13:45 〜 15:15
[MGI26-P11] Impact of atmospheric forcing on SST biases in the LETKF-based Ocean Research Analysis (LORA)
キーワード：海洋データ同化、大気強制、海面水温、混合層水温収支解析、適応型観測誤差膨張
Various ocean analysis products have been produced by research institutions and colleges and used for geoscience research. In the Pacific region, to our best knowledge, there are four high-resolution regional analysis datasets [JCOPE2M (Miyazawa et al. 2017) and FRA-ROMS II (Kuroda et al. 2017) with 3D-VAR; NPR-4DVAR (Hirose et al. 2019); and DREAMS with Kalman filter (Hirose et al. 2013)], but there are no EnKF-based analysis datasets.
Recently geostationary satellites have provided sea surface temperatures (SSTs) at higher spatiotemporal resolution than before. To take advantage of such observations, we have developed an EnKF-based ocean data assimilation system with a short assimilation interval of 1 day, and demonstrated that the combination of three schemes [incremental analysis update (IAU; Bloom et al. 1996), relaxation-to-prior perturbation (RTPP; Zhang et al. 2004), and adaptive observation error inflation (AOEI; Minamide and Zhang 2017)] significantly improves geostrophic balance and analysis accuracy (Ohishi et al. 2022a, b). With the recent enhancement of computational resources, we have developed higher-resolution ocean data assimilation systems sufficient to resolve fronts and eddies and produced ensemble analysis products in the western North Pacific (WNP) and Maritime Continent (MC) regions referred to as the LETKF-based Ocean Research Analysis (LORA)-WNP and -MC, respectively (Ohishi et al. in review). The validation results show that the LORA has sufficient accuracy for geoscience research. However, high SST biases over 1.0 degree C are detected near the coastal regions, where coarse atmospheric reanalysis datasets might not accurately capture the coastlines. Therefore, this study aims to investigate the impacts of atmospheric forcing on the nearshore SST biases and to examine the mechanisms of the improvement of the SST biases.
We have conducted sensitivity experiments using JRA-55 (Kobayashi et al. 2015) and JRA-55do (Tsujino et al. 2018) with horizontal resolution of 1.25 and 0.5 degree, respectively, which are referred to as the JRA55 and JRA55do runs. We note that the setting of the JRA55 run is the same as Ohishi et al. (in review) and that JRA-55do is a surface atmospheric dataset for driving ocean-sea ice models and is created by adjusting JRA-55 toward reference datasets such as CERES-EBAF-Surface_Ed2.8 data (Kako et al. 2013).
The validation results show that the SST biases and RMSDs relative to assimilated satellite and independent in-situ coastal data are improved in the JRA55do run, especially near the coastal regions. The mixed layer temperature budget analysis indicates that stronger latent heat release by nearshore stronger wind speed and weaker downward shortwave radiation by the adjustment in JRA-55do is the main cause of the improvement of the high SST biases in September-October. This results in further improvement in November-January, because the smaller absolute innovation reduces the frequency of the AOEI application. As a result, cooling in the analysis increments is stronger in the JRA55do run. This study indicates the importance of the quality of atmospheric forcing for EnKF-based ocean data assimilation systems. It would be important to have access to surface atmospheric datasets for driving ocean-sea ice models.
Recently geostationary satellites have provided sea surface temperatures (SSTs) at higher spatiotemporal resolution than before. To take advantage of such observations, we have developed an EnKF-based ocean data assimilation system with a short assimilation interval of 1 day, and demonstrated that the combination of three schemes [incremental analysis update (IAU; Bloom et al. 1996), relaxation-to-prior perturbation (RTPP; Zhang et al. 2004), and adaptive observation error inflation (AOEI; Minamide and Zhang 2017)] significantly improves geostrophic balance and analysis accuracy (Ohishi et al. 2022a, b). With the recent enhancement of computational resources, we have developed higher-resolution ocean data assimilation systems sufficient to resolve fronts and eddies and produced ensemble analysis products in the western North Pacific (WNP) and Maritime Continent (MC) regions referred to as the LETKF-based Ocean Research Analysis (LORA)-WNP and -MC, respectively (Ohishi et al. in review). The validation results show that the LORA has sufficient accuracy for geoscience research. However, high SST biases over 1.0 degree C are detected near the coastal regions, where coarse atmospheric reanalysis datasets might not accurately capture the coastlines. Therefore, this study aims to investigate the impacts of atmospheric forcing on the nearshore SST biases and to examine the mechanisms of the improvement of the SST biases.
We have conducted sensitivity experiments using JRA-55 (Kobayashi et al. 2015) and JRA-55do (Tsujino et al. 2018) with horizontal resolution of 1.25 and 0.5 degree, respectively, which are referred to as the JRA55 and JRA55do runs. We note that the setting of the JRA55 run is the same as Ohishi et al. (in review) and that JRA-55do is a surface atmospheric dataset for driving ocean-sea ice models and is created by adjusting JRA-55 toward reference datasets such as CERES-EBAF-Surface_Ed2.8 data (Kako et al. 2013).
The validation results show that the SST biases and RMSDs relative to assimilated satellite and independent in-situ coastal data are improved in the JRA55do run, especially near the coastal regions. The mixed layer temperature budget analysis indicates that stronger latent heat release by nearshore stronger wind speed and weaker downward shortwave radiation by the adjustment in JRA-55do is the main cause of the improvement of the high SST biases in September-October. This results in further improvement in November-January, because the smaller absolute innovation reduces the frequency of the AOEI application. As a result, cooling in the analysis increments is stronger in the JRA55do run. This study indicates the importance of the quality of atmospheric forcing for EnKF-based ocean data assimilation systems. It would be important to have access to surface atmospheric datasets for driving ocean-sea ice models.