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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, ChinaAugust 27-30, 2013Katmandu, Nepal
ContentContent
IntroductionIntroductionExperiment designExperiment designObserved dataObserved data
Simulation resultSimulation resultClimatologyClimatologyVariabilityVariabilityPDFs and quantilesPDFs and quantilesExtreme indicesExtreme indices
SummarySummary
IntroductionIntroduction
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
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
IntroductionIntroduction
Experiment designExperiment design
Model Configuration
Model prototype RegCM3
Governing equations Hydrostatic
Grids and resolution 110×78, 45km &15km
Vertical layers (top)
18 sigma layers(50hPa)
Cumulus convection Grell
PBL Holtslag
Land Surface BATS
Initial and boundary conditions ERA-interim reanalysis
Simulation period
1989.1.1-2009.12.31
Experiment designExperiment 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
Observed dataObserved 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
Simulation resultSimulation result
ClimatologyClimatology
precipitation bias
Statistical index
Whole region Source Region
Summer Winter Summer Winter
45km
BIAS(%) 19.770 268.322 14.760 122.096
Spatial R 0.865 0.768 0.786 0.842
RMSE(mm/day) 3.412 3.999 1.869 2.005
15km
BIAS(%) 13.368 227.880 12.055 80.987
Spatial R 0.883 0.792 0.873 0.880
RMSE(mm/day) 2.882 3.546 1.753 1.887
Overestimation , especially in winterSource Region of Yellow River, better simulatedLargest bias, Qaidam BasinUnderestimation, Tanggula Mountain, Sichuan Basin
high-resolution, remarkable improvement15km simulation
bias and RMSE, much smallerspatial correlation coefficient, much higher
surface air temperature bias
Statistical indexWhole region Source Region
Summer Winter Summ
er Winter
45km
BIAS(℃) -3.399 -3.961 -1.821 -1.713
Spatial R 0.703 0.544 0.687 0.313
RMSE(℃) 0.677 0.386 0.593 0.202
15km
BIAS(℃) -2.867 -3.506 -1.704 -1.594
Spatial R 0.786 0.594 0.899 0.787
RMSE(℃) 0.512 0.328 0.528 0.138
cold bias Maximum bias, surroundings of Tanggula Mountainlocations 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
Precipitation and surface air temperature at different surface elevations
Simulation resultSimulation resultVariabilityVariability
Inter annual variability of precipitation and surface air temperature averaged over the whole analysis domain
Taylor Diagram of interannual variability in the 12 surface stations in the Source Region of Yellow River
Annual cycle of precipitation and surface air temperature averaged over the whole analysis domain
Simulation resultSimulation result
PDFs and quantiles PDFs and quantiles
PDFs of daily mean precipitation over the whole analysis region and the Source Region of Yellow River
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
PDFs of daily mean surface air temperature over the whole analysis region and the Source Region of Yellow River
Quantiles (from 0.05 to 1 in steps of 0.05) of daily mean surface air temperature
Simulation resultSimulation result
Extreme indexExtreme index
Variable name Definition
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. > 95th percentile
Precipitation extreme index definitions
Extreme precipitation index
Temperature extreme index definitions
Variable name Definition
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 1st July of at least 6 consecutive days with daily mean temperature below 5℃
Extreme temperature index
SummarySummary
a) The RegCM3 model displays wet bias and cold bias with a better performance in the 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 correlation coefficients
c) It can also well simulate the probability distribution (PDFs) of precipitation but underestimate 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 indices of precipitation and temperature but tends to overestimate the heavy rainfall and cold days
e) The simulation ability is improved in a great degree over Source Region of Yellow River by using higher resolution
Thank You!Thank You!