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- W4220991036 abstract "<p>We combine sea surface temperature (SST) and OHC indices derived from the gridded datasets to construct a phase space for data-driven ENSO models. Using a Bayesian optimization method, we construct linear as well as nonlinear models for these indices. We show that the joint SST-OHC optimal models yield significant benefits in predicting both the SST and OHC as compared with the separate SST or OHC models. It is shown that these models substantially reduces seasonal predictability barriers in each variable &#8211; the spring barrier in the SST index and the winter barrier in the OHC index. We find significant nonlinear relationships between the ENSO variables manifesting on interannual scales, which opens prospects for improving yearly ENSO forecasting. The work is supported by he Russian Science Foundation (grant #20-62-46056).</p>" @default.
- W4220991036 created "2022-04-03" @default.
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- W4220991036 date "2022-03-28" @default.
- W4220991036 modified "2023-10-14" @default.
- W4220991036 title "Studying ENSO nonlinearity using observations of SST and ocean heat content" @default.
- W4220991036 doi "https://doi.org/10.5194/egusphere-egu22-11745" @default.
- W4220991036 hasPublicationYear "2022" @default.
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