Skip to main content

Advertisement

Log in

Uncertainty of land surface model and land use data on WRF model simulations over China

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

The land–atmosphere interaction has been considered one of the most important part for weather prediction and climate modeling. To evaluate the uncertainty coming from land surface models (LSMs) and land use (LU) data in WRF simulated climatology over China, we have conducted fifteen 10-year simulations from 1996 to 2005 with three LSMs (NOAH, CLM and RUC) and five LU data sets (MODIS, HYDE, HH, RF and CESM). Compared to the MODIS, the most major differences for HYDE, HH and RF include the reduction of the barren or sparsely vegetated area and the CESM map shows the largest arid and semi-arid area. Based on performance evaluation of WRF model, the uncertainties of LSMs and LU data are analyzed in a three-dimension aspect: the magnitudes of response, spatial and temporal patterns. The impact of LSM and LU data is statistically significant in some regions and the LSM effect is substantially higher than the LU data especially for precipitation. The temporal effect of combinations of LSM and LU data varied across regions. For temperature, we find that the effects of LSMs and LU data on the spatial pattern and magnitude are one order smaller than those on temporal pattern, and the uncertainties from LSMs and LU datasets are as the same order when considering the temporal and spatial patterns. The results also indicate that the uncertainty of LU data on precipitation is much smaller than that of LSMs on magnitude and spatial patterns. These findings reflect that the relative importance of LSMs and LU data in the WRF climate modeling largely depends on the specific LSM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Arino O, Bicheron P, Achard F et al (2008) The most detailed portrait of Earth. Eur Space Agency 136:25–31

    Google Scholar 

  • Arneth A, Harrison SP, Zaehle S et al (2010) Terrestrial biogeochemical feedbacks in the climate system. Nat Geosci 3:525

    Article  Google Scholar 

  • Arneth A, Sitch S, Pongratz J et al (2017) Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat Geosci 10:79–84

    Article  Google Scholar 

  • Bartholome E, Belward AS (2005) GLC2000: a new approach to global land cover mapping from Earth observation data. Int J Remote Sens 26:1959–1977

    Article  Google Scholar 

  • Benjamin SG, Dévényi D, Weygandt SS et al (2004) An hourly assimilation–forecast cycle: the RUC. Mon Weather Rev 132:495–518

    Article  Google Scholar 

  • Chen F, Dudhia J (2001a) Coupling an advanced land surface-hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569–585

    Article  Google Scholar 

  • Chen F, Dudhia J (2001b) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part II: preliminary model validation. Mon Weather Rev 129:587–604

    Article  Google Scholar 

  • Collins WD, Rasch PJ, Boville BA et al (2004) Description of the NCAR community atmosphere model (CAM 3.0). NCAR Tech Note NCAR/TN-464+ STR 226

  • Constantinidou K, Hadjinicolaou P, Zittis G et al (2020) Sensitivity of simulated climate over the MENA region related to different land surface schemes in the WRF model. Theor Appl Climatol 141:1431–1449

    Article  Google Scholar 

  • de Noblet-Ducoudré N, Boisier JP, Pitman A et al (2012) Determining robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: results from the first set of LUCID experiments. J Clim 25:3261–3281

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons A et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597

    Article  Google Scholar 

  • Devaraju N, de Noblet-Ducoudré N, Quesada B et al (2018) Quantifying the relative importance of direct and indirect biophysical effects of deforestation on surface temperature and teleconnections. J Clim 31:3811–3829

    Article  Google Scholar 

  • Dickinson RE (1995) Land-atmosphere interaction. Rev Geophys 33:917–922

    Article  Google Scholar 

  • FAO (2006) Global forest resources assessment 2005. FAO Forestry Paper 147. FAO Rome, Italy

  • Friedl MA, McIver DK, Hodges JC et al (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83:287–302

    Article  Google Scholar 

  • Friedl MA, Sulla-Menashe D, Tan B et al (2010) MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114:168–182

    Article  Google Scholar 

  • Gao H, Jia G (2013) Assessing disagreement and tolerance of misclassification of satellite-derived land cover products used in WRF model applications. Adv Atmos Sci 30:125–141

    Article  Google Scholar 

  • Ge J, Pitman AJ, Guo W et al (2019) Do uncertainties in the reconstruction of land cover affect the simulation of air temperature and rainfall in the CORDEX region of East Asia? J Geophys Res Atmos 124:3647–3670

    Article  Google Scholar 

  • Goldewijk KK (2001) Estimating global land use change over the past 300 years: the HYDE database. Glob Biogeochem Cycles 15:417–433

    Article  Google Scholar 

  • Grassi G, House J, Dentener F et al (2017) The key role of forests in meeting climate targets requires science for credible mitigation. Nat Clim Chang 7:220

    Article  Google Scholar 

  • Hansen MC, DeFries RS, Townshend JR et al (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21:1331–1364

    Article  Google Scholar 

  • Herold M, Mayaux P, Woodcock C et al (2008) Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sens Environ 112:2538–2556

    Article  Google Scholar 

  • Hong S-Y, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341

    Article  Google Scholar 

  • Houghton RA (2008) Carbon flux to the atmosphere from land-use changes: 1850–2005. TRENDS: a compendium of data on global change: 1850–2005

  • Houghton RA, Hackler JL, Cushman RM (2001) Carbon flux to the atmosphere from land-use changes: 1850 to 1990. Carbon Dioxide Information Center: Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge

  • Hu Z, Xu Z, Zhou N et al (2014) Evaluation of the WRF model with different land surface schemes: a drought event simulation in Southwest China during 2009–10. Atmos Ocean Sci Lett 7:168–173

    Article  Google Scholar 

  • Jin J, Miller NL, Schlegel N (2010) Sensitivity study of four land surface schemes in the WRF model. Adv Meteorol 2010:185–194. https://doi.org/10.1155/2010/167436

    Article  Google Scholar 

  • Jung M, Vetter M, Herold M et al (2007) Uncertainties of modeling gross primary productivity over Europe: a systematic study on the effects of using different drivers and terrestrial biosphere models. Glob Biogeochem Cycles. https://doi.org/10.1029/2006GB002915

    Article  Google Scholar 

  • Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802

    Article  Google Scholar 

  • Lawrence PJ, Chase TN (2007) Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0). J Geophys Res Biogeosci. https://doi.org/10.1029/2006JG000168

    Article  Google Scholar 

  • Li Y, Zhao C, Zhang T et al (2018) Impacts of land-use data on the simulation of surface air temperature in Northwest China. J Meteorol Res 32:896–908

    Article  Google Scholar 

  • Lim K-SS, Hong S-Y (2010) Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon Weather Rev 138:1587–1612

    Article  Google Scholar 

  • Loveland TR, Reed BC, Brown JF et al (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens 21:1303–1330

    Article  Google Scholar 

  • Mahmood R, Pielke RA Sr, Hubbard KG et al (2014) Land cover changes and their biogeophysical effects on climate. Int J Climatol 34:929–953

    Article  Google Scholar 

  • Maussion F, Scherer D, Finkelnburg R et al (2011) WRF simulation of a precipitation event over the Tibetan Plateau, China–an assessment using remote sensing and ground observations. Hydrol Earth Syst Sci 15:1795–1817

    Article  Google Scholar 

  • Meiyappan P, Jain AK (2012) Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Front Earth Sci 6:122–139

    Article  Google Scholar 

  • Oleson K, Dai Y, Bonan B et al (2004) Technical description of the community land model (CLM). Tech. Note NCAR/TN-461+ STR

  • Pitman AJ, de Noblet-Ducoudré N, Cruz F et al (2009) Uncertainties in climate responses to past land cover change: first results from the LUCID intercomparison study. Geophys Res Lett. https://doi.org/10.1029/2009GL039076

    Article  Google Scholar 

  • Ramankutty N (2012) Global cropland and pasture data from 1700–2007. Montreal, Canada

  • Ramankutty N, Foley JA (1999) Estimating historical changes in global land cover: croplands from 1700 to 1992. Glob Biogeochem Cycles 13:997–1027

    Article  Google Scholar 

  • Ramankutty N, Evan AT, Monfreda C et al (2008) Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob Biogeochem Cycles. https://doi.org/10.1029/2007GB002952

    Article  Google Scholar 

  • Reynolds RW, Rayner NA, Smith TM et al (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625

    Article  Google Scholar 

  • Rummukainen M (2010) State-of-the-art with regional climate models. Wiley Interdiscip Rev Clim Change 1:82–96

    Article  Google Scholar 

  • Skamarock W, Klemp J, Dudhia J et al (2008) A description of the advanced research WRF Version 3. NCAR Tech Note NCAR/TN-475+ STR

  • Smirnova TG, Brown JM, Benjamin SG (1997) Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon Weather Rev 125:1870–1884

    Article  Google Scholar 

  • Smirnova TG, Brown JM, Benjamin SG et al (2000) Parameterization of cold-season processes in the MAPS land-surface scheme. J Geophys Res Atmos 105:4077–4086

    Article  Google Scholar 

  • Tang J, Wang S, Niu X et al (2017) Impact of spectral nudging on regional climate simulation over CORDEX East Asia using WRF. Clim Dyn 48:2339–2357

    Article  Google Scholar 

  • Teklay A, Dile YT, Asfaw DH et al (2019) Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia. Heliyon 5:e02469

    Article  Google Scholar 

  • Wu J, Gao X (2013) A gridded daily observation dataset over China region and comparison with the other datasets. Chin J Geophys 56:1102–1111

    Google Scholar 

  • Yang Y, Xiao P, Feng X et al (2017) Accuracy assessment of seven global land cover datasets over China. ISPRS J Photogramm Remote Sens 125:156–173

    Article  Google Scholar 

  • Zhang Y, Yan D, Wen X et al (2020) Comparative analysis of the meteorological elements simulated by different land surface process schemes in the WRF model in the Yellow River source region. Theor Appl Climatol 139:145–162

    Article  Google Scholar 

Download references

Acknowledgements

The work is jointly funded by the National Key Research and Development Program of China (2018YFA0606003, 2016YFA0600303) and the National Natural Science Foundation of China (41875124). This work is also supported by the Chinese Jiangsu Collaborative Innovation Center for Climate Change. The authors also acknowledge with thanks the ECMWF for providing the ERA-interim reanalysis data as driving fields in the simulations, and NOAA for providing OI SST weekly data as oceanic boundary conditions and SSTs. We furthermore thank the National Climate Center for providing CN05.1 observational dataset. And we declare that we have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jianping Tang or Shuyu Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, Y., Tang, J., Wang, S. et al. Uncertainty of land surface model and land use data on WRF model simulations over China. Clim Dyn 57, 1833–1851 (2021). https://doi.org/10.1007/s00382-021-05778-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-021-05778-w

Keywords

Navigation