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Title: New estimates of uncertainty in the marine surface temperature record
Author: Carella, Giulia
ISNI:       0000 0004 6496 2524
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2017
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Sea Surface Temperature (SST) represents the marine component of surface global temperature, the indicator underpinning the Paris Agreement. This thesis presents major advances in the understanding of the systematic biases and their uncertainty associated with changes in the observing protocol in the ship-only SST record since about 1850. First, by developing a method that probabilistically groups the observations in plausible ship tracks (and therefore potentially associates observations made with the same measurement method), the length of the tracks and the percentage of reports associated with individual platforms increased substantially. Following this analysis, the consistency of the SST was also found to have improved. Secondly, by comparing the SST diurnal variations observed by individual ships with a reference derived from drifting buoys, the SST measurement method was verified or estimated. Following this new classification of the changing ratio of bucket to engine-room inlet (ERI) observations, the difference between bucket and ERI SST anomalies in the period 1955 - 70 increased more rapidly when compared to existing estimates. Better and well validated physical models of SST biases in observations made with buckets were developed by comparing measurements made in the laboratory to predictions of models used in common gridded analyses to bias adjust SST observations made with buckets. Uncertainties due to the effects of turbulence and the assumption of well-mixed water samples were identified as a substantial limiting factor for the direct application of these models to the historical record. Building on the improved platform and observational metadata, SST observations from ships in the period 1992 - 2007 were bias adjusted by modelling their differences from climate-quality satellite data within a Bayesian hierarchical spatial model and as a function of the leading drivers characteristic to the observational biases for each measurement type. A comparison with existing bias adjustments, showed that current SST estimates for the past two decades might be characterized by undetected biases, especially in the ERI record, that could affect the estimates of global and regional surface temperature trends.
Supervisor: Kent, Elizabeth Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available