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Korean Meteorological Society (한국기상학회)
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Impact of Cumulus Parameterization Schemes on the Regional Climate Simulation for the Domain of CORDEX-East Asia Phase 2 Using WRF Model

WRF 모형의 적운 모수화 방안이 CORDEX 동아시아 2단계 지역의 기후 모의에 미치는 영향

  • Choi, Yeon-Woo (Division of Earth Environmental System, Pusan National University) ;
  • Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University)
  • 최연우 (부산대학교 지구환경시스템학부) ;
  • 안중배 (부산대학교 지구환경시스템학부)
  • Received : 2016.12.14
  • Accepted : 2017.02.23
  • Published : 2017.03.31
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Abstract

This study assesses the performance of the Weather Research and Forecasting (WRF) model in reproducing regional climate over CORDEX-East Asia Phase 2 domain with different cumulus parameterization schemes [Kain-Fritch (KF), Betts-Miller-Janjic (BM), and Grell-Devenyi-Ensemble (GD)]. The model is integrated for 27 months from January 1979 to March 1981 and the initial and boundary conditions are derived from European Centre for Medium-Range Weather Forecast Interim Reanalysis (ERA-Interim). The WRF model reasonably reproduces the temperature and precipitation characteristics over East Asia, but the regional scale responses are very sensitive to cumulus parameterization schemes. In terms of mean bias, WRF model with BM scheme shows the best performance in terms of summer/winter mean precipitation as well as summer mean temperature throughout the North East Asia. In contrast, the seasonal mean precipitation is generally overestimated (underestimated) by KF (GD) scheme. In addition, the seasonal variation of the temperature and precipitation is well simulated by WRF model, but with an overestimation in summer precipitation derived from KF experiment and with an underestimation in wet season precipitation from BM and GD schemes. Also, the frequency distribution of daily precipitation derived from KF and BM experiments (GD experiment) is well reproduced, except for the overestimation (underestimation) in the intensity range above (less) then $2.5mm\;d^{-1}$. In the case of the amount of daily precipitation, all experiments tend to underestimate (overestimate) the amount of daily precipitation in the low-intensity range < $4mm\;d^{-1}$ (high-intensity range > $12mm\;d^{-1}$). This type of error is largest in the KF experiment.

Keywords

References

  1. Adler, R. F., and Coauthors, 2003: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979 - present). J. Hydrometeorol., 4, 1147-1167. https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2
  2. Ahn, J. B., J. Hur, and K. M. Shim, 2010a: A simulation of agro-climate index over the Korean peninsula using dynamical downscaling with a numerical weather prediction model. Korean J. Agr. Forest Meteorol., 12, 1-10, doi:10.5532/KJAFM.2010.12.1.001 (in Korean with English abstract).
  3. Ahn, J. B., J. Y. Hong, and K. M. Shim, 2010b: Agro-Climatic Indices Changes over the Korean Peninsula in CO2 Doubled Climate Induced by Atmosphere-Ocean-Land-Ice Coupled General Circulation Model. Korean J. Agr. Forest Meteorol., 12, 11-22, doi:10.5532/KJAFM. 2010.12.1.011 (in Korean with English abstract).
  4. Ahn, J. B., Y. W. Choi, S. R. Jo, and J. Y. Hong, 2014: Projection of 21st century climate over Korean Peninsula: Temperature and precipitation simulated by WRFV3.4 based on RCP4.5 and 8.5 scenarios. Atmosphere, 24, 541-554, doi:10.14191/Atmos.2014.24.4.541 (in Korean with English abstract).
  5. Ahn, J. B., and Coauthors, 2016: Changes of precipitation extremes over South Korea projected by the 5 RCMs under RCP scenarios. Asia-Pac. J. Atmos. Sci., 52, 223-236, doi:10.1007/s13143-016-0021-0.
  6. Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, doi:10.1029/2005JD006290.
  7. Baek, H. J., and Coauthors, 2013: Climate change in the 21st century simulated by HadGEM2-AO under representative concentration pathways. Asia-Pac. J. Atmos. Sci., 49, 603-618, doi:10.1007/s13143-013-0053-7.
  8. Betts, A., and M. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693-709.
  9. Boo, K. O., W. T. Kwon, and J. K. Kim, 2004: Vegetation changes in the regional surface climate over East Asia due to global warming using BIOME4. Geophys. Space Phys., 27, 317-327.
  10. Cha, D. H., D. K. Lee, and S. Y. Hong, 2008: Impact of boundary layer processes on seasonal simulation of the East Asian summer monsoon using a regional climate model. Meteorol. Atmos. Phys., 100, 53-72, doi:10.1007/s00703-008-0295-6.
  11. Cha, D. H., and D. K. Lee, 2009: Reduction of systematic errors in regional climate simulations of the summer monsoon over East Asia and the western North Pacific by applying the spectral nudging technique. J. Geophys. Res., 114, D14, doi:10.1029/2008JD011176.
  12. Cha, D. H., D. K. Lee, and Coauthors, 2016: Future changes in summer precipitation in regional climate simulations over the Korean Peninsula forced by multi-RCP scenarios of HadGEM2-AO. Asia-Pac. J. Atmos. Sci., 52, 139-149, doi:10.1007/s13143-016-0015-y.
  13. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569-585. https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2
  14. Choi, Y. W., and Coauthors, 2016: Future changes in drought characteristics over South Korea using multi regional climate models with the standardized precipitation index. Asia-Pac. J. Atmos. Sci., 52, 209-222, doi:10.1007/s13143-016-0020-1.
  15. Collins, W. D., J. K. Hackney, and D. P. Edwards, 2002: An updated parameterization for infrared emission and absorption by water vapor in the National Center for Atmospheric Research Community Atmosphere Model. J. Geophys. Res., 107, 1-20, doi:10.1029/2001JD001365.
  16. Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood, 2005: A new dynamical core for the Met Office's global and regional modelling of the atmosphere. Quart. J. Roy. Meteor. Soc., 131, 1759-1782, doi:10.1256/qj.04.101.
  17. Easterling, D. R., G. A. Meehl, C. Parmesan, S. A. Changnon, T. R. Karl, and L. O. Mearns, 2000: Climate extremes: Observation, modeling, and impacts. Science, 289, 2068-2074, doi:10.1126/sci-ence.289.5487.206.
  18. Giorgetta, M. A., and Coauthors, 2013: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst., 5, 572-597, doi:10.1002/jame.20038.
  19. Giorgi, F., and L. O. Mearns, 1999: Introduction to special section: Introduction to special section: Regional climate modeling revisited. J. Geophys. Res., 104, 6335-6532, doi:10.1029/98JD02072.
  20. Giorgi, F., and Coauthors, 2012: RegCM4: Model description and preliminary tests over multiple CORDEX domains. Climate Res., 52, 7-29, doi:10.3354/cr01018.
  21. Grell, G. A., and D. Devenyi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 38-1-38-4, doi:10.1029/2002GL015311.
  22. Ham, S. R., S. J. Park, C. H. Bang, B. J. Jung, and S. Y. Hong, 2005: Intercomparison of the East-Asian summer monsoon on 11-18 July 2004, simulated by WRF, MM5, and RSM models. Atmosphere, 15, 91-99 (in Korean with English abstract).
  23. Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. Int. J. Climatol., 34, 623-642, doi:10.1002/joc.3711.
  24. Hong, J. Y., and J. B. Ahn, 2015: Changes of early summer precipitation in the Korean Peninsula and nearby regions based on RCP simulations. J. Climate, 28, 3557-3578, doi:10.1175/JCLI-D-14-00504.1.
  25. Hong, S. Y., J. Dudhia, and S. H. Chen, 2004: A Revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103-120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.
  26. Hong, S. Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341, doi:10.1175/MWR3199.1.
  27. Hong, S. Y., and Coauthors, 2013: The Global/Regional Integrated Model system (GRIMs). Asia-Pac. J. Atmos. Sci., 49, 219-243, doi:10.1007/s13143-013-0023-0.
  28. Im, E. S., J. B. Ahn, A. R. Remedio, and W. T. Kwon, 2008: Sensitivity of the regional climate of East/Southeast Asia to convective parameterizations in the RegCM3 modelling system. Part 1: Focus on the Korean peninsula. Int. J. Climatol., 28, 1861-1877, doi:10.1002/joc.1664.
  29. Im, E. S., J. B. Ahn, and S. R. Jo, 2015: Regional climate projection over South Korea simulated by the Had-GEM2-AO and WRF model chain under RCP emission scenarios. Climate Res., 63, 249-266, doi:10.3354/cr01292.
  30. Im, E. S., Y. W. Choi, and J. B. Ahn, 2016: Robust intensification of hydroclimatic intensity over East Asia from multi-model ensemble regional projections. Theor. Appl. Climatol., doi:10.1007/s00704-016-1846-2, in press.
  31. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of The Intergovernmental Panel on Climate Change. Stocker, T. F. et al. Eds., Cambridge University Press, 1535 pp.
  32. Janjic, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927-945, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.
  33. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteorol., 43, 170-181, doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.
  34. Lee, D. H., and Coauthors, 2016a: Time of emergence of anthropogenic warming signals in the Northeast Asia assessed from multi-regional climate models. Asia-Pac. J. Atmos. Sci., 52, 129-137, doi:10.1007/s13143-016-0014-z.
  35. Lee, D. H., C. Park, Y. H. Kim, and S. K. Min, 2016b: Evaluation of the COSMO-CLM for East Asia climate simulations: Sensitivity to spectral nudging. Climate Res., 11, 69-85 (in Korean with English abstract). https://doi.org/10.14383/cri.2016.11.1.69
  36. Lee, D. K., D. H. Cha, and H. S. Kang, 2004: Regional climate simulation of the 1998 summer flood over East Asia. J. Meteor. Soc. Japan, 82, 1735-1753, doi:10.2151/jmsj.82.1735.
  37. Lee, J. W., S. Y. Hong, E. C. Chang, M. S. Suh, and H. S. Kang, 2014: Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP. Climate Dyn., 42, 733-747, doi:10.1007/s00382-013-1841-6.
  38. Moss, R., and Coauthors, 2008: Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies. Intergovernmental Panel on Climate Change (IPCC), 132 pp.
  39. Oh, S. G., M. S. Suh, J. S. Myoung, and D. H. Cha, 2011: Impact of boundary conditions and cumulus parameterization schemes on regional climate simulation over South-Korea in the CORDEX-East Asia domain using the RegCM4 model. J. Korean Earth Sci. Soc., 32, 373-387, doi:10.5467/JKESS.2011.32.4.373 (in Korean with English abstract).
  40. Oh, S. G., J. H. Park, S. H. Lee, and M. S. Suh, 2014: Assessment of the RegCM4 over East Asia and future precipitation change adapted to the RCP scenarios. J. Geophys. Res., 119, 2913-2927, doi:10.1002/2013JD020693.
  41. Oh, S. G., and Coauthors, 2016: Projections of high resolution climate changes for South Korea using multipleregional climate models based on four RCP scenarios. Part 2: Precipitation. Asia-Pac. J. Atmos. Sci., 52, 171-189, doi:10.1007/s13143-016-0018-8.
  42. Park, C., and Coauthors, 2015: Evaluation of multiple regional climate models for summer climate extremes over East Asia. Climate Dyn., 46, 2469-2486, doi:10.1007/s00382-015-2713-z.
  43. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the advanced research WRF version 2. NCAR/TN-468+STR, 88 pp.
  44. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Technical Note, NCAR/TN-475+ STR, 125 pp, doi:10.5065/D68S4MVH.
  45. Simmons, A., S. Uppala, D. Dee, and S. Kobayashi, 2007: ERA-Interim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newsl., 110, 25-35.
  46. Suh, M. S., S. G. Oh, D. K. Lee, D. H. Cha, S. J. Choi, C.-S. Jin, and S.-Y. Hong, 2012: Development of new ensemble methods based on the performance skills of regional climate models over South Korea. J. Climate, 25, 7067-7082, doi:10.1175/JCLI-D-11-00457.1.
  47. Suh, M. S., and Coauthors, 2016: Projections of high resolution climate changes for South Korea using multipleregional climate models based on four RCP scenarios. Part 1: Surface air temperature. Asia-Pac. J. Atmos. Sci., 52, 151-169, doi:10.1007/s13143-016-0017-9.
  48. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183-7192, doi:10.1029/2000JD900719.
  49. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485-498, doi:10.1175/BAMSD-11-00094.1.
  50. Von Storch, H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 3664-3673, doi:10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2.