Skip to main content
SpringerLink
Log in

Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

  • Research Article
  • Published:
Frontiers of Earth Science Aims and scope Submit manuscript

Abstract

In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere–land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Brasnett B (1999). A global analysis of snow depth for numerical weather prediction. J Appl Meteorol, 38(6): 726–740

    Article  Google Scholar 

  • Cai X, Yang Z-L, Xia Y, Huang M, Wei H, Leung L R, Ek M B (2014). Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS test bed. Journal of Geophysical Research: Atmospheres, 119(13): 13751–13770

    Google Scholar 

  • Chou M D, Suarez M J (1999). A solar radiation parameterization for atmospheric studies. NASA/TM-1999-104606/VOL15. NASA Technical Report. Greenbelt, MD: Goddard Space Flight Center

    Google Scholar 

  • Dee D P, Uppala S M, Simmons A J, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M A, Balsamo G, Bauer P, Bechtold P, Beljaars A C M, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer A J, Haimberger L, Healy S B, Hersbach H, Hólm E V, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally A P, Monge-Sanz B M, Morcrette J J, Park B K, Peubey C, de Rosnay P, Tavolato C, Thépaut J N, Vitart F (2011). The ERAInterim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc, 137(656): 553–597

    Article  Google Scholar 

  • Douville H (2010). Relative contribution of soil moisture and snow mass to seasonal climate predictability: a pilot study. Clim Dyn, 34(6): 797–818

    Article  Google Scholar 

  • Du J (2007). Uncertainty and ensemble forecast. National Weather Service, Office of Science & Technology, Science & Technology Infusion Lecture Series, 42 pp

    Google Scholar 

  • EkMB (2003). Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res, 108(D22): 8851

  • Grimit E P, Mass C F (2007). Measuring the ensemble spread-error relationship with a probabilistic approach: Stochastic ensemble results. Mon Weather Rev, 135(1): 203–221

    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(9): 2318–2341

    Article  Google Scholar 

  • Hu Z H, Xu Z F, Zhou N F, Ma Z G, Li G P(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(2): 168–173

    Article  Google Scholar 

  • Huffman G J, Adler R F, MorrisseyMM, Bolvin D T, Curtis S, Joyce R, McGavock B, Susskind J (2001). Global precipitation at one-degree daily resolution from multisatellite observations. J Hydrometeorol, 2 (1): 36–50

    Article  Google Scholar 

  • Iacono M J, Delamere J S, Mlawer E J, Shephard M W, Clough S A, Collins W D (2008). Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res, 113(D13): D13103

    Article  Google Scholar 

  • Jin J, Miller N L, Schlegel N (2010). Sensitivity study of four land surface schemes in the WRF model. Adv Meteorol, 2010: 1–11

    Google Scholar 

  • Kain J S (2004). The Kain–Fritsch convective parameterization: an update. J Appl Meteorol, 43(1): 170–181

    Article  Google Scholar 

  • Lin Y L, Farley R D, Orville H D (1983). Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol, 22(6): 1065–1092

    Article  Google Scholar 

  • Mahrt L, Ek M (1984). The influence of atmospheric stability on potential evaporation. J Clim Appl Meteorol, 23(2): 222–234

    Article  Google Scholar 

  • Niu G Y, Yang Z L, Mitchell K E, Chen F, Ek M B, Barlage M, Kumar A, Manning K, Niyogi D, Rosero E, Tewari M, Xia Y (2011). The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with localscale measurements. J Geophys Res, 116(D12): 12109

    Google Scholar 

  • Orth R, Dutra E, Pappenberger F (2016). Improving weather predictability by including land surface model parameter uncertainty. Mon Weather Rev, 144(4): 1551–1569

    Article  Google Scholar 

  • Roulston M S (2005). A comparison of predictors of the error of weather forecasts. Nonlinear Process Geophys, 12(6): 1021–1032

    Article  Google Scholar 

  • Skamarock W C, Klemp J B, Dudhia J, Gill D O (2008). A description of the advanced research WRF Version 3. NCAR Technical note-475 + STR, 113 pp

    Google Scholar 

  • Suzuki K, Kodama Y, Nakai T, Liston G E, Yamamoto K, Ohata T, Ishii Y, Sumida A, Hara T, Ohta T (2011). Impact of land-use changes on snow in a forested region with heavy snowfall in Hokkaido, Japan. Hydrol Sci J, 56(3): 443–467

    Article  Google Scholar 

  • Suzuki K, Konohira E, Yamazaki Y, Kubota J, Ohata T, Vuglinsky V (2006a). Transport of organic carbon from the Mogot Experimental Watershed in the southern mountainous taiga of eastern Siberia. Nord Hydrol, 37(3): 303–312

    Article  Google Scholar 

  • Suzuki K, Kubota J, Ohata T, Vuglinsky V (2006b). Influence of snow ablation and frozen ground on spring runoff generation in the Mogot Experimental Watershed, southern mountainous taiga of eastern Siberia. Hydrol Res, 37: 21–29

    Article  Google Scholar 

  • Suzuki K, Liston G E, Kodama Y (2015a). Variations of winter surface net shortwave radiation caused by land-use change in northern Hokkaido, Japan. J For Res, 20(2): 281–292

    Article  Google Scholar 

  • Suzuki K, Liston G E, Matsuo K (2015b). Estimation of continentalbasin- scale sublimation in the Lena River basin, Siberia. Adv Meteorol, 2015: 1–14

    Article  Google Scholar 

  • Suzuki K, Matsuo K, Hiyama T (2016). Satellite gravimetry-based analysis of terrestrial water storage and its relationship with run-off from the Lena River in eastern Siberia. Int J Remote Sens, 37(10): 2198–2210

    Article  Google Scholar 

  • Suzuki K, Zupanski M, Zupanski D (2017). A case study involving single observation experiments performed over snowy Siberia using a coupled atmosphere-land modelling system. Atmos Sci Lett, 18(3): 106–111

    Article  Google Scholar 

  • Whitaker J S, Loughe A F (1998). The relationship between ensemble spread and ensemble mean skill. Mon Weather Rev, 126(12): 3292–3302

    Article  Google Scholar 

  • Yu M, Wang G, Chen H (2016). Quantifying the impacts of land surface schemes and dynamic vegetation on the model dependency of projected changes in surface energy and water budgets. J Adv Model Earth Syst, 8(1): 370–386

    Article  Google Scholar 

  • Zeng X M, Wang N, Wang Y, Zheng Y, Zhou Z, Wang G, Chen C, Liu H (2015). WRF-simulated sensitivity to land surface schemes in short and medium ranges for a high-temperature event in East China: a comparative study. J Adv Model Earth Syst, 7(3): 1305–1325

    Article  Google Scholar 

  • Zupanski M (2005). Maximum likelihood ensemble filter: theoretical aspects. Mon Weather Rev, 133(6): 1710–1726

    Article  Google Scholar 

Download references

Acknowledgments

We thank Prof. Steven R. Fassnacht and two anonymous reviewers for their contributions to improving the first draft of this article. We declare no conflicts of interest and no financial disclosure. Parts of this study were supported by a Grant-in-Aid for Scientific Research (C) (No. 16K00581), and a Grant-in-Aid for Challenging Exploratory Research (No. 25550022). The second author acknowledges partial support from the Office of Naval Research under contract N000149169192040.

Author information

Authors and Affiliations

  1. Institute of Arctic Climate and Environment Change Research (IACE), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, 236-0001, Japan

    Kazuyoshi Suzuki

  2. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, 80523-1375, USA

    Milija Zupanski

Authors
  1. Kazuyoshi Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milija Zupanski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kazuyoshi Suzuki.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suzuki, K., Zupanski, M. Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models. Front. Earth Sci. 12, 672–682 (2018). https://doi.org/10.1007/s11707-018-0691-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11707-018-0691-2

Keywords

Search

Navigation