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Climate Dynamics

, Volume 54, Issue 1–2, pp 159–172 | Cite as

How can CMIP5 AGCMs’ resolution influence precipitation in mountain areas: the Hengduan Mountains?

  • Weichen Tao
  • Gang HuangEmail author
  • William K. M. Lau
  • Danhong Dong
  • Pengfei Wang
  • Guanhuan Wen
Article
  • 82 Downloads

Abstract

The precipitation over the Hengduan Mountains (HMs) during rainy seasons is investigated based on observations, reanalysis datasets, and 28 atmospheric general circulation models (AGCMs) from Coupled Model Intercomparison Project phase 5 (CMIP5). Most CMIP5 AGCMs generally capture two observed precipitation centers over the southwestern HMs and on the west side of Sichuan basin (WSSB), but their location, range, and magnitude vary with models. As the horizontal resolution increases, the details of simulated precipitation pattern are improved and closer to observation and reanalysis, as well as the increasing magnitude of precipitation over the southwestern HMs. However, the simulated precipitation on the WSSB is overestimated regardless of resolution. Mechanisms involved in resolution affecting precipitation pattern and biases of precipitation on the WSSB are explored. Representation of topography in AGCMs influences orographic effect, which contributes to simulations of both horizontal and vertical moisture flux convergence and further precipitation over the HMs. The biases of WSSB precipitation between reanalysis and AGCMs are attributed to the discrepancy in the vertical distribution of upward motions. The simulated upward motions can reach a higher level than reanalysis, and a spurious center of upward motions develops at 400 hPa due to the overestimation of circulation-precipitation feedback in AGCMs.

Keywords

The Hengduan Mountains Rainy-season precipitation CMIP5 Model resolution Topography Orographic effect Circulation-precipitation feedback 

Notes

Acknowledgements

We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank two anonymous reviewers as well as the editor for their useful comments. This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060501), the National Key R&D Program of China (2018YFA0605904), the National Natural Science Foundation of China (41705068, 41831175, 41425019, and 41721004), the China Postdoctoral Science Foundation (2016LH0005 and 2016M600116), the scholarship from China Scholarships Council under the State Scholarship Fund (201704910055), and the Natural Science Foundation of Guangdong Province (2016A030310009).

Supplementary material

382_2019_4993_MOESM1_ESM.pdf (618 kb)
Supplementary material 1 (PDF 618 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkUSA
  3. 3.Laboratory for Regional Oceanography and Numerical ModelingQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  4. 4.Joint Center for Global Change StudiesBeijingChina
  5. 5.University of Chinese Academy of SciencesBeijingChina
  6. 6.Center for Monsoon System Research, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  7. 7.Guangzhou Institute of Tropical and Marine MeteorologyChina Meteorological AdministrationGuangzhouChina

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