Estimating Uncertainty in Simulated ENSO Statistics.

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. Plain Language Summary: Earth's climate naturally fluctuates on intraseasonal to interdecadal timescales, confounding the evaluation of climate models and the detection of trends linked to climate change. To tackle this challenge, scientists produce ensembles of simulations with identical external forcings (e.g., volcanic eruptions, greenhouse gas emissions) but plausibly different initial conditions. In this study, we analyze how these ensembles can be used to reduce the uncertainty of the simulated climate, to help guide the design of future ensembles and optimize the use of available computing resources. Key Points: Large ensembles of climate simulations are analyzed to characterize the sampling uncertainty of El Niño–Southern Oscillation statisticsAs expected, uncertainty of these statistics decreases with the square root of the number of simulated years provided by the ensembleA simple equation developed to predict the ensemble size required to limit this sampling uncertainty to within a given tolerance [ABSTRACT FROM AUTHOR]