https://nova.newcastle.edu.au/vital/access/manager/Index ${session.getAttribute("locale")} 5 https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:47773 Wed 22 Mar 2023 18:06:21 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:38151 Wed 16 Feb 2022 14:50:03 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:40468 Wed 13 Jul 2022 12:00:38 AEST ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11218 Wed 11 Apr 2018 10:32:36 AEST ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:14919 Wed 05 Dec 2018 17:37:29 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:51994 Tue 26 Sep 2023 11:00:44 AEST ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:45155 18O records from an updated version of the Speleothem Isotopes Synthesis and Analysis (SISAL) database and palaeoclimate simulations generated using the ECHAM5-wiso isotope-enabled atmospheric circulation model. We show that the SISAL records reproduce the first-order spatial patterns of isotopic variability in the modern day, strongly supporting the application of this dataset for evaluating model-derived isotope variability into the past. However, the discontinuous nature of many speleothem records complicates the process of procuring large numbers of records if data–model comparisons are made using the traditional approach of comparing anomalies between a control period and a given palaeoclimate experiment. To circumvent this issue, we illustrate techniques through which the absolute isotope values during any time period could be used for model evaluation. Specifically, we show that speleothem isotope records allow an assessment of a model's ability to simulate spatial isotopic trends. Our analyses provide a protocol for using speleothem isotope data for model evaluation, including screening the observations to take into account the impact of speleothem mineralogy on δ18O values, the optimum period for the modern observational baseline and the selection of an appropriate time window for creating means of the isotope data for palaeo-time-slices.]]> Thu 27 Oct 2022 14:49:38 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:54914 Thu 21 Mar 2024 12:03:59 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:33942 Thu 20 Oct 2022 13:20:55 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:17998 Sat 24 Mar 2018 07:56:35 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:20152 Sat 24 Mar 2018 07:51:37 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:28918 Botryococcus) in the Greta coals explains the high HI in the perhydrous type. The Gangamopteris flora that is reported in the Greta coal measures, grew after plants in a taiga like the recent birch forests in Russia (Retallack, 1980). The Glossopteris flora that is reported in the Tomago coal measures, grew in a swamp forest. Based upon the botanic zonation, this flora was located in the cold temperate biome that was located in Gondwanaland, except for Antarctica which was in the glacial biome. Diterpane analysis results reveal alternation of wet and dry periods existed during the deposition of Lewis coals in the Greta coal measures during the Kungurian, and an increase of dryness is noted from Upper Donaldson to Beresfield in the Tomago coal measures during the Capitanian. Based on analysis of aromatics and diterpanes, the same periods of dryness and wetness alternate during the coal deposition in the Sydney Coal Basin. These climatic changes correspond to high frequency cycles (< 100 ka, Goldhammer et al., 1994). The presence of aromatics linked with combustion in the studied samples confirms the hypothesis of fire in peat land to explain high inertinite content. A low to medium biodegradation by bacteria was observed for saturates and aromatics from the studied samples as noted previously in the Sydney and Bowen Basins. This biodegradation concerns short chain n-alkanes, naphtalenes and phenanthrenes and does not alter the paleoenvironmental and paleoclimatic interpretation. The comparison between biomarkers from coals and isotopes from marine (Birgenheier et al., 2010) and terrestrial (Retallack et al., 2011) deposits allow us to identify precise dry/wet climate and glacial/interglacial periods during the Permian.]]> Sat 24 Mar 2018 07:25:58 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3398 Sat 24 Mar 2018 07:21:40 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3183 Sat 24 Mar 2018 07:18:09 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:46284 Mon 14 Nov 2022 15:48:51 AEDT ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:43051 k (M-STAR)(k) is a nonlinear multivariate time series model able to capture regime changes in the conditional mean. The main aim of this paper is to develop a Bayesian estimation scheme for the M-STAR(k) model that includes the coefficient parameter matrix, transition function parameters, covariance parameter matrix, and the model order k as parameters to estimate. To achieve this aim, the joint posterior distribution of the parameters for the M-STAR(k) model is derived. The conditional posterior distributions are then shown, followed by the design of a posterior simulator using a combination of Markov chain Monte Carlo (MCMC) algorithms that includes the Metropolis-Hastings, Gibbs sampler, and reversible jump MCMC algorithms. Following this, extensive simulation studies, as well as case studies, are detailed at the end.]]> Mon 12 Sep 2022 14:16:24 AEST ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:41273 Mon 01 Aug 2022 09:56:26 AEST ]]> https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:47316 Fri 13 Jan 2023 11:00:08 AEDT ]]>