Approximating snow surface temperature from standard temperature and humidity data: New possibilities for snow model and remote sensing evaluation

Snow surface temperature (Ts) is important to the snowmelt energy balance and land-atmosphere interactions, but in situ measurements are rare, thus limiting evaluation of remote sensing data sets and distributed models. Here we test simple Ts approximations with standard height (2-4 m) air temperature (Ta), wet-bulb temperature (Tw), and dew point temperature (Td), which are more readily available than Ts. We used hourly measurements from seven sites to understand which Ts approximation is most robust and how Ts representation varies with climate, time of day, and atmospheric conditions (stability and radiation). Td approximated Ts with the lowest overall bias, ranging from -2.3 to +2.6°C across sites and from -2.8 to 1.5°C across the diurnal cycle. Prior studies have approximated Ts with Ta, which was the least robust predictor of Ts at all sites. Approximation of Ts with Td was most reliable at night, at sites with infrequent clear sky conditions, and at windier sites (i.e., more frequent turbulent instability). We illustrate how mean daily Td can help detect surface energy balance bias in a physically based snowmelt model. The results imply that spatial Td data sets may be useful for evaluating snow models and remote sensing products in data sparse regions, such as alpine, cold prairie, or Arctic regions. To realize this potential, more routine observations of humidity are needed. Improved understanding of Td variations will advance understanding of Ts in space and time, providing a simple yet robust measure of snow surface feedback to the atmosphere.