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Simulations of a boreal grassland hydrology at Valdai, Russia: PILPS phase 2(d)

  • Author(s): Schlosser, CA
  • Slater, AG
  • Robock, A
  • Pitman, AJ
  • Vinnikov, KY
  • Henderson-Sellers, A
  • Speranskaya, NA
  • Mitchell, K
  • Boone, A
  • Braden, H
  • Chen, F
  • Cox, P
  • De Rosnay, P
  • Desborough, CE
  • Dickenson, RE
  • Dai, YJ
  • Duan, Q
  • Entin, J
  • Etchevers, P
  • Gedney, N
  • Gusev, YM
  • Habets, F
  • Kim, J
  • Koren, V
  • Kowalczyk, E
  • Nasonova, ON
  • Noilhan, J
  • Schaake, J
  • Shmakin, AB
  • Smirnova, TG
  • Verseghy, D
  • Wetzel, P
  • Xue, Y
  • Yang, ZL
  • et al.
Abstract

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966-83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs' sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpaek to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

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