Shift in seasonal snowpack melt-out date due to light-absorbing particles at a high-altitude site in Central Himalaya
Abstract. Snow darkening by deposited light-absorbing particles (LAP) has the potential to accelerate snowmelt and shift the snow melt-out date. Here we investigate the sensitivity of the seasonal snow cover duration to changes in LAP at a high altitude valley site in the Central Himalayas, India. First, the variation of the albedo of the seasonal snow was emulated using two seasons of automatic weather station (AWS) data and applying a constant, but realistic deposition of LAP to the snow. Then, the number of days with snowmelt were evaluated based on the estimated net energy budget of the seasonal snow cover and the derived surface temperature. The impact on the energy budget by LAP combined with the melt-day analysis resulted in very simple relations to determine the contribution of LAP to the number of days with snowmelt of the seasonal snow in Himalaya. Above a concentration of 1 ng g-1 (Elemental Carbon equivalent, ECeq, which in this study includes EC and the absorption equivalent EC contribution by other light absorbing particles, such as mineral dust) in new snow, the number of days with snowmelt can be estimated by; days=0.0109(log(〖EC〗_eq )+1)PP±0.0033(log(〖EC〗_eq )+1)PP, where PP is the seasonal precipitation in mm snow water equivalent. A change in ECeq by a factor of two corresponds to about 1/3 of a day per 100 mm precipitation. Although the change in the number of days with melt caused by the changes in ECeq is small, the estimated total change in the snow melt-out date by LAP can be significant. For our realistic base case scenario for the Sunderdhunga Valley, Central Himalayas, India, of ECeq=100 ng g-1 and PP=400 mm, this yields in an advancement of the melt-out date of about 13 days.
Johan Ström et al.
Johan Ström et al.
Johan Ström et al.
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This manuscript explores the shift in melt-out date due to deposited LAP at a Central Himalayan site using AWS data. The paper is fairly well written, and the results presented in the article are interesting. However, I have few minor questions before I can recommend it for publishing.
Page 3, Line 104: Authors mentioned that “data was screened for inconsistencies.” However, no clear methodology has been provided on how the inconsistencies were screened. What makes a data point inconsistent?
Page 4, Line 106: Paper uses median for albedo and SD, while a daily average for other data. Why so? Does it create any impact on results if authors use the daily average for albedo and SD as well?
Page 4, Line110: Paper states, “Using a lower emissivity would result in higher Ts, but will not affect the interpretation of the data.” Why so? Some explanation is needed.
Page 11, Line 309-310: “Compared to other reported values for snow these estimates are high, but are close to those reported for ice.” This statement is not entirely clear and needs more explanation on why such a thing will happen if all parameters are used for snow? Also, provide some references for values reported for snow so that a fair comparison can be made.
Page 14, Line 383-385: Authors mentioned “an overestimation of the melting compared to pristine snow.” How much overestimation and compared to which data? Provide some references for comparison and quantify the overestimation.
Minimal references are provided in many places, especially in the Introduction. Including suitable and more recent references make it easier for comparison and improvement made from previous studies. Some references in the manuscript are quite old and do not reflect the current state of knowledge with associated research. Here are few key references, which authors should consider including in the manuscript (List is not exhaustive):
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