Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water
equivalent uncertainty across North America via ensemble land surface
modeling
Abstract
The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a
baseline characterization of snow water equivalent (SWE) uncertainty
across North America with the goal of informing global snow
observational needs. An ensemble-based modeling approach, encompassing a
suite of current operational models, is used to assess the uncertainty
in SWE and total snow storage (SWS) estimation during the 2009-2017
period. The highest modeled SWE uncertainty is observed in mountainous
regions, likely due to the relatively deep snow, forcing uncertainties,
and variability between the different models in resolving the snow
processes over complex terrain. This highlights a need for
high-resolution observations in mountains to capture the high spatial
SWE variability. The greatest SWS is found in Tundra regions where, even
though the spatiotemporal variability in modeled SWE is low, there is
considerable uncertainty in the SWS estimates due to the large areal
extent over which those estimates are spread. This highlights the need
for high accuracy in snow estimations across the Tundra. In mid-latitude
boreal forests, large uncertainties in both SWE and SWS indicate that
vegetation-snow impacts are a critical area where focused improvements
to modeled snow estimation efforts need to be made. Finally, the SEUP
results indicate that SWE uncertainty is driving runoff uncertainty and
measurements may be beneficial in reducing uncertainty in SWE and
runoff, during the melt season at high latitudes (e.g., Tundra and Taiga
regions) and in the Western mountain regions, whereas observations at
(or near) peak SWE accumulation are more helpful over the mid-latitudes.