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ABSTRACT Some of the major drivers of extreme temperatures in Australia are related to large-scale variations in the climate. While many of the relationships between climate variability and heatwaves have been described, the preceding climate conditions and the circulation patterns responsible for interannual heatwave variations are still poorly understood. We have used rotated principal component analysis ( PCA) to describe the variance of frequency, duration, magnitude and timing of Australian summer heatwaves. This provides a simple means of investigating both the spatial patterns of circulation and the preceding climate mode conditions of summer heatwaves. We find that spatial patterns of heatwave variability associated with El Niño-Southern Oscillation ( ENSO) were focused in the north and northeast of Australia for heatwave occurrence. Patterns of heatwave amplitude in southeastern Australia were also weakly related to ENSO. The PCA did not find any direct, concurrent Indian Ocean Dipole ( IOD) or Southern Annular Mode ( SAM) signal. However, lead correlations indicate that winter IOD conditions can have a weak indirect effect on heatwave occurrence in northeast Australia and similarly, winter SAM conditions can affect heatwave timing in central eastern Australia. Regressions reveal that ENSO related heatwave duration and frequency in the northeast are related to the movement of the Walker circulation, while southeastern heatwave amplitude is associated with a north-south movement of the Tasman high pressure system. These results may lead to improvements in understanding the predictability and general preparedness for heatwaves in the summer season. [ABSTRACT FROM AUTHOR]

ABSTRACT Extratropical and subtropical cyclones, and their associated fronts, produce the majority of rainfall and extreme weather in the mid-latitudes around the world. In the Southern Hemisphere in particular, where observations are sparse, we have limited knowledge of their long-term variability and trends. While the long time period of the 20th Century Reanalysis ( 20CR) offers potential to assess longer-term changes, the lack of observational data sets makes it difficult to assess its reliability. We assess the skill of the 20CR at representing mid-latitude cyclones through a case study on the east coast of Australia. In this region, a comprehensive database has recently been developed of all severe floods between 1871 and 2012, of which >70% are associated with a coastal low-pressure system. Through use of the full 20CR 56-member ensemble, we are able to identify the majority of severe Australian east coast lows as early as the late 19th century, as well as recreate the cyclone tracks for some of the most severe events. The analysis shows that the full ensemble can be used for a long-term assessment of interannual variability in cyclone frequency from at least 1911, despite large inhomogeneities in the ensemble mean pressure fields. [ABSTRACT FROM AUTHOR]

ABSTRACT Heatwaves have been linked to increased rates of human mortality and morbidity. Given these adverse health impacts, it is crucial to improve our understanding of future changes in these extreme events to inform health impacts studies and adaptation planning. While this information would be most beneficial at a local scale, Global Climate Models provide projections on much coarser resolutions. Regional Climate Models, such as those used in the New South Wales/Australian Capital Territory Regional Climate Modelling (NARCliM) project, provide simulations at a finer scale more appropriate for regional assessments. This study uses NARCliM output to investigate the ability of a Regional Climate Model ensemble to represent heatwave characteristics through the Excess Heat Factor, an index that includes factors that are known to be important to the heat-health relationship. Both uncorrected and bias-corrected model output is evaluated against observationally-derived heatwave characteristics for the period 1990-2009. The effect of bias-correction on future changes in heatwave characteristics is also assessed. Overall, the simulations provided a good representation of the recent climate and bias-correction did not greatly change simulated heatwave characteristics. Some regions were more affected by bias-correction than others, with bias-correction being most beneficial for coastal regions. We emphasise that these results may not apply to all indices measuring extreme heat, and demonstrate that results for an index based on a fixed absolute temperature threshold are substantially affected when bias-correction is applied. While supporting bias-correction, this study demonstrates that it is not necessarily required when evaluating a relative measure such as the Excess Heat Factor. [ABSTRACT FROM AUTHOR]

ABSTRACT Five gridded datasets containing measures of temperature and precipitation extremes over the past five decades over China are compared. Time series, spatial averages and trends in China's coldest and warmest days and nights and wettest days are estimated from these datasets that vary in their station network density, interpolation procedures and the order in which extremes are calculated. We find that country-wide trends in temperature extremes are coherent irrespective of dataset choice although actual values are generally smaller in the dataset in which indices are calculated from daily interpolated grids rather than from station points. There are also some regional differences in trends especially over regions with sparse data networks, e.g. the Tibetan Plateau. Averaged across China, trends calculated over the period 1961-2009 vary from a minimum of 0.13 °C decade−1 (hottest day) to 0.65 °C decade−1 (coldest night). The coldest day and night have tended to warm faster than the warmest day and night. Trends in precipitation extremes are much less coherent spatially and can be positive or negative depending on the choice of dataset. The length of station record chosen is also vital to eliminate spurious trends. The results have important implications for detection and attribution and model evaluation studies. [ABSTRACT FROM AUTHOR]

A 0.05° × 0.05° gridded dataset of daily observed rainfall is compared with high-quality station data at 119 sites across Australia for performance in capturing extreme rainfall characteristics. A range of statistics was calculated and analysed for a selection of extreme indices representing the frequency and intensity of heavy rainfall events, and their contribution to total rainfall. As is often found for interpolated data, we show that the gridded dataset tends to underestimate the intensity of extreme heavy rainfall events and the contribution of these events to total annual rainfall as well as overestimating the frequency and intensity of very low rainfall events. The interpolated dataset captures the interannual variability in extreme indices. The spatial extent of significant trends in the frequency of extreme rainfall events is also reproduced to some degree. An investigation into the performance of this gridded dataset in remote areas reveals issues, such as the appearance of spurious trends, when stations come in and out of use. We recommend masking over areas of low station density for this particular gridded data. It is likely that in areas of low station density, gridded datasets will, in general, not perform as well. Therefore, caution should be exercised when examining trends and variability in these regions. We conclude that this gridded product is suitable for use in studies on trends and variability in rainfall extremes across much of Australia. The methodology employed in this study, to examine extreme rainfall over Australia in a gridded dataset, may be applied to other areas of the world. While our study indicates that, in general, gridded datasets can be used to investigate extreme rainfall trends and variability, the data should first be subjected to tests similar to those employed here. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one on the convention of the International Union of Geophysics and Geodesics (IUGG) and the American Geophysical Union (AGU) and another on various initiatives preventing the worsening impact of climate change.