摘要：In situ observations of summer (June through August, or JJA) albedo are presented for the period 2002–2017 from Haig Glacier in the Canadian Rocky Mountains. The observations provide insight into the seasonal evolution and interannual variability of snow and ice albedo, including the effects of summer snowfall, the decay of snow albedo through the melt season, and the potential short-term impacts of regional wildfire activity on glacier-albedo reductions. Mean JJA albedo (± 1σ) recorded at an automatic weather station in the upper ablation zone of the glacier was αS=0.55 ± 0.07 over this period, with no evidence of long-term trends in surface albedo. Each summer the surface conditions at the weather station undergo a transition from a dry, reflective spring snowpack (αS∼0.8) to a wet, homogeneous midsummer snowpack (αS∼0.5) to exposed, impurity-rich glacier ice, with a measured albedo of 0.21 ± 0.06 over the study period. The ice albedo drops to ∼ 0.12 during years of intense regional wildfire activity such as 2003 and 2017, but it recovers from this in subsequent years. This seasonal albedo decline is well simulated through a parameterization of snow-albedo decay based on cumulative positive degree days (PDDs), but the parameterization does not capture the impact of summer snowfall events, which cause transient increases in albedo and significantly reduce glacier melt. We introduce this effect through a stochastic parameterization of summer precipitation events within a surface energy balance model. The amount of precipitation and the date of snowfall are randomly selected for each model realization based on a predefined number of summer snow events. This stochastic parameterization provides an improved representation of the mean summer albedo and mass balance at Haig Glacier. We also suggest modifications to conventional degree-day melt factors to better capture the effects of seasonal albedo evolution in temperature-index or positive-degree-day melt models on mountain glaciers. Climate, hydrology, or glacier mass balance models that use these methods typically use a binary rather than continuum approach to prescribing melt factors, with one melt factor for snow and one for ice. As alternatives, monthly melt factors effectively capture the seasonal albedo evolution, or melt factors can be estimated as a function of the albedo where these data are available.
