Cullmann, J.

Dang, T.

Fink, M.

Fischer, Ch.

Flügel, W.

Laux, P.

Neuditt, A.

Nguyen, P.

Phan, V.T.

Ribbe, L.

Tran, T.

Zander, F.

English

Phan, V. T.

KITopen

Precipitation [mm] High-resolution Climate Information for the Vu Gia-Thu Bon River Basin (1961-2050) for Climate Impact Modelers 1 Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research (KIT, IMK-IFU), 82467 Garmisch-Partenkirchen, Germany 2 IHP/HWRP Secretariat, Federal Institute of Hydrology, 56068 Koblenz, Germany 3 Vietnam Institute of Meteorology, Hydrology and Environment (IMHEN), Hanoi, Vietnam 4 Hanoi University of Science, Department of Meteorology, Hanoi, Vietnam 5 Cologne University of Applied Sciences, Institute for Technology and Resources Management in the Tropics and Subtropics (ITT), Cologne, Germany 6 Friedrich-Schiller University, Department of Geoinformatics, Hydrology and Modeling, Jena, Germany  P. Laux1, P. Nguyen1,2,3, T. Dang1,3, J. Cullmann2, V.T. Phan4, T. Tran3, A. Nauditt5, L. Ribbe5, M. Fink6, Ch. Fischer6, F. Zander6, W. Flügel6  Introduction High-resolution hydrometeorological information is crucial for regional and local impact studies, especially for data sparse regions such as Vietnam (Souvignet et al., 2013). Regional Climate Models (RCMs) are suitable to dynamically downscale large-scale climate information to regional and even local scales. Transient (1961-2050) Weather Research and Forecast (WRF) simulations are forced by ECHAM5 and the A1B and B1 SRES scenarios using a nested approach, finally resulting in climate information of 45 km, 15 km and 5 km, respectively (Fig. 1). After appropriate bias correction, the data can be used for climate impact studies such as future estimations of water availability, crop yield, renewable wind and solar energy productivity.  Regional Climate Simulations using WRF Fig. 1: WRF simulation domains  (red boxes) as used for the transient climate simulations. Expected Change of Extreme Precipitation  Laux, P., Phan, V.T., Lorenz, Ch., Thuc, T., Ribbe, L., Kunstmann, H. (2012), Setting up Regional Climate Simulations for Southeast Asia. High Performance Computing in Science and Engineering ' 12, Conference Proceedings, Wolfgang E. Nagel, Dietmar B. Kröner, Michael M. Resch (Eds.), Springer Laux, P., Phan, V.T., Kunstmann, H. (2013) High Resolution Climate Change Information for the Lower Mekong River Basin of Southeast Asia. High Performance Computing in Science and Engineering '13, Conference Proceedings, Wolfgang E. Nagel, Dietmar B. Kröner, Michael M. Resch (Eds.), p. 543-551, Springer.   Parameterization experiments (Table 1) are conducted using reanalysis data (NCEP/NCAR, ERA40) to identify a suitable set of physical schemes for Microphysics, Planetary Boundary Layer, and Cumulus Convection. The patterns of WRF rainfall and temperature (D02) are compared to the gridded APHRODITE and CRU data for rainfall and temperature, respectively (Fig. 2). See Laux et al. (2012 & 2013) for more information. D01 D02 D03 Domain 1: - horizontal: 45 km (99x99 grid  cells) - vertical: 50 layers up to 50 hPa - time step: 180 s Domain 2 horizontal: 15 km (142x145 grid cells) vertical: 50 layers up to 50 hPa time step: 120 s Domain 3 - horizontal: 5 km (66x75 grid  cells) - vertical: 50 layers up to 5000 Pa - time step: 30 s Run Microphysic schemes PBL physic schemes Cumulus physic schemes B Lin et al.  Hong et al. Betts-Miller-Janjic  C Lin et al. Nakanishi and Niino Betts-Miller-Janjic D Lin et al.  Nakanishi and Niino New SAS E Lin et al. Hong et al.  New SAS F WRF Single-Moment 3-class  Hong et al.  Betts-Miller-Janjic G WRF Single-Moment 3-class  Nakanishi and Niino Betts-Miller-Janjic H WRF Single-Moment 3-class  Hong et al.  New SAS I WRF Single-Moment 3-class  Nakanishi and Niino New SAS J WRF Double-Moment 6-class  Hong et al.  Betts-Miller-Janjic K WRF Double-Moment 6-class Nakanishi and Niino Betts-Miller-Janjic L WRF Double-Moment 6-class  Nakanishi and Niino New SAS M WRF Double-Moment 6-class Hong et al.  New SAS Table 1: 12 combinations of  parameterization schemes used for both NCEP/NCAR and ERA40 reanalysis data:  Rainfall Temperature WRF-ERA40 Observation data Fig. 2: Rainfall amount and mean temperature for 2000 of observations (left column) and WRF simulations using ERA40 reanalysis data and parameter combination of run G (see Table 1). Fig. 3: Taylor diagram showing performance of  WRF to simulate precipitation (run B to M) compared  to Aphrodite (A).  - Precipitation: patterns well captured - Temperature: patterns very well captured, low sensitivity of parameterization runs (not shown here) Pearson Correlation Coefficient Root Mean Squared Error Standard Deviation 2001-2030 minus (1970-2000)   (2021-2050) minus (1970-2000)  Long-term climate simulations (based on parameterization G) performed, Climate data are available at RBIS database (http://www.lucci-vietnam.info/), access on request A1B B1 Zooming In: Reliability for the VGTB Basin [mm] [mm] Contact: patrick.laux@kit.edu - WRF captures precipitation well (seasonality, amounts) - Still deviations between local scale (rain gauges) and WRF grid cells ( if required, data from further statistical bias correction for local impact studies provided)    Fig. 4: Validation of precipitation in the VGTB basin. Top: mean monthly precipitation (red: mean of 23 observation stations (OBS), black: modeled (WRF) with a resolution of 5 km, Domain 3). Bottom: relative deviations (OBS-WRF)/OBS.     Fig. 5: Expected change of extreme precipitation (q95) frequencies in the VGTB basin (outlined in red) as simulated by WRF and ECHAM5, based on two different scenarios (A1B (business-as-usual), B1 (moderate emissions). 

High-resolution climate information for the Vu Gia-Thu Bon river basin (1961-2050) for climate impact modelers

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High-resolution climate information for the Vu Gia-Thu Bon river basin (1961-2050) for climate impact modelers

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