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• An ocean forcing dataset JRA55-do significantly improves nearshore warm SST biases. • The improvement mechanisms of the warm SST biases are quantitatively investigated. • Ocean forcing datasets are essential for ocean data assimilation systems. In the previous study, the authors have produced an eddy-resolving ocean ensemble analysis product called the local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) over the western North Pacific and Maritime Continent regions using an ocean data assimilation system driven by the Japanese operational atmospheric reanalysis dataset known as the JRA-55. However, the LORA includes warm biases in sea surface temperatures (SSTs) in coastal regions during the boreal winter. In this study, we perform sensitivity experiments with atmospheric forcing using an ocean forcing dataset known as the JRA55-do, which adjusts the JRA-55 to high-quality reference datasets to reduce biases and uncertainties. The results show that the nearshore warm SST biases are significantly improved by the JRA55-do. During the boreal autumn, the improvement comes from mainly two factors: (i) enhancement of surface cooling by latent heat releases caused by removing contamination of weak winds at the land grid cells, and (ii) weakening surface heating by downward shortwave radiation through the adjustment in the JRA55-do. During the boreal winter, enhanced cooling by the analysis increments suppresses the growth of the warm SST biases when the JRA55-do is used. However, if the JRA-55 dataset is used, the adaptive observation error inflation (AOEI) scheme acts negatively to keep the nearshore SST biases in winter. Based on the innovation statistics, the AOEI inflates the observation errors when the differences between the squared observation-minus-forecast innovations and the squared forecast ensemble spreads are larger than the prescribed observation error variance, and improves the accuracy in the open ocean, especially around the frontal regions. However, when substantial warm SST biases are formed in the previous season, AOEI's observation error inflation makes the analysis increments smaller and cannot suppress the warm biases. We also validate the analysis accuracy using various data such as sea surface height and horizontal velocities and find that the JRA55-do has significant advantages. Therefore, continuous maintenance and development of ocean forcing datasets are essential for ocean modeling and data assimilation. [ABSTRACT FROM AUTHOR]

Local ensemble transform Kalman filter (LETKF)‐based Ocean Research Analysis (LORA) version 1.0 datasets for western North Pacific (WNP) and Maritime Continent (MC) regions (LORA‐WNP and ‐MC, respectively) are released through the JAXA‐RIKEN Ocean Analysis website. The LORA datasets are created using an eddy‐resolving LETKF‐based ocean data assimilation system with satellite sea‐surface temperature, salinity, and height data and with in‐situ temperature and salinity data assimilated daily. The LORA datasets include 128‐member ensemble analyses at the sea surface (2D), each term of mixed‐layer temperature and salinity budget equations, and the related variables (2D) such as mixed‐layer depth and heat and freshwater fluxes as well as system grid information and analysis ensemble mean and spread (3D), from August 2015 to January 2024 (as of June 2024). The LORA datasets are useful for geoscience research and practical applications, especially for particle tracking, boundary conditions of atmospheric models, and research on spatiotemporal variations in sea‐surface temperature and salinity. [ABSTRACT FROM AUTHOR]