Abstract

Abstract. To facilitate the construction of a satellite-derived 2 m air temperature (T2 m) product for the snow- and ice-covered regions in the Arctic, observations from weather stations are used to quantify the relationship between the T2 m and skin temperature (Tskin). Multiyear data records of simultaneous Tskin and T2 m from 29 different in situ sites have been analysed for five regions, covering the lower and upper ablation zone and the accumulation zone of the Greenland Ice Sheet (GrIS), sea ice in the Arctic Ocean, and seasonal snow-covered land in northern Alaska. The diurnal and seasonal temperature variabilities and the impacts from clouds and wind on the T2 m–Tskin differences are quantified. Tskin is often (85 % of the time, all sites weighted equally) lower than T2 m, with the largest differences occurring when the temperatures are well below 0 ∘C or when the surface is melting. Considering all regions, T2 m is on average 0.65–2.65 ∘C higher than Tskin, with the largest differences for the lower ablation area and smallest differences for the seasonal snow-covered sites. A negative net surface radiation balance generally cools the surface with respect to the atmosphere, resulting in a surface-driven surface air temperature inversion. However, Tskin and T2 m are often highly correlated, and the two temperatures can be almost identical (<0.5 ∘C difference), with the smallest T2–Tskin differences around noon and early afternoon during spring, autumn and summer during non-melting conditions. In general, the inversion strength increases with decreasing wind speeds, but for the sites on the GrIS the maximum inversion occurs at wind speeds of about 5 m s−1 due to the katabatic winds. Clouds tend to reduce the vertical temperature gradient, by warming the surface, resulting in a mean overcast T2 m–Tskin difference ranging from −0.08 to 1.63 ∘C, with the largest differences for the sites in the low-ablation zone and the smallest differences for the seasonal snow-covered sites. To assess the effect of using cloud-limited infrared satellite observations, the influence of clouds on temporally averaged Tskin has been studied by comparing averaged clear-sky Tskin with averaged all-sky Tskin. To this end, we test three different temporal averaging windows: 24 h, 72 h and 1 month. The largest clear-sky biases are generally found when 1-month averages are used and the smallest clear-sky biases are found for 24 h. In most cases, all-sky averages are warmer than clear-sky averages, with the smallest bias during summer when the Tskin range is smallest.

Highlights

  • The Arctic region is warming about twice as much as the global average because of Arctic amplification (Graversen et al, 2008)

  • The extensive data set used in this study represents a wide range of conditions including all-year observations from Arctic sea ice, land ice in northern Alaska, and low- and high-altitude land ice covering the lower, middle and upper ablation zones and the accumulation region of the Greenland Ice Sheet (GrIS)

  • It has been found that for each region there is a good correspondence between the Tskin and T2 m and that the main factors influencing the relationship between Tskin and T2 m are seasonal variations, wind speed and cloud cover

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Summary

Introduction

The Arctic region is warming about twice as much as the global average because of Arctic amplification (Graversen et al, 2008). The warming leads to a declining mass balance of the Greenland Ice Sheet (GrIS), contributing to global sea level rise. P. Nielsen-Englyst et al.: Ice surface skin temperature and 2 m air temperature relationships of the GrIS partly comes from increased calving rates, while the other part is a result of increased surface melt (Rignot, 2006), which is driven by changes in the surface energy balance. Current global surface temperature products are fundamental for the assessment of climate change (Stocker et al, 2014), but in the Arctic these data traditionally include only near-surface air temperatures from buoys and automatic weather stations (AWSs; Hansen et al, 2010; Jones et al, 2012; Rayner, 2003). Crucial climatic signals and trends could be missed in the assessment of the Arctic climate changes

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