Multi-sensor Improved Sea Surface Temperature: continuing the GHRSST Partnership and Arctic Data

Lead PI: Chelle Gentemann, Earth and Space Research
Start Year: 2018 | Duration: 4 years
Partners: University of Colorado, University of Miami , University of Washington, University of Maryland, NASA JPL, NOAA NCEI, NOAA PMEL, NOAA NESDIS STAR,NOAA/OAR/ESRL NOAA/NMFS/SWFSC

The Arctic Seas were until recently ice-covered for all or most of the year, so studies of Arctic Sea Surface Temperature (SST) were not particularly interesting. This has changed dramatically in recent years, owing to extreme seasonal sea ice melt-back and other climate impacts. In fact, this is now one of the most exciting areas of the world to study SST, in order to understand a variety of phenomena including heat exchange in the coupled air-sea system. However, satellite SST products in this region are presently very poorly validated, and are generally tuned to lower latitude in situ observations. The time is right to address these problems, given new observations that have been collected in recent years, in concert with the advantages that come from multiple passes of polar orbiting satellites at high latitude.

Improving our understanding of dynamical processes in the Arctic requires improving both the accuracy and characterization of observations used. To address this gap, this project proposes to collect, quality control, and re-distribute a large number of existing ‘research’ Arctic in situ datasets that have not been included in broadly used SST databases. Additionally, the collection of new Arctic SST profile data using unmanned surface vehicles is proposed in Option 1.These existing and new Arctic in situ data will be integrated into existing satellite-in situ matchup databases. As these matchup databases are the basis for most satellite SST algorithm development and uncertainty assessments, the addition of these new in situ data will greatly improve SST accuracy and uncertainty estimates in the Arctic. All satellite data, in situ data, and matchup databases developed in this project will be distributed publicly, to benefit this project, our international partners, and the wider scientific community.

Most researchers use global, gap-free, daily SST analyses, or Level-4 (L4) SSTs. L4 SSTs not only have large uncertainties in the Arctic but can vary by several degrees depending on which product is selected. The errors in L4 SSTs are partially due to the L4 analysis procedures (different handling of SSTs in the Marginal Ice Zone (MIZ),different bias corrections, different filtering of data, etc.) and partially due to errors in the orbital Level-2 (L2) satellite SSTs that are used to create the L4 SSTs. Improvements in L2 data accuracy, characterization and quality control, combined with better understanding of air-sea interactions and other processes, will feed through into better high-latitude L4 SST analyses that underpin so much Arctic research.

While improving SST data in the Arctic is important, the ability to reach the scientific community who will use the data is just as important. This project proposes to evolve (along with our international partners, in coordination with the GHRSST Project Office) from the existing centralized data distribution approach to a more distributed approach that reduces redundancies and will improve the user experience.

Finally, within this project, research into diurnal wanning in the Arctic, SST variability in the MIZ, and air-sea-ice interactions are all planned. These research topics directly benefit this project by improving our understanding of different dynamical processes that affect flagging of SST retrievals, accuracy of SST observations, and how to construct daily SST averages. The GHRSST international collaboration of scientists is an important component of this project. Through the proposed work, new in situ data, matchup databases, SST algorithms, and processing capabilities will be developed both within this project and jointly with international partners. This project will increase availability of Arctic in situ data, accuracy of Arctic SSTs, distribution of satellite SST data, and understanding of Arctic SST variability, resulting in substantial improvements in our understanding and prediction of the Arctic environment.