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Significance of Internal Variability for Numerical Experimentation and Analysis
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Version 1
: Received: 26 July 2024 / Approved: 27 July 2024 / Online: 29 July 2024 (11:03:01 CEST)
How to cite: von Storch, H.; Lin, L. Significance of Internal Variability for Numerical Experimentation and Analysis. Preprints 2024, 2024072261. https://doi.org/10.20944/preprints202407.2261.v1 von Storch, H.; Lin, L. Significance of Internal Variability for Numerical Experimentation and Analysis. Preprints 2024, 2024072261. https://doi.org/10.20944/preprints202407.2261.v1
Abstract
When limited area models of the hydrodynamics of the atmosphere and of the ocean are 1
run over an extended time, variability unrelated to external "drivers" emerge - this variability is 2
colloquially named "hydrodynamical noise" or just "noise". This article summarises what we have 3
learned in the past few years about the properties of such noise, and the implications for numerical 4
experimentation and analysis. The presence of this noise can be identified easily in ensembles of 5
numerical simulations, and it turns out that the intensity of the noise is closely linked to the scale- 6
dependent "memory". At the system level, this "memory" term as given by Hasselmann’s Stochastic 7
Climate model plays a key role. In the case of marginal seas, the process of baroclinic instability 8
modulated by tides and the formation of seasonal thermoclines are significant aspects. Some more 9
general aspects are discussed, such as the applicability of the Stochastic Climate model to systems 10
outside of atmospheric and oceanic dynamics, the irreversibility of tipping points and the challenges 11
of detecting changes beyond a noise level and of the attribution of causes of change.
Keywords
hydrodynamic noise; marginal seas; numerical experimentation; dynamical models; 13 Stochastic climate model; seeding noise
Subject
Environmental and Earth Sciences, Oceanography
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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