EGU21-3826
https://doi.org/10.5194/egusphere-egu21-3826
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Evaluation of FIO-ESM v1.0 Seasonal Prediction Skills Over the North Pacific

Yajuan Song and Xunqiang Yin
Yajuan Song and Xunqiang Yin
  • First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China,

Accurate prediction over the North Pacific, especially for the key parameter of sea
surface temperature (SST), remains a challenge for short-term climate prediction. In
this study, seasonal predicted skills of the First Institute of Oceanography Earth System
Model version 1.0 (FIO-ESM v1.0) over the North Pacific were assessed. Ensemble
adjustment Kalman filter (EAKF) and Projection Optimal Interpolation (Projection-OI) data
assimilation schemes were used to provide initial conditions for FIO-ESM v1.0 hindcasts
that were started from the first day of each month between 1993 and 2017. Evolution
and spacial distribution of SST anomalies over the North Pacific were reasonably
reproduced in EAKF and Projection-OI assimilation output. Two hindcast experiments
show that the skill of FIO-ESM v1.0 with the EAKF data assimilation scheme to predict
SST over the North Pacific is considerably higher than that with Projection-OI data
assimilation for all lead times of 1–6 months, especially in the central North Pacific where
the subsurface ocean temperature in the initial conditions is significantly improved by
EAKF data assimilation. For the Kuroshio–Oyashio extension (KOE) region, the errors
in the initial conditions have more rapid propagation resulting in large discrepancies
between simulated and observed values, which are reduced by inducing surface
waves into the climate model. Incorporation of realistic initial conditions and reasonable
physical processes into the coupled model is essential to improving seasonal prediction
skill. These results provide a solid basis for the development of operational seasonal
prediction systems for the North Pacific.

How to cite: Song, Y. and Yin, X.: Evaluation of FIO-ESM v1.0 Seasonal Prediction Skills Over the North Pacific, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3826, https://doi.org/10.5194/egusphere-egu21-3826, 2021.

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