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.
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.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
16 Nov 2023
Methods for assessment of models |
| 16 Nov 2023
| 16 Nov 2023
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Benjamin S. Grandey
CORRESPONDING AUTHOR
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
Zhi Yang Koh
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
Dhrubajyoti Samanta
Earth Observatory of Singapore, Nanyang Technological University, Singapore
Benjamin P. Horton
Earth Observatory of Singapore, Nanyang Technological University, Singapore
Asian School of the Environment, Nanyang Technological University, Singapore
Justin Dauwels
Department of Microelectronics, Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology (TU Delft), Delft, the Netherlands
Lock Yue Chew
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
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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.
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can...
