Climate Dynamics

, Volume 51, Issue 1–2, pp 639–666 | Cite as

Australian snowpack in the NARCliM ensemble: evaluation, bias correction and future projections

  • Alejandro Di LucaEmail author
  • Jason P. Evans
  • Fei Ji


In this study we evaluate the ability of an ensemble of high-resolution Regional Climate Model simulations to represent snow cover characteristics over the Australian Alps and go on to asses future projections of snowpack characteristics. Our results show that the ensemble presents a cold temperature bias and overestimates total precipitation leading to a general overestimation of the snow cover as compared with MODIS satellite data. We then produce a new set of snowpack characteristics by running a temperature based snow melt/accumulation model forced by bias corrected temperature and precipitation fields. While some positive snow cover biases remain, the bias corrected (BC) dataset show large improvements regarding the simulation of total amounts, seasonality and spatial distribution of the snow cover compared with MODIS products. Both the raw and BC datasets are then used to assess future changes in the snowpack characteristics. Both datasets show robust increases in near-surface temperatures and decreases in snowfall that lead to a substantial reduction of the snowpack over the Australian Alps. The snowpack decreases by about 15 and 60% by 2030 and 2070 respectively. While the BC data introduce large differences in the simulation of the present climate snowpack, in relative terms future changes appear to be similar to those obtained using the raw data. Future temperature projections show a clear dependence with elevation through the snow-albedo feedback effect that affects snowpack projections. Uncertainties in future projections of the snowpack are large in both datasets and are mainly dominated by the choice of the lateral boundary conditions.


Snowfall Snow cover Climate change Mountains High resolution 



Support for this work was provided by the New South Wales (NSW) Office of Environment and Heritage to build on the NSW/ACT Regional Climate Modelling (NARCliM) Project. This work was made possible through the Merit Allocation Scheme award from the NCI (National Computational Infrastructure) National Facility at the Australian National University. The authors would like to thank Kathryn J. Bormann and Jeffery A. Thompson for providing the MODIS derived satellite datasets and the scientists involved in generating AWAP observational datasets that are used in this study. The authors also acknowledge the administration of Climate Change Research Centre at the University of New South Wales for the logistical support, the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the World Climate Research Programme (WCRP) CMIP3 multimodel data set. Support of this data set is provided by the Office of Science, U.S. Department of Energy. We thank the scientists at NCAR Mesoscale and Microscale Meteorology Division for maintaining the Weather Research and Forecasting Model.

Supplementary material

382_2017_3946_MOESM1_ESM.pdf (6.5 mb)
Supplementary material 1 (PDF 6,683 KB)


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Climate Change Research Centre and ARC Centre of Excellence for Climate System ScienceUniversity of New South WalesSydneyAustralia
  2. 2.Office of Environment and HeritageDepartment of Planning and EnvironmentQueanbeyanAustralia

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