Richter, I., Ratnam, J.V., Martineau, P., Oettli, P., Doi, T., Ogata, T., Kataoka, T., Counillon, F. 2024: A simple statistical post-processing scheme for enhancing the skill of seasonal SST predictions in the tropics. Monthly Weather Review. https://doi.org/10.1175/MWR-D-23-0266.1

Summary: The prediction of year-to-year climate variability patterns, such as El Niño, offers potential benefits to society by aiding mitigation and adaptation efforts. Current prediction systems, however, may still have substantial room for improvement due to systematic model errors and due to imperfect initialization of the oceanic state at the start of predictions. Here we develop a statistical correction scheme to improve prediction skill after forecasts have been completed. The scheme shows some moderate success in improving the skill for predicting El Niño and similar climate patterns in seven prediction systems. Our results not only indicate a potential for improving prediction skill after the fact but also point to the importance of improving the way prediction systems are initialized.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.