Meteorological Conditions and Cloud Effects on Surface Radiation Balance Near Helheim Glacier and Jakobshavn Isbræ (Greenland) Using Ground-Based Observations

Abstract

The surface radiation budget is an essential component of the total energy exchange between the atmosphere and the Earth’s surface. Measurements of radiative fluxes near/on ice surfaces are sparse in the polar regions, including on the Greenland Ice Sheet (GrIS), and the effects of cloud on radiative fluxes are still poorly studied. In this work, we assess the impacts of cloud on radiative fluxes using two metrics: the longwave-equivalent cloudiness, derived from long-wave radiation measurements, and the cloud transmittance factor, obtained from short-wave radiation data. The metrics are applied to radiation data from two automatic weather stations located over the bare ground near the ice front of Helheim (HG, 66.3290°N, 38.1460°W) and Jakobshavn Isbræ(JI, 69.2220°N, 49.8150°W) on the GrIS. Comparisons of meteorological parameters, surface radiation fluxes, and cloud metrics show significant differences between the two sites. The cloud transmittance factor is higher at HG than at JI, and the incoming short-wave radiation in the summer at HG is about 50.0 W m−2 larger than at JI. Cloud metrics derived at the two sites reveal partly cloudy conditions were frequent (42 and 65% of the period at HG and JI) with a high dependency on the wind direction. The total cloud radiative effect (CREnet) generally increases during melt season at the two stations due to long-wave CRE enhancement by cloud fraction. CREnet decreases from May to June and increases afterward, due to the strengthened short-wave CRE. The annually averaged CREnet were 3.0 ± 7.4 W m−2 and 1.9±15.1 W m−2 at JI and HG. CREnet estimated from AWS indicates that clouds cool the JI and HG during melt season at different rates.

Modeling cloud properties over the 79 N Glacier (Nioghalvfjerdsfjorden, NE Greenland) for an intense summer melt period in 2019

“…As in many previous studies (e.g., Intrieri, 2002; Cox et al ., 2015; Wang et al ., 2018; Niwano et al ., 2019; Wang et al ., 2019; Izeboud et al ., 2020; Djoumna et al ., 2021), we used the cloud radiative effect (CRE) as a metric to assess the impact of clouds on the surface energy balance. It is defined as the difference in W·m −2 between cloudy and clear‐sky surface radiative fluxes under the assumption of unchanged meteorological conditions.…”

“…Most of the previous studies analyzing the near‐surface impact of Arctic clouds covered the whole Arctic region at a low spatial resolution (e.g., Cesana et al ., 2012; Cox et al ., 2015; Kay et al ., 2016; Hines and Bromwich, 2017; Cho et al ., 2020) or the whole of Greenland (e.g., van Tricht et al ., 2016; Hofer et al ., 2017; Lacour et al ., 2018; Niwano et al ., 2019; Hahn et al ., 2020; Lenaerts et al ., 2020), impeding the ability to draw reliable conclusions on smaller regions or single locations. Many of the more small‐scale studies focused either on the southeast (Djoumna et al ., 2021), the west (van den Broeke et al ., 2008; Izeboud et al ., 2020; Djoumna et al ., 2021) or the interior (i.e., Summit Station; Lacour et al ., 2018; Miller et al ., 2015; Solomon et al ., 2017). Given the large spatial variability in cloud forcing on near‐surface conditions (Wang et al ., 2019) and the disparate findings, directly transferring their results to other regions such as the NEGIS is not appropriate.…”

Coastal climate variability in Northeast Greenland and the role of changing sea ice and fjord ice

“…The cloud cover fraction (CCF) is then calculated by linear interpolation of the LWRin between the clear-sky and overcast estimates. Essentially, the CCF computed with observed variables is closely associated with sky emissivity, rather than the physical fraction of the sky covered by clouds (Djoumna et al, 2021). See…”

Multi-domain Anomalous Relationships in Heterogeneous Temporal Data

“…Analyzing the correlation between domain pairs with overlapping anomalous relationship Table 5 indicates that the pair of strd and tcc has the highest correlation score, the correlation score at the overlapping anomalous windows is higher than correlation of all the data points. [5,21,22] analyzed and discussed the relationship between cloud cover and thermal radiation. Cloud cover and thermal radiation are some of the significant features that influenced two of the biggest extreme snowmelt events in the Arctic.…”