Using Observational Evidence and Process Understanding to Improve Predictions of Extreme Rainfall Change

Lead Research Organisation: UNIVERSITY OF EXETER
Department Name: Engineering Computer Science and Maths


Climate change is one of the most important challenges facing societies in the coming century but there are important gaps in our understanding of how climate change might affect local and regional scale hydrology. In particular, we do not know how European rainfall patterns might change. Observations of rainfall suggest that there have been increases in northern and central Europe, especially in winter, and also increases in rainfall intensity. These changes are consistent with atmospheric physics which indicate that warmer air can hold more moisture. We use climate models to examine how climate might change in the future and these suggest more frequent and intense heavy rainfall even in regions experiencing lower rainfall totals. This may cause an increase in the risk of flooding of the sort witnessed over the last decade across the UK and Europe. Although climate model ability to simulate observed processes has improved in recent years, there are still biases in their outputs due to uncertainties in the levels of future greenhouse gas emissions, due to the large-scale resolution of climate models compared to many natural processes and due to natural variations in the climate. There is also a lack of climate model simulations on the small scale needed to model some of the heaviest rainfall events, in particular summer storms. This research advances the study of extreme climate events by looking at the causes of climate model biases in the simulation of extreme rainfall, particularly with regards to heavy summer storms. We will first identify the historical characteristics of heavy rainfall using observed storms and, after we have identified the atmospheric causes for these events, we will try to provide physically-based explanations for any detected trends. Climate models represent physical processes in different ways and this can have an important influence on the simulation of heavy rainfall. We will assess which of these affect the simulation of heavy rainfall by comparing different model simulations with observations. Weather forecasting and climate models will also be run at a 1.5km resolution to see if such models are able to tell us more about how heavy rainfall events such as thunderstorms might change in the future. This research will provide new estimates of future changes to heavy rainfall and examine the atmospheric mechanisms responsible for such changes. This information will tell us which aspects of heavy rainfall and relevant processes are simulated well by models and which projections for the future we should use in informing any adaptation to climate change. Those that are not will be identified and this research will provide guidance on improvements that are needed in the next generation of climate models as well as weather forecasting models. As we use many different climate models, we can also produce estimates of how uncertain we are about future changes in extreme rainfall and flood risk. The summer 2007 floods cost the UK over £3 billion and the UK Government has announced increased annual budgets for flood risk management that will reach £800 million by 2010 but when and should this investment be prioritised. The Pitt Review in 2008 suggested that more information is needed for 'urgent and fundamental changes in the way the country is adapting to the likelihood of more frequent and intense periods of heavy rainfall'. We need to know how heavy rainfall and flood risks may change in the future, particularly for surface water flooding which is very poorly understood. The information provided by this research is vital for agencies responsible for future flood risk planning and management such as the Environment Agency, DEFRA and the Emergency Services and crucial for updating the climate change allowances used in flood risk management.
Description We have developed a new way of analyzing climate models to measure sources of variation in the simulation of rainfall. In particular, our approach measures the relative importance of 1) simulated variation over time of related weather variables such as humidity, 2) variation in the simulated time-average values of such variables, and 3) variation in the simulated relationships between such variables and rainfall.
Exploitation Route Our approach can be used to understand why climate models differ in their simulation of rainfall, potentially informing model improvements and interpretations of climate projections.
Sectors Environment