Simulated Sea Surface Salinity Data from a 1/48° Ocean Model

Area of Science:

• Oceanography
• Remote Sensing
• Climate Modeling

Background:

• Accurate sea surface salinity (SSS) measurements are crucial for understanding ocean circulation and climate.
• Satellite missions like Aquarius, SMAP, and SMOS provide global SSS data, but validation requires high-quality reference data.
• The ECCO global ocean model offers a high-resolution dataset for simulating satellite and in situ SSS.

Purpose of the Study:

• To generate simulated satellite and in situ SSS data for validating satellite SSS measurements.
• To investigate sampling errors, matchups, and subfootprint variability in SSS data.
• To assess the validation process for SSS data at Level 2 and Level 3.

Main Methods:

• Utilized the ECCO (Estimating the Circulation and Climate of the Oceans) 1/48° global ocean model simulation.
• Extracted satellite ground tracks (Aquarius, SMAP, SMOS) to sample model data, simulating Level 2 (L2) SSS.
• Averaged L2 data onto a regular grid to produce simulated Level 3 (L3) SSS and generated simulated Argo and tropical mooring datasets.

Main Results:

• Generated one year of simulated SSS data (November 2011-October 2012) at L2 and L3 resolutions.
• Created simulated Argo and tropical mooring SSS datasets, including a gridded monthly 1° Argo product.
• The simulated data enabled the study of sampling errors, matchups, and subfootprint variability.

Conclusions:

• The simulated SSS datasets provide a valuable tool for studying satellite SSS validation processes.
• Understanding subfootprint variability and sampling errors is essential for accurate SSS data validation.
• This approach enhances the reliability of satellite-derived SSS for climate and oceanographic research.