1. Regional Climate Modeling in the Source Region of Yellow River with complex topography using the RegCM3: Model validation Pinhong Hui, Jianping Tang School of Atmospheric Sciences Nanjing University, China August 27-30, 2013 Katmandu, Nepal

2. Content Introduction Introduction Experiment design Experiment design Observed data Observed data Simulation result Simulation result  Climatology  Variability  PDFs and quantiles  Extreme indices Summary Summary

3. Introduction The Tibet Plateau  The highest plateau all over the world  complex topography and fragile ecosystem  one of the most sensitive areas to climate change The Source Region of Yellow River  located in the Tibet Plateau  climatology may have dramatic impact on hydrology and ecosystem over the whole Yellow River Basin Spatial distribution of precipitation and temperature  displays a strong relationship with the topography in scale under 10km  However, most of this region lacks meteorological observations Use of regional climate models(RCMs)  necessary for reproducing the main climatic features in complex terrain

4. The regional climate models has been successfully applied in many regional climate studies around the world  Heikkila et al. (2011) did dynamical downscaling of the ERA-40 reanalysis with the WRFV  Afiesimama et al. (2006) use the RegCM3 to study the West African monsoon  Dimri and Ganju (2007) simulated wintertime Seasonal Scale over Western Himalaya Using RegCM3  Park et al. (2008) Characteristics of an East-Asian summer monsoon climatology simulated by the RegCM3  Caldwell et al. (2009) Evaluation of a WRF dynamical downscaling simulation over California  …... There is little research work on regional climate modeling in the Source Region of Yellow River with high resolution using the RegCM3 model Introduction

5. Experiment design Model Configuration Model prototypeRegCM3 Governing equationsHydrostatic Grids and resolution110×78, 45km &15km Vertical layers (top) 18 sigma layers (50hPa) Cumulus convectionGrell PBLHoltslag Land SurfaceBATS Initial and boundary conditionsERA-interim reanalysis Simulation period 1989.1.1-2009.12.31

6. Experiment design First figure: the larger domain with 45km resolution covering the whole China with a 15km nest covering the Source Region of Yellow River Second figure  shaded color: terrain height in the nest  large red rectangle: analysis domain(92-106°E, 29-39°N ）  black contour line: location of the Source Region of Yellow River  small red circles: surface observation stations

7. Observed data Daily surface observations from the China Meteorological Administration (CMA) Precipitation surface air temperature daily maximum and minimum surface air temperature Consists of 756 meteorological stations, covering the whole country and provides the best data available for China 116 stations included in our analysis domain Interpolated the model results onto the station locations and evaluated the quality of the simulations

8. Simulation result Climatology

9. precipitation bias Statistical index Whole regionSource Region Summe r Winter Summe r Winter 45 km BIAS(%) 19.770268.32214.760122.096 Spatial R 0.8650.7680.7860.842 RMSE (mm/day) 3.4123.9991.8692.005 15 km BIAS(%) 13.368227.88012.05580.987 Spatial R 0.8830.7920.8730.880 RMSE (mm/day) 2.8823.5461.7531.887  Overestimation, especially in winter  Source Region of Yellow River, better simulated  Largest bias, Qaidam Basin  Underestimation, Tanggula Mountain, Sichuan Basin  high-resolution, remarkable improvement  15km simulation bias and RMSE, much smaller spatial correlation coefficient, much higher

10. surface air temperature bias Statistical index Whole regionSource Region Summ er Winter Summ er Winter 45km BIAS( ℃ ) -3.399-3.961-1.821-1.713 Spatial R 0.7030.5440.6870.313 RMSE( ℃ ) 0.6770.3860.5930.202 15km BIAS( ℃ ) -2.867-3.506-1.704-1.594 Spatial R 0.7860.5940.8990.787 RMSE( ℃ ) 0.5120.3280.5280.138  cold bias  Maximum bias, surroundings of Tanggula Mountain  locations of cold bias are in good agreement with the wet bias regions  15km simulation outperforms the 45km simulation, especially in the Source Region of Yellow River higher spatial correlation coefficient lower bias and RMSE

11. Precipitation and surface air temperature at different surface elevations

12. Simulation result Variability

13. Inter annual variability of precipitation and surface air temperature averaged over the whole analysis domain

14. Taylor Diagram of interannual variability in the 12 surface stations in the Source Region of Yellow River

15. Annual cycle of precipitation and surface air temperature averaged over the whole analysis domain

16. Simulation result PDFs and quantiles

17. PDFs of daily mean precipitation over the whole analysis region and the Source Region of Yellow River

18. Quantiles (0.025, 0.1, 0.25, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95 and 0.99) of daily mean precipitation

19. PDFs of daily mean surface air temperature over the whole analysis region and the Source Region of Yellow River

20. Quantiles (from 0.05 to 1 in steps of 0.05) of daily mean surface air temperature

21. Simulation result Extreme index

22. Variable nameDefinition Consecutive dry days (CDD) Maximum number of consecutive days with Precipitation < 1mm Number of heavy precipitation days (R10) Annual count of days when Precipitation >= 10mm Maximum 5-day precipitation amount (Rx5 day) Annual maximum consecutive 5-day precipitation Very wet days (R95) Annual total precipitation when Pre. > 95 th percentile Precipitation extreme index definitions

23. Extreme precipitation index

24. Temperature extreme index definitions Variable nameDefinition Summer day (SU) Daily maximum temperature over 25 ℃ Consecutive frost days (CFD) Days with daily minimum temperature below 0 ℃ Growing season length (GSL) The number of days between the first occurrence of at least 6 consecutive days with daily mean temperature above 5 ℃ and the first occurrence after 1 st July of at least 6 consecutive days with daily mean temperature below 5 ℃

25. Extreme temperature index

26. Summary a) The RegCM3 model displays wet bias and cold bias with a better performance in th e Source Region of Yellow River. And the wet bias is significantly larger in percent during winter b) The model accurately captures the interannual variability and annual cycle of both precipitation and temperature averaging over the entire region with high correlati on coefficients c) It can also well simulate the probability distribution (PDFs) of precipitation but und erestimate the extreme precipitation in summer and overestimate it in winter. The simulated temperature PDFs are shifted towards the lower temperatures d) The RegCM3 model generally reproduces the spatial patterns of the extreme indice s of precipitation and temperature but tends to overestimate the heavy rainfall an d cold days e) The simulation ability is improved in a great degree over Source Region of Yellow R iver by using higher resolution

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