Supermodelling as a high-level AI approach

Climate models are plagued by long-standing biases that degrade predictions. While increasing resolution of global climate models to km scales promises to reduce biases, there is little evidence so far of improvements with currently available computing power. Supermodelling is a high-level AI approach that combines existing models with machine learning. This alternative approach has demonstrated reductions in long-standing biases, such as the double ITCZ and tropical SST biases, at a fraction of the computational cost of km scale models. A supermodel is a combination of models that interact during their simulations to mitigate errors before they develop into large-scale biases. Here, I will present recent results from a supermodel based on three Earth System Models (NorESM, CESM, MPIESM). The models were combined using ocean data assimilation and trained on observed SST data. The simulation of tropical climate is markedly improved compared to that of the respective standalone models. We have performed the seasonal predictions using this supermodel and compared them with those from the standalone models. Our results show that while model biases are reduced, seasonal predictions are not necessarily improved. Reduction in biases, however, does lead to improved teleconnections, improving skill over some continental regions.