Articles | Volume 19, issue 4
https://doi.org/10.5194/os-19-1253-2023
© 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/os-19-1253-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Aug 2023
Research article |
| 21 Aug 2023
| 21 Aug 2023
Potential artifacts in conservation laws and invariants inferred from sequential state estimation
Carl Wunsch
Department of Earth and Planetary Science, Harvard University, Cambridge, MA, USA
Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
Patrick Heimbach
Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA
Institute for Geophysics, University of Texas at Austin, Austin, TX, USA
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Short summary
Data assimilation methods that couple observations with dynamical models are essential for understanding climate change. Here,
climateincludes all sub-elements (ocean, atmosphere, ice, etc.). A common form of combination arises from sequential estimation theory, a methodology susceptible to a variety of errors that can accumulate through time for long records. Using two simple analogs, examples of these errors are identified and discussed, along with suggestions for accommodating them.
Data assimilation methods that couple observations with dynamical models are essential for...
