On the Predictability of Short-term Climate Simulations of African Easterly Waves within a Global Mesoscale Model: A View with a Generalized Lorenz Model

Recent advances in global weather/climate models and generalized Lorenz models have revealed opportunities for examining the role of butterfly effects as well as multiscale processes in the predictability of (high-impact) weather systems. In this study, we first provide a brief summary on the recent case studies with a remarkable predictability achieved using a global mesoscale model. We then present and apply a generalized Lorenz model (GLM) with many modes to improve our understanding of the predictability when a strong forcing is present.

Previously, we have shown that a global mesoscale model is capable of producing realistic 30-days simulations for the initiation and propagation of six consecutive AEWs in late August 2006 as well as the mean state of the African easterly jet (AEJ) over both Africa and downstream in the tropical Atlantic (e.g., Shen et al., 2010). Results suggest that (1) accurate representations of non-linear interactions between the atmosphere and land processes are crucial for improving the simulations of the AEWs and the AEJ; (2) improved simulations of an individual AEW and its interaction with local environments (e.g., the Guinea Highlands) could provide determinism for TC formation downstream. To assure the validity of the simulations, we calculated correlation coefficients from the 30-day simulations and then revealed the downscaling processes associated with large-scale waves using the parallel ensemble empirical mode decomposition (PEEMD) in a 10-year data analysis (Wu and Shen, 2016; Shen et al., 2017). To provide the theoretical foundation for the remarkable predictability, we applied the GLM to reveal the role of the nonlinear feedback loop (NFL) in providing aggregated negative feedback to stabilize solutions in higher-dimensional Lorenz models (e.g., Shen 2014) and in producing nonlinear oscillatory components (e.g., non-chaotic limit cycle solutions). All of the aforementioned results lead to the view of "weather is a superset that consists of chaotic and non-chaotic processes." (e.g., Shen et al., 2018). Detailed discussions using the global mesoscale model and the generalized Lorenz model will be provided.

References: (https://bwshen.sdsu.edu/shen_selected.html)