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Assessing IPCC AR4 model simulation of present and future changes in extreme indices

Assessing IPCC AR4 model simulation of present and future changes in extreme indices. Speaker: Tung Yu-Shiang Adviser: Chen Cheng-Ta. Outline. Motivation Data Definition of Extreme Rainfall Indices Validation of Rainfall Extremes Simulation in Present Climate Attribution of the model bias

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Assessing IPCC AR4 model simulation of present and future changes in extreme indices

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  1. Assessing IPCC AR4 model simulation of present and future changes in extreme indices Speaker: Tung Yu-Shiang Adviser: Chen Cheng-Ta

  2. Outline • Motivation • Data • Definition of Extreme Rainfall Indices • Validation of Rainfall Extremes Simulation in Present Climate • Attribution of the model bias • Future Climate Projection

  3. Motivation and Aims of Study • Extreme rainfall events have much more significant impact on human society and nature environment. • Can current generation of climate models (such as in IPCC AR4 data archive) properly simulate extreme rainfall (after considering the effect of spatial scale of model gridded output)? • What leads to the model bias in precipitation extreme indices? • Future projections of changes in extreme rainfall indices from IPCC models • What are the major contributions to the projected change? Is the change in basic thermodynamic condition (moisture supply) enough to explain?

  4. Data Observation data • CMAP 1981-2000 monthly precipitation • GPCP 1997-2007 daily precipitation • Gridded daily rainfall analysis based on gauge data (Aphrodite, Yatagai et al, 2007) Model-simulation data • IPCC AR4 data archive 15 models (daily rainfall available and resolution higher than T42): daily precipitation from1981-2000 period of 20c3m run and from 2081-2100 period of A1B scenario run Considering the impact of spatial scale on gridded model rainfall, we conservatively interpolate all data to T42 resolution before identify rainfall extremes.

  5. IPCC AR4 models list(1)

  6. IPCC AR4 models list(2)

  7. Definition of Extreme index Expert Team (ET) on climate change detection and indices (ETCCDI) • RX1day Highest 1-day precipitation amount(mm) Let RRij be the daily precipitation amount for day i of period j. Then maximum 1-day values for periodj are: RX1day j =max(RRij) • SDII Simple daily intensity index(mm/wet day) Let RRwj be the daily precipitation amount for wet day w (RR ≧ 1.0 mm) of period j. Then the mean precipitation amount at wet days is given by:

  8. Eaten Asia station data RX1day is similar with GPCP(1997-2007, 11 years) Eaten Asia station RX1day different with GPCP on north India. Regional RX1day comparison for observation data (Aphrodite)

  9. RX1day (20th) (mm)

  10. RX1day (20th)-Taylor Diagram JJA DJF

  11. Model bias comparison mean precipitation RX1day

  12. SDII (20th) (mm/wet day)

  13. Wet day frequency(RR1) Days/year

  14. Model bias comparison SDII Wet day frequency

  15. Extreme indices zonal mean(20th) RX1day Mean precipitation • For RX1day, models have much spread in tropical region • For mean precipitation, models are very similar with mean precipitation, but in south tropical, models over estimated.

  16. SDII Wet day frequency • For SDII, GPCP is higher than ensemble mean • GPCP have less wet days frequency than most model

  17. Future climate

  18. RX1day increase percentage %

  19. Projected change in mean total precipitable water

  20. Total precipitable water Zonal mean change in Rx1day temperature • RX1day increase in tropical region • Temperature increase in north high latitude region, total precipitation has the same result.

  21. Zonal mean change SDII wet day frequency mean precipitation

  22. Thanks for your listening ~ the end

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