Decomposing global warming using Bayesian statistics

dc.contributor.authorLjungqvist, Gustav
dc.contributor.departmentChalmers tekniska högskola / Institutionen för energi och miljösv
dc.contributor.departmentChalmers University of Technology / Department of Energy and Environmenten
dc.description.abstractIn this thesis an energy balance model and regression with internal climate variability indices are employed to model ocean heat content and global mean surface temperatures. The energy balance model takes radiative forcing as input. The nature of the contribution of anthropogenic aerosol emissions to the radiative forcing is not very well-known, and in previous research its path is usually scaled by some factor. Here the path is allowed to vary, which reflects the historical uncertainty. Parameters are estimated using Markov chain Monte Carlo methods. The results show that the aerosol path flexibility substantially increases the probability of very high values of the equilibrium climate sensitivity (ECS), but marginally decreases the most probable value. The inclusion of long-term internal climate variability in the form of the Atlantic Multidecadal Oscillation (AMO) in the regression does not reduce the average error of the estimated temperature. This indicates that observed historical multidecadal temperature oscillations might be better explained by changes in external forcing than by the AMO. It is also shown that including AMO only affects the estimated ECS to a small extent in most scenarios.
dc.relation.ispartofseriesRapportserie för Avdelningen för fysisk resursteori : 2015:05
dc.subjectÖvrig annan teknik
dc.subjectOther Engineering and Technologies not elsewhere specified
dc.titleDecomposing global warming using Bayesian statistics
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
local.programmeEngineering mathematics and computational science (MPENM), MSc
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