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Title: Quantification of uncertainties in global temperatures using multi-resolution lattice kriging
Author: Ilyas, Maryam
ISNI:       0000 0004 7429 307X
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Temperature measurements are subject to uncertainties. Temperature observations are sparsely available over the surface of the earth. The uncertainties in temperatures due to these gaps in spatial coverage is quanti fied using multi-resolution lattice kriging (MRLK). These uncertainties are combined with the existing estimates of the observational uncertainties. It results in a monthly temperature data product from 1850-2016. A new approximate Bayesian methodology is proposed for spatial data analysis. It relies on spatial dependence of the data using the variogram. This methodology is integrated with the multi-resolution lattice kriging (MRLK) model. It results in an approximate Bayesian inference for MRLK. The MRLK with the approximate Bayesian framework is used to generate another temperature data set. It samples the observational and coverage uncertainties in temperatures but also accounts for the model parametric uncertainties. The two sets of monthly temperature data products created in this thesis provide the uncertainties in temperatures at a regional scale. Therefore, a probabilistic El Niño Southern Oscillation (ENSO) index is invented that reflects the regional estimates of temperature uncertainties. This defi nition is applied to both versions of temperature data sets. During the pre-industrial period, fewer temperature measurements are available. Therefore, there is uncertainty in the pre-industrial baseline temperatures. Uncertainties in the pre-industrial baseline are integrated with the observational, coverage and parametric uncertainties. The results suggest that the uncertainties mainly dominate early temperature records. However, the uncertainty in temperatures due to the uncertain pre-industrial baseline stays same throughout the time series.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available