Changes to sub-daily rainfall in Australia

1. Changes to sub-daily rainfall in Australia Dr Seth Westra

6. Presentation overview • Part 1: The sub-daily rainfall dataset in Australia • Part 2: The observed relationship between temperature, humidity and rainfall intensity • Part 3: Detection of trends in sub-daily rainfall • Part 4: Towards a downscaling algorithm for sub-daily rainfall • Part 5: Evaluating regional climate model (WRF) performance using the diurnal cycle of sub-daily precipitation

7. Part 1: Australian rainfall record • More than 19000 daily precipitation stations (read at 9am daily) • More than 1500 pluviograph stations (6-minute resolution)

8. Pluviograph (sub-daily)

9. Daily (only locations > 100 years)

10. Australian rainfall record – record length Pluviograph Daily

11. Part 2: Link between temperature and extreme rainfall Extreme rainfall will scale at C-C rate of ~7%/C or “super C-C” rate of ~15%/C

12. Methodology • Reproduce this work using Australia-wide data: • 137 long pluviograph records (average length 32 years, with average of 6% missing) • Mean and maximum daily 2m air temperature extracted for each wet day • Data grouped into 15 bins by temperature – and different percentile (e.g. 50, 99%ile) rainfall extracted in each bin • Where available, relative humidity also extracted

13. Methodology Hardwick-Jones, R., Westra, S. & Sharma, A., 2010, “Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity”, Geophysical Research Letters, 37, L22805

14. 60-minute rainfall intensity against average daily temperature Blue = 99 percentile rainfall (representing behaviour of ‘extremes’)Red = 50 percentile rainfall (representing behaviour of ‘average’ events)

15. 60-minute rainfall intensity against average daily temperature Blue = 99 percentile rainfall (representing behaviour of ‘extremes’)Red = 50 percentile rainfall (representing behaviour of ‘average’ events)

21. Does relative humidity stay constant with temp?

22. Summary of temperature scaling work • Clear scaling of rainfall with temperature across Australia • Scaling depends on duration of storm burst, and exceedance probability • Scaling also depends on atmospheric temperature – negative scaling with high temperatures! • Likely to be due to access to atmospheric moisture • BUT: Does a historical scaling relationship imply similar future changes?

23. Part 2: Detection of trends in Australian rainfall • We wish to detect whether there are trends or other types of climatic non-stationarity in extreme precipitation data • Consider the following hypothetical example: • ‘Extreme’ precipitation will scale at a rate of 7%/C in proportion to the water holding capacity of the atmosphere • Global warming trend has been ~0.74C over the 20th century • Therefore would need to be able to detect a ~5% change

24. Motivation • Assuming 50 years of data, such a trend would be detected at the 5% significance level in only 8% of samples (and a negative trend detected in 2% of samples!)

25. What is a max-stable process? • Formal definition: suppose for , i = 1,..., n, are independent realisations of a continuous process. If the limit: exists for all s with normalising constants an(s) and bn(s), then is a max-stable process. • Spatial analogue of multivariate extreme value models, which accounts for both data-leveldependence and parameter-level dependence. • Distinct from ‘Spatial GEV’ models which only account for parameter-level dependence.

26. Illustration of max-stable process • The ‘storm profile’ model:

27. Benefits for trend detection • Can improve the strength of the trend that can be detected (given by value of parameter ‘β1’), depending on the amount of spatial correlation.

28. Application to Australian rainfall data • Of Australia’s ~1400 sub-daily records, we selected the 35 most complete stations with records from 1965-2005. • Extracted annual maximum data for 6-minute through to 72 hour storm bursts • Also considered high quality daily data from 1910 to 2005

29. Application to Australian rainfall • Trends in annual maximum 6-minuterainfall • Blue/red indicates increasing/decreasing trend • Filled circles indicatestatistically significantat the 5% level

30. Is there an increasing trend in east-Australian precipitation?

31. Sensitivity to gauge changes • Many sub-daily stations had at least one gauge change over the record, usually from Dines pluviograph to TBRG • Tested sensitivity by extracting any ‘step change’ in the year the gauge change occurred, and then re-fitting the trend. • Did not make any significant difference to the strength of the trends in the previous slide

32. Summary of trend detection work • Max-stable processes provide an elegant way of detecting non-stationarity in hydroclimatic data • Enables substitution of ‘space-for-time’ while accounting for spatial dependence • In east-Australia an increasing trend in sub-daily (particularly sub-hourly) precipitation data could be detected, but not for daily data • This would suggest that sub-daily precipitation is increasing much more quickly than expected • Also highlights that daily data cannot be usedfor inference at shorter timescales Westra, S. & Sisson, A., 2011, “Detection of non-stationarity in precipitation extremes using a max-stable process model”, Journal of Hydrology, 406

33. Part 4: Disaggregating from daily to sub-daily rainfall under a future climate • We have shown that the scaling of rainfall with atmospheric temperature depends on storm burst duration, exceedance probability, and moisture availability • How can this be used for estimating change in sub-daily rainfall under a future climate? • Various techniques are available for downscaling daily rainfall under a future climate • We have developed an algorithm to disaggregate from daily to sub-daily rainfall under a future climate.

34. Importance of seasonality on daily to sub-daily scaling • Scaling from daily to sub-daily rainfall strongly depends on atmospheric temperature

35. Plotting against both temperature and day of year • BUT – most of the annual variation can actually be attributed to atmospheric temperature!

36. Influence of location – before and after regressing against atmospheric temperature

37. Considering a broader range of atmospheric variables...

38. Algorithm Assume we have future sequences of daily rainfall available (e.g. from a statistical or dynamical downscaling algorithm), as well as atmospheric covariates • Given a future daily rainfall amount and associated atmospheric covariates (e.g. temperature, relative humidity, geopotential height...) • Find days in the historical record which have a ‘similar’ atmospheric state and daily rainfall amount and also the complete sub-daily rainfall sequence • Sample from one of those days

39. A disaggregation algorithm for downscaling sub-daily rainfall Westra, S., Evans, J., Mehrotra, R. & Sharma, A., “Disaggregating from daily to sub-daily rainfall under a future climate”, submitted to Journal of Climate

40. Summary of sub-daily disaggregation • Disaggregation algorithm is a simple ‘analogues’ based approach for understanding sub-daily rainfall behaviour under a future climate • Requires daily downscaling information, but such information is often readily available • Shows substantial changes can be expected at hourly or sub-hourly timescales.

41. Part 5: Diurnal cycle of modelled and observed rainfall • Good performance of a dynamical model in capturing the diurnal cycle provides a positive indication that the processes of sub-daily precipitation are correctly represented. Evans, J. & Westra, S., “Investigating the mechanisms of diurnal rainfall variability using a Regional Climate Model”, submitted to Journal of Climate

42. Diurnal cycle of different precipitation generating mechanisms

43. Conclusions and ongoing work • Evaluated scaling relationships of sub-daily rainfall and found strong dependence on temperature and atmospheric moisture • Trend detection work also shows increasing trends in fine time-scale (particularly sub-hourly) rainfall • Significant implications for urban flood risk and risk of flash flooding • Developed statistical disaggregation algorithm to generate sub-daily rainfall sequences conditional to daily rainfall, under a future climate. • Also collaborating with dynamical climate modellers to evaluate capacity of regional climate models to simulate sub-daily precipitation

45. References • Hardwick-Jones, R., Westra, S. & Sharma, A., 2010, “Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity”, Geophysical Research Letters, 37, L22805 • Westra, S. & Sisson, A., 2011, “Detection of non-stationarity in precipitation extremes using a max-stable process model”, Journal of Hydrology, 406 • Westra, S., Mehrotra, R., Sharma, A. & Srikanthan, S., 2012, Continuous rainfall simulation: 1. A regionalised sub-daily disaggregation approach, Water Resources Research, 48 (W01535). • Westra, S., Evans, J., Mehrotra, R. & Sharma, A., “Disaggregating from daily to sub-daily rainfall under a future climate”, submitted to Journal of Climate • Evans, J. & Westra, S., “Investigating the mechanisms of diurnal rainfall variability using a Regional Climate Model”, submitted to Journal of Climate