16:45 〜 17:00
[SCG55-35] Evaluation of vertical oceanic processes affecting seafloor pressure using MRI.COM and DONET observations
キーワード：Ocean bottom pressure records 、Oceanograhpic processes、MOVE/MRI.COM–JPN
The ocean bottom pressure (OBP) gauge enables the observation of vertical deformation of the seafloor. However, the OBP records include not only tectonic signals but also tides, instrumental drift, and non-tidal oceanographic fluctuations. Oceanographic fluctuations particularly obstruct high-accuracy tectonic signal observations when focusing on transient tectonic signals such as slow slip events (SSEs). The OBP derived from ocean global circulation models (OGCMs) can effectively reduce oceanographic fluctuations, as they do not include any tectonic signals. Thus, they are typically utilized to reduce oceanographic fluctuations in OBP data by subtracting the modeled time series from observations (e.g., Hino et al., 2014; Woods et al., 2022), although every model has the challenge of reproducibility. MOVE/MRI.COM–JPN (hereafter, MRI.COM; Hirose et al., 2020) is an OGCM with high spatial resolution and high reproducibility (Otsuka et al., in prep.). The horizontal structure of oceanic signals calculated by MRI.COM was also similar to the observations. Nevertheless, after subtracting the modeled pressure data by MRI.COM from observations, the fluctuations of several hPa still remain. Therefore, to evaluate the vertical structure of oceanic processes reproduced by the model and their respective influences on seafloor pressure, we examined the relationship between density variations in each layer of MRI.COM and seafloor pressure.
In this study, we focused on the off-Kii region because the spatiotemporal characteristics of the observed and modeled seafloor pressure data were well-evaluated (Otsuka et al., 2023; in prep.). MRI.COM provides potential temperature and salinity data with a spatial resolution of approximately 2 km across 60 layers from 2008 to 2019 (Hirose et al., 2020). We determined the potential density of each layer based on the provided temperature and salinity using TEOS-10. Additionally, we calculated the OBP from MRI.COM by integrating the density of the layers from the ocean floor to the sea surface. When computing OBP, we considered the anomaly of sea surface pressure, sea surface height, tidal effects, z* coordinate, and partial cell based on the topography of the seafloor (Sakamoto et al., 2019). We assessed the vertical and horizontal characteristics of these parameters at the DONET observation sites.
The density time series in adjacent layers were similar; however, the magnitude of density anomaly decreased with the increase of depth. The significant annual variation was remarkable in the shallower layers. To analyze the depth range affected by surface density variations, we calculated the correlation coefficients between the normalized density time series of different layers. The results indicated that although there were differences among observation points, the effect of density variations in the uppermost layer (layer 1) leveled off around layer 10 (sea depth: ~60 m). Below this depth, the correlation coefficient (CC) was either zero or negative around layer 40 (sea depth: ~900 m). This suggests that the influence of shallow layers does not extend further or that density variations compensating for those in the upper layers have been observed.
To evaluate the horizontal structure of the density, we applied empirical orthogonal function (EOF) to the density time series on the same layer. As a result, EOF1 exhibited components common across all observation sites, while EOF2 and EOF3 displayed spatial dependencies, such as those in the east-west direction. EOF2 of OBP showed a notable depth dependence (Otsuka et al., 2023); however, the influence of depth differences on the upper layers was considered minimal. Additionally, the contribution rate of EOF1 was ~97% in the surface layer and generally decreased with increasing depth, reaching approximately ~52% at layer 40. The large-scale processes in the surface layer did not synchronize with the variations in the deeper layers. In the presentation, we will also discuss the influence of sea surface pressure and height for a comprehensive evaluation of the oceanographic process on the seafloor.
In this study, we focused on the off-Kii region because the spatiotemporal characteristics of the observed and modeled seafloor pressure data were well-evaluated (Otsuka et al., 2023; in prep.). MRI.COM provides potential temperature and salinity data with a spatial resolution of approximately 2 km across 60 layers from 2008 to 2019 (Hirose et al., 2020). We determined the potential density of each layer based on the provided temperature and salinity using TEOS-10. Additionally, we calculated the OBP from MRI.COM by integrating the density of the layers from the ocean floor to the sea surface. When computing OBP, we considered the anomaly of sea surface pressure, sea surface height, tidal effects, z* coordinate, and partial cell based on the topography of the seafloor (Sakamoto et al., 2019). We assessed the vertical and horizontal characteristics of these parameters at the DONET observation sites.
The density time series in adjacent layers were similar; however, the magnitude of density anomaly decreased with the increase of depth. The significant annual variation was remarkable in the shallower layers. To analyze the depth range affected by surface density variations, we calculated the correlation coefficients between the normalized density time series of different layers. The results indicated that although there were differences among observation points, the effect of density variations in the uppermost layer (layer 1) leveled off around layer 10 (sea depth: ~60 m). Below this depth, the correlation coefficient (CC) was either zero or negative around layer 40 (sea depth: ~900 m). This suggests that the influence of shallow layers does not extend further or that density variations compensating for those in the upper layers have been observed.
To evaluate the horizontal structure of the density, we applied empirical orthogonal function (EOF) to the density time series on the same layer. As a result, EOF1 exhibited components common across all observation sites, while EOF2 and EOF3 displayed spatial dependencies, such as those in the east-west direction. EOF2 of OBP showed a notable depth dependence (Otsuka et al., 2023); however, the influence of depth differences on the upper layers was considered minimal. Additionally, the contribution rate of EOF1 was ~97% in the surface layer and generally decreased with increasing depth, reaching approximately ~52% at layer 40. The large-scale processes in the surface layer did not synchronize with the variations in the deeper layers. In the presentation, we will also discuss the influence of sea surface pressure and height for a comprehensive evaluation of the oceanographic process on the seafloor.