Theoretical and Applied Climatology

, Volume 135, Issue 3–4, pp 1641–1658 | Cite as

Downstream effect of Hengduan Mountains on East China in the REMO regional climate model

  • Jingwei XuEmail author
  • Nikolay V. Koldunov
  • Armelle Reca C. Remedio
  • Dmitry V. Sein
  • Diana Rechid
  • Xiefei Zhi
  • Xi Jiang
  • Min Xu
  • Xiuhua Zhu
  • Klaus Fraedrich
  • Daniela Jacob
Original Paper


The Hengduan Mountains and Tibetan Plateau possess unique topographical characteristics that serve as an effective blocking of the movement of the westerly wind in the middle and lower troposphere towards East China. This study examines results from a regional climate model (REMO) at the resolutions of 25 and 50 km for the period 1980–2012. The model is run using lateral boundary conditions from ERA-Interim (European Centre for Medium-Range Weather Forecasts interim reanalysis). There are only a few differences between 25 and 50 km in land surface/vegetation characteristics, but the major differences in this region are due to the orography. Results show that the high-resolution simulation performance is poor in winter, when southwesterly wind prevails, whereas it performs well in summer, when the westerly wind is substantially weakened in southern China. In comparison to the ERA-Interim wind field, the high-resolution simulation overestimates the air flow over the Hengduan Mountains near the ground surface, which influences the transport of atmospheric water vapor in the downstream region, i.e., over southern China. Specifically, with the help of the overestimated southwesterly wind, the amount of atmospheric water vapor transported increases considerably perennially by up to 20% in southern China, while it decreases remarkably by more than 5% throughout the year in a large area of Central and North China. These features lead to excessive precipitation and underestimated cloud cover in southern China, which probably causes the overestimated 2-m temperature in southern China. Our study emphasizes that, in such high-resolution-model studies for East Asia, special attention should be paid to the near-surface winds over the Hengduan Mountains.



We thank anonymous reviewers for comments and suggestions that helped to improve the manuscript. We thank the ECMWF and CMA for providing the gridded data. We thank the Climate System Department at GERICS for the consultations that took place regarding the REMO model. The simulations were carried out at the German Climate Computing Center (DKRZ).

Funding information

This work is supported by a project entitled “Relationships between glacier changes and atmospheric circulation in High Mountain Asia,” supported by the National Natural Science Foundation of China (Nos. 41871053, 41371095, 91337218), the China Special Fund for Meteorological Research in the Public Interest (No. GYHY 201406008), project S1 (Diagnosis and Metrics in Climate Models) of the Collaborative Research Centre TRR 181 Energy Transfer in Atmosphere and Ocean program funded by the German Research Foundation, EC project PRIMAVERA under grant agreement no. 641727, and the state assignment of FASO Russia (theme Nos. 0149-2019-0015, 0149-2018-0014). This study was also funded by “the Priority Academic Program Development of Jiangsu Higher Education Institutions” (PAPD).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.


  1. Asselin R (1972) Frequency filter for time integrations. Mon Wea Rev 100:487–490CrossRefGoogle Scholar
  2. Borscheid P (2015) Temporal and spatial scaling impacts on extreme precipitation. Atmo Chem Phys 15:2157–2196Google Scholar
  3. Casanueva A et al (2015) Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations. Clim Dyn 31:1–19Google Scholar
  4. Davies HC (1983) Limitations of some common lateral boundary schemes used in regional NWP models. Mon Weather Rev 111:1002–1012CrossRefGoogle Scholar
  5. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  6. Déqué M, Rowell DP, Lüthi D, Giorgi F, Christensen JH, Rockel B, Jacob D, Kjellström E, de Castro M, van den Hurk B (2007) An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim Chang 81:53–70CrossRefGoogle Scholar
  7. Gao X, Xu Y, Zhao Z, Pal J, Giorgi F (2006) On the role of resolution and topography in the simulation of East Asia precipitation. Theor Appl Climatol 86:173–185CrossRefGoogle Scholar
  8. Gao Y, Cuo L, Zhang Y (2014) Changes in moisture flux over the Tibetan Plateau during 1979–2011 and possible mechanisms. J Clim 27:1876–1893CrossRefGoogle Scholar
  9. Gao Y, Leung LR, Zhang Y, Cuo L (2015a) Changes in moisture flux over the Tibetan Plateau during 1979–2011: insights from a high-resolution simulation. J Clim 28:4185–4197CrossRefGoogle Scholar
  10. Gao Y, Xu J, Chen D (2015b) Evaluation of WRF mesoscale climate simulations over the Tibetan Plateau during 1979–2011. J Clim 28:2823–2841CrossRefGoogle Scholar
  11. Hagemann S (2002) An improved land surface parameter dataset for global and regional climate models. Report 336, Max Planck Institute for Meteorology, HamburgGoogle Scholar
  12. Hagemann S, Botzet M, Dümenil L, Machenhauer B (1999) Derivation of global GCM boundary conditions from 1 km land use satellite data. Report 289, Max Planck Institute for Meteorology, HamburgGoogle Scholar
  13. Jacob D (2001) A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin. Meteorog Atmos Phys 77:61–73CrossRefGoogle Scholar
  14. Jacob D, Bärring L, Christensen OB, Christensen JH, de Castro M, Déqué M, Giorgi F, Hagemann S, Hirschi M, Jones R, Kjellström E, Lenderink G, Rockel B, Sánchez E, Schär C, Seneviratne SI, Somot S, van Ulden A, van den Hurk B (2007) An inter-comparison of regional climate models for Europe: model performance in present-day climate. Clim Chang 81:31–52CrossRefGoogle Scholar
  15. Jacob D, Elizalde A, Haensler A, Hagemann S, Kumar P, Podzun R, Rechid D, Remedio AR, Saeed F, Sieck K, Teichmann C, Wilhelm C (2012) Assessing the transferability of the regional climate model REMO to different coordinated regional climate downscaling experiment (CORDEX) regions. Atmosphere 3:181–199CrossRefGoogle Scholar
  16. Jacob D et al (2001) A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period. Meteorog Atmos Phys 77:19–43CrossRefGoogle Scholar
  17. Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, van Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana JF, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Chang 14:563–578CrossRefGoogle Scholar
  18. Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, van Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana JF, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2013) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Chang 14:563–578. CrossRefGoogle Scholar
  19. Jacob D, Podzun R (1997) Sensitivity studies with the regional climate model REMO. Meteorog Atmos Phys 63:119–129CrossRefGoogle Scholar
  20. Koldunov NV, Kumar P, Rasmussen R, Ramanathan AL, Nesje A, Engelhardt M, Tewari M, Haensler A, Jacob D (2016) Identifying climate change information needs for the Himalayan region: results from the GLACINDIA stakeholder workshop and training program. Bull Am Meteorol Soc 97:ES37–ES40CrossRefGoogle Scholar
  21. Kumar P, Podzun R, Hagemann S, Jacob D (2014) Impact of modified soil thermal characteristic on the simulated monsoon climate over South Asia. J Earth Syst Sci 123:151–160CrossRefGoogle Scholar
  22. Luca AD, Elía RD, Laprise R (2012) Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations. Clim Dyn 38:1229–1247CrossRefGoogle Scholar
  23. Lucas-Picher P, Wulff-Nielsen M, Christensen JH, Mottram R, Simonsen SB (2012) Very high resolution regional climate model simulations over Greenland: identifying added value. J Geophys Res Atmos 117:262–269CrossRefGoogle Scholar
  24. Majewski D (1991) The Europa-Modell of the Deutscher Wetterdienst. In: ECMWF seminar on numerical methods in atmospheric models, pp 147–191Google Scholar
  25. Prein AF et al. (2016) Precipitation in the EURO-CORDEX 0.11° and 0.44° simulations: high resolution, high benefits? Clim Dyn 46:383–412Google Scholar
  26. Rechid D, Hagemann S, Jacob D (2009) Sensitivity of climate models to seasonal variability of snow-free land surface albedo. Theor Appl Climatol 95:197–221CrossRefGoogle Scholar
  27. Redler R (2015) YAC 1.2. 0: an extendable coupling software for earth system modelling. Geosci Model DevGoogle Scholar
  28. Ritter B, Geleyn J-F (1992) A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon Weather Rev 120:303–325CrossRefGoogle Scholar
  29. Roeckner E et al (1996) The atmospheric general circulation model ECHAM-4: model description and simulation of present-day climate. Comptes Rendus Des Séances De La Société De Biologie Et De Ses Filiales 151:361–363Google Scholar
  30. Roeckner E et al. (2003) The atmospheric general circulation model ECHAM 5. Part I: model description Report 349, Max Planck Institute for Meteorology, HamburgGoogle Scholar
  31. Saeed F, Hagemann S, Jacob D (2012) A framework for the evaluation of the South Asian summer monsoon in a regional climate model applied to REMO. Int J Climatol 32:430–440. CrossRefGoogle Scholar
  32. Sein DV, Mikolajewicz U, Gröger M, Fast I, Cabos W, Pinto JG, Hagemann S, Semmler T, Izquierdo A, Jacob D (2015) Regionally coupled atmosphere-ocean-sea ice-marine biogeochemistry model ROM: 1. Description and validation. J Adv Model Earth Syst 7:268–304CrossRefGoogle Scholar
  33. Simmons AJ, Burridge DM (1981) An energy and angular-momentum conserving vertical finite-difference scheme and hybrid vertical coordinates. Mon Weather Rev 109:758–766CrossRefGoogle Scholar
  34. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. CrossRefGoogle Scholar
  35. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117:1779–1800CrossRefGoogle Scholar
  36. Wang A, Zeng X (2012) Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau. J Geophys Res Atmos 117Google Scholar
  37. Wang QW, Tan ZM (2014) Multi-scale topographic control of southwest vortex formation in Tibetan Plateau region in an idealized simulation. J Geophys Res Atmos 119Google Scholar
  38. Wu J, Gao X (2013) A gridded daily observation dataset over China region and comparison with the other datasets. Chin J Geophys 56:1102–1111Google Scholar
  39. Xu J et al (2018) On the role of horizontal resolution over the Tibetan Plateau in the REMO regional climate model. Clim Dyn:1–18Google Scholar
  40. Xu Y, Gao X, Shen Y, Xu C, Shi Y, Giorgi F (2009) A daily temperature dataset over China and its application in validating a RCM simulation. Adv Atmos Sci 26:763–772CrossRefGoogle Scholar
  41. Zhang Y, Gao H, Lammel G (2005) Simulation of monsoon seasonal variation of regional climate model REMO in East Asia (in Chinese). Climatic and Environmental Research 10:41–55Google Scholar
  42. Zhou TJ, Yu RC (2005) Atmospheric water vapor transport associated with typical anomalous summer rainfall patterns in China. J Geophys Res Atmos:110Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Jingwei Xu
    • 1
    • 2
    • 3
    Email author
  • Nikolay V. Koldunov
    • 4
    • 5
  • Armelle Reca C. Remedio
    • 2
  • Dmitry V. Sein
    • 5
    • 6
  • Diana Rechid
    • 2
  • Xiefei Zhi
    • 1
  • Xi Jiang
    • 1
  • Min Xu
    • 7
  • Xiuhua Zhu
    • 8
  • Klaus Fraedrich
    • 1
    • 3
  • Daniela Jacob
    • 2
  1. 1.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Joint Center for Data Assimilation Research and ApplicationsNanjing University of Information Sciences and Technology (NUIST)NanjingChina
  2. 2.Climate Service Center Germany (GERICS)/Helmholtz-Zentrum Geesthacht (HZG)HamburgGermany
  3. 3.Max Planck Institute for MeteorologyHamburgGermany
  4. 4.MARUM—Center for Marine Environmental SciencesBremenGermany
  5. 5.Alfred Wegener Institute (AWI)BremerhavenGermany
  6. 6.Russian Academy of ScienceShirshov Institute of OceanologyMoscowRussia
  7. 7.Meteorological Bureau of Jiangsu ProvinceNanjingChina
  8. 8.Center for Earth System Research and Sustainability, CliSAPUniversity of HamburgHamburgGermany

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