Does Increasing Horizontal Resolution Improve Seasonal Prediction of Indian Summer Monsoon?: A Climate Forecast System Model Perspective

The seasonal prediction skill of climate forecast system model with two different resolutions, namely T126 and T382, is studied using hindcast data. Using novel diagnostic tools such as total variation distance and two-state Markov Chain analysis, it is shown that increasing the horizontal resolution of the model has minimal impact on the quality of seasonal prediction. The underlying rain distribution and associated transition probabilities are very similar in both model versions. The Markov chain analysis also provides critical clues about the issues associated with convective processes in the model. Both the models produce longer (shorter) wet (dry) spells compared to the observations. Models are trying to bring the atmosphere closer to convective quasi-equilibrium, leading to a substantial departure from observed features. Although the conventional error metrics are helpful to assess the prediction skills, the new metrics used in the study provide further insights on possible pathways to improve model moist physics.