Theoretical and Applied Climatology

, Volume 133, Issue 3–4, pp 763–773 | Cite as

Modeling of a severe winter drought in eastern China using different initial and lateral boundary forcing datasets

  • Chunchun Meng
  • Yaoming Ma
  • Weiqiang Ma
  • Yinlong Xu
Original Paper


This paper describes the performance of the Regional Atmospheric Modeling System (RAMS) in simulating a winter drought event, based on two different forcing datasets. We ran EC (ERA-Interim reanalysis data as initial and lateral boundary forcing conditions) and FNL (NCEP-FNL reanalysis data) simulations for the 2008/2009 winter drought event to quantify the impact of any uncertainty in the different initial and lateral boundary forcing data on regional model outputs. The response of the winter mean atmospheric states to the variations in the initial and lateral boundary conditions was investigated on the basis of these simulation results. The spatio-temporal features of precipitation from the EC and FNL runs closely resembled those measured from the Global Summary Of the Day (GSOD) observations, although the EC run data outperformed the FNL run data in both their spatial distribution patterns and precipitation values. The water vapor flux values explain how the differences in the precipitation values between the EC and the FNL runs were generated, whereas temperature values were not sensitive to any changes in forcing data. The model results from these runs also slightly overestimated temperature on both spatial and temporal scales. For the tropospheric atmospheric data recorded at the Fuyang Meteorological Station in Anhui Province, neither the time series nor the statistical analyses showed any evidence of superiority between the two different driver datasets compared with radiosonde data. However, on closer inspection, the influence of different initial and lateral boundary conditions on modeling the tropospheric atmospheric data appeared to be evident.



The authors should like to thank Dr. Lixin Lu and Mr. Steve Saleeby at Colorado State University for their kind support and help in using RAMS. This work was supported by the Chinese Academy of Sciences (Grant No. XDB03030201), the National Natural Science Foundation of China (Grant Nos. 91337212, 41275010, and 41375009), the External Cooperation Program of the Chinese Academy of Sciences (Grant No. GJHZ1207), the CMA Special Fund for Scientific Research in the Public Interest (Grant No. GYHY201406001), the EU-FP7 “CORE-CLIMAX” Project (Grant No. 313085), the Key Projects of China’s national twelfth 5-year Science and Technology Pillar Program (2013BAC09B04), and the Chinese Academy of Sciences “Hundred Talent” program (Dr. Weiqiang Ma).


  1. Arritt RW, Rummukainen M (2011) Challenges in regional-scale climate modeling. Bull Am Meteorol Soc 92:365. doi: 10.1175/2010BAMS2971.1 CrossRefGoogle Scholar
  2. Brands S, Gutiérrez JM, Herrera S et al (2012) On the use of reanalysis data for downscaling. J Clim 25(7):2517–2526. doi: 10.1175/jcli-d-11-00251.1 CrossRefGoogle Scholar
  3. Castro CL, Sr RAP, Leoncini G (2005) Dynamical downscaling: assessment of value retained and added using the regional atmospheric modeling system (rams). J Geophys Res Atmos 110(D5):851–862. doi: 10.1029/2004JD004721 CrossRefGoogle Scholar
  4. Castro CL, Pielke RA, Adegoke JO (2007) Investigation of the summer climate of the contiguous United States and Mexico using the regional atmospheric modeling system(RAMS). Part I: model climatology (1950–2002). J Clim 20:3844–3865. doi: 10.1175/JCLI4212.1 CrossRefGoogle Scholar
  5. Chen W (2002) Impacts of el Nino and la Nina on the cycle of the East Asian winter and summer monsoon. Chin J Atmos Sci 26:595–610Google Scholar
  6. Chen W, Graf HF, Huang R (2000) The interannual variability of East Asian winter monsoon and its relation to the summer monsoon. Adv Atmos Sci 01:48–60. doi: 10.1007/s00376-000-0042-5 Google Scholar
  7. Cotton WR et al (2003) RAMS 2001:current status and future directions. Meteorog Atmos Phys 82:5–29. doi: 10.1007/s00703-001-0584-9 CrossRefGoogle Scholar
  8. Dai A, Trenberth KE, Qian T (2004) A global data set of palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming. J Hydrometeorol 5:1117–1130. doi: 10.1175/JHM-386.1 CrossRefGoogle Scholar
  9. Druyan LM, Feng J, Cook KH et al (2010) The WAMME regional model intercomparison study. Clim Dyn 35(1):175–192. doi: 10.1007/s00382-009-0676-7 CrossRefGoogle Scholar
  10. European Centre for Medium-Range Weather (2009) ERA-Interim project, in, research data Archive at the National Center for Atmospheric Research. Computational and Information Systems Laboratory, BoulderGoogle Scholar
  11. Harrington JY (1997) The effects of radiative and microphysical processes on simulated warm and transition season Arctic stratus (Doctoral dissertation, Colorado State University)Google Scholar
  12. Hasler N, Avissar R, Liston GE (2005) Issues in simulating the annual precipitation of a semiarid region in Central Spain. J Hydrometeorol 6:409–422. doi: 10.1175/JHM418.1 CrossRefGoogle Scholar
  13. Jacob D, Podzun R (1997) Sensitivity studies with the regional climate model REMO. Meteorog Atmos Phys 63(1):119–129. doi: 10.1007/B F01025368 CrossRefGoogle Scholar
  14. Jiang P, Ye S, Chen D, Liu Y, Xia P (2016) Retrieving precipitable water vapor data using gps zenith delays and global reanalysis data in china. Remote Sens 8(5):389. doi: 10.3390/rs8050389 CrossRefGoogle Scholar
  15. Kain JS (1993) Convective parametrization for mesoscale models: the Kain-Fritsch scheme[J]. Meteorol Monogr 46:165–170. doi: 10.1007/978-1-935704-13-3_16 Google Scholar
  16. Kim J, Waliser DE, Mattmann CA et al (2014) Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors. Clim Dyn 42(5–6):1189–1202. doi: 10.1007/s00382-013-1751-7 CrossRefGoogle Scholar
  17. Li Z, Song L, Ma H et al (2017) Observed surface wind speed declining induced by urbanization in East China. Clim Dyn. doi: 10.1007/s00382-017-3637-6
  18. Liang XZ, Kunkel KE, Samel AN, Kunkel KE, Samel AN (2001) Development of a regional climate model for U.S. Midwest applications. Part I: sensitivity to buffer zone treatment. J Clim 14:4363–4378. doi: 10.1175/1520-0442(2001)014<4363:DOARCM>2.0.CO;2 CrossRefGoogle Scholar
  19. Liang XZ, Li L, Kunkel KE, Ting M, Wang JXL (2004) Regional climate model simulation of U.S. precipitation during 1982-2002. Part I: annual cycle. J Clim 17:3510–3529. doi: 10.1175/1520-0442(2004)017<3510:RCMSOU>2.0.CO;2 CrossRefGoogle Scholar
  20. Liu L, Zuo RZA (2014) Intercomparison of spring soil moisture among multiple reanalysis data sets over eastern china. J Geophys Res Atmos 1(1):54–64. doi: 10.1002/2013JD020940 CrossRefGoogle Scholar
  21. Lu L, Shuttleworth WJ (2002) Incorporating NDVI-derived LAI into the climate version of rams and its impact on regional climate. J Hydrometeorol 3:347–362. doi: 10.1175/1525-7541(2002)003 CrossRefGoogle Scholar
  22. Mellor GL, Yamada T (1974) A hierarchy of turbulence closure models for planetary boundary layers. J Atmos Sci 31:1791–1806. doi: 10.1175/1520-0469(1974)031<1791:AHOTCM>2.0.CO;2 CrossRefGoogle Scholar
  23. Meng C, Ma Y, Han C, Gou P (2016) Effect of reducing the topographical altitude of the Tibetan Plateau on a severe winter drought in eastern China as determined using RAMS[J]. Theor Appl Climatol. doi: 10.1007/s00704-016-1817-7
  24. National Centers for Environmental Prediction, N. W. S., NOAA, U. S. Department of Commerce (2000) NCEP FNL operational model global tropospheric analyses, continuing from July 1999[C]// research data Archive at the National Center for Atmospheric Research, computational and information systems Laboratory. Boulder, COGoogle Scholar
  25. Pielke RA Sr, Wilby RL (2012) Regional climate downscaling: what's the point? Eos trans. AGU 93–52Google Scholar
  26. Pielke R et al (1992) A comprehensive meteorological modeling system-RAMS. Meteorog Atmos Phys 49:69–91. doi: 10.1007/BF01025401 CrossRefGoogle Scholar
  27. Saleeby SM, Cotton WR (2004) Simulations of the north American monsoon system. Part I: model analysis of the 1993 monsoon season. J Clim 17:1997–2018. doi: 10.1175/1520-0442(2004)017<1997:SOTNAM>2.0.CO;2 CrossRefGoogle Scholar
  28. Saleeby SM, van den Heever SC (2013) Developments in the CSU-RAMS aerosol model: emissions, nucleation, regeneration, deposition, and radiation. J Appl Meteorol Climatol 52:2601–2622. doi: 10.1175/JAMC-D-12-0312.1 CrossRefGoogle Scholar
  29. Simmons AJ, Uppala SM, Dee D, Kobayashi S (2007) ERA-Interim: new ECMWF reanalysis products from 1989 onwards. Ecmwf Newsletter 110:25–35Google Scholar
  30. Skamarock WC, Klemp JB, Dudhia J et al (2005) A description of the advanced research WRF version 2. NCAR tech notes-468 + STRGoogle Scholar
  31. Song, L. C., Z. Y. Deng, and A. X. Dong (2003) Drought, China Meteorol. Press, BeijingGoogle Scholar
  32. Tao S, Wei J, Sun J, Zhao S (2009) The severe drought in East China dring November, December and January 2008-2009(in Chinese). Meteorol Mon 35:3–10Google Scholar
  33. Vukicevic T, Errico RM (1990) The influence of artificial and physical factors upon predictability estimates using a complex limited-area model. Mon Weather Rev 118:1460–1482. doi: 10.1175/1520-0493(1990)118<1460:TIOAAP>2.0.CO;2 CrossRefGoogle Scholar
  34. Walko RL, Tremback CJ (2005) ATMET technical note 1, modifications for the transition from LEAF-2 to LEAF-3, ATMET, LLC, Boulder, Colorado 80308–2195Google Scholar
  35. Wang H (2001) The weakening of the Asian monsoon circulation after the end of 1970's. Adv Atmos Sci 18:376–386. doi: 10.1007/BF02919316 CrossRefGoogle Scholar
  36. Wang B, Yang H (2008) Hydrological issues in lateral boundary conditions for regional climate modeling: simulation of east asian summer monsoon in 1998. Clim Dyn 31(4):477–490CrossRefGoogle Scholar
  37. Wang JJ, Hu X, Guo XR (2001) Comparison experiments on cumulus parameterization schemes of the MM5 [J]. Quart J Appl Meteor 12(1):41–53Google Scholar
  38. Warner TT, Peterson RA, Treadon RE (1997) A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull Am Meteorol Soc 78:2599–2617. doi: 10.1175/1520-0477(1997)078<2599:ATOLBC>2. 0.CO;2 CrossRefGoogle Scholar
  39. Wu W, Lynch AH, Rivers A (2005) Estimating the uncertainty in a regional climate model related to initial and lateral boundary conditions. J Clim 18:917–933. doi: 10.1175/JCLI-3293.1 CrossRefGoogle Scholar
  40. Xue Y, Vasic R, Janjic Z, Mesinger F, Mitchell KE (2007) Assessment of dynamic downscaling of the continental US regional climate using the eta/SSiB regional climate model. J Clim 20(16):4172–4193. doi: 10.1175/JCLI4239.1 CrossRefGoogle Scholar
  41. Xue Y, Janjic Z, Dudhia J et al (2014) A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmos Res 147:68–85. doi: 10.1016/j.atmosres.2014.05.001 CrossRefGoogle Scholar
  42. Yang H, Wang B, Wang B (2012) Reduction of systematic biases in regional climate downscaling through ensemble forcing. Clim Dyn 38(3–4):655–665. doi: 10.1007/s00382-011-1006-4
  43. Zhang GL (2005) A profile of the south-north water diversion project(in Chinese). Water Conservancy and Hydropower Construction 2:19–27Google Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  • Chunchun Meng
    • 1
    • 2
    • 3
  • Yaoming Ma
    • 2
    • 3
  • Weiqiang Ma
    • 2
  • Yinlong Xu
    • 1
  1. 1.Institute of Environment and Sustainable Development in AgricultureChinese Academy of Agricultural SciencesBeijingChina
  2. 2.Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau ResearchChinese Academy of Sciences, CAS Center for Excellence in Tibetan Plateau Earth SciencesBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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