Estimating uncertainties in future global warming using a simple climate model
AuthorBodman, Roger William
AffiliationSchool of Earth Sciences
Document TypePhD thesis
CitationsBodman, R. W. (2011). Estimating uncertainties in future global warming using a simple climate model. PhD thesis, School of Earth Sciences, The University of Melbourne.
Access StatusOpen Access
© 2011 Dr. Roger William Bodman
This research has investigated the sources of uncertainty that apply to global–mean temperature change projections. Uncertainties in climate system processes have led to a wide range of projections for future temperature changes, which are compounded by the range of possible future greenhouse–gas emissions. For example, the 2007 Intergovernmental Panel on Climate Change Fourth Assessment Report reported that, by 2100, the global–mean temperature increase relative to 1990 is likely to be in the range 1.1°C to 6.4°C, a result that reflects uncertainties in both future emissions and the response of the climate system. However, such a wide range is not particularly helpful for policy and planning purposes, especially in the absence of probabilities. This research has investigated the reasons for this wide range, assessed the sources of uncertainty and developed a method for producing probabilistic temperature change projections. A simple climate model was selected as the research tool for this investigation, instead of a complex three–dimensional model. The model chosen was the latest version of MAGICC (Model for the Assessment of Greenhouse–gas Induced Climate Change), which represents many of the important processes that determine variations of the Earth’s climate, including radiative forcing, heat uptake in the ocean and the carbon cycle, albeit highly simplified and only for temperature changes at the global scale. One of the features of this research is the development of alternative approaches to constraining the model’s primary climate system and carbon cycle parameters. It was found that indices using land minus ocean and Northern Hemisphere minus Southern Hemisphere temperature anomalies are only weakly correlated with global–mean temperatures, and therefore provide additional independent information that can assist in better estimating some model parameters. A ratio of sea–surface temperature to ocean heat content was also found to have a low correlation to the sea– surface temperatures, creating an alternate measure for constraining ocean parameters. This ratio, as well as the vertical ocean temperature change profile, led to revised estimates for the ocean vertical diffusivity parameter, resulting in a new estimate that is nearly a quarter of the previously standard setting used with the Third and Fourth IPCC assessment report versions of MAGICC. In addition to constraining individual model parameters with targeted observational information, a Bayesian statistical technique, the Monte Carlo Metropolis–Hastings algorithm (MCMH), was applied to constraining groups of model parameters using historical observations. One advantage of the MCMH technique is that it addresses uncertainty that arises from observations, model structure and climate system response. This resulted in probability distributions for the key model parameters which then allowed the production of probabilistic temperature change projections. The carbon cycle was included in the MCMH process, leading to a successful calibration of MAGICC’s key carbon cycle parameters with observations for the first time. The MCMH technique was applied to a number of emissions scenarios, enabling probabilistic estimates to be made of global–mean temperature changes to the end of this century. These show reduced uncertainty ranges for future warming projections, with higher lower bounds for warming due to business–as–usual emissions as compared to the results reported in the IPCC’s Fourth Assessment Report. The upper bound for the likely range is also considerably reduced. For the highest emissions scenario, the SRES A1FI, there is a 50% probability of exceeding 2°C by 2042, with a 73% probability of exceeding 4°C by 2100. Analysis of stabilisation scenarios shows that limiting further increases in global–mean temperature to 2°C above pre-industrial requires massive reductions in anthropogenic greenhouse–gas emissions, to the extent that almost zero CO2 emissions are required by the end of this century. Even then, the temperature increase will peak around mid-century, with the upper bound of the likely range temperature change exceeding 2°C, which then entails the risk of irreversible changes to the climate system.
Keywordsclimate change; simple climate model
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