Internal vs Forced Variability Metrics for Geophysical Flows Using Information Theory

Abstract

This a preprint and has not been peer reviewed. Data may be preliminary.

Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

“…Other systems, and perhaps the kinetic energy in this system, are dominated by internal variability rather than forced variability. A companion paper (Sane et al., 2021) expands on this topic for coastal modeling, where a variety of different boundary forcing mechanisms can contribute.…”

“…(2018) state that a noise to signal ratio of less than 0.5 is sufficient so that external forcing is dominant in setting the ensemble mean variability over internal chaos, indicating also that model trend is captured sufficiently with this number of ensemble members. An upcoming companion paper by the authors (Sane et al., 2021) expands on the approach of Llovel et al. (2018) using information theory techniques to quantify forced versus internal variability even for nonGaussian and nonindependent datasets.…”

“…A companion paper in review by Sane et al. (2021) develops a nonparametric information theory approach to quantifying the amount of internal versus forced variability similar to the ensemble approach of (Llovel et al., 2018), and uses this metric to quantify the relative importance of different choices in boundary forcing. As the balance of sources of variability depends on forcing, resolution, classes of flow, etc., the magnitude of the forced versus intrinsic variability depends on the specifics of the model, rather than being a general description of the waterways under study.…”