Seasonal, synoptic, and intraseasonal variability of the West African monsoon

2012 Fall.Includes bibliographical references.The simulation of the West African monsoon is examined in two coupled general circulation models (CGCMs). The first model is the standard Community Climate System Model (CCSM) which uses traditional parameterizations to represent convective processes. The second model is the superparameterized-CCSM (SP-CCSM), in which convective parameterizations have been replaced by embedding a two-dimensional cloud resolving model into each gridbox. Superparameterization is intended to improve simulation of the complex multiscale interactions that occur between the large-scale environment and clouds. Key features of West African climate are analyzed in both models including: the mean annual cycle of the monsoon, African easterly wave (AEW) activity and dynamics, and the intraseasonal modulation of precipitation. Adding superparameterization improves the position and intensity of the summer maximum in precipitation which is shifted from over the Gulf of Guinea in CCSM (not realistic), to over the continent in SP-CCSM which is in keeping with the observations. AEWs and their relationship with convection are also improved in the SP-CCSM: In the standard model, little to no easterly wave activity occurs over West Africa, and the relationship with convection is tenuous at best. SP-CCSM on the other hand produces strong AEWs over the region that exhibit similar horizontal and vertical structures to observations. AEWs in SP-CCSM are strongly coupled to convection, more so than is supported by observations. An examination of the energetics of the simulated AEWs suggests that convection drives the generation and propagation the waves in SP- CCSM. Consistent with observations, intraseasonal variations in West African precipitation in SP-CCSM appear to be linked to variations in convection in the Indo-Pacific region corresponding with the MJO and the Indian monsoon. Because of these physically-realistic relationships, SP-CCSM has potential to deepen our understanding of the teleconnections between the MJO and West Africa, helping to improve seasonal rainfall forecasts

Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations

We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differential equations (SDE) for stochastic modeling. The ideal LES models the LES flow by treating each full-order trajectory as a random realization of the underlying dynamics, as such, the effect of small-scales is marginalized to obtain the deterministic evolution of the LES state. However, ideal LES is analytically intractable. In our work, we use a latent neural SDE to model the evolution of the stochastic process and an encoder-decoder pair for transforming between the latent space and the desired ideal flow field. This stands in sharp contrast to other types of neural parameterization of closure models where each trajectory is treated as a deterministic realization of the dynamics. We show the effectiveness of our approach (niLES - neural ideal LES) on a challenging chaotic dynamical system: Kolmogorov flow at a Reynolds number of 20,000. Compared to competing methods, our method can handle non-uniform geometries using unstructured meshes seamlessly. In particular, niLES leads to trajectories with more accurate statistics and enhances stability, particularly for long-horizon rollouts.Comment: 18 page