Search

Polar and subpolar oceans play a particularly important role in the global climate and its temporal changes, yet these regions are less well sampled than the rest of the global ocean. To better understand the physical or biogeochemical properties and their variabilities in these regions, accurate data mapping is crucial. In this paper, we introduce a mapping methodology that includes a water column shrinking and stretching constraint (SSC) based on the principle of conservation of potential vorticity. To demonstrate the mapping scheme efficiency, we map the ocean temperature in the southern Sea of Okhotsk, where the bottom topography comprises a broad and shallow shelf, a sharp continental slope, and a deep oceanic basin. Such topographic features are typical of polar and subpolar marginal seas. Results reveal that the SSC integrated (SSCI) mapping strongly reduces the mapping error in the broad and shallow shelf compared with a recently introduced topographic constraint integrated (TCI) mapping procedure. We also tested our mapping scheme in the Southern Ocean, which has a comparatively slanted shelf, a wider and gentler slope, and a deep and broad oceanic basin. We found that the SSCI and TCI methods are practically equivalent there. The SSCI mapping is thus an effective method to map the ocean's properties in various topographic environments and should be adequate in all polar and subpolar regions. Importantly, we introduced a standardized procedure for determining the decorrelation length scales—a necessary step prior to implementing any mapping scheme—in any topographic conditions. [ABSTRACT FROM AUTHOR]

Long-term quantification of sea ice production in coastal polynyas (thin sea ice areas) is an important issue to understand the global overturning circulation and its changes. The Special Sensor Microwave Imager (SSM/I), which has nearly 30 years of observation, is a powerful tool for that purpose owing to its ability to detect thin ice areas. However, previous SSM/I thin ice thickness algorithms differ between regions, probably due to the difference in dominant type of thin sea ice in each region. In this study, we developed an SSM/I thin ice thickness algorithm that accounts for three types of thin sea ice (active frazil, thin solid ice, and a mixture of two types), using the polarization and gradient ratios. The algorithm is based on comparison with the ice thickness derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for 22 polynya events off the Ross Ice Shelf, off Cape Darnley, and off the Ronne Ice Shelf in the Southern Ocean. The algorithm can properly discriminate the ice type in coastal polynyas and estimate the thickness of thin sea ice (≤20 cm) with an error range of less than 6 cm. We also confirmed that the algorithm can be applied to other passive microwave radiometers with higher spatial resolution to obtain more accurate and detailed distributions of ice type and thickness. The validation of this algorithm in the Arctic Ocean suggests its applicability to the global oceans. [ABSTRACT FROM AUTHOR]

The quantification of sea ice production in coastal polynyas is a key issue to understand the global climate system. In this study, we directly compared Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data with the sea ice thickness distribution obtained from a mooring observation during the winter of 2003 off Sakhalin in the Sea of Okhotsk to evaluate the algorithm for estimation of sea ice thickness in coastal polynyas. By using thermal ice thickness as a target physical quantity, we found that the obtained relationship between the polarization ratio (PR) and ice thickness can provide an appropriate AMSR-E algorithm to estimate thin ice thickness, irrespective of the uniform or nonuniform ice thickness field. The relationship between the PR value and thermal ice thickness is likewise consistent with the local PR–thickness relationship that is observed at individual ice floes. This is because both the PR value and thermal ice thickness are more sensitive to thinner ice. Furthermore, we evaluated the method for detection of active frazil in a coastal polynya by comparing with the mooring data, and subsequently modified it to classify the coastal polynya into three thin ice types, namely, active frazil, thin solid ice, and mixed ice (mixture of active frazil and thin solid ice). The improved algorithm successfully represents the thermal ice thickness even for a relatively small-scale polynya off Sakhalin and is expected to be useful for better quantification of sea ice production in the global ocean owing to its high versatility. [ABSTRACT FROM AUTHOR]

This study investigated a method for creating a climatological dataset with improved reproducibility and reliability for the Southern Ocean. Despite sparse observational sampling, the Southern Ocean has a dominant physical characteristic of a strong topographic constraint formed under weak stratification and strong Coriolis effect. To increase the fidelity of gridded data, the topographic constraint is incorporated into the interpolation method, the weighting function of which includes a contribution from bottom depth differences and horizontal distances. Spatial variability of physical properties was also analyzed to estimate a realistic decorrelation scale for horizontal distance and bottom depth differences using hydrographic datasets. A new gridded dataset, the topographic constraint incorporated (TCI), was then developed for temperature, salinity, and dissolved oxygen, using the newly derived weighting function and decorrelation scales. The root-meansquare (RMS) of the difference between the interpolated values and the neighboring observed values (RMS difference) was compared among available gridded datasets. That the RMS differences are smaller for the TCI than for the previous datasets by 12%-21% and 8%-20% for potential temperature and salinity, respectively, demonstrates the effectiveness of incorporating the topographic constraint and realistic decorrelation scales. Furthermore, a comparison of decorrelation scales and an analysis of interpolation error suggests that the decorrelation scales adopted in previous gridded datasets are 2 times or more larger than realistic scales and that the overestimation would increase the interpolation error. The interpolation method proposed in this study can be applied to other high-latitude oceans, which are weakly stratified but undersampled. [ABSTRACT FROM AUTHOR]

Antarctic coastal polynyas are important areas of high sea ice production and dense water formation, and thus their detection including an estimate of thin ice thickness is essential. In this paper, the authors propose an algorithm that estimates thin ice thickness and detects fast ice using Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) data in the Antarctic Ocean. Detection and estimation of sea ice thicknesses of <0.2 m are based on the SSM/I 85- and 37-GHz polarization ratios (PR85 and PR37) through a comparison with sea ice thicknesses estimated from the Advanced Very High Resolution Radiometer (AVHRR) data. The exclusion of data affected by atmospheric water vapor is discussed. Because thin ice and fast ice (specifically ice shelves, glacier tongues, icebergs, and landfast ice) have similar PR signatures, a scheme was developed to separate these two surface types before the application of the thin ice algorithm to coastal polynyas. The probability that the algorithm correctly distinguishes thin ice from thick ice and from fast ice is ∼95%, relative to the ice thicknesses estimated from AVHRR. Although the standard deviation of the difference between the thin ice thicknesses estimated from the SSM/I algorithm and AVHRR is ∼0.05 m and thus not small, the estimated ice thicknesses from the microwave algorithm appear to have small biases and the accuracies are independent of region and season. A distribution map of thin ice occurrences derived from the SSM/I algorithm represents the Ross Sea coastal polynya being by far the largest among the Antarctic coastal polynyas; the Weddell Sea coastal polynyas are much smaller. Along the coast of East Antarctica, coastal polynyas frequently form on the western side of peninsulas and glacier tongues, downstream of the Antarctic Coastal Current. [ABSTRACT FROM AUTHOR]