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compared to SNOTEL. The departures can partly be
attributed to the too high temperatures simulated by
NARCCAP RCMs compared to SNOTEL. Another reason
for the partially great deviations is that SNOTEL probably in
general overestimates snow cover depth. The SNOTEL
stations were placed based on the goal to collect information
primarily for run off forecasting purposes, and not to
measure and monitor snow as a climate variable. Therefore,
SNOTEL sites are mostly located in troughs, shadowed
areas and generally, where the seasonal snow pack lasts a
relatively long time.
16th Conference on Climate Variations and Change. 9-13
January, 2005. Paper
16 J6.10, pp. 235-238 Washington, D.C.
In order to gain some insights into possible reasons and
sources leading to the biases found, some analyses regarding
the LSS were undertaken. The LSS dictates and forces the
processes related to snow in a RCM. Here, there was no
obvious larger similarity found between RCMs using the
same LSS. In particular also NARR, which uses the same
LSS (NOAH), as the RCMs WRF, MM5 and ECPC, did not
show specific similarities with the respective RCMs.
Therefore, it seems that the LSS’s function is marginal for
the final output regarding snow and that the biases found can
not directly be attributed to the LSS used by the RCM.
4. Conclusion and Perspectives
It can be concluded so far that the annual snow cycle is, in
general, represented relatively well by the NARCCAP
simulations but underestimates the amount of snow.
Quantifying this underestimation in absolute values is,
however, difficult. This is because the SNOTEL
measurements most probably overestimate the effective
snow depth due to the primary goal of the SNOTEL network
as mentioned above. Furthermore, a comparison between
point measurements and gridded data is always somewhat
questionable. Additionally, a detailed attribution of the
biases to e.g. the LSS or the elevation differences between
models and reality is finally not possible. Because of the
many uncertainties associated with such analyses, it is
advised and proposed to refer to such analyses as ‘an
assessment or evaluation of the performance of the models’
rather than as ‘a validation of models’.
Nevertheless such analyses are fundamental for the impacts
community. It is essential that impacts modeler know about
the range of uncertainty associated with RCM outputs and
that they account accordingly for it in their studies.
References
Barnett, T.P., Adam, J.C., Lettenmaier, D.P., Potential
impacts of a warming climate on water availability in snowdominated
regions, Nature 438, 303-309, 2005
Cohen, J., & Rind, D., The effect of snow cover on the
climate, J. Climate, 689-706, 1991
Christensen, N.S., Wood, A.W., Voisin, N., Lettenmaier,
D.P., Palmer, N., The effect of climate change on the
hydrology and water resources of the Colorado River Basin.
Climate Change 62, 337-363, 2004
Leung, L., R. & Qian, Y., The sensitivity of precipitation
and snowpack simulations to model resolution via nesting in
regions of complex terrain, J. Hydrometerology 4, 1025-
1043, 2003
Mearns, L.O. et al., 2005: NARCCAP, North American
Regional Climate Change Assessment Program, A multiple
AOGCM and RCM climate scenario project over North
America. Preprints of the American Meteorological Society