Ocean Bottom Pressure Variability: Can It Be Reliably Modeled?

Abstract

Ocean bottom pressure (OBP) variability serves as a proxy of ocean mass variability, the knowledge of which is needed in geophysical applications. The question of how well it can be modeled by the present general ocean circulation models on time scales in excess of 1 day is addressed here by comparing the simulated OBP variability with the observed one. To this end, a new multiyear data set is used, obtained with an array of bottom pressure gauges deployed deeply along a transect across the Southern Ocean. We present a brief description of OBP data and show large‐scale correlations over several thousand kilometers at all time scales using daily and monthly averaged data. Annual and semiannual cycles are weak. Close to the Agulhas Retroflection, signals of up to 30 cm equivalent water height are detected. Further south, signals are mostly intermittent and noisy. It is shown that the models simulate consistent patterns of bottom pressure variability on monthly and longer scales except for areas with high mesoscale eddy activity, where high resolution is needed to capture the variability due to eddies. Furthermore, despite good agreement in the amplitude of variability, the in situ and simulated OBP show only modest correlation.

Convergence of Daily GRACE Solutions and Models of Submonthly Ocean Bottom Pressure Variability

“…Neither dataset gives access to the full spectrum of rapid p b variability relevant to GRACE. Coverage with seafloor pressure measurements is sparse in both space and time, and variance of particular deployments may reflect eddies or other localized effects (Androsov et al., 2020). In contrast, satellite altimetry provides good sampling over much of the ocean, but differences with modeled bottom pressure are not easily interpreted in a quantitative sense where baroclinic signals contribute to observed sea‐level changes.…”

On the seasonal variations of ocean bottom pressure in the world oceans

“…In the tropical western Pacific (Region A) and southeastern South Indian Ocean (Region B), the annual cycle of OBP, sea level and steric anomalies simulated by the model are quite similar to the observations (Figs. 2 and 5) and results based on volume-conserving models 1a) (e.g., Ponte 1999;Ponte et al 2007;Bingham and Hughes 2008;Köhl et al 2012;Kuhlmann et al 2013;Poropat et al 2018;Androsov et al 2020). Figures 4 and 5 indicate that the PCOM can simulate the seasonal cycle of regional OBP quite well; thus, this model can be used to study the dynamics of OBP variability.…”

“…Besides satellite and hydrographic observations, numerical models can also serve as good tools for the study of sea level variations and correlative physical processes. However, most currently used models are based on the Boussinesq approximations (e.g., Ponte 1999;Ponte et al 2007;Bingham and Hughes 2008;Köhl et al 2012;Kuhlmann et al 2013;Poropat et al 2018;Androsov et al 2020); such models cannot properly represent the OBP changes associated with thermal expansion or contraction. Surface heating gives rise to decline of bottom pressure in the Boussinesq models; while in a mass-conserving model surface heating/cooling does not directly change bottom pressure.…”

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

“…The column water mass on the combined ocean and atmosphere is called the Ocean Bottom Pressure (OBP) [171]. OBP variability is due to three important factors [172]: (1) variations in wind stress, curl, and circulation (this is called internal ocean mass redistribution), (2) water mass entering and leaving the ocean (this is considered as part of the global water cycle), and (3) atmospheric mass exchange between the ocean and land. Although direct measurement of OBP is difficult, GRACE can effectively measure global OBP (see Fig.…”