The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

1. Introduction
2. Data and Methods
    2.1. GloSea5 hindcast and ARGO float data
    2.2. Data preprocessing
    2.3. Brier score (BS) and Brier skill score (BSS)
3. Results
    3.1. Evaluation of forecasting performancein GloSea5
    3.2. BS and BSS in GloSea5
4. Conclusion
Acknowledgment
References

• Jieun Wie(Division of Science Education and Institute of Fusion Science, Jeonbuk National University, Jeonju 54896, Korea)
• Jae-Young Byon(Forecast Research Department, National Institute of Meteorological Sciences, Seogwipo 63568, Korea)
• Byung-Kwon Moon(Division of Science Education and Institute of Fusion Science, Jeonbuk National University, Jeonju 54896, Korea) Corresponding author