Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K−1), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state‐of‐the‐art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water and natural hazard management are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined

Continuous rainfall simulation in a warmer climate

Continuous sub-daily precipitation sequences are required for many hydrological applications. Unfortunately sub-daily precipitation data is often unavailable due to the paucity of measurements. To overcome this, statistical methods are used to synthetically generate continuous precipitation sequences. These methods generally assume the climate is stationary, that is, the future climate will behave in the same way as the past climate. Anthropogenic climate change implies that the assumption of stationarity is no longer valid. Sub-daily precipitation is expected to change with greater precipitation intensities associated with warmer temperatures, causing greater flood related extremes and disasters. This thesis examines the relationship between extreme precipitation and temperature and proposes to use the relationship between precipitation and temperature to simulate sub-daily precipitation for a future warmer climate.Quantile regression is presented as a superior alternative to current binning techniques in quantifying the relationship between precipitation and temperature. It is found the relationship between precipitation intensity and temperature is modulated by storm duration. Using precipitation from an accumulation of differing storm duration results in a different relationship to when individual storm durations are considered.It is shown that, at higher temperatures, storm patterns are temporally less uniform with more precipitation occurring in a shorter duration. Likewise, the spatial pattern of precipitation also changes with more moisture concentrated in the storm centre at higher temperatures. The results suggest a change to flood peaks at higher temperatures, however, an investigation of the scaling relationship of streamflow and temperature presented little evidence of greater discharges at higher temperatures. It is concluded that antecedent conditions are likely to dominate flooding in a future climate with only the most extreme storms dominated by changes to the flood causing precipitation.Two non-stationary Poisson process continuous sub-daily precipitation models are presented. The first is conditioned on climatic state and the second on temperature, presenting methodologies that can be used to generate precipitation sequences that better reflect the future climate. The thesis concludes by arguing for the use of the alternatives presented here as a basis for planning and designing water resources infrastructure in future settings

Reduced spatial extent of extreme storms at higher temperatures

Extreme precipitation intensity is expected to increase in proportion to the water-holding capacity of the atmosphere. However, increases beyond this expectation have been observed, implying that changes in storm dynamics may be occurring alongside changes in moisture availability. Such changes imply shifts in the spatial organization of storms, and we test this by analyzing present-day sensitivities between storm spatial organization and near-surface atmospheric temperature. We show that both the total precipitation depth and the peak precipitation intensity increases with temperature, while the storm’s spatial extent decreases. This suggests that storm cells intensify at warmer temperatures, with a greater total amount of moisture in the storm, as well as a redistribution of moisture toward the storm center. The results have significant implications for the severity of flooding, as precipitation may become both more intense and spatially concentrated in a warming climate.Conrad Wasko, Ashish Sharma, and Seth Westr

Impact of atmospheric circulation on the rainfall-temperature relationship in Australia

International audienceAnthropogenic climate change is leading to the intensification of extreme rainfall due to an increase in atmospheric water holding capacity at higher temperatures as governed by the Clausius-Clapeyron (C-C) relationship. However, the rainfall-temperature sensitivity (termed scaling) often deviates from the CC relationship. This manuscript uses classifications prescribed by regional-scale atmospheric circulation patterns to investigate whether deviations from the CC relationship in tropical Australia can be explained by differing weather types (WT). We show that the rainfall-temperature scaling differs depending on the WTs, with the difference increasing with rainfall magnitude. All monsoonal WTs have similar scaling, in excess of the CC relationship, while trade winds (the driest WTs) result in the greatest scaling, up to twice that of the CC relationship. Finally, we show the scaling for each WT also varies spatially, illustrating that both local factors and the WT will contribute to the behaviour of rainfall under warming