Articles | Volume 12, issue 7
Development and technical paper
17 Jul 2019
Development and technical paper |  | 17 Jul 2019

Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation

Sihan Li, David E. Rupp, Linnia Hawkins, Philip W. Mote, Doug McNeall, Sarah N. Sparrow, David C. H. Wallom, Richard A. Betts, and Justin J. Wettstein


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Sihan Li on behalf of the Authors (11 Apr 2019)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (23 Apr 2019) by James Annan
RR by Anonymous Referee #1 (06 May 2019)
ED: Publish as is (07 May 2019) by James Annan
AR by Sihan Li on behalf of the Authors (20 May 2019)  Manuscript 
Short summary
Understanding the unfolding challenges of climate change relies on climate models, many of which have regional biases larger than the expected climate signal over the next half-century. This work shows the potential for improving climate model simulations through a multiphased parameter refinement approach. Regional warm biases are substantially reduced, suggesting this iterative approach is one path to improving climate models and simulations of present and future climate.