Improving

By N. Loader. 2018

. While dynamical (cid:58)(cid:58)(cid:58)(cid:58) Earth (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) system (cid:58) models are essential for seasonal Arctic sea ice prediction, they often exhibit significant errors that are challenging to correct. In this study, we integrate a multilayer perceptron (MLP) machine learning (ML) model into the Norwegian Climate Prediction Model (NorCPM) to improve seasonal sea ice predictions. We compare the online and offline error correction approaches. In the online approach, ML corrects errors in the model’s instantaneous state during the model simulation, while in the offline approach, ML post-processes and calibrates predictions after the model simulation. Our 5 results show that the ML models effectively learn and correct (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) dynamical (cid:58) model errors in both methods (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) approaches, leading to improved predictions of Arctic sea ice during test periods (cid:58)(cid:58)(cid:58) the (cid:58)(cid:58)(cid:58) test (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) period (i.e., 2003-2021). Both methods (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) approaches

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1. O. Güven
2. E. Moura
3. J. Drelich
4. S. Monteiro
5. Frederik Benzing
6. Mehdi Abbana Bennani
7. Liyuan Wang

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