| 26 Aug 2026
No significant trends in snow free days at three long-term monitoring sites in the high-, mid- and low- Arctic Greenland as seen by MODIS Terra from 2000–2025
Abstract. Snow cover is a key component of Arctic ecosystems, regulating both biotic and abiotic processes such as plant phenology, soil thermal regimes, greenhouse gas fluxes, and the timing of glacier melt. We present a 26-year (2000–2025) record of snow-free days derived from the MODIS Terra cloud-gap-filled snow cover product at three long-term monitoring sites in high, mid, and low-Arctic Greenland. The MODIS-derived counts correlated well with in situ observations, most strongly at the high-arctic site, the only location where a direct comparison to in situ observations was possible. The mean snow-free season length is 76, 78 and 103 days at high-, mid-, and low-Arctic respectively, with a standard deviation of approximately two weeks at each site despite their contrasting latitudinal positions. We find no statistically significant trends in the number of snow-free days at any site over the record, consistent with the absence of significant trends in Northern Hemisphere snow cover in the Greenland and North American sector over the same period. The dominant driver of interannual variability shifts along the latitudinal gradient: snowfall was the dominant predictor of snow free day counts at the high arctic site, positive degree-days become co-limiting alongside snowfall at the low-Arctic site and at the mid-Arctic site positive degree-days are the only significant predictor, allthought snowfall and positive-degree-days together explain little of the interannual variance there, which point to additional physical controls. The high interannual variability in snow-free season length underscores the necessity of monitoring records spanning multiple decades in order to detect climate-change signals in snow cover and the many Arctic ecosystem processes that depend on it.
This manuscript has used a 26 year time series of MODIS data to analyse trends and variability in the number of snow-free days at three sites across Greenland. The authors have also attempted to determine which climatic drivers are responsible for the interannual variability in snow-free days at the different sites by fitting an ordinary least squares model. They conclude that there is large variability in number of snow free days but no significant trends over the time series analysed, but that the sites exhibit different climatic drivers of the variability. Overall it is an interesting paper that uses similar datasets/approaches to earlier work, making the results comparable and complementary to existing knowledge on how snow cover is changing in the Arctic.
General comments
While I don't think it was necessary to assess the number of snow-free days using NDSI thresholds > 50%, the majority of my comments relate to the analysis of the climate drivers. I also think that rather than only analysing the MODIS data at specific sites/areas across Greenland, it would also have been interesting to map the derived variables and their trends for Greenland as a whole so that any changes and trends (or lack of) can be assessed in the wider context. Moreover, the authors also emphasise in the last part of their discussion, the importance of monitoring trends in terms of ecological impacts – and mapping these variables over a larger spatial area would therefore allow potentially vulnerable zones to be identified.
Specific comments
L82-83: I am a bit confused about why it was necessary to test thresholds >50% for computing the number of snow-free days. In my mind it would be hard to argue that a grid cell can be considered snow-free if more than half of it is snow-covered. Indeed the authors also note that the higher thresholds are more relevant to identifying melt onset, making the results somewhat irrelevant for snow-free conditions.
L114: Snowfall is summed over a hydrological year but PDD is summed over a calendar year? Would this potentially impact the OLS model results if the time series are acquired from summing over different time periods?
Also I might expect correlations between snow-free days and snowfall/PDD to be different depending on the time of year. The rate of snowmelt would likely control the variability in number of snow-free days in the spring through timing of snow disappearance, and this depends on how much snow there is to melt and also the temperature, whereas in the autumn I might expect other factors to control the number of snow-free days through variability in snow onset date, which is maybe more dependent on temperature because this determines whether the precipitation actually falls as snow or not.
Have the authors considered fitting the OLS model for two parts of the year 1. the start of the hydrological year through to snowmelt, and 2. following snowmelt until snow onset (which can also be determined from MODIS)? One might expect that the roles of snowfall/PDD would differ during different periods of the year eg. PDD may steer the number of snow-free days more strongly when the ground is already snow-free (greater PDDs = lower likelihood of snowfall onset = more snowfree days) whereas during the snow-covered season perhaps both variables play more equal roles?
L131. Snowfall and temperature/PDD are two factors that influence the number of snow-free days but this view seems a little simplistic. What about precipitation as rain falling on an existing snowpack? ROS events are known to accelerate melt processes more than temperature alone so I would expect rain could be an important explanatory variable as well as PDD and snowfall. Moreover earlier studies mentioned in the results (Liu et al. 2024) have identified wind as a factor since it can redistribute snow – why not download this dataset as well since it is also available from CARRA? Perhaps the variables identified as the strongest drivers of snow-free days no longer remain the strongest if accounting for other meteorological variables such as wind and rainfall?