Development of a new comprehensive ocean atlas for Indian ocean utilizing ARGO data

Abstract

The World Ocean Atlas (WOA), also termed ‘Levitus Climatology’, is a global ocean climatology containing monthly, seasonal and annual means of temperature (T) and salinity (S) fields at standard ocean depths. The monthly climatology for T and S is available for standard depths up to 1000 m. The database used in the preparation of this climatology (WOA) are historical records of Conductivity, Temperature and Depth (CTD) casts and other available marine observations collected in the past. The methodology used in preparation of this WOA is objective analysis which is essentially non-synoptic and widely scattered in the space domain. We understand that ARGO data has so far not been blended with WOA, nor has its impact for improving WOA climatology been attempted. Presently, with the wealth of marine data from ARGO profilers in the Indian Ocean, we propose a new approach to reconstruct T and S fields optimally utilizing the ARGO data. Here we develop a new model using Delaunay Tessellation with QHull algorithm delivering three-dimensional T and S fields from a non-uniform scattered database up to a depth of 1000 m. For gaps in a data-sparse region, we use all available quality-checked Ocean Station Data (OSD) and Profiling Float Data (PFL) information on T and S, in addition to the existing ARGO data. The initiative here was to replace WOA data points with realistic information from ARGO and in situ data, thereby producing a new climatology atlas. We demonstrate the robustness of our approach, and the final climatology on T and S is better compared with the existing state-of-the-art WOA. The advantage of the proposed methodology is the scope of improving the ocean atlas with the addition of more ARGO data in the near future. The clustered approach in modelling enables ocean parameter retrieval in geometrically disconnected regions with an option for hot restart. We believe that the new climatology will benefit the research community immensely.