Search Results
Abstract
Experiments using OGCM output have been performed to assess sampling strategies for the Argo array in the Indian Ocean. The results suggest that spatial sampling is critical for resolving intraseasonal oscillations in the upper ocean, that is, about 500 km in the zonal and about 100 km in the equatorial meridional direction. Frequent temporal sampling becomes particularly important in dynamically active areas such as the western boundary current regime and the equatorial waveguide. High-frequency sampling is required in these areas to maintain an acceptable signal-to-noise ratio, suggesting a minimum sampling interval of 5 days for capturing intraseasonal oscillations in the upper Indian Ocean. Sampling of seasonal and longer-term variability down to 2000-m depth is less critical within the range of sampling options of Argo floats, as signal-to-noise ratios for sampling intervals up to about 20 days are almost always larger than one. However, these results are based on a single OGCM and are subject to model characteristics and errors. Based on a coordinated effort, results from various models could provide more robust estimates by minimizing the impact of individual model errors on sampling strategies.
Abstract
Experiments using OGCM output have been performed to assess sampling strategies for the Argo array in the Indian Ocean. The results suggest that spatial sampling is critical for resolving intraseasonal oscillations in the upper ocean, that is, about 500 km in the zonal and about 100 km in the equatorial meridional direction. Frequent temporal sampling becomes particularly important in dynamically active areas such as the western boundary current regime and the equatorial waveguide. High-frequency sampling is required in these areas to maintain an acceptable signal-to-noise ratio, suggesting a minimum sampling interval of 5 days for capturing intraseasonal oscillations in the upper Indian Ocean. Sampling of seasonal and longer-term variability down to 2000-m depth is less critical within the range of sampling options of Argo floats, as signal-to-noise ratios for sampling intervals up to about 20 days are almost always larger than one. However, these results are based on a single OGCM and are subject to model characteristics and errors. Based on a coordinated effort, results from various models could provide more robust estimates by minimizing the impact of individual model errors on sampling strategies.
Static and Dynamic Performance of the RBRargo
3 CTDAbstract
The static and dynamic performances of the RBRargo 3 are investigated using a combination of laboratory-based and in situ datasets from floats deployed as part of an Argo pilot program. Temperature and pressure measurements compare well to co-located reference data acquired from shipboard CTDs. Static accuracy of salinity measurements is significantly improved using 1) a time lag for temperature, 2) a quadratic pressure dependence, and 3) a unit-based calibration for each RBRargo 3 over its full pressure range. Long-term deployments show no significant drift in the RBRargo 3 accuracy. The dynamic response of the RBRargo 3 demonstrates the presence of two different adjustment time scales: a long-term adjustment O(120) s, driven by the temperature difference between the interior of the conductivity cell and the water, and a short-term adjustment O(5–10) s, associated to the initial exchange of heat between the water and the inner ceramic. Corrections for these effects, including dependence on profiling speed, are developed.
Abstract
The static and dynamic performances of the RBRargo 3 are investigated using a combination of laboratory-based and in situ datasets from floats deployed as part of an Argo pilot program. Temperature and pressure measurements compare well to co-located reference data acquired from shipboard CTDs. Static accuracy of salinity measurements is significantly improved using 1) a time lag for temperature, 2) a quadratic pressure dependence, and 3) a unit-based calibration for each RBRargo 3 over its full pressure range. Long-term deployments show no significant drift in the RBRargo 3 accuracy. The dynamic response of the RBRargo 3 demonstrates the presence of two different adjustment time scales: a long-term adjustment O(120) s, driven by the temperature difference between the interior of the conductivity cell and the water, and a short-term adjustment O(5–10) s, associated to the initial exchange of heat between the water and the inner ceramic. Corrections for these effects, including dependence on profiling speed, are developed.
