Relevance of Satellite-derived SSS for Climate and Oceanographic Studies: The Case of Superficial Ocean Heat Content

• Main Authors: Arias, Manuel, Olmedo, Estrella, Turiel, Antonio, Catany, Rafael
• Format: Conference Object
• Language: unknown
• Published: European Space Agency 2019
• Subjects: Willis; North Atlantic; North Atlantic oscillation
• Online Access: http://hdl.handle.net/10261/205060

2019 Living Planet Symposium, 13-17 May 2019, Milan, Italy Ocean heat content (OHC) is a key variable to understand the role of the oceans in climate regulation (Johnson et al, 2013). Ocean heat transport and budget are fundamental aspects of some climate phenomena such as ENSO, the North Atlantic Oscillation, hurricanes, sea level rise, the global energy budget, and to constrain global warming scenarios. Levitus et al. (2000) showed that OHC has a significant correlation with global warming and climate indexes, with seawater acting as a sink for atmospheric heat. They employed historical data and in situ data made available in the 90s by CLIVAR. Levitus et al (2001) reported that addition of new data into the existing datasets meant a significant variation of OHC trends respect to previous reports, and found a strong correlation with concentration of atmospheric anthropogenic gases. Levitus et al (2009), however, did a substantial correction over these results, as consequence of incorporating Argo data and performing a better quality control of the historical datasets. Levitus et al (2012) reviewed again the work to add a denser network of Argo data and further quality control. This was necessary after the detection of existing biases within a subset of Argo profiles (Willis et al, 2009), which caused an unexpected cooling of the oceans with respect previous works, for the period 2003-2005. Cheng et al (2014) also proved that incorporation of early Argo data was responsible for the significant OHC jump observed previously for the period 2001-2003, as result of the error sampling. Hence, OHC estimations are very sensible to observational errors, including issues with the heterogeneity in both spatial distribution and data quality found in Argo measurements. Those are fundamental factors when estimating OHC from in situ data. Ocean modelling has become a basic tool for the creation of the various scenarios covering climate change. Models are also significantly dependent of the sampling errors biases. Lymann et ...