Interannual variability of mean and extreme rainfall and relationship with large-scale circulation Malcolm Haylock A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy by Publication at the University of East Anglia December 2004 © This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that no quotation from the thesis, nor any information derived therefrom, may be published without the author’s prior, written consent. Interannual variability of mean and extreme rainfall and relationship with large-scale circulation Malcolm Haylock 2004 Abstract Seven journal publications are presented documenting historical trends and interannual variability of mean and extreme rainfall. The seven studies cover diverse geographical regions including Australia, southeast Asia and the Pacific, Europe and South America. While two of the studies solely present trends in mean and extreme rainfall in recent decades, the remainder seek causes for the observed trends. The first paper explains the reduction in winter rainfall observed in recent decades in southwest Western Australia. Composites of sea level pressure reveal that during anomalously dry years there was a westward expansion of the continental anticyclone along with an intensification of the trough at higher latitudes, thus confining the rain bearing storms to the south of the continent. The second paper examines the seasonal dependence of the spatial coherence of Indonesian rainfall, showing that high coherence coincides with a strong relationship with the El Niño – Southern Oscillation phenomenon (ENSO). The third paper extends this work to cover the entire Maritime Continent, showing that the ENSO-affected region is strongly related to large-scale changes in the Pacific-wide ENSO related anomalies of sea surface temperature. The fourth paper uses several indices of daily extreme rainfall to document whether there has been an observed change in extreme rainfall over Australia and to determine if the results are sensitive to the choice of rainfall indices. The fifth paper presents results from a southeast Asia and Pacific regional workshop that examined trends in indices of extreme daily rainfall and temperature. While extreme temperatures have increased in recent decades, rainfall trends are more region- dependent. The sixth paper shows extreme rainfall trends from a similar workshop held in South America. A strong regional dependence of the observed trends is shown to be the result of changes in the behaviour of ENSO and a weakening of the continental trough in surface pressure. The seventh paper relates changes in European winter extreme rainfall to changes in pressure. The large-scale latitude dependent trend is the result of changes in the North Atlantic Oscillation (NAO). The seven studies show that, despite the expectation that a warmer atmosphere means an enhanced hydrological cycle, mean and extreme rainfall has shown no consistent increase. Moreover, long-term trends in rainfall are dominated by changes in large scale climate signals, such as ENSO and the NAO. Contents Chapter 1: Introduction ............................................................................................. 2 How has winter rainfall changed in southwest Western Australia and what has caused the observed change? ................................................................................................ 5 How does rainfall vary over Indonesia and can we conclude anything about predictability?........................................................................................................... 9 How does rainfall vary over the Maritime Continent and what causes this?............. 12 How has extreme rainfall changed over Australia and are results sensitive to the choice of indices? ................................................................................................... 15 How have extreme rainfall and temperature changed over Southeast Asia? ............. 19 How has extreme rainfall changed over South America and what has caused this?.. 23 How does extreme winter rainfall vary over Europe and what causes this?.............. 27 Conclusions ............................................................................................................ 31 References .............................................................................................................. 32 Chapter 2: Allan and Haylock, 1993 Chapter 3: Haylock and McBride, 2001 Chapter 4: McBride et al., 2003 Chapter 5: Haylock and Nicholls, 2000 Chapter 6: Manton et al., 2001 Chapter 7: Haylock et al., 2004 Chapter 8: Haylock and Goodess, 2004 Appendix 1: Personal Contribution Appendix 2: Citations Appendix 3: Publications Acknowledgements 1 Chapter 1: Introduction There are two main reasons why we would wish to study the climate: to increase our knowledge for the sake of learning; and to be able to determine what is the most likely climate at varying timescales in the future. Climate forecasting is particularly important for rainfall. It is one of the key climate variables that have important influence on both our lives and the health of all ecosystems. Interannual variability of mean and extreme rainfall can have devastating consequences. The El Niño-induced drought in southeast Australia in summer 2002-3 was combined with unusually warm temperatures to produce one of the worst wildfire seasons on record, with the destruction of almost 4 million Ha of forest (Nairn, 2003). In contrast, extreme and persistent rainfall in central and eastern Europe in summer 2002 led to severe flooding causing over €21 billion in economic loss and the loss of over 100 lives (Munich Re, 2003). Knowledge in advance of such events would enable the chance to take precautionary steps to reduce the impact. Longer-term forecasts are important for less dramatic, but equally important reasons, such as the ability to plan for any major changes to the spatial and temporal distribution of rainfall (e.g. building dams to provide water resources) or, in the case of human-induced climate change, to take steps now to mitigate such changes. There are two common timescales on which much current work is involved: seasonal forecasting and climate change scenarios. Seasonal forecasts are routinely issued by several national meteorological services, which give the most probable climate for the next few months. Climate change scenarios are currently issued based on available output from climate model experiments, generally up to the end of the 21st century. Two common methods are currently in use for operational seasonal forecasts: statistical and dynamical. Statistical methods use current conditions to predict likely future conditions. Since rainfall has very little temporal autocorrelation i.e. given that it is currently an unusually wet month, we can’t determine with sufficient statistical confidence what will probably happen in several months time, we need to use other variables with which to forecast rainfall. Slowly evolving sea surface temperature (SST) observations are usually used, provided there is sufficient predictive skill. In this method, we are not trying to forecast individual rainfall events, but rather the mean conditions, although there may be sufficient skill to also model other parts of the rainfall distribution such as the probability of observing extreme events. 2 Dynamical models are used increasingly for seasonal forecasting, which involves running usually an ensemble of climate models (the same model with different initialisation states and/or different models) to determine a probability distribution for future climate variables. Unfortunately, rainfall is very sensitive to the hydrological cycle parameterisations within dynamical models, as well as to local effects such as topography and surface processes which are not easily reproduced in the model. Therefore, statistical downscaling is often used to determine a more representative rainfall from the model. These techniques use either the model’s rainfall or, more commonly, other larger-scale variables (with higher spatial autocorrelation) such as circulation measures that are more reliably simulated by the models. Climate change experiments, which by their nature are examining the climate many years from the present, cannot directly use current conditions to forecast. Therefore a dynamical global circulation model (GCM) is required, giving coarse resolution output on a grid of the order of several degrees. For finer scale resolution we must rely on downscaling of climate model data, achieved using statistical methods or by nesting a higher resolution regional model within the global model (dynamical downscaling). Therefore, in statistical seasonal forecasting, as well as statistical downscaling of dynamical seasonal forecasts or climate change experiments, we need to relate rainfall to something larger scale. For downscaling we require that these variables have a higher spatial autocorrelation, for example mean sea level pressure (MSLP). For statistical seasonal forecasting we require spatial and temporal autocorrelation (e.g. SST) and use this to model rainfall. In order to find these relationships between rainfall and other variables, we therefore need to look at the past record in order to look forwards in time with any confidence. The seven papers in this thesis are all concerned with examining historic rainfall data (or a proxy of),