Snow Mass over North America: Observations and Results from the Second Phase of the Atmospheric Model Intercomparison Project

Abstract

Eighteen global atmospheric general circulation models (AGCMs) participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2) are evaluated for their ability to simulate the observed spatial and temporal variability in snow mass, or water equivalent (SWE), over North America during the AMIP-2 period (1979–95). The evaluation is based on a new gridded SWE dataset developed from objective analysis of daily snow depth observations from Canada and the United States with snow density estimated from a simple snowpack model. Most AMIP-2 models simulate the seasonal timing and the relative spatial patterns of continental-scale SWE fairly well. However, there is a tendency to overestimate the rate of ablation during spring, and significant between-model variability is found in every aspect of the simulations, and at every spatial scale analyzed. For example, on the continental scale, the peak monthly SWE integrated over the North American continent in AMIP-2 models varies between ±50% of the observed value of ∼1500 km3. The volume of water in the snowpack, and the magnitudes of model errors, are significant in comparison to major fluxes in the continental water balance. It also appears that the median result from the suite of models tends to do a better job of estimating climatological mean features than any individual model. Year-to-year variations in large-scale SWE are only weakly correlated to observed variations, indicating that sea surface temperatures (specified from observations as boundary conditions) do not drive interannual variations of SWE in these models. These results have implications for simulations of the large-scale hydrologic cycle and for climate change impact assessments.

An Improved Snow Scheme for the ECMWF Land Surface Model: Description and Offline Validation

“…During spring, both schemes tend to ablate snow cover too quickly, with increasing underestimation. This behavior was also documented by Frei et al (2005) LWDR_SC (all new components of NEW except the exposed and forest albedo) simulation partially reduces the bias of CTR with a negative bias in snow-covered area of 2.5 and 4.4 million km 2 , during the whole period and spring, respectively, showing that the new snow cover fraction [Eq. (8)] has an important effect in the model-simulated snow cover extent.…”

SNOWMIP2: An Evaluation of Forest Snow Process Simulations

“…Some previous intercomparisons of land surface process models have found that multimodel means performed better in comparison with observations than individual models (Frei et al 2005;Guo et al 2007). Because the SWE observations frequently lie outside the interquartile range of the simulations, it is clear from Fig.…”

“…Salvage logging may exacerbate the impact on snow hydrology, with much greater changes to snowmelt rates following the removal of dead trees (Boon 2007). Remote sensing of snow properties is important for the assimilation in hydrological and numerical weather prediction models (Drusch et al 2004), evaluation of climate models (Frei et al 2003), and detection of climate trends (Dye 2002), but exposed vegetation complicates the signatures of snow-covered ground in both visible and microwave bands (Klein et al 1998;Chang et al 1996;Pullianen et al 2001). Conversely, the presence of snow complicates retrievals of vegetation indices (Robin et al 2007).…”

Decadal to century scale trends in North American snow extent in coupled atmosphere‐ocean general circulation models

“…No apparent relationship is observed in this study between model spatial resolution, mean 20th century snow extent, and the magnitude of 21st century trends. In AMIP-2 AGCMs, truncation of high elevation topography due to coarse spatial resolution plays a role in, but is not the primary cause of, errors in snow simulations [Frei et al, 2005]: the same is likely to be true in IPCC-AR4 AOGCMs. The answers may lie in the relationship between snow extent and meteorological variations.…”

“…[3] Recent evaluations of snow simulations for the period 1979 -1995 by fifteen atmospheric general circulation models (AGCMs) participating in the Second Phase of the Atmospheric Model Intercomparison Project (AMIP-2) found significant between-model variability in SCE [Frei et al, 2003] and snow water equivalent (SWE) (Frei et al 2005). Earlier evaluations of AGCM snow simulations using less consistent model experiments found similarly mixed results [Foster et al, 1996;Zhong, 1996;Yang et al, 1999].…”