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Abstract Kelvin‐Helmholtz Instabilities (KHI) are known to be significant drivers of atmospheric turbulence. Recent observations show KHI forming with misaligned or angled billow segments that develop connecting vortex tubes and knots (T&K); these features promote distinctive, event‐defining instability and mixing characteristics that were not accounted for in prior idealized studies. Though T&K have been shown to increase mixing in KHI events with low Richardson numbers (Ri), their influence in weakly KH‐unstable, less‐idealized environments is unknown. Here we present modeling results of KHI in the stratosphere to assess the impact of T&K dynamics in weakly KH‐unstable environments. Radiosonde wind and temperature profiles from 22 February 2006 near Lamont, Oklahoma, measured vertically offset shear and stability peaks near 16.2 km with a minimum Ri = 0.11. Direct numerical simulations (DNS) of this event reveal decreasing shear and increasing stratification, where Ri increases to 0.2 as the shear and stratification peaks move to a common altitude. The resulting KHI exhibit T&K features forming adjacent to, and in superposition with, secondary convective instabilities (CI) rather than superseding them as in prior T&K studies with Ri = 0.05. Newly discovered “crankshaft” instabilities distort the billows and give rise to secondary KHI with delayed, elevated dissipation. KHI that exhibit T&K dynamics are found to accumulate % greater mixing than axially uniform KHI with equal or lower mixing efficiency. The substantial increase in mixing suggests significant contributions of T&K dynamics to KHI events throughout the atmosphere that remain unaddressed in general circulation models' turbulence parameterizations.
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Predicting Ocean Turbulence across Orders of Magnitude Using Neural Networks Trained on Multiyear Observations
Abstract Turbulence quantities in geophysical fluids roughly follow a lognormal distribution. Consequently, extreme values dominate arithmetic means, and it is challenging for regression algorithms to accurately predict point-to-point and mean values. We train neural networks (NNs) to predict logarithmic values of turbulence diffusivityKTfrom multiyear observational records of turbulence in the deep-cycle layer (DCL) of the upper ocean at Earth’s equator, with the objective of accurately predicting long-term statistics ofKT. Depending on prescribed input variables, temporal averaging, and the number of internal parametersNnn, NNs can predict instantaneous values of log10KTwith correlation coefficientsRas high as 0.6–0.65, root-mean-square error less than an order of magnitude, and long-term averages ofKTwithin a factor of 2 between predictions and observations. Prescribing smallNnncompared to the training dataset size results in poor representation of the distribution’s tails. Conversely, prescribing largeNnncauses overfitting degrading the instantaneous predictability. NNs reproduce the observed spread inKTof multiple orders of magnitude at given gradient Richardson number Ri, unlike commonly used physics-based parameterizations which are single-valued functions of Ri. Predictions of the log10of vertical turbulent heat fluxJqare qualitatively similar to those of log10KTbut with poorer correlation because of differences between the observed distributions. Tests for spatial generalizability show that when training on two of three equatorial locations, each having DCLs, with multiyear records (140°, 23°, and 10°W), predictions at the third location are less accurate than when training from the same site. Significance StatementBy enhancing thermodynamic mixing, ocean turbulence transports heat from the surface to the ocean interior. Directly quantifying this transport requires careful, small-scale, long-term observations. Since such observations are rare in both space and time, it is necessary to infer turbulence parameters from conventional measurements like temperature or current velocity. Machine learning predictive algorithms, trained using existing long-term observations, show promise as a means to achieve this goal. A challenge to overcome is how to characterize the lognormal-like distribution of turbulence levels, in which values vary over orders of magnitude and a few extreme values dominate the arithmetic mean. Reproducing this distribution with neural networks is not trivial, and identifying how to do so is a focus of this paper.
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- Award ID(s):
- 2048631
- PAR ID:
- 10668831
- Publisher / Repository:
- ams
- Date Published:
- Journal Name:
- Artificial Intelligence for the Earth Systems
- Volume:
- 4
- Issue:
- 3
- ISSN:
- 2769-7525
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1175/AIES-D-24-0093.1
