Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 22
Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6593-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6593-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 22
Methods for assessment of models
 | 
16 Nov 2023
Methods for assessment of models |  | 16 Nov 2023

Monte Carlo drift correction – quantifying the drift uncertainty of global climate models

Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew

Viewed

Total article views: 1,192 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
886 249 57 1,192 88 54 47
  • HTML: 886
  • PDF: 249
  • XML: 57
  • Total: 1,192
  • Supplement: 88
  • BibTeX: 54
  • EndNote: 47
Views and downloads (calculated since 14 Feb 2023)
Cumulative views and downloads (calculated since 14 Feb 2023)

Viewed (geographical distribution)

Total article views: 1,192 (including HTML, PDF, and XML) Thereof 1,168 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Nov 2024
Download
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Read more