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The representation of Arctic surface radiative fluxes in atmospheric models and reanalyses is integral to understanding relevant physical processes, yet testing of these models is confounded by a scarcity of in situ observations of near-surface atmospheric state profiles, cloud vertical structure, cloud phase, and surface properties. Here, airborne measurements obtained from the Arctic Radiation IceBridge Sea&Ice Experiment (ARISE) during fall 2014 are compared with concurrent products from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). Both data sets are then used as input to a radiative transfer model to produce surface radiative fluxes over multiple locations in the Beaufort Sea under the various conditions observed during ARISE. The sensitivity of the simulated fluxes is assessed and compared between these two data sets. Then, the relative contribution of atmospheric state, boundary layer clouds, and their properties to the sensitivity of the simulated surface fluxes is assessed. In our comparisons with ARISE observations we found that MERRA-2 has a warm temperature bias near the surface and it underestimates near-surface clouds and cloud liquid and ice water content. These prevail over both open water and sea ice surfaces. Our sensitivity analysis showed that boundary layer cloud vertical structure and water content account for more than 70% of the difference between MERRA-2 and the radiative fluxes calculated from airborne observations and that differences in boundary layer atmospheric state parameters contribute about 10-20% to the positive bias in the longwave surface flux.

Plain Language Summary The research focuses on airborne measurement conducted over the Beaufort Sea during September 2014. We compare our airborne measurement to predicted fields by reanalysis model and use both to reconstruct the surface radiative fluxes and compare them. We find that the reanalysis overestimates near-surface temperatures over open water areas and also over sea ice-covered areas. However, it underestimates relative humidity and clouds close to the surface. Comparing the simulated surface fluxes, we found that the reanalysis shows higher (more positive) surface fluxes in compare with measurements. It also underestimates the amount of ice water content in the low-level Arctic clouds during this time of the year, which results in an overestimation of the predicted fluxes by the model in comparison with those from observations. Overall, we found that cloud vertical structure and water content are the main difference sources (explain more than 70% of the differences) between observation and the modeled fields, with cloud phase and atmospheric parameters second (explain 20% of the differences).