The Significance of Internal Variability for Numerical Experimentation and Analysis
Abstract
:1. Introduction
- Firstly, noise is expected to arise in systems influenced by short-term weather variations, which lack strong damping but possess robust memory. Such systems are foremost all atmospheric and oceanic hydrodynamical systems with short-term variations related to eddies, internal tides, fronts, and other phenomena. A very different case of such systems encompasses regional morphodynamics, as highlighted by a CNN report (see “Damaged US Navy sub was operating in one of the world’s most difficult undersea environments, analysts say”, CNN, 8 October 2021) following a US submarine incident in the South China Sea, which emphasised the ongoing, albeit gradual, changes in the environment and seafloor. This underscores the necessity for continuous bottom contour mapping in the region. Additionally, ecosystem dynamics may also be affected by such noise [30,31,32].
- Another aspect pertains to the realm of numerical experimentation, where alterations in factors such as parametrisations, boundary conditions, and atmospheric composition are introduced in simulations. In such experiments, appropriately designed ensembles are crucial for estimating the extent of inherent variability, determining whether changes between ensembles can be attributed solely to internal variability or if external factors play a role (an issue akin to detection) (e.g., [33]).
- The third one is the conventional “detection and attribution” challenge [34]: whether observed variations may be understood in the framework of internal variability or if an external factor needs to be determined to explain the observed change, which brings us back to the initial observations of the need to separate forced and unforced climate variations, which was mentioned at the beginning of this paper.
2. Methods
3. Results
3.1. Emerging Noise in Marginal Seas
3.2. Sensitivity to Tides and Changing Seasonal Conditions
3.3. Seeding Noise
4. Discussion
4.1. First Experiments of the Effect of Disturbances on Morphodynamics
4.2. Assessing the Outcome of Numerical Experiments
4.3. Detection and Attribution
4.4. Multiple Equilibria
5. Conclusions
- Low-frequency noise can be understood within the framework of Hasselmann’s stochastic climate model [1].
- Internal variability, which refers to variations that cannot be attributed to specific external drivers, is an intrinsic part of the system’s dynamics rather than just a nuisance.
- In the absence of external drivers, the system exhibits variability across all spatial and temporal scales.
- In simulations, identifying this noise is relatively straightforward. It can be achieved by constructing ensembles of simulations with minor, insignificant variations introduced by shifting the initial time or using different computer platforms.
- To determine the impact of external factors, statistical testing is required, using “no effect” as the null hypothesis. This can be carried out through numerical experiments with ensembles of simulations. Attributing causal mechanisms, especially when multiple causes are possible, can be approached with a plausibility argument.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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von Storch, H.; Lin, L. The Significance of Internal Variability for Numerical Experimentation and Analysis. Atmosphere 2024, 15, 1317. https://doi.org/10.3390/atmos15111317
von Storch H, Lin L. The Significance of Internal Variability for Numerical Experimentation and Analysis. Atmosphere. 2024; 15(11):1317. https://doi.org/10.3390/atmos15111317
Chicago/Turabian Stylevon Storch, Hans, and Lin Lin. 2024. "The Significance of Internal Variability for Numerical Experimentation and Analysis" Atmosphere 15, no. 11: 1317. https://doi.org/10.3390/atmos15111317
APA Stylevon Storch, H., & Lin, L. (2024). The Significance of Internal Variability for Numerical Experimentation and Analysis. Atmosphere, 15(11), 1317. https://doi.org/10.3390/atmos15111317