1. School of Information Technologies The University of Sydney Australia Spatio-Temporal Analysis of the relationship between South American Precipitation Extremes and the El Niño Southern Oscillation Elizabeth Wu and Sanjay Chawla

2. SSTDM 20072 Overview School of Information Technologies The University of Sydney Australia Aims Motivation Background Experiments Future Research Questions

3. SSTDM 20073 School of Information Technologies The University of Sydney Australia To discover the spatial and temporal relationships of high precipitation extremes between regions over South America To compare the spatial and temporal behaviour of high precipitation extremes to the weather phenomenon known as the El Niño Southern Oscillation (ENSO), which is said to have a teleconnection with rainfall patterns Aims

4. SSTDM 20074 School of Information Technologies The University of Sydney Australia Why look at high precipitation extremes? High precipitation extremes can bring both devastation (destruction of property, disease, etc) and rejuvenation (replenish dry areas) Why choose South America? Data is available from the NOAA since 1940 South Americans are particularly vulnerable to the effects of flooding Why compare the behaviour of precipitation extremes to the Southern Oscillation Index (SOI)? Further understanding of the teleconnection between precipitation extremes and the El Niño Southern Oscillation (ENSO) is required. Motivation

5. SSTDM 20075 School of Information Technologies The University of Sydney Australia Previous research has looked at the temporal nature of precipitation extremes and drawn qualitative spatial conclusions from their results This research provides quantitative analysis of the spatial and temporal relationship of precipitation extremes Motivation

6. SSTDM 20076 School of Information Technologies The University of Sydney Australia Provided by the NOAA (National Oceanic and Atmospheric Administration) NetCDF format Daily data 2.5° grids – data is averaged for each day from all stations in the grid About 7900 stations Background: South American Precipitation Data

7. SSTDM 20077 School of Information Technologies The University of Sydney Australia Considerations: (a)Extremes: -Fixed threshold – doesn’t consider seasonal variations -x th -percentile (b)Independent and Identical Distribution (iid) -Daily data is not independent, so deseasonalised weekly maximum data is used instead (c)Time Intervals -Selected data from ‘strong’ (as classified by the NOAA) El Nino events from 1978-2004 (d)Locations -Latitude 60°S to 15°N (31) -Longitude 85°W to 35°W (23) -Total number of regions: 713 -from all stations in the grid Background: South American Precipitation Data

8. SSTDM 20078 School of Information Technologies The University of Sydney Australia Considerations: (e)Deseasonalisation -Consider a period eg. 1970-1989. -Take the weekly max of all weeks in that period -Subtract the period average of that particular week of the year (between 1-53) from each week. -Average is calculated as the sum of all non-missing values for that period divided by the total number of non-missing values. (f) Peak Over Threshold (POT) approach to selecting extreme values -Rather than using a pre-defined threshold, we use the top 95 th percentile of weekly maxima residuals Background: South American Precipitation Data

9. SSTDM 20079 School of Information Technologies The University of Sydney Australia What are extreme precipitation values? Significant deviations from the normal rainfall for a particular time of year - must be deseasonalised In our study, they are the 95th percentile of precipitation values (top 5%) How does EVT help to analyse them? What are the advantages over other techniques? EVT only looks at the extreme values to understand past and future extremes, rather than looking at all of the data (ie. Looks at the tail of a distribution) How is EVT applied to this study? EVT is used to model precipitation extremes over different periods for each grid From this, we obtain the parameters of the distributions Use Moran’s I to determine the extent that the parameters from one region influence the parameter values of nearby regions Background: Extreme Value Theory

10. SSTDM 200710 School of Information Technologies The University of Sydney Australia Moran’s I Statistic is a measure of spatial autocorrelation Can be used to measure global and local correlation Global models may not take into account spatial structural instability (large variations between regions), and so Local Indicators of Spatial Association (LISA) are best used for this purpose Moran’s I may indicate Positive autocorrelation: an event in one region increases the likelihood of the same event in a neighbouring region Negative autocorrelation: an event in one region decreases the likelihood of the same event in a neighbouring region No autocorrelation: an event in one region will have no effect on the likelihood of events in neighbouring region (random) Background: Moran’s I Statistic

11. SSTDM 200711 School of Information Technologies The University of Sydney Australia A naturally occurring phenomenon consisting of two phases: El Niño (Warm) La Nina (Cold) El Niño is often associated with heavy precipitation in South America due to the warming of the East Pacific Ocean Three measures of ENSO phases and strengths are: 1) Southern Oscillation Index (SOI) – atmospheric - measures the difference in Sea Level Pressure (SLP) between Tahiti and Darwin relative to the ‘normal’ SLP. 2) Sea Surface Temperatures (SST) - oceanic 3) Multivariate ENSO Index (MEI) – considers both atmospheric and oceanic measures Background: El Nino Southern Oscillation

12. SSTDM 200712 School of Information Technologies The University of Sydney Australia The relationship between parameters of the extreme value distributions were evaluated using the Local Moran I statistic Compared El Niño Southern Oscillation Index (SOI) for several strong El Niño periods with the average Local Moran I values over South America Experiments

13. SSTDM 200713 School of Information Technologies The University of Sydney Australia Strong El Nino periods obtained from the NOAA website: Experiments Event #El Nino EventPeriod StartPeriod End 11939-194101-Jan-194030-Jun-1949 21957-195901-Jul-194930-Jun-1966 31972-197301-Jul-196631-Dec-1977 41982-198301-Jan-197831-Dec-1986 51990-199301-Jan-198730-Jun-1995 61997-199801-Jul-199531-Jun-2000 7Remaining01-Jul-200031-Dec-2004

14. SSTDM 200714 School of Information Technologies The University of Sydney Australia Of the 713 periods, only some contain data: Experiments

15. SSTDM 200715 School of Information Technologies The University of Sydney Australia Experiments

16. SSTDM 200716 School of Information Technologies The University of Sydney Australia Bootstrap Analysis Experiments

17. SSTDM 200717 School of Information Technologies The University of Sydney Australia Compare the average Local Moran I to other ENSO indicators such as the SST and the MEI Try other methods of spatial autocorrelation Future Research

18. SSTDM 200718 School of Information Technologies The University of Sydney Australia Compare the average Local Moran I to other ENSO indicators such as the SST and the MEI Try other methods of spatial autocorrelation Develop spatio-temporal data mining techniques to discover new and interesting patterns about extreme weather from data sets Future Research

19. SSTDM 200719 School of Information Technologies The University of Sydney Australia Questions