Articles | Volume 28, issue 4
https://doi.org/10.5194/npg-28-633-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-633-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Dec 2021
Research article |
| 23 Dec 2021
| 23 Dec 2021
Inferring the instability of a dynamical system from the skill of data assimilation exercises
Department of Meteorology and NCEO, University of Reading, Reading, UK
Centre for the Mathematics of Planet Earth, University of Reading, Reading, UK
Alberto Carrassi
Department of Meteorology and NCEO, University of Reading, Reading, UK
Department of Physics and Astronomy “Augusto Righi”, University of Bologna, Bologna, Italy
Centre for the Mathematics of Planet Earth, University of Reading, Reading, UK
Valerio Lucarini
Centre for the Mathematics of Planet Earth, University of Reading, Reading, UK
Department of Mathematics and Statistics, University of Reading, Reading, UK
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Short summary
Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.
Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate...
