Asia-Pacific Journal of Atmospheric Sciences

, Volume 52, Issue 2, pp 107–127

Evaluation of regional climate simulations over the CORDEX-EA-II domain using the COSMO-CLM model

  • Weidan Zhou
  • Jianping Tang
  • Xueyuan Wang
  • Shuyu Wang
  • Xiaorui Niu
  • Yuan Wang
Article
  • 119 Downloads

Abstract

The COSMO-CLM (CCLM) model is applied to perform regional climate simulation over the second phase of CORDEX-East Asia (CORDEX-EA-II) domain in this study. Driven by the ERAInterim reanalysis data, the model was integrated from 1988 to 2010 with a high resolution of 0.22°. The model’s ability to reproduce mean climatology and climatic extremes is evaluated based on various aspects. The CCLM model is capable of capturing the basic features of the East Asia climate, including the seasonal mean patterns, interannual variations, annual cycles and climate extreme indices for both surface air temperature and precipitation. Some biases are evident in certain areas and seasons. Warm and wet biases appear in the arid and semi-arid areas over the northwestern and northern parts of the domain. The simulated climate over the Tibetan Plateau is colder and wetter than the observations, while South China, East China, and India are drier. The model biases may be caused by the simulated anticyclonic and cyclonic biases in low-level circulations, the simulated water vapor content biases, and the inadequate physical parameterizations in the CCLM model. A parallel 0.44° simulation is conducted and the comparison results show some added value introduced by the higher resolution 0.22° simulation. As a result, the CCLM model could be an adequate member for the next stage of the CORDEX-EA project, while further studies should be encouraged.

Key words

Regional climate modeling Model evaluation CORDEXEast Asia COSMO-CLM ERA-Interim 

References

  1. AghaKouchak, A., D. Easterling, K. Hsu, S. Schubert, and S. Sorooshian, Eds., 2013: Extremes in a changing climate: detection, analysis and uncertainty. Water Science and Technology Library, Vol. 65, Springer Netherlands, 426 pp.CrossRefGoogle Scholar
  2. Ahmed, K. F., G. Wang, J. Silander, A. M. Wilson, J. M. Allen, R. Horton, and R. Anyah, 2013: Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast. Glob. Planet. Change, 100, 320–332.CrossRefGoogle Scholar
  3. Arakawa, A., and V. R. Lamb, 1977: Computational design of the basic dynamical processes of the UCLA general circulation model. Methods in Computational Physics, 17, 173–265.Google Scholar
  4. Bowden, J. H., T. L. Otte, C. G. Nolte, and M. J. Otte, 2012: Examining interior grid nudging techniques using two-way nesting in the WRF model for regional climate modeling. J. Climate, 25, 2805–2823.CrossRefGoogle Scholar
  5. Bucchignani, E., M. Montesarchio, L. Cattaneo, M. P. Manzi, and P. Mercogliano, 2014: Regional climate modeling over China with COSMO-CLM: Performance assessment and climate projections. J. Geophys. Res. Atmos., 119, 12151–12170.CrossRefGoogle Scholar
  6. Bucchignani, E., M. Montesarchio, A. L. Zollo, and P. Mercogliano, 2015a: High-resolution climate simulations with COSMO-CLM over Italy: performance evaluation and climate projections for the 21st century. Int. J. Climatol., 36, 735–756.CrossRefGoogle Scholar
  7. Bucchignani, E., L. Cattaneo, H.-J. Panitz, and P. Mercogliano, 2015b: Sensitivity analysis with the regional climate model COSMO-CLM over the CORDEX-MENA domain. Meteorol. Atmos. Phys., 128, 73–95.CrossRefGoogle Scholar
  8. Bukovsky, M. S., and D. J. Karoly, 2009: Precipitation simulations using WRF as a nested regional climate model. J. Appl. Meteor. Climatol., 48, 2152–2159.CrossRefGoogle Scholar
  9. Castro, C. L., R. A. Pielke Sr., and G. Leoncini, 2005: Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res., 110, D05108, doi:10.1029/2004JD004721.CrossRefGoogle Scholar
  10. Cha, D.-H., C.-S. Jin, and D.-K. Lee, 2011: Impact of local SST anomaly over the western North Pacific on extreme East Asian summer monsoon. Clim. Dynam., 37, 1691–1705.CrossRefGoogle Scholar
  11. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597.CrossRefGoogle Scholar
  12. Dickinson, R. E., R. M. Errico, F. Giorgi, and G. T. Bates, 1989: A regional climate model for the western United States. Climatic Change, 15, 383–422.Google Scholar
  13. Dobler, A., and B. Ahrens, 2010: Analysis of the Indian summer monsoon system in the regional climate model COSMO-CLM. J. Geophys. Res., 115, D16101, doi:10.1029/2009JD013497.CrossRefGoogle Scholar
  14. Doms, G., and Coauthors, 2011: A description of the nonhydrostatic regional COSMO model part II: physical parameterization. DWD, Offenbach, Germany, 161 pp. [Available online at http://www.cosmomodel.org/content/model/documentation/core/].Google Scholar
  15. Doms, G., and M. Baldauf, 2015: A description of the nonhydrostatic regional COSMO model part I: dynamics and numerics. DWD, Offenbach, Germany, 164 pp. [Available online at http://www.cosmomodel.org/content/model/documentation/core/].Google Scholar
  16. Dosio, A., H. J. Panitz, M. Schubert-Frisius, and D. Lüthi, 2015: Dynamical downscaling of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value. Clim. Dynam., 44, 2637–2661.CrossRefGoogle Scholar
  17. Frich, P., L. V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. M. G. Klein Tank, and T. Peterson, 2002: Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res., 19, 193–212.CrossRefGoogle Scholar
  18. Fu, C., and Z. Zheng, 1998: Monsoon regions: The highest rate of precipitation changes observed from global data. Chinese Sci. Bull., 43, 662–666.CrossRefGoogle Scholar
  19. Fu, C., S. Wang, Z. Xiong, W. J. Gutowski, D.-K. Lee, J. L. McGregor, Y. Sato, H. Kato, J.-W. Kim, and M.-S. Suh, 2005: Regional Climate Model Intercomparison Project for Asia. Bull. Amer. Meteor. Soc., 86, 257–266.CrossRefGoogle Scholar
  20. Gao, X., Y. Xu, Z. Zhao, J. S. Pal, and F. Giorgi, 2006: On the role of resolution and topography in the simulation of East Asia precipitation. Theor. Appl. Climatol., 86, 173–185.CrossRefGoogle Scholar
  21. Giorgi, F., and G. T. Bates, 1989: The climatological skill of a regional model over complex terrain. Mon. Wea. Rev., 117, 2325–2347.CrossRefGoogle Scholar
  22. Giorgi, F., and L. O. Mearns, 1999: Introduction to special section: Regional climate modeling revisited. J. Geophys. Res., 104, 6335–6352.CrossRefGoogle Scholar
  23. Giorgi, F., C. Jones, and G. Asrar, 2009: Addressing climate information needs at the regional level: the CORDEX framework. WMO Bull., 58, 175–183.Google Scholar
  24. Giorgi, F., and W. J. Gutowski, Jr., 2015: Regional Dynamical Downscaling and the CORDEX Initiative. Annu. Rev. Environ. Resour., 40, 467–490.CrossRefGoogle Scholar
  25. Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated highresolution grids of monthly climatic observations - the CRU TS3.10 Dataset. Int. J. Climatol., 34, 623–642.CrossRefGoogle Scholar
  26. Haslinger, K., I. Anders, and M. Hofstätter, 2013: Regional climate modelling over complex terrain: an evaluation study of COSMO-CLM hindcast model runs for the Greater Alpine Region. Clim. Dynam., 40, 511–529.CrossRefGoogle Scholar
  27. Hong, S.-Y., and E.-C. Chang, 2012: Spectral nudging sensitivity experiments in a regional climate model. Asia-Pac. J. Atmos. Sci., 48, 345–355.CrossRefGoogle Scholar
  28. Hong, S.-Y., and M. Kanamitsu, 2014: Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations. Asia-Pac. J. Atmos. Sci., 50, 83–104.CrossRefGoogle Scholar
  29. Huang, B., S. Polanski, and U. Cubasch, 2015: Assessment of precipitation climatology in an ensemble of CORDEX-East Asia regional climate simulations. Clim. Res., 64, 141–158.CrossRefGoogle Scholar
  30. Huffman, G. J, R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the Global Precipitation Record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.CrossRefGoogle Scholar
  31. Im, E.-S, B.-J. Lee, J.-H. Kwon, S.-R. In, and S.-O. Han, 2012a: Potential increase of flood hazards in Korea due to global warming from a highresolution regional climate simulation. Asia-Pac. J. Atmos. Sci., 48, 107–113.CrossRefGoogle Scholar
  32. Im, E.-S, J.-B. Ahn, and D.-W. Kim, 2012b: An assessment of future dryness over Korea based on the ECHAM5-RegCM3 model chain under A1B emission scenario. Asia-Pac. J. Atmos. Sci., 48, 325–337.CrossRefGoogle Scholar
  33. 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. Cambridge University Press, 1535 pp.Google Scholar
  34. IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 688 pp.Google Scholar
  35. Jaeger, E. B., I. Anders, D. Lüthi, B. Rockel, C. Schär, and S. I. Seneviratne, 2008: Analysis of ERA40-driven CLM simulations for Europe. Meteorol. Z., 17, 349–367.CrossRefGoogle Scholar
  36. Jones, P. W., 1999: First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Wea. Rev., 127, 2204–2210.CrossRefGoogle Scholar
  37. Kothe, S., D. Lüthi, and B. Ahrens, 2014: Analysis of the West African Monsoon system in the regional climate model COSMO-CLM. Int. J. Climatol., 34, 481–493.CrossRefGoogle Scholar
  38. Lee, E.-J., S.-W. Yeh, J.-G. Jhun, and B.-K. Moon, 2006: Seasonal change in anomalous WNPSH associated with the strong East Asian summer monsoon. Geophys. Res. Lett., 33, L21702, doi:10.1029/2006GL027474.CrossRefGoogle Scholar
  39. Lee, D.-K., D.-H. Cha, C.-S. Jin, and S.-J. Choi, 2013: A regional climate change simulation over East Asia. Asia-Pac. J. Atmos. Sci., 49, 655–664.CrossRefGoogle Scholar
  40. 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. Clim. Dyn., 42, 733–747.CrossRefGoogle Scholar
  41. Li, J. P., and Q. C. Zeng, 2002: A unified monsoon index. Geophys. Res. Lett., 29, doi:10.1029/2001GL013874.Google Scholar
  42. Mironov, D., and M. Raschendorfer, 2001: Evaluation of empirical parameters of the new LM surface-layer parameterization Scheme: results from numerical experiments including soil moisture analysis. COSMO technical report No.1. DWD, Offenbach, Germany, 12 pp. [Available online at http://www.cosmo-model.org/content/model/documentation/techReports/docs/techReport01.pdf].Google Scholar
  43. Montesarchio, M., A. L. Zollo, E. Bucchignani, P. Mercogliano, and S. Castellari, 2014: Performance evaluation of high-resolution regional climate simulations in the Alpine space and analysis of extreme events. J. Geophys. Res., 119, 3222–3237.Google Scholar
  44. 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.CrossRefGoogle Scholar
  45. Panitz, H. J., A. Dosio, M. Büchner, D. Lüthi, and K. Keuler, 2014: COSMO-CLM (CCLM) climate simulations over CORDEX-Africa domain: analysis of the ERA-Interim driven simulations at 0.44° and 0.22° resolution. Clim. Dyn., 42, 3015–3038.CrossRefGoogle Scholar
  46. Park, J.-H., S.-G. Oh, and M.-S. Suh, 2013: Impacts of boundary conditions on the precipitation simulation of RegCM4 in the CORDEX East Asia domain. J. Geophys. Res., 118, 1652–1667.Google Scholar
  47. Ritter, B., and J. F. Geleyn, 1992: A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Wea. Rev., 120, 303–325.CrossRefGoogle Scholar
  48. Rockel, B., A. Will, and A. Hense, 2008: The regional climate model COSMO-CLM (CCLM). Meteorol. Z., 17, 347–348.CrossRefGoogle Scholar
  49. Rockel, B., and B. Geyer, 2008: The performance of the regional climate model CLM in different climate regions, based on the example of precipitation. Meteorol. Z., 17, 487–498.CrossRefGoogle Scholar
  50. Rummukainen, M., 2010: State-of-the-art with regional climate models. WIREs Clim. Change, 1, 82–96.CrossRefGoogle Scholar
  51. Schrodin, R., and E. Heise, 2001: The Multi-Layer Version of the DWD Soil Model TERRA-LM. COSMO Technical Report No. 2, DWD, offenhach, Germany, 16 pp. [Available online at http://www.cosmo-model.org/content/model/documentation/techReports/docs/techReport02.pdf].Google Scholar
  52. Seifert, A., and K. D. Beheng, 2001: A double-moment parameterization for simulating autoconversion, accretion and selfcollection. Atmos. Res., 59-60, 265–281.CrossRefGoogle Scholar
  53. Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J. Geophys. Res., 118, 2473–2493.Google Scholar
  54. Smiatek, G., 2014: Time invariant boundary data of regional climate models COSMO-CLM and WRF and their application in COSMOCLM. J. Geophys. Res., 119, 7332–7347.Google Scholar
  55. Suh, M.-S., and D.-K. Lee, 2004: Impacts of land use/cover changes on surface climate over east Asia for extreme climate cases using RegCM2. J. Geophys. Res., 109, D02108, doi:10.1029/2003JD003681.CrossRefGoogle Scholar
  56. Sun, Q. H., C. Y. Miao, Q. Y. Duan, D. X. Kong, A. Z. Ye, Z. H. Di, and W. Gong, 2014: Would the ‘Real’ Observed Dataset Stand Up? A Critical Examination of Eight Observed Gridded Climate Datasets for China. Environ. Res. Lett., 9, 015001, doi:10.1088/1748-9326/9/1/015001.CrossRefGoogle Scholar
  57. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183–7192.CrossRefGoogle Scholar
  58. Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800, doi:10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.CrossRefGoogle Scholar
  59. Wang, B., Ed., 2006: The Asian Monsoon. Springer/Praxis Publishing Co., New York, pp. 788, doi:10.1007/3-540-37722-0.Google Scholar
  60. Wang, B., Z. Wu, J. Li, J. Liu, C.-P. Chang, Y. Ding, and G. Wu, 2008: How to measure the strength of the East Asian summer monsoon. J. Climate, 21, 4449–4463.CrossRefGoogle Scholar
  61. Wang D., C. Menz, T. Simon, C. Simmer, and C. Ohlwein, 2013: Regional dynamical downscaling with CCLM over East Asia. Meteorol. Atmos. Phys., 121, 39–53.CrossRefGoogle Scholar
  62. Wicker, L. J., and W. C. Skamarock, 2002: Time-splitting methods for elastic models using forward time schemes. Mon. Wea. Rev., 130, 2088–2097.CrossRefGoogle Scholar
  63. Wu, J., and X. J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese J. Geophys., 56, 1102–1111 (in Chinese).Google Scholar
  64. Xue, Y., Z. Jajnic, J. Dudhia, R. Vasic, and F. De Sales, 2014: A review on regional dynamical downscaling in intra-seasonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmos. Res., 147-148, 68–85.CrossRefGoogle Scholar
  65. Yun, K.-S., K.-Y. Heo, J.-E. Chu, K.-J. Ha, E.-J. Lee, Y. Choi, and A. Kitoh, 2012: Changes in climate classification and extreme climate indices from a high-resolution future projection in Korea. Asia-Pac. J. Atmos. Sci., 48, 213–226.CrossRefGoogle Scholar
  66. Zhao, D., 2013: Performance of Regional Integrated Environment Modeling System (RIEMS) in precipitation simulations over East Asia. Clim. Dyn., 40, 1767–1787.CrossRefGoogle Scholar
  67. Zou, L., Y. Qian, T. Zhou, and B. Yang, 2014a: Parameter Tuning and Calibration of RegCM3 with MIT-Emanuel Cumulus Parameterization Scheme over CORDEX East Asia Domain. J. Climate, 27, 7687–7701.CrossRefGoogle Scholar
  68. Zou, H., J. Zhu, L. Zhou, P. Li, and S. Ma, 2014b: Validation and application of reanalysis temperature data over the Tibetan plateau. J. Meteorol. Res., 28, 139–149.Google Scholar

Copyright information

© Korean Meteorological Society and Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Weidan Zhou
    • 1
    • 2
  • Jianping Tang
    • 1
    • 2
    • 3
    • 4
  • Xueyuan Wang
    • 1
  • Shuyu Wang
    • 1
    • 3
  • Xiaorui Niu
    • 1
    • 3
  • Yuan Wang
    • 1
    • 2
  1. 1.School of Atmospheric SciencesNanjing UniversityNanjingChina
  2. 2.Key Laboratory of Mesoscale Severe Weather/Ministry of EducationNanjing UniversityNanjingChina
  3. 3.Institute for Climate and Global Change ResearchNanjing UniversityNanjingChina
  4. 4.School of Atmospheric SciencesNanjing UniversityNanjingChina

Personalised recommendations