Advertisement

Advances in Atmospheric Sciences

, Volume 36, Issue 4, pp 363–377 | Cite as

Assessment of Temperature Extremes in China Using RegCM4 and WRF

  • Xianghui Kong
  • Aihui WangEmail author
  • Xunqiang Bi
  • Dan Wang
Original Paper
  • 30 Downloads

Abstract

This study assesses the performance of temperature extremes over China in two regional climate models (RCMs), RegCM4 and WRF, driven by the ECMWF’s 20th century reanalysis. Based on the advice of the Expert Team on Climate Change Detection and Indices (ETCCDI), 12 extreme temperature indices (i.e., TXx, TXn, TNx, TNn, TX90p, TN90p, TX10p, TN10p WSDI, ID, FD, and CSDI) are derived from the simulations of two RCMs and compared with those from the daily station-based observational data for the period 1981–2010. Overall, the two RCMs demonstrate satisfactory capability in representing the spatiotemporal distribution of the extreme indices over most regions. RegCM performs better than WRF in reproducing the mean temperature extremes, especially over the Tibetan Plateau (TP). Moreover, both models capture well the decreasing trends in ID, FD, CSDI, TX10p, and TN10p, and the increasing trends in TXx, TXn, TNx, TNn, WSDI, TX90p, and TN90p, over China. Compared with observation, RegCM tends to underestimate the trends of temperature extremes, while WRF tends to overestimate them over the TP. For instance, the linear trends of TXx over the TP from observation, RegCM, and WRF are 0.53°C (10 yr)−1, 0.44°C (10 yr)−1, and 0.75°C (10 yr)−1, respectively. However, WRF performs better than RegCM in reproducing the interannual variability of the extreme-temperature indices. Our findings are helpful towards improving our understanding of the physical realism of RCMs in terms of different time scales, thus enabling us in future work to address the sources of model biases.

Key words

dynamical downscaling extreme-temperature index observation RegCM WRF 

摘 要

以欧洲中心的二十世纪再分析资料为初始场和侧边界场, 利用两个区域气候模式—RegCM4.5 和 WRF3.6—对东亚地区的气候进行了水平分辨率为 50km 的动力降尺度模拟. 本文评估了两个模式在中国的极端气温模拟性能. 基于气候变化检测和指数专家组的建议, 对比分析了 1981–2010 年两个模式模拟的12个极端气温指数 (即TXx, TXn, TNx, TNn, TX90p, TN90p, TX10p, TN10p WSDI, ID, FD, 和CSDI) 及对应的观测资料. 总体而言, 在中国大多数地区, 两个区域气候模式在表征极端气温指数的时空分布特征上有令人满意的表征能力. RegCM模式再现极端气温的平均气候态上性能优于 WRF 模式, 尤其是在青藏高原地区. 此外, 两个模式都很好地抓住了 1981–2010 年间 ID, FD, CSDI, TX10p 和 TN10p 的下降趋势, 以及TXx, TXn, TNx, TNn, WSDI, TX90p 和 TN90p 的上升趋势. 在青藏高原地区, 与观测资料相比表明, RegCM 模式低估了极端气温的趋势, 而 WRF 模式高估了极端气温的趋势. 例如, 在青藏高原地区中TXx的线性趋势在观测资料, RegCM和WRF模式分别是 0.53ºC (10 yr)−1, 0.44ºC (10 yr)−1 和 0.75ºC (10 yr)−1. 在模拟极端气温的年际变率方面, WRF模式的表现优于 RegCM 模式. 我们的发现有助于提高对区域气候模式在不同时间尺度上物理过程的理解, 从而使我们在今后的工作中追踪模式偏差的来源

关键词

动力降尺度 极端气温指数 观测 RegCM WRF 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was supported by the Key Project of the Ministry of Science and Technology of China (Grant No. 2016YFA0602401) and National Natural Science Foundation of China (Grant No. 41575089).

References

  1. Argüeso, D., J. M. Hidalgo-Muñoz, S. R. Gámiz-Fortis, M. J. Esteban-Parra, J. Dudhia, and Y. Castro-Díez, 2011: Evaluation of WRF parameterizations for climate studies over Southern Spain using a multistep regionalization. J. Climate, 24, 5633–5651,  https://doi.org/10.1175/JCLI-D-11-00073.1.CrossRefGoogle Scholar
  2. Awan, N. K., H. Truhetz, and A. Gobiet, 2011: Parameterization-Induced error characteristics of MM5 and WRF operated in climate mode over the Alpine Region: An ensemble-based analysis. J. Climate, 24, 3107–3123,  https://doi.org/10.1175/2011JCLI3674.1.CrossRefGoogle Scholar
  3. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surfacehydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585,  https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.CrossRefGoogle Scholar
  4. Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Rep. NASA/TM-1999-10460, 15, NASA Goddard Space Flight Cent., Greenbelt. Md, 38 pp.Google Scholar
  5. Dai, A. G., K. E. Trenberth, and T. T. Qian, 2004: A global dataset of Palmer Drought Severity Index for 1870–2002: Relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology, 5, 1117–1130,  https://doi.org/10.1175/JHM-386.1.CrossRefGoogle Scholar
  6. Dickinson, R. E., A. Henderson-Sellers, and P. J. Kennedy, 1993: Biosphere-atmosphere transfer scheme (bats) version 1E as coupled to the NCAR community climate model. NCAR Technical Note NCAR/TN-387+STR,  https://doi.org/10.5065/D67W6959.Google Scholar
  7. Easterling, D. R., J. L. Evans, P. Y. Groisman, T. R. Karl, K. E. Kunkel, and P. Ambenje, 2000: Observed variability and trends in extreme climate events: A brief review. Bull. Amer. Meteor. Soc., 81, 417–426,  https://doi.org/10.1175/1520-0477(2000)081<0417:OVATIE>2.3.CO;2.CrossRefGoogle Scholar
  8. Emanuel, K. A., and M. Živković-Rothman, 1999: Development and evaluation of a convection scheme for use in climate models. J. Atmos. Sci., 56, 1766–1782,  https://doi.org/10.1175/1520-0469(1999)056<1766:DAEOAC>2.0.CO;2.CrossRefGoogle Scholar
  9. Feng, J. M., Y. L. Wang, and Z. G. Ma, 2015: Long-term simulation of large-scale urbanization effect on the East Asian monsoon. Climatic Change, 129, 511–523,  https://doi.org/10.1007/s10584-013-0885-2.CrossRefGoogle Scholar
  10. 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. Climate Research, 19, 193–212,  https://doi.org/10.3354/cr019193.CrossRefGoogle Scholar
  11. Gao, X. J., Z. C. Zhao, and F. Giorgi, 2002: Changes of extreme events in regional climate simulations over East Asia. Adv. Atmos. Sci., 19(5), 927–942,  https://doi.org/10.1007/s00376-002-0056-2.CrossRefGoogle Scholar
  12. Gao, X. J., Y. Shi, Z. Y. Han, M. L. Wang, J. Wu, D. F. Zhang, Y. Xu, and F. Giorgi, 2017: Performance of RegCM4 over major river basins in China. Adv. Atmos. Sci., 34(4), 441–455,  https://doi.org/10.1007/s00376-016-6179-7.CrossRefGoogle Scholar
  13. Giorgi, F., M. R. Marinucci, G. Bates, and G. DeCanio, 1993: Development of a second generation regional climate model (RegCM2). II. Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev., 121, 2814–2832,  https://doi.org/10.1175/1520-0493(1993)121<2814:DOASGR>2.0.CO;2.CrossRefGoogle Scholar
  14. Giorgi, F., C. Jones, and G. R. Asrar, 2009: Addressing climate information needs at the regional level: The CORDEX framework. WMO Bulletin, 58, 175–183.Google Scholar
  15. Giorgi, F, and Coauthors, 2012: RegCM4: Model description and preliminary tests over multiple CORDEX domains. Climate Research, 52, 7–29,  https://doi.org/10.3354/cr01018.CrossRefGoogle Scholar
  16. Girogi, F., and W. G. Gutowski, 2015: Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40, 467–490,  https://doi.org/10.1146/annurev-environ-102014-021217.CrossRefGoogle Scholar
  17. Guo, D. L., E. T. Yu, and H. J. Wang, 2016: Will the Tibetan Plateau warming depend on elevation in the future? J. Geophys. Res., 121, 3639–3978,  https://doi.org/10.1002/2016JD024871.Google Scholar
  18. Han, J., and H. L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 2, 520–533,  https://doi.org/10.1175/WAF-D-10-05038.1.CrossRefGoogle Scholar
  19. Hersbach, H. C., C. Peubey, A. Simmons, P. Berrisford, P, Poli, and D. Dee, 2015: ERA-20CM: A twentieth-century atmospheric model ensemble. Quart. J. Roy. Meteor. Soc., 141, 2350–2375,  https://doi.org/10.1002/qj.2528.CrossRefGoogle Scholar
  20. Holtslag, A. A. M., de Bruijn, E. I. F., and H. L. Pan, 1990: A high resolution air mass transformation model for short-range weather forecasting. Mon. Wea. Rev., 118, 1561–1575,  https://doi.org/10.1175/1520-0493(1990)118<1561:AHRAMT>2.0.CO;2.CrossRefGoogle Scholar
  21. Hong, S. Y., and J. O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). Journal of the Korean Meteorological Society, 42, 129–151.Google Scholar
  22. Hong, Y., M. G. Donat, L. V. Alexander, and Y. Sun, 2015: Multidataset comparison of gridded observed temperature and precipitation extremes over China. International Journal of Climatology, 35, 2809–2827,  https://doi.org/10.1002/joc.4174.CrossRefGoogle Scholar
  23. Hui, P. H., J. P. Tang, S. Y. Wang, X. R. Niu, P. S. Zong, and X. N. Dong, 2018: Climate change projections over China using regional climate models forced by two CMIP5 global models. Part I: Evaluation of historical simulations. International Journal of Climatology, 38, e57–e77,  https://doi.org/10.1002/joc.5351.CrossRefGoogle Scholar
  24. IPCC, 2012: Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, C. B. Field, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 582 pp.Google Scholar
  25. 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, T. F. Stocker, et al., Eds., Cambridge University Press, Cambridge, United Kindom and New York, NY, USA, 1535 pp.Google Scholar
  26. Jacob, D., and Coauthors, 2014: EURO-CORDEX: New highresolution climate change projections for European impactresearch. Regional Environmental Change, 14, 563–578,  https://doi.org/10.1007/s10113-013-0499-2.CrossRefGoogle Scholar
  27. Ji, Z. M., and S. C. Kang, 2015: Evaluation of extreme climate events using a regional climate model for China. International Journal of Climatology, 35, 888–902,  https://doi.org/10.1002/joc.4024.CrossRefGoogle Scholar
  28. Karl, T. R., N. Nicholls, and A. Ghazi, 1999: Clivar/GCOS/WMO workshop on indices and indicators for climate extremes workshop summary. Climate Change, 42, 3–7,  https://doi.org/10.1023/A:1005491526870.CrossRefGoogle Scholar
  29. Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch, 1996: Description of the NCAR Community Climate Model (CCM3). NCAR Technical Note, NCAR/TN-420+STR,  https://doi.org/10.5065/D6FF3Q99.Google Scholar
  30. Liang, X. Z., and Coauthors, 2018: CWRF performance at downscaling China climate characteristics. Climate Dyn.,  https://doi.org/10.1007/s00382-018-4257-5.Google Scholar
  31. Liu, B. H., M. Henderson, and M. Xu, 2008: Spatiotemporal change in China’s frost days and frost-free season, 1955–2000. J. Geophys. Res., 113, D12104,  https://doi.org/10.1029/2007JD009259.CrossRefGoogle Scholar
  32. Liu, X. D., and B. D. Chen, 2000: Climatic warming in the Tibetan Plateau during recent decades. International Journal of Climatology, 20, 1729–1742,  https://doi.org/10.1002/1097-0088(20001130)20:14<1729::AID-JOC556>3.0.CO;2-Y.CrossRefGoogle Scholar
  33. Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts: The results of two years of real-time numerical weather prediction over the Pacific Northwest. Bull. Amer. Meteor. Soc., 83, 407–430,  https://doi.org/10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.CrossRefGoogle Scholar
  34. Niu, X. R., and Coauthors, 2018: Ensemble evaluation and projection of climate extremes in China using RMIP models. International Journal of Climatology, 38, 2039–2055,  https://doi.org/10.1002/joc.5315.CrossRefGoogle Scholar
  35. Pal, J. S., E. Small, and E. A. B. Eltahir, 2000: Simulation of regional-scale water and energy budgets: Representation of subgrid cloud and precipitation processes within RegCM. J. Geophys. Res., 105, 29 579–29 594,  https://doi.org/10.1029/2000JD900415.CrossRefGoogle Scholar
  36. Park, C., and Coauthors, 2016: Evaluation of multiple regional climate models for summer climate extremes over East Asia. Climate Dyn., 46, 2469–2486,  https://doi.org/10.1007/s00382-015-2713-z.CrossRefGoogle Scholar
  37. Pepin, N., and Coauthors, 2015: Elevation-dependent warming in mountain regions of the world. Nature Climate Change, 5, 424–430,  https://doi.org/10.1038/nclimate2563.CrossRefGoogle Scholar
  38. Qian, Y., and L. R. Leung, 2007: A long-term regional simulation and observations of the hydroclimate in China. J. Geophys. Res., 112, D14104,  https://doi.org/10.1029/2006JD008134.CrossRefGoogle Scholar
  39. Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Technical Note, NCAR/TN-475+STR,  https://doi.org/10.5065/D68S4MVH.Google Scholar
  40. Sukoriansky, S., B. Galperin, and V. Perov, 2006: A quasi-normal scale elimination model of turbulence and its application to stably stratified flows. Nonlinear Processes in Geophysics, 13, 9–22,  https://doi.org/10.5194/npg-13-9-2006.CrossRefGoogle Scholar
  41. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183–7192,  https://doi.org/10.1029/2000JD900719.CrossRefGoogle Scholar
  42. Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800,  https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.CrossRefGoogle Scholar
  43. Toreti, A., and Coauthors, 2013: Projections of global changes in precipitation extremes from Coupled Model Intercomparison Project Phase 5 models. Geophys. Res. Lett., 40, 4887–4892,  https://doi.org/10.1002/grl.50940.CrossRefGoogle Scholar
  44. Wang, A. H., and J. J. Fu, 2013: Changes in daily climate extremes of observed temperature and precipitation in China. Atmos. Oceanic Sci. Lett., 6, 312–319,  https://doi.org/10.1080/16742834.2013.11447100.CrossRefGoogle Scholar
  45. Wang, S. Y., and Coauthors, 2015: Regional integrated environmental modeling system: Development and application. Climate Change, 129, 499–510,  https://doi.org/10.1007/s10584-013-0973-3.CrossRefGoogle Scholar
  46. Wang, Z. Y., S. Yang, Z. J. Ke, and X. W. Jiang, 2014: Largescale atmospheric and oceanic conditions for extensive and persistent icing events in China. J. Applied Meteorology and Climatology, 53, 2698–2709,  https://doi.org/10.1175/JAMCD-14-0062.1.CrossRefGoogle Scholar
  47. Wigley, T. M. L., 1985: Climatology: Impact of extreme events. Nature, 316, 106–107,  https://doi.org/10.1038/316106a0.CrossRefGoogle Scholar
  48. Wu, J., and X. J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Journal of Geophysics, 56, 1102–1111,  https://doi.org/10.6038/cjg20130406. (in Chinese)Google Scholar
  49. Xu, J. W., and Coauthors, 2018: On the role of horizontal resolution over the Tibetan Plateau in the REMO regional climate model. Climate Dyn., 51, 4525–4542,  https://doi.org/10.1007/s00382-018-4085-7.CrossRefGoogle Scholar
  50. Yu, E. T., J. Q. Sun, H. P. Chen, and W. L. Xiang, 2015: Evaluation of a high-resolution historical simulation over China: Climatology and extremes. Climate Dyn., 45, 2013–2031,  https://doi.org/10.1007/s00382-014-2452-6.CrossRefGoogle Scholar
  51. Zeng, X. B., and A. Beljaars, 2005: A prognostic scheme of sea surface skin temperature for modeling and data assimilation. Geophys. Res. Lett., 32, L14605,  https://doi.org/10.1029/2005GL023030.CrossRefGoogle Scholar
  52. Zeng, X. B., M. Zhao, and R. E. Dickinson, 1998: Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J. Climate, 11, 2628–2644,  https://doi.org/10.1175/1520-0442(1998)011<2628:IOBAAF>2.0.CO;2.CrossRefGoogle Scholar
  53. Zhai, P. M., X. B. Zhang, H. Wan, and X. H. Pan, 2005: Trends in total precipitation and frequency of daily precipitation extremes over China. J. Climate, 18, 1096–1108,  https://doi.org/10.1175/JCLI-3318.1.CrossRefGoogle Scholar
  54. Zhang, Y. X., V. Dulière, P. W. Mote, and E. P. Jr. Salathé, 2009: Evaluation of WRF and HadRM mesoscale climate simulations over the U.S. Pacific Northwest. J. Climate, 22, 5511–5526,  https://doi.org/10.1175/2009JCLI2875.1.Google Scholar
  55. Zwiers, W. F., X. B. Zhang, and F. Yang, 2011: Anthropogenic influence on long return period daily temperature extremes at regional scales. J. Climate, 24, 881–892,  https://doi.org/10.1175/2010JCLI3908.1.CrossRefGoogle Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xianghui Kong
    • 1
  • Aihui Wang
    • 1
    Email author
  • Xunqiang Bi
    • 2
  • Dan Wang
    • 1
  1. 1.Nansen-Zhu International Research Centre, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Climate Change Research Center, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

Personalised recommendations