Tree-ring based climate reconstructions have been critical for understanding past variability and recent trends in climate worldwide, but they are scarce in Australia. This is particularly the case for temperature: only one tree-ring width based temperature reconstruction – based on Huon Pine trees from Mt Read, Tasmania – exists for Australia. Here, we investigate whether additional tree- ring parameters derived from Athrotaxis cupressoides trees growing in the same region have potential to provide robust proxy records of past temperature variability. We measured wood properties, including tree-ring width (TRW), mean density, mean cell wall thickness (CWT), and tracheid radial diameter (TRD) of annual growth rings in Athrotaxis cupressoides, a long-lived, high-elevation conifer in central Tasmania, Australia. Mean density and CWT were strongly and negatively correlated with summer temperatures. In contrast, the summer temperature signal in TRW was weakly positive. The strongest climate signal in any of the tree-ring parameters was maximum temperature in January (mid-summer; JanTmax) and we chose this as the target climate variable for reconstruction. The model that explained most of the variance in JanTmax was based on TRW and mean density as predictors. TRW and mean density provided complementary proxies with mean density showing greater high-frequency (inter-annual to multi-year) variability and TRW showing more low-frequency (decadal to centennial-scale) variability. The final reconstruction model is robust, explaining 55% of the variance in JanTmax, and was used to reconstruct JanTmax for the last five centuries (1530–2010 C.E.). The reconstruction suggests that the most recent 60 years have been warmer than average in the context of the last ca. 500 years. This unusually warm period is likely linked to a coincident increase in the intensity of the subtropical ridge and dominance of the positive phase of the Southern Annular Mode in summer, which weaken the influence of the band of prevailing westerly winds and storms on Tasmanian climate. Our findings indicate that wood properties, such as mean density, are likely to provide significant contributions toward the development of robust climate reconstructions in the Southern Hemisphere and thus toward an improved understanding of past climate in Australasia.

An artificial neural network (ANN) is used to model nonlinear, large deformation plastic behavior of a material. This ANN model establishes a relationship between flow stress and dislocation structure content. The density of geometrically necessary dislocations (GNDs) was calculated based on analysis of local lattice curvature evolution. The model includes essential statistical measures extracted from the distributions of dislocation microstructures, including substructure cell size, wall thickness, and GND density as the input variables to the ANN model. The model was able to successfully predict the flow stress of aluminum alloy 6022 as a function of its dislocation structure content. Furthermore, a sensitivity analysis was performed to identify the significance of individual dislocation parameters on the flow stress. The results show that an ANN model can be used to calibrate and predict inelastic material properties that are often cumbersome to model with rigorous dislocation-based plasticity models.