School of Earth Sciences - Theses
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ItemThe impacts of climate variability and change on severe thunderstorm environments in Australia( 2012)Severe thunderstorms present a relatively infrequent but significant threat to property and life in Australia during the spring and summer. These thunderstorms can produce hailstones over 2cm in diameter, winds in excess of 90kmh-1 and less frequently tornadoes. Any of these phenomena can result in localised high impact severe events. Recent examples of this potential are illustrated by damage caused by the 1999 Sydney Hailstorm, 2008 Gap Microburst and the 2010 and 2011 Melbourne Hailstorms. This risk makes the implications of a changing and variable climate on severe thunderstorms important to understand. Recent studies into the impacts of anthropogenic climate change on severe weather events, including thunderstorms, suggest a potential increasing trend in both frequency and intensity for Australia. While current convective parameterisations in both global and regional climate models limit direct assessments of future convection, the use of environmental parameters to estimate changes in severe thunderstorm environments has been successful in other geographical regions. This study seeks an answer to the question “Is the frequency and distribution of severe thunderstorm environments in Australia likely to change in the future?” A database of 1550 independent severe thunderstorm reports in Australia has been developed for the period March 2003 to April 2010. Severe thunderstorm reports are then used to identify relationships with their associated environments estimated using proximal soundings from a mesoscale numerical weather assimilation and prediction model (MesoLAPS). This proximity climatology of known severe thunderstorm environments has been successfully used to derive covariate discriminants that identify the potential of an environment to produce severe thunderstorms. These covariates use variables describing the potential for organised convection (deep-layer wind shear), and the potential for instability over the depth of the atmosphere (convective available potential energy). Applying these discriminants to a reanalysis dataset (ERA-Interim), a climatology of the frequency and spatial distribution of environments favourable to the development of severe and significant severe thunderstorms for Australia has been developed for warm seasons during the period 1979-2011. This climatology demonstrates that inter-annual variability in terms of both the frequency and spatial distribution of environments is influenced by El Niño- Southern Oscillation. La Niña conditions are typically associated with an increased frequency and an inland shift of favourable environments over eastern Australia, while El Niño typically results in fewer environments, particularly along the coastal fringe. Applying this climatology, the environments simulated by two climate models (CSIRO Mk3.6 and CCAM) for the 20-season period 1980-2000 are examined over Australia and tested against the reanalysis climatology. In particular, the ability of the models to resolve the intra-annual variability and both quantify and simulate the spatial distribution of convective variables are analysed, and are found to perform reasonably well, especially in the case of the higher resolution CCAM. Finally, future simulations of severe thunderstorm environments from high emissions projections for the period 2079-2099 are presented for both models. Comparing these simulations to the 20th century, a potential small increase in the frequency of severe thunderstorm environments appears likely for southeast and eastern Australia under a warming climate.
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ItemEstimating uncertainties in future global warming using a simple climate model( 2011)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.