Preprints
https://doi.org/10.5194/gmd-2022-60
https://doi.org/10.5194/gmd-2022-60
Submitted as: model experiment description paper
16 Mar 2022
Submitted as: model experiment description paper | 16 Mar 2022
Status: this preprint is currently under review for the journal GMD.

The Seasonal-to-Multiyear Large Ensemble (SMYLE) Prediction System using the Community Earth System Model Version 2

Stephen Gerald Yeager1, Nan Rosenbloom1, Anne A. Glanville1, Xian Wu1, Isla Simpson1, Hui Li1, Maria J. Molina1, Kristen Krumhardt1, Samuel Mogen2, Keith Lindsay1, Danica Lombardozzi1, Will Wieder1, Who Myung Kim1, Jadwiga H. Richter1, Matthew Long1, Gokhan Danabasoglu1, David Bailey1, Marika Holland1, Nicole Lovenduski2, and Warren G. Strand1 Stephen Gerald Yeager et al.
  • 1National Center for Atmospheric Research, Boulder, Colorado, USA
  • 2Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado, USA

Abstract. The potential for multiyear prediction of impactful Earth system change remains relatively underexplored compared to shorter (subseasonal to seasonal) and longer (decadal) timescales. In this study, we introduce a new initialized prediction system using the Community Earth System Model Version 2 (CESM2) that is specifically designed to probe potential and actual prediction skill at lead times ranging from 1 month out to 2 years. The Seasonal-to-Multiyear Large Ensemble (SMYLE) consists of 2-year long hindcast simulations that cover the period from 1970 to 2019, with 4 initializations per year and an ensemble size of 20. A full suite of output is available for exploring near-term predictability of all Earth system components represented in CESM2. We show that SMYLE skill for El Niño-Southern Oscillation is competitive with other prominent seasonal prediction systems, with correlations exceeding 0.5 beyond a lead time of 12 months. A broad overview of prediction skill reveals varying degrees of potential for useful multiyear predictions of seasonal anomalies in the atmosphere, ocean, land, and sea ice. The SMYLE dataset, experimental design, model, initial conditions, and associated analysis tools are all publicly available, providing a foundation for research on multiyear prediction of environmental change by the wider community.

Stephen Gerald Yeager et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-60', Anonymous Referee #1, 11 Apr 2022
  • RC2: 'Comment on gmd-2022-60', Anonymous Referee #2, 26 Apr 2022

Stephen Gerald Yeager et al.

Stephen Gerald Yeager et al.

Viewed

Total article views: 600 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
451 136 13 600 30 6 6
  • HTML: 451
  • PDF: 136
  • XML: 13
  • Total: 600
  • Supplement: 30
  • BibTeX: 6
  • EndNote: 6
Views and downloads (calculated since 16 Mar 2022)
Cumulative views and downloads (calculated since 16 Mar 2022)

Viewed (geographical distribution)

Total article views: 554 (including HTML, PDF, and XML) Thereof 554 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 May 2022
Download
Short summary
The natural environment changes over a range of time and space scales and some of these changes are predictable in advance. Short-term weather forecasts are most familiar to many, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a myriad of changes in the natural world.