Preprints
https://doi.org/10.5194/esd-2021-53
https://doi.org/10.5194/esd-2021-53

  06 Jul 2021

06 Jul 2021

Review status: a revised version of this preprint was accepted for the journal ESD and is expected to appear here in due course.

Extreme Metrics and Large Ensembles

Claudia Tebaldi1, Kalyn Dorheim1, Michael Wehner2, and Ruby Leung3 Claudia Tebaldi et al.
  • 1Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA
  • 2Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  • 3Pacific Northwest National Laboratory, Richland, WA, USA

Abstract. We consider the problem of estimating the ensemble sizes required to characterize the forced component and the internal variability of a range of extreme metrics. While we exploit existing large ensembles contributed to the CLIVAR Large Ensemble Project, our perspective is that of a modeling center wanting to estimate a-priori such sizes on the basis of an existing small ensemble (we use five members here). We therefore ask if such small-size ensemble is sufficient to estimate the population variance in a way accurate enough to apply a well established formula that quantifies the expected error as a function of n (the ensemble size). We find that indeed we can anticipate errors in the estimation of the forced component for temperature and precipitation extreme metrics as a function of n by applying the population variance derived by five members in the formula. For a range of spatial and temporal scales, forcing levels (we use RCP8.5 simulations), and both models considered here as our proof of concept, CESM1-CAM5 and CanESM2, it appears that an ensemble size of 20 or 25 members can provide estimates of the forced component for the extreme metrics considered that remain within small absolute and percentage errors. Additional members beyond 20 or 25 add only marginal precision to the estimate, which remains true when extreme value analysis is used. We then ask about the ensemble size required to estimate the ensemble variance (a measure of internal variability) along the length of the simulation, and – importantly – about the ensemble size required to detect significant changes in such variance along the simulation with increased external forcings. When an F-test is applied to the ratio of the variances in question, one estimated on the basis of only 5 or 10 ensemble members, one estimated using the full ensemble (up to 50 members in our study) we do not obtain significant results even when the analysis is conducted at the grid-point scale. While we recognize that there will always exist applications and metric definitions requiring larger statistical power and therefore ensemble sizes, our results suggest that for a wide range of analysis targets and scales an effective estimate of both forced component and internal variability can be achieved with sizes below 30 members. This invites consideration of the possibility of exploring additional sources of uncertainty, like physics parameter settings, when designing ensemble simulations.

Claudia Tebaldi et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2021-53', Anonymous Referee #1, 30 Jul 2021
    • AC1: 'Reply on RC1', Claudia Tebaldi, 26 Aug 2021
  • RC2: 'Comment on esd-2021-53', Anonymous Referee #2, 09 Aug 2021
    • AC2: 'Reply on RC2', Claudia Tebaldi, 26 Aug 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2021-53', Anonymous Referee #1, 30 Jul 2021
    • AC1: 'Reply on RC1', Claudia Tebaldi, 26 Aug 2021
  • RC2: 'Comment on esd-2021-53', Anonymous Referee #2, 09 Aug 2021
    • AC2: 'Reply on RC2', Claudia Tebaldi, 26 Aug 2021

Claudia Tebaldi et al.

Claudia Tebaldi et al.

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Short summary
We address the question of how large an initial condition ensemble of climate model simulations should be, if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered) an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
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