Duo Chan

(Woods Hole Oceanographic Institution)

"Combining Statistical, Physical, and Historical Evidence to Improve Historical In-situ Sea Surface Temperature (SST) Records"

What meteo colloquium
When Jan 19, 2022
from 03:30 pm to 04:30 pm
Where Zoom Webinar
Contact Name Yifei Fan
Contact email
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This talk is presented as a Zoom Webinar and requires a passcode. For anyone outside the department; If you would like to attend, email Lan Lewis, lan5340@psu.edu

Duo Chan


How to reconstruct past sea-surface temperatures (SSTs) from historical measurements containing more than 100 million ship-based observations taken by over 500,000 ships from more than 150 countries using a variety of methodologies creates a wide range of historical, scientific, and statistical challenges.  The reconstruction of historical SSTs for studying climate change is particularly challenging because SST measurements are uncertain and contain systematic biases of order 0.1°C to 1°C --- these systematic biases are in the range of the historical global warming signal of approximately 1°C.  The biases are complicated and have generally been addressed using simplified corrections.   In this talk, I will introduce a history of SST observations, review a statistical method developed for quantifying SST biases and illustrate scientific insights obtained from adjusted SSTs.  The statistical method for correcting SSTs (i.e., a linear-mixed-effect inter-comparison framework) depends on identifying systematic offsets between inter-comparable groups of SST observations.  Combining estimated offsets with physical and historical evidence has allowed for correcting discrepancies associated with SSTs, including the North Atlantic warming twice as fast as the North Pacific in the early 20th century and anomalously warm SSTs during World War II.   Corrections also permit better hindcasting of Atlantic hurricanes.  Bringing observational estimates into accord with our current knowledge of forcing, climate sensitivity, and internal variability leads to greater confidence in future predictions of global warming made by climate models.