Kjelstrup, L.

Madsen, H.

Rosbjerg, Dan

Sunyer Pinya, Maria Antonia

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Use of a Neyman-Scott Rectangular Pulses model for downscaling extreme events forclimate change projectionsKjelstrup, L.; Sunyer Pinya, Maria Antonia; Madsen, H.; Rosbjerg, DanPublication date:2011Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Kjelstrup, L., Sunyer Pinya, M. A., Madsen, H., & Rosbjerg, D. (2011). Use of a Neyman-Scott RectangularPulses model for downscaling extreme events for climate change projections. Poster session presented at IUGG25th General Assembly Earth on the Edge, Melbourne, Australia.Use of a Neyman-Scott Rectangular Pulses model for downscaling extreme events for climate change projectionsLise Kjelstrup1, Maria Sunyer1, Henrik Madsen2 and Dan Rosbjerg1*1 DTU Environment, Miljoevej, Building 113,  2800 Kgs. Lyngby,  Denmark. *daro@env.dtu.dk.2 DHI Water Environment Health, Agern Allé 5, 2970 Hørsholm, Denmark.In recent years, Weather Generators (WG) have been used inclimate change impact studies as a statistical downscalingtool. The Neyman-Scott Rectangular pulses is probably one ofthe most comprehensive WGs that can be tuned to putemphasis on extreme events.Introduction• Observed daily statistics were used to calibrate the NSRP model. During the calibration weights were assigned to each statistic. 1000 years were simulated.Neyman-Scott Rectangular PulsesThis WG is based on a clustering approach, where rainfall isassociated with clusters of rain cells making up storm events.The NSRP WG model implemented in the RainSim2 softwarehas been used in this study.1) Use of NSRP for current climate:• Statistics form the observed data are used to simulatesynthetic time series.2) Use of NSRP for future climate conditions:• Statistics for the future climate are estimated using changefactors (change of one statistic from the control to the futurescenario using the information contained in the RCM).• The statistics estimated for the future are used to generatesynthetic time series.1) NSRP – Current climate 2) NSRP – Future climate (2071-2100)• RCM projections cannot be used directly in climate change impact studies       Need for statistical downscaling.• NSRP is able to reproduce adequately observed extremeevents.• Large uncertainty in changes projected by the RCMs,especially in skewness and probability of dry days.• All RCMs considered in this study point to an increase ofextreme events in the future (2071-2100) for this region.• However, there is large uncertainty in the value of thechange, the uncertainty increases for large return periods.(1) van der Linden, P., Mitchell, J.F.B. (eds.), 2009. ENSEMBLES: Climate Change and its Impacts. Summary of research and results from the ENSEMBLES project, Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK, 160pp.(2) Burton, A., Kilsby, C.G., Fowler, H.J., Cowpertwait, P.S.P., O‟Connell, P.E., 2008. RainSim: A spatial-temporal stochastic rainfall modelling system. Environmental Modelling & Software 23, 1356-1369.ConclusionsCurrent climate – Observations vs. RCMs ............ScenarioGCMRCMStatistical D.This study analyses the ability of the NSRP for extreme events and its use as statistical downscaling tool. • Firstly, change factors were calculated.• The same set of statistics and weights  as in the use of NSRP for current climate were used to calibrate the WG. 1000 years were simulated.• The change in extreme events was then estimated using the synthetic time series for the future and current climate.Data and Case studyAarhusA location in Aarhus (Denmark) has beenselected as a case study.Data used in the analysis:• Observed daily rainfall for 1981-2010.• Daily rainfall from 20 RCMs from  ENSEMBLES1 from 1951-2100.0123456Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMean [mm/day]01234567Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecSt.Dev. [mm/day]0123456789Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecSkewness [-]0102030405060708090Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecProb. Dry Day [%]0204060801001201 10 100Intensity  [mm/day]Return period [years]Extreme EventsStatisticThr. / lag timeWeightMean - 6St.Dev - 1Skewness - 6Prob. Dry day 0.2mm 7Autocorrelation 1 60123Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0123456Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec4050607080Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0123456Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0204060801001 10 100Intensity  [mm/day]Return period [years]Extreme Events0.60.811.21.41.61.822.20.5 2.5 4.5 6.5 8.5MeanSummerMeanWinterSt.Dev.WinterSt.DevSummerSkewWinterSkewSummerPDryWinterPDrySummer0.811.21.41.61.822.22.41 10 100Return period [years]Skewness [-]Mean [mm/day]St.Dev [mm/day] Prob. Dry Day [%]RCMsObserved95% Conf.IntervalRCMsAverageNo changeRCMsAverageNo changeSimulatedObservedFitted• Statistics from the simulated time series and the extremeevents were compared.tPr [mm]References

Use of a Neyman-Scott Rectangular Pulses model for downscaling extreme events for climate change projections

http://orbit.dtu.dk/ws/files/51310277/MariaSunyer_Kjelstrup_IUGGPoster_Final.pdf

Use of a Neyman-Scott Rectangular Pulses model for downscaling extreme events for climate change projections

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