SUPERPARAMETERIZATION - A Promise towards a better Global Climate Model : An Indian Summer Monsoon perspective

The atmosphere is comprised of convection of all scales ranging from few kilometers to several thousands of kilometers. In a numerical model irrespective of its kind e. g. General Circulation Model (GCM) etc., the scale which is bigger than the grid resolution are resolved and rest all remain sub-grid and unresolved. The model uncertainties are largely attributed to the parameterization of sub-grid scale convection, although the simulation of the resolved large-scale processes too shows biases. This is because of the fact that there is no scale separation between the resolved scales and the unresolved sub-grid scales. Henceforth, the efficiency of all the models depends on their ability to realistically represent these sub-grid scale processes. Because of the multiscale character of organized convection, it has always been a challenge for the GCMs to simulate the tropical precipitation. Traditionally, the way to account for the sub-grid scale processes in a GCM is through parameterization, which is to include the effect of clouds as a function of large scale parameters. We here discuss a newer approach, the Superparameterization (SP) or the Multiscale Modelling Framework (MMF). The MMF guarantees better resolved sub-grid scale processes in a GCM, through a few embedded Cloud Resolving Models in each grid of the GCM, compared to the traditional parameterization. Superparameterized models, though computationally a little demanding, are a good compromise between the coarse resolution GCMs and highly computationally expensive global cloud resolving models. Most importantly, the MMF approach serves as a "laboratory" to better understand the shortcomings in models with traditional convective-parameterization. We here intend to discuss the fidelity of MMF to represent the unresolved sub-grid scale convective processes in an earth system model by analyzing the simulated Indian summer monsoon and its variability. We have taken up two version of the same global climate model, one uncoupled,SPCAM (AMIP style run) and the other coupled,SPCCSM, both superparameterized. The motive behind this is, to assess the impact of superparameterization in a greater detail with coupling on and off. We not only analyze the superparameterized model output for Indian summer monsoon, but also explore the root cause behind the biases and propose a way to correct them.