Abstract. Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration rainfall extremes but records are typically not readily accessible and data are often subject to errors and inhomogeneities. Furthermore, these events are poorly quantified in projections of future climate change making adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in high-resolution climate modelling to better understand key processes and likely future change. The project has so far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and large-scale atmospheric circulation in the generation of these events and how these might change in the future.

We describe the first effort within the Coordinated Regional Climate Downscaling Experiment - Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of 21st century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over 9 CORDEX domains at ~25 km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors and climate service activities. The future of the CORDEX-CORE initiative is also discussed.

Coastal inundation has both potential marine and inland contributions. Using a suite of Global Circulation Models, their skill in representing the key fundamental coastal engineering design forcings (mean sea level pressure, wind and precipitation) has been quantified at the 20 year ARI. Skill is assessed by comparison with measured and assembled data along the temperate east Australian coast. Clear extreme distributions are available from GCM output which show no sign of saturation within the tails of extreme distributions. Extreme surface pressures and winds are comparable with the available data giving confidence to the coastal engineering community that GCMs provide data that is suitable for coastal engineering design. GCMs also provide much longer and more detailed data than is available from equivalent measured records. When changes under the A2 scenario are considered, the consensus of the models is that little change in 20 year extreme surface pressures and rainfall are anticipated over the next 100 years with an accompanying 10% decrease in design wind.

Modes of climate variability can drive significant changes to regional climate affecting extremes such as droughts, floods and bushfires. The need to forecast these extremes and expected future increases in their intensity and frequency motivates a need to better understand the physical processes that connect climate modes to regional precipitation. Focusing on east Australia, where precipitation is driven by multiple interacting climate modes, this study provides a new perspective into the links between large-scale modes of climate variability and precipitation. Using a Lagrangian back-trajectory approach, we examine how the El Niño Southern Oscillation (ENSO) modifies the supply of evaporative moisture for precipitation, and how this is modulated by the Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM). We demonstrate that La Niña modifies large-scale moisture transport together with local thermodynamic changes to facilitate local precipitation generation, whereas below average precipitation during El Niño stems predominantly from increased regional subsidence. These dynamic-thermodynamic processes were often more pronounced during co-occurring La Niña/negative IOD and El Niño/positive IOD periods. As the SAM is less strongly correlated with ENSO, the impact of co-occurring ENSO and SAM largely depended on the state of ENSO. La Niña-related processes were exacerbated when combined with +SAM and dampened when combined with -SAM, and vice versa during El Niño. This new perspective on how interacting climate modes physically influence regional precipitation can help elucidate how model biases affect the simulation of Australian climate, facilitating model improvement and understanding of regional impacts from long-term changes in these modes.

No abstract

Improving modeling capacities requires a better understanding of both the physical relationship between the variables and climate models with a higher degree of skill than is currently achieved by Global Climate Models (GCMs). Although Regional Climate Models (RCMs) are commonly used to resolve finer scales, their application is restricted by the inherent systematic biases within the GCM datasets that can be propagated into the RCM simulation through the model input boundaries. Hence, it is advisable to remove the systematic biases in the GCM simulations prior to downscaling, forming improved input boundary conditions for the RCMs. Various mathematical approaches have been formulated to correct such biases. Most of the techniques, however, correct each variable independently leading to physical inconsistencies across the variables in dynamically linked fields. Here, we investigate bias corrections ranging from simple to more complex techniques to correct biases of RCM input boundary conditions. The results show that substantial improvements in model performance are achieved after applying bias correction to the boundaries of RCM. This work identifies that the effectiveness of increasingly sophisticated techniques is able to improve the simulated rainfall characteristics. An RCM with multivariate bias correction, which corrects temporal persistence and inter-variable relationships, better represents extreme events relative to univariate bias correction techniques, which do not account for the physical relationship between the variables.

We examine the relative impact of population increases and climate change in affecting future water demand for Sydney, Australia. We use the Weather and Research Forecasting model, a water demand model and a stochastic weather generator to downscale four different global climate models for the present (1990–2010), near (2020–2040) and far (2060–2080) future. Projected climate change would increase median metered consumption, at 2019/2020 population levels, from around 484 GL under present climate to 484–494 GL under near future climate and 495–505 GL under far future climate. Population changes from 2014/2015 to 2024/2025 have a far larger impact, increasing median metered consumption from 457 to 508 GL under present climate, 463 to 515 GL under near future climate and from 471 to 524 GL under far future climate. The projected changes in consumption are sensitive to the climate model used. Overall, while population growth is a far stronger driver of increasing water demand than climate change for Sydney, both act in parallel to reduce the time it would take for all storage to be exhausted. Failing to account for climate change would therefore lead to overconfidence in the reliability of Sydney's water supply.

Recently, Barbero et al. (2018) examined temperature‐extreme precipitation scaling and argue that the local cooling effect leading to negative apparent scaling in Darwin found by Bao et al. (2017a) was simply a statistical artefact. Barbero et al. (2018) also propose that dew point temperature drives extreme precipitation, and should be used as a scaling variable. Here we address some of their criticisms and further clarify some conclusions. We maintain that scaling analyses via “binning methods” cannot be reliably applied to climate changes due to fundamental problems of misinterpreted causality, and that using dew point temperature does not solve these problems.