HFIP Telecon Meeting | August 04, 2021
Channel: Hurricane Forecast Improvement Program
Date: 2021-08-12

Bo-Wen Shen
Following Lorenz’s studies (Lorenz 1963, 1969, 1972), deterministic, chaotic solutions, and closure- based, linearly unstable solutions have been an explicit and/or implicit focus for understanding predictability in weather and climate. In this talk, I will provide the physical relevance of findings within Lorenz models for real world problems by showing mathematical universality between the Lorenz and Pedlosky models (Shen 2021), as well as amongst the non-dissipative Lorenz model and the Duffing, the Nonlinear Schrodinger, and the Korteweg–de Vries equations (Shen 2020). Using the classical Lorenz 1963 (L63), 1969 (L69), and generalized Lorenz models (GLM) (e.g., Lorenz 1963, 1969; Shen 2014- 2019), I will then discuss various types of solutions, two kinds of attractor coexistence, time-varying multistability, and two types of sensitivities in support of the revised view on the dual nature of chaos and order in weather and climate (Shen et al. 2021a, b). The above findings are applied in order to revisit a conceptual model for illustrating the predictability dependence of medium-scale processes on the modulation of large-scale processes and aggregated feedback by small-scale processes. Examples from earlier studies, published by myself and collaborators, have examined the relationship between:

Tropical Cyclone (TC) Nargis (2008) and an equatorial Rossby wave (Shen et al. 2010a);
Twin TCs (2002) and a mixed Rossby gravity wave (Shen et al. 2012) during an active phase of a Madden–Julian oscillation (MJO);
Hurricane Helene (2006) and an intensifying African Easterly Wave (Shen et al. 2010b; Wu and Shen 2016; Shen 2019b); and
Hurricane Sandy (2012) and upper-level tropical waves associated with a MJO (Shen et al. 2013a; Shen et al. 2016).
At the end of the talk, I will discuss new opportunities and challenges in predictability research, including computational chaos and saturation dependence on various types of solution, in order to improve our understanding of the butterfly effect and predictions at extended-range time scales, including sub-seasonal to seasonal time scales.