK
- School of Earth Atmosphere and Environment Monash University Clayton VIC Australia, ARC Centre of Excellence for Climate Extremes Monash University Clayton VIC Australia
Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- NONE; AC52-07NA27344
- OSTI ID:
- 2440096
- Journal Information:
- Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 9 Vol. 16; ISSN 1942-2466
- Publisher:
- American Geophysical Union (AGU)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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