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Journal of Meteorological Research

, Volume 32, Issue 6, pp 862–880 | Cite as

An Assessment of CAMS-CSM in Simulating Land–Atmosphere Heat and Water Exchanges

  • Guo ZhangEmail author
  • Jianduo Li
  • Xinyao Rong
  • Yufei Xin
  • Jian Li
  • Haoming Chen
  • Jingzhi Su
  • Lijuan Hua
Special Collection on CAMS-CSM
  • 202 Downloads

Abstract

The Chinese Academy of Meteorological Sciences (CAMS) has been devoted to developing a climate system model (CSM) to meet demand for climate simulation and prediction for the East Asian region. In this study, we evaluated the performance of CAMS-CSM in regard to sensible heat flux (H), latent heat flux (LE), surface temperature, soil moisture, and snow depth, focusing on the Atmospheric Model Intercomparison Project experiment, with the aim of participating in the Coupled Model Intercomparison Project phase 6. We systematically assessed the simulation results achieved by CAMS-CSM for these variables against various reference products and ground observations, including the FLUXNET model tree ensembles H and LE data, Climate Prediction Center soil moisture data, snow depth climatology data, and Chinese ground observations of snow depth and winter surface temperature. We compared these results with data from the ECMWF Interim reanalysis (ERA-Interim) and Global Land Data Assimilation System (GLDAS). Our results indicated that CAMS-CSM simulations were better than or comparable to ERAInterim reanalysis for snow depth and winter surface temperature at regional scales, but slightly worse when simulating total column soil moisture. The root-mean-square differences of H in CAMS-CSM were all greater than those from the ERA-Interim reanalysis, but less than or comparable to those from GLDAS. The spatial correlations for H in CAMS-CSM were the lowest in nearly all regions, except for North America. CAMS-CSM LE produced the lowest bias in Siberia, North America, and South America, but with the lowest spatial correlation coefficients. Therefore, there are still scopes for improving H and LE simulations in CAMS-CSM, particularly for LE.

Key words

Climate System Model of the Chinese Academy of Meteorological Sciences Atmospheric Model Intercomparison Project sensible heat flux latent heat flux 

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References

  1. Albergel, C., P. De Rosnay, G. Balsamo, et al., 2012: Soil moisture analyses at ECMWF: Evaluation using global groundbased in situ observations. J. Hydrometeorol., 13, 1442–1460, doi: 10.1175/JHM-D-11-0107.1.CrossRefGoogle Scholar
  2. Armstrong, R. L., and M. J. Brodzik, 2001: Recent Northern Hemisphere snow extent: A comparison of data derived from visible and microwave satellite sensors. Geophys. Res. Lett., 28, 3673–3676, doi: 10.1029/2000GL012556.CrossRefGoogle Scholar
  3. Balsamo, G., S. Boussetta, E. Dutra, et al., 2011: Evolution of land-surface processes in the IFS. ECMWF Newsletter, No. 127, 17–22. doi:10.21957/x1j3i7bz.Google Scholar
  4. Barlage, M., F. Chen, M. Tewari, et al., 2010: Noah land surface model modifications to improve snowpack prediction in the Colorado Rocky Mountains. J. Geophys. Res. Atmos., 115, D22101, doi: 10.1029/2009JD013470.CrossRefGoogle Scholar
  5. Berrisford, P., D. Dee, K. Fielding, et al., 2009: The ERA-Interim Archive. ECMWF, Reading, UK, 16 pp.Google Scholar
  6. Betts, A. K., F. Chen, K. E. Mitchell, et al., 1997: Assessment of the land surface and boundary layer models in two operational versions of the NCEP Eta model using FIFE data. Mon. Wea. Rev., 125, 2896–2916, doi: 10.1175/1520-0493(1997)125 <2896:AOTLSA>2.0.CO;2.CrossRefGoogle Scholar
  7. Bonan, G. B., 1996: A Land Surface model (LSM version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User’s Guide. NCAR Technical Note: NCAR/TN-417+STR, National Center for Atmospheric Research, Boulder, CO, doi:10.5065/D6DF6P5X.Google Scholar
  8. Cai, X. T., Z. L. Yang, Y. L. Xia, et al., 2014: Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS test bed. J. Geophys. Res. Atmos., 119, 13751–13770, doi: 10.1002/2014JD022113.CrossRefGoogle Scholar
  9. Chen, Y. Y., K. Yang, J. Qin, et al., 2013: Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. J. Geophys. Res. Atmos., 118, 4466–4475, doi: 10.1002/jgrd.50301.CrossRefGoogle Scholar
  10. Clapp, R. B., and G. M. Hornberger, 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14, 601–604, doi: 10.1029/WR014i004p00601.CrossRefGoogle Scholar
  11. Dai, Y. J., and Q. C. Zeng, 1997: A land surface model (IAP94) for climate studies Part I: Formulation and validation in offline experiments. Adv. Atmos. Sci., 14, 433–460, doi: 10.1007/s00376-997-0063-4.CrossRefGoogle Scholar
  12. Dai, Y. J., X. B. Zeng, R. E. Dickinson, et al., 2003: The common land model. Bull. Amer. Meteor. Soc., 84, 1013–1024, doi: 10.1175/BAMS-84-8-1013.CrossRefGoogle Scholar
  13. Dee, D. P., S. M. Uppala, A. J. Simmons, et al., 2011: The ERAInterim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi: 10.1002/qj.828.CrossRefGoogle Scholar
  14. 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, National Center for Atmospheric Research, Boulder, CO, doi: 10.5065/D67W6959.Google Scholar
  15. Dirmeyer, P. A., Z. C. Guo, and X. Gao, 2004: Comparison, validation, and transferability of eight multiyear global soil wetness products. J. Hydrometeorol., 5, 1011–1033, doi: 10.1175/JHM-388.1.CrossRefGoogle Scholar
  16. Dorigo, W., R. de Jeu, D. Chung, et al., 2012: Evaluating global trends (1988–2010) in harmonized multi-satellite surface soil moisture. Geophys. Res. Lett., 39, L18405, doi: 10.1029/2012 GL052988.CrossRefGoogle Scholar
  17. Dorigo, W. A., A. Gruber, R. A. M. de Jeu, et al., 2015: Evaluation of the ESA CCI soil moisture product using groundbased observations. Remote Sens. Environ., 162, 380–395, doi: 10.1016/j.rse.2014.07.023.CrossRefGoogle Scholar
  18. Douville, H., J. F. Royer, and J. F. Mahfouf, 1995: A new snow parameterization for the Météo-France climate model. Part I: Validation in stand-alone experiments. Climate Dyn., 12, 21–35, doi: 10.1007/BF00208760.Google Scholar
  19. Dutra, E., G. Balsamo, P. Viterbo, et al., 2010: An improved snow scheme for the ECMWF land surface model: Description and offline validation. J. Hydrometeorol., 11, 899–916, doi: 10.11 75/2010JHM1249.1.CrossRefGoogle Scholar
  20. Ebita, A., S. Kobayashi, Y. Ota, et al., 2011: The Japanese 55-year reanalysis “JRA-55”: An interim report. SOLA, 7, 149–152, doi: 10.2151/sola.2011-038.CrossRefGoogle Scholar
  21. Essery, R., 1997: Seasonal snow cover and climate change in the Hadley Centre GCM. Ann. Glaciol., 25, 362–366, doi: 10.31 89/S0260305500014282.CrossRefGoogle Scholar
  22. Eyring, V., S. Bony, G. A. Meehl, et al., 2016: Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958, doi: 10.5194/gmd-9-1937-2016.CrossRefGoogle Scholar
  23. Fan, Y., and H. van den Dool, 2004: Climate Prediction Center global monthly soil moisture data set at 0.5° resolution for 1948 to present. J. Geophys. Res. Atmos., 109, D10102, doi: 10.1029/2003JD004345.CrossRefGoogle Scholar
  24. Foster, D. F. Jr., and R. D. Davy, 1988: Global Snow Depth Climatology. USAF Environmental Technical Application Center, Scott Air Force Base, Illinois, USA, 48 pp.CrossRefGoogle Scholar
  25. Frankenberg, C., J. B. Fisher, J. Worden, et al., 2011: New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett., 38, L17706, doi: 10.1029/2011GL 048738.CrossRefGoogle Scholar
  26. Griffies, S. M., 2010: Elements of MOM4p1. GFDL Ocean Group Technical Report No. 6, NOAA/Geophysical Fluid Dynamics Laboratory, 444 pp.Google Scholar
  27. Huang, J., H. M. van den Dool, and K. P. Georgarakos, 1996: Analysis of model-calculated soil moisture over the United States (1931–1993) and applications to long-range temperature forecasts. J. Climate, 9, 1350–1362, doi: 10.1175/1520-0442 (1996)009<1350:AOMCSM>2.0.CO;2.CrossRefGoogle Scholar
  28. Ikeda, K., R. Rasmussen, C. H. Liu, et al., 2010: Simulation of seasonal snowfall over Colorado. Atmos. Res., 97, 462–477, doi: 10.1016/j.atmosres.2010.04.010.CrossRefGoogle Scholar
  29. Ji, D., L. Wang, J. Feng, et al., 2014: Description and basic evalu-ation of Beijing Normal University Earth System Model (BNU-ESM) version 1. Geosci. Model Dev., 7, 2039–2064, doi: 10.5194/gmd-7-2039-2014.CrossRefGoogle Scholar
  30. Ji, D. and Y. Dai, 2010: The Common land model (CoLM) technical guide. [available online at http://globalchange.bnu.edu.cn/download/doc/CoLM/CoLM_Technical_Guide.pdf].Google Scholar
  31. Jung, M., M. Reichstein, H. A. Margolis, et al., 2011: Global patterns of land–atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. Biogeosci., 116, G00J07, doi: 10.1029/2010JG001566.CrossRefGoogle Scholar
  32. Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–472, doi: 10.1175/1520-0477(1996)077<0437:TNY RP>2.0.CO;2.CrossRefGoogle Scholar
  33. Koster, R. D., Z. C. Guo, R. Q. Yang, et al., 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 4322–4335, doi: 10.1175/2009JCLI2832.1.CrossRefGoogle Scholar
  34. Kumar, S. V., C. D. Peters-Lidard, Y. Tian, et al., 2006: Land Information System: An interoperable framework for high resolution land surface modeling. Environ. Modell. Softw., 21, 1402–1415, doi: 10.1016/j.envsoft.2005.07.004.CrossRefGoogle Scholar
  35. Li, C. W., H. Lu, K. Yang, et al., 2017: Evaluation of the common land model (CoLM) from the perspective of water and energy budget simulation: Towards inclusion in CMIP6. Atmosphere, 8, 141, doi: 10.3390/atmos8080141.CrossRefGoogle Scholar
  36. Li, L. H., Y. P. Wang, V. K. Arora, et al., 2018: Evaluating global land surface models in CMIP5: Analysis of ecosystem waterand light-use efficiencies and rainfall partitioning. J. Climate, 31, 2995–3008, doi: 10.1175/JCLI-D-16-0177.1.CrossRefGoogle Scholar
  37. Li, Z. C., Z. G. Wei, S. H. Lv, et al., 2014: Effect of land surface processes on the Tibetan Plateau’s past and its predicted response to global warming: An analytical investigation based on simulation results from the CMIP5 model. Environ. Earth Sci., 72, 1155–1166, doi: 10.1007/s12665-013-3034-3.CrossRefGoogle Scholar
  38. Ma, N., G. Y. Niu, Y. L. Xia, et al., 2017: A systematic evaluation of Noah-MP in simulating land–atmosphere energy, water, and carbon exchanges over the continental United States. J. Geophys. Res. Atmos., 122, 12245–12268, doi: 10.1002/2017 JD027597.CrossRefGoogle Scholar
  39. Mahfouf, J. F., and J. Noilhan, 1991: Comparative study of various formulations of evaporations from bare soil using in situ data. J. Appl. Meteor., 30, 1351–1362, doi: 10.1175/1520-0450(1991)030<1354:CSOVFO>2.0.CO;2.Google Scholar
  40. Mueller, B., and S. I. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys. Res. Lett., 41, 128–134, doi: 10.1002/2013GL058055.CrossRefGoogle Scholar
  41. Niu, G. Y., and Z. L. Yang, 2006: Effects of frozen soil on snowmelt runoff and soil water storage at a continental scale. J. Hydrometeorol., 7, 937–952, doi: 10.1175/JHM538.1.CrossRefGoogle Scholar
  42. Niu, G.-Y., and Z.-L. Yang, 2007: An observation-based formulation of snow cover fraction and its evaluation over large North American river basins. J. Geophys. Res. Atmos., 112, D21101, doi: 10.1029/2007JD008674.CrossRefGoogle Scholar
  43. Pielke, R. A., G. Marland, R. A. Betts, et al., 2002: The influence of land-use change and landscape dynamics on the climate system: Relevance to climate-change policy beyond the radiative effect of greenhouse gases. Philosophical Transactions of The Royal Society A: Mathematical, Physical and Engineering Sciences, 360, 1705–1719, doi: 10.1098/rsta.2002. 1027.CrossRefGoogle Scholar
  44. Qin, Y. H., T. H. Wu, X. D. Wu, et al., 2017: Assessment of reanalysis soil moisture products in the permafrost regions of the central of the Qinghai–Tibet Plateau. Hydrol. Processes, 31, 4647–4659, doi: 10.1002/hyp.11383.CrossRefGoogle Scholar
  45. Robock, A., K. Y. Vinnikov, G. Srinivasan, et al., 2000: The global soil moisture data bank. Bull. Amer. Meteor. Soc., 81, 1281–1300, doi: 10.1175/1520-0477(2000)081<1281:TGSMDB> 2.3.CO;2.CrossRefGoogle Scholar
  46. Rodell, M., P. R. Houser, U. Jambor, et al., 2004: The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381–394, doi: 10.1175/BAMS-85-3-381.CrossRefGoogle Scholar
  47. Roeckner, E., G. B äuml, L. Bonaventura, et al., 2003: The Atmospheric General Circulation Model ECHAM 5. PART I: Model Description. MPI-Report No. 349, Max Planck Institute for Meteorology, Hamburg, 140 pp.Google Scholar
  48. Roesch, A., and E. Roeckner, 2006: Assessment of snow cover and surface albedo in the ECHAM5 general circulation model. J. Climate, 19, 3828–3843, doi: 10.1175/JCLI3825.1.CrossRefGoogle Scholar
  49. Rong, X. Y., J. Li, H. M. Chen, et al., 2018: The CAMS climate system model and a basic evaluation of its climate state and variability simulation. J. Meteor. Res., 32, 839–861, doi: 10.1007/s13351-018-8058-x.CrossRefGoogle Scholar
  50. Su, B. D., A. Q. Wang, G. J. Wang, et al., 2016: Spatiotemporal variations of soil moisture in the Tarim River basin, China. International Journal of Applied Earth Observation and Geoinformation, 48, 122–130, doi: 10.1016/j.jag.2015.06.012.CrossRefGoogle Scholar
  51. Swenson, S. C., and D. M. Lawrence, 2012: A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance. J. Geophys. Res. Atmos., 117, D21107, doi: 10.1029/2012JD018178.CrossRefGoogle Scholar
  52. Valayamkunnath, P., V. Sridhar, W. G. Zhao, et al., 2018: Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate. Agric. Forest Meteor., 248, 22–47, doi: 10.1016/j. agrformet.2017.08.025.CrossRefGoogle Scholar
  53. van den Hurk, B. J. J. M., P. Viterbo, A. C. M. Beljaars, et al., 2000: Offline Validation of the ERA40 Surface Scheme. Technical Memorandum No. 295, ECMWF, Reading, UK, 43 pp.Google Scholar
  54. Viterbo, P., and A. C. M. Beljaars, 1995: An improved land surface parameterization scheme in the ECMWF model and its validation. J. Climate, 8, 2716–2748, doi: 10.1175/1520-0442 (1995)008<2716:AILSPS>2.0.CO;2.CrossRefGoogle Scholar
  55. Wang, A. H., X. B. Zeng, and D. L. Guo, 2016: Estimates of global surface hydrology and heat fluxes from the community land model (CLM4.5) with four atmospheric forcing datasets. J. Hydrometeorol., 17, 2493–2510, doi: 10.1175/JHM-D-16-0041.1.CrossRefGoogle Scholar
  56. Xia, K., B. Wang, L. J. Li, et al., 2014: Evaluation of snow depth and snow cover fraction simulated by two versions of the flexible global ocean–atmosphere–land system model. Adv. Atmos. Sci., 31, 407–420, doi: 10.1007/s00376-013-3026-y.CrossRefGoogle Scholar
  57. Xia, Y. L., B. A. Cosgrove, K. E. Mitchell, et al., 2016: Basinscale assessment of the land surface water budget in the National Centers for Environmental Prediction operational and research NLDAS-2 systems. J. Geophys. Res. Atmos., 121, 2750–2779, doi: 10.1002/2015JD023733.CrossRefGoogle Scholar
  58. Yanai, M., and G. X. Wu, 2006: Effects of the Tibetan Plateau. The Asian Monsoon, B. Wang, Ed., Springer, Berlin, Heidelberg, Germany, 513–549.Google Scholar
  59. Yang, K., X. F. Guo, and B. Y. Wu, 2011: Recent trends in surface sensible heat flux on the Tibetan Plateau. Sci. China Earth Sci., 54, 19–28, doi: 10.1007/s11430-010-4036-6.CrossRefGoogle Scholar
  60. Yu, R. C., 1994: A two—step shape—preserving advection scheme. Adv. Atmos. Sci., 11, 479–490, doi: 10.1007/BF0265 8169.CrossRefGoogle Scholar
  61. Zhang, G., G. S. Zhou, F. Chen, et al., 2014: A trial to improve surface heat exchange simulation through sensitivity experiments over a desert steppe site. J. Hydrometeorol., 15, 664–684, doi: 10.1175/JHM-D-13-0113.1.CrossRefGoogle Scholar
  62. Zhang, H., G. Y. Shi, T. Nakajima, et al., 2006a: The effects of the choice of the k-interval number on radiative calculations. Journal of Quantitative Spectroscopy and Radiative Transfer, 98, 31–43, doi: 10.1016/j.jqsrt.2005.05.090.CrossRefGoogle Scholar
  63. Zhang, H., T. Suzuki, T. Nakajima, et al., 2006b: Effects of band division on radiative calculations. Optical Engineering, 45, 016002, doi: 10.1117/1.2160521.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Guo Zhang
    • 1
    Email author
  • Jianduo Li
    • 1
  • Xinyao Rong
    • 1
  • Yufei Xin
    • 1
  • Jian Li
    • 1
  • Haoming Chen
    • 1
  • Jingzhi Su
    • 1
  • Lijuan Hua
    • 1
  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina

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