11:20 〜 11:35
[ACG44-03] The SINTEX-F seasonal prediction system and some experiments to understand the role of ocean observations
キーワード：季節予測、熱帯
We have been developing a dynamical seasonal prediction system based on a climate model called the Scale Interaction Experiment-Frontier (SINTEX-F) under the EU–Japan collaborative framework. It has demonstrated high skills in the prediction of climate phenomena in the tropical Pacific and Indian Oceans (e.g., El Niño/Southern Oscillation (ENSO), ENSO-Modoki, and Indian Ocean Dipole (IOD) events) and their teleconnections.
Based on the system, we conducted two reforecast experiments to understand the role of subsurface ocean measurements: one used only SST for the initialization, and the other used SST plus subsurface temperature and salinity aggregated from in situ observations, such as XBTs, moored buoys, and Argo floats. Although the ENSO prediction skill did not change significantly between the two experiments, the IOD prediction skill was significantly improved.
We also found that the prediction of regional sea surface temperatures around the Arafura Sea was significantly improved at 3–4 months' lead time by adding the assimilation of subsurface temperature measurements obtained from sea turtles.
Very recently, we also explored the impacts of interannual variations of surface chlorophyll on seasonal predictions of the tropical Pacific by the SINTEX-F2 dynamical climate prediction system. The improvements are noticeable in the predictions of sea surface temperature over the eastern edge of the Western Pacific Warm Pool.
Those results showed that enhancement of the existing Global Ocean Observing System is crucial to improving global/regional seasonal prediction.
Based on the system, we conducted two reforecast experiments to understand the role of subsurface ocean measurements: one used only SST for the initialization, and the other used SST plus subsurface temperature and salinity aggregated from in situ observations, such as XBTs, moored buoys, and Argo floats. Although the ENSO prediction skill did not change significantly between the two experiments, the IOD prediction skill was significantly improved.
We also found that the prediction of regional sea surface temperatures around the Arafura Sea was significantly improved at 3–4 months' lead time by adding the assimilation of subsurface temperature measurements obtained from sea turtles.
Very recently, we also explored the impacts of interannual variations of surface chlorophyll on seasonal predictions of the tropical Pacific by the SINTEX-F2 dynamical climate prediction system. The improvements are noticeable in the predictions of sea surface temperature over the eastern edge of the Western Pacific Warm Pool.
Those results showed that enhancement of the existing Global Ocean Observing System is crucial to improving global/regional seasonal prediction.