Description: 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.

Description: Aim: Species distribution models (SDMs) are essential tools in ecology and conservation. However, the scarcity of visual sightings of marine mammals in remote polar areas hinders the effective application of SDMs there. Passive acoustic monitoring (PAM) data provide year-round information and overcome foul weather limitations faced by visual surveys. However, the use of PAM data in SDMs has been sparse so far. Here, we use PAM-based SDMs to investigate the spatiotemporal distribution of the critically endangered Antarctic blue whale in the Weddell Sea. Location: The Weddell Sea. Methods: We used presence-only dynamic SDMs employing visual sightings and PAM detections in independent models. We compared the two independent models with a third combined model that integrated both visual and PAM data, aiming at leveraging the advantages of each data type: the extensive spatial extent of visual data and the broader temporal/environmental range of PAM data. Results: Visual and PAM data prove complementary, as indicated by a low spatial overlap between daily predictions and the low predictability of each model at detections of other data types. Combined data models reproduced suitable habitats as given by both independent models. Visual data models indicate areas close to the sea ice edge (SIE) and with low-to-moderate sea ice concentrations (SIC) as suitable, while PAM data models identified suitable habitats at a broader range of distances to SIE and relatively higher SIC. Main Conclusions: The results demonstrate the potential of PAM data to predict year-round marine mammal habitat suitability at large spatial scales. We provide reasons for discrepancies between SDMs based on either data type and give methodological recommendations on using PAM data in SDMs. Combining visual and PAM data in future SDMs is promising for studying vocalized animals, particularly when using recent advances in integrated distribution modelling methods.