Heat flow from the Southeast Indian Ridge flanks between 80°E and 140°E: Data review and analysis - art. no. B01101

We analyze available heat flow data from the flanks of the Southeast Indian Ridge adjacent to or within the Australian-Antarctic Discordance (AAD), an area with patchy sediment cover and highly fractured seafloor as dissected by ridge- and fracture-parallel faults. The data set includes 23 new data points collected along a 14-Ma old isochron and 19 existing measurements from the 20- to 24-Ma old crust. Most sites of measurements exhibit low heat flux (from 2 to 50 mW m(-2)) with near-linear temperature-depth profiles except at a few sites, where recent bottom water temperature change may have caused nonlinearity toward the sediment surface. Because the igneous basement is expected to outcrop a short distance away from any measurement site, we hypothesize that horizontally channelized water circulation within the uppermost crust is the primary process for the widespread low heat flow values. The process may be further influenced by vertical fluid flow along numerous fault zones that crisscross the AAD seafloor. Systematic measurements along and across the fault zones of interest as well as seismic profiling for sediment distribution are required to confirm this possible, suspected effect.

The EMSO-ERIC Pan-European Consortium: Data Benefits and Lessons Learned as the Legal Entity Forms

The European Multidisciplinary Seafloor and water-column Observatory (EMSO) European Research Infrastructure Consortium (ERIC) provides power, communications, sensors, and data infrastructure for continuous, high-resolution, (near-)real-time, interactive ocean observations across a multidisciplinary and interdisciplinary range of research areas including biology, geology, chemistry, physics, engineering, and computer science, from polar to subtropical environments, through the water column down to the abyss. Eleven deep-sea and four shallow nodes span from the Arctic through the Atlantic and Mediterranean, to the Black Sea. Coordination among the consortium nodes is being strengthened through the EMSOdev project (H2020), which will produce the EMSO Generic Instrument Module (EGIM). Early installations are now being upgraded, for example, at the Ligurian, Ionian, Azores, and Porcupine Abyssal Plain (PAP) nodes. Significant findings have been flowing in over the years; for example, high-frequency surface and subsurface water-column measurements of the PAP node show an increase in seawater pCO2 (from 339 μatm in 2003 to 353 μatm in 2011) with little variability in the mean air-sea CO2 flux. In the Central Eastern Atlantic, the Oceanic Platform of the Canary Islands open-ocean canary node (aka ESTOC station) has a long-standing time series on water column physical, biogeochemical, and acidification processes that have contributed to the assessment efforts of the Intergovernmental Panel on Climate Change (IPCC). EMSO not only brings together countries and disciplines but also allows the pooling of resources and coordination to assemble harmonized data into a comprehensive regional ocean picture, which will then be made available to researchers and stakeholders worldwide on an open and interoperable access basis.

Description of the Argo GDAC File Checks: Data Format and Consistency Checks

Every Argo data file submitted by a DAC for distribution on the GDAC has its format and data consistency checked by the Argo FileChecker. Two types of checks are applied: 1. Format checks. Ensures the file formats match the Argo standards precisely. 2. Data consistency checks. Additional data consistency checks are performed on a file after it passes the format checks. These checks do not duplicate any of the quality control checks performed elsewhere. These checks can be thought of as “sanity checks” to ensure that the data are consistent with each other. The data consistency checks enforce data standards and ensure that certain data values are reasonable and/or consistent with other information in the files. Examples of the “data standard” checks are the “mandatory parameters” defined for meta-data files and the technical parameter names in technical data files. Files with format or consistency errors are rejected by the GDAC and are not distributed. Less serious problems will generate warnings and the file will still be distributed on the GDAC. Reference Tables and Data Standards: Many of the consistency checks involve comparing the data to the published reference tables and data standards. These tables are documented in the User’s Manual. (The FileChecker implements “text versions” of these tables.)

Detection of mesoscale thermal fronts from 4km data using smoothing techniques: Gradient-based fronts classification and basin scale application

In order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. Fronts are usually detected at 1 km resolution using the histogram-based, single image edge detection (SIED) algorithm developed by Cayula and Cornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 × 3 pixel kernel. Here, detections are performed in three study regions (off Morocco, the Mozambique Channel, and north-western Australia) and across the Indian Ocean basin using the combination of multiple windows (CMW) method developed by Nieto, Demarcq and McClatchie in 2012 which improves on the original Cayula and Cornillon algorithm. Detections at 4 km and 1 km of resolution are compared. Fronts are divided in two intensity classes (“weak” and “strong”) according to their thermal gradient. A preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and Gaussian) combined with four kernel sizes (3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels) and three detection window sizes (16 × 16, 24 × 24 and 32 × 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. The performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. We find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 × 16 pixel window size, and a 5 × 5 pixel kernel for strong fronts and a 7 × 7 pixel kernel for weak fronts. Results show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. Despite the small window used (16 × 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. This optimal preliminary smoothing and the CMW detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the Indian Ocean basin. In general strong fronts are observed in coastal areas whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. This study shows that adequate noise reduction done by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 × 3 convolution) in terms of detectability, length and location. This method, using 4 km data is easily applicable to large regions or at the global scale with far less constraints of data manipulation and processing time relative to 1 km data.