Downwelling longwave flux over Summit, Greenland, 2010–2012: Analysis of surface‐based observations and evaluation of ERA‐Interim using wavelets

Abstract

This study analyzes the downwelling longwave radiation (DLW) over the Greenland Ice Sheet (GrIS) using surface-based observations from Summit Station (72°N, 38°W; 3210 m) and the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) DLW fields. Since surface-based observations are sparse in the Arctic, the accuracy of including reanalyses for spatial context is assessed. First, the DLW at Summit is reported, including the significant time scales of variability using time-frequency decomposition (wavelet analysis). A new method for evaluating reanalyses is then introduced that also uses wavelet analysis. ERA-Interim DLW performs reasonably well at Summit, but because it includes too many thin clouds and too few thick clouds, it is biased low overall. The correlation between the observations and ERA-Interim drops from r 2 > 0.8 to near 0 for time series reconstructed from time scales less than~4 days. These low correlations and additional analyses suggest that the spatial resolution of the data sets is a factor in representing variability on short time scales. The bias is low across all time scales and is thus likely tied to cloud generation processes in the model rather than the spatial representation of the atmosphere across the GrIS. The exception is autumn, when ERA-Interim overestimates the influence of clouds at time scales of 1 and 4 weeks. The spatial distribution of cloud influence on the DLW across the GrIS indicates that Summit is located in a transition zone with respect to cloud properties. The gradient across this transition zone is steepest near Summit in autumn, so the spatial characteristics of the atmosphere near Summit may contribute to the ERA-Interim bias during this time.

The influence of winter cloud on summer sea ice in the Arctic, 1983–2013

“…Similarly, Cox et al . [] found that ERA‐Interim performed well for shortwave and longwave surface fluxes over Summit, Greenland, but encountered some difficulty simulating thick clouds during winter.…”

Greenland Ice Sheet Surface Mass Loss: Recent Developments in Observation and Modeling

“…In turn, these models must be evaluated with as many as possible in situ SEB observations, to assess whether the partitioning of melt energy, and therefore the sensitivity of melt to changing atmospheric/surface conditions is correctly represented [62,73,75,76]. An accurate observational estimate of melt energy requires dedicated experiments or automatic weather stations (AWS) that are operated at the ice sheet surface and measure all relevant parameters (including all radiation fluxes) sufficiently accurately to close the SEB and calculate melt energy as a residual; such stations are, e.g., operated at Summit and Swiss Camp by GC-Net [20], in the PROMICE network [14], and along the K-transect in west Greenland [57,90].…”

International Arctic Systems for Observing the Atmosphere: An International Polar Year Legacy Consortium

“…5) shows the regionally diverse range in the timing of the transition from snow-covered to snow-free conditions for Ny-Ålesund, Alert, Barrow, and Tiksi, as inferred from albedo measurements. Factors such as snow accumulation, temperature, and cloudiness influence the timing of snowmelt in spring (Stone et al 2002;Stanitski and Stone 2014;Cox et al 2014), which in turn modulates the annual surface radiation budget.…”

“…Clouds, and particularly mixed-phase and high-latitude clouds, induce some of the largest uncertainties in numerical modeling (Chaudhuri et al 2014;de Boer et al 2012). Some of the challenges with simulation of Arctic clouds have come from obstacles involved with validating the model performance through the comparison of grid-boxaverage cloud properties to temporal averages derived at observatories using upward-looking sensors (e.g., Cox et al 2014). While some of the observatories are adding scanning instrument capabilities that allow for data products that compare more favorably with gridbox averages, and while continued advancement of instrument simulators should provide a more direct comparison with the models, caution must still be exercised when making comparisons between models and observatories at coastal sites (e.g., Barrow), sites that are situated within extreme topography (e.g., Eureka), and on islands (e.g., Ny-Ålesund).…”

“…Characterization and improved process understanding of Arctic clouds has been identified as an important focal area; a Cloud Working Group to address this area is currently being organized. In recent years, significant advancement has been made in understanding Arctic mixed-phase clouds, cloudradiation-turbulence interactions, and the influence of aerosol-cloud-radiation interactions on the surface energy budgets (Shupe et al 2013;Morrison et al 2012;Vavrus 2004;Verlinde et al 2007;Cox et al 2015); however, the topic is complex and significant challenges remain. The radars, lidars, and radiometers at four of the IASOA observatories (Barrow, Oliktok Point, Eureka, and Summit) are powerful tools for determining cloud properties and for obtaining process measurements that are helpful in improving our understanding of Arctic cloud life cycles.…”