Data and Code for Yeager et al., 2022: Enhanced Skill and Signal-to-noise in an Eddy-Resolving Decadal Prediction System

The sensitivity of decadal prediction system performance to model resolution is examined by comparing results from low- and high-resolution (LR and HR) predictions conducted with the Community Earth System Model (CESM). The primary difference between the two systems is the horizontal grid spacing of the ocean and atmosphere models (1° for both in LR; 0.1° and 0.25°, respectively, in HR), permitting a first direct comparison of how skill and signal-to-noise characteristics change when moving to the ocean eddy-resolved modeling regime. HR exhibits significantly increased skill and enhanced signal-to-noise for atmospheric fields compared to LR. This result suggests that mesoscale atmosphere-ocean interaction, which is present in HR but absent in LR, is a key mechanism involved in the transmission of predictable signals from the ocean to the atmosphere. Climate predictions can potentially be improved (and the signal-to-noise paradox alleviated) through explicit representation of ocean eddies and their interactions with the atmosphere.

To Access Resource:

Questions? Email Resource Support Contact:

  • Stephen Yeager
    yeager@ucar.edu
    UCAR/NCAR - Climate and Global Dynamics Laboratory

Temporal Range

  • Begin:  1982
    End:  2016

Keywords

Resource Type dataset
Temporal Range Begin 1982
Temporal Range End 2016
Temporal Resolution N/A
Bounding Box North Lat 90.0
Bounding Box South Lat -90.0
Bounding Box West Long -180.0
Bounding Box East Long 180.0
Spatial Representation N/A
Spatial Resolution 5.0 degreesLongitude
5.0 degreesLatitude
Related Links N/A
Additional Information N/A
Resource Format GNU tar Compressed File Archive (GNU Tape Archive) (application/x-gtar)
application/x-netcdf
application/x-tar
Standardized Resource Format Archive
NetCDF
Asset Size N/A
Legal Constraints

Creative Commons Attribution 4.0 International License.


Access Constraints None
Software Implementation Language N/A

Resource Support Name Stephen Yeager
Resource Support Email yeager@ucar.edu
Resource Support Organization UCAR/NCAR - Climate and Global Dynamics Laboratory
Distributor N/A
Metadata Contact Name GDEX Curator
Metadata Contact Email gdex@ucar.edu
Metadata Contact Organization UCAR/NCAR - GDEX

Author Yeager, Stephen
Chang, Ping
Danabasoglu, Gokhan
Wu, Lixin
Rosenbloom, Nan
Zhang, Qiuying
Castruccio, Frederic
Gopal, Abishek
Rencurrel, M. Cameron
Publisher UCAR/NCAR - GDEX
Publication Date 2023-08-04
Digital Object Identifier (DOI) https://doi.org/10.5065/9t56-sm14
Alternate Identifier N/A
Resource Version N/A
Topic Category N/A
Progress N/A
Metadata Date 2023-08-04T14:02:10-06:00
Metadata Record Identifier edu.ucar.gdex::8265a707-5a27-40a8-b3fb-bdf7feba816f
Metadata Language eng; USA
Suggested Citation Yeager, Stephen, Chang, Ping, Danabasoglu, Gokhan, Wu, Lixin, Rosenbloom, Nan, Zhang, Qiuying, Castruccio, Frederic, Gopal, Abishek, Rencurrel, M. Cameron. (2023). Data and Code for Yeager et al., 2022: Enhanced Skill and Signal-to-noise in an Eddy-Resolving Decadal Prediction System. UCAR/NCAR - GDEX. https://doi.org/10.5065/9t56-sm14. Accessed 22 July 2024.

Harvest Source