Thermally-driven upvalley wind in the upper East River Valley in the Colorado Rocky Mountains often unexpectedly stops in mid-morning and reverses back to downvalley wind. We use a comprehensive observational data set for a nearly two-year long period to analyze the wind system and boundary layer evolution in this high-altitude valley and determine the reason for this early wind reversal. Days with short upvalley wind predominantly occur during the warm season when the valley floor is free of snow and the convective boundary layer grows well above the height of the surrounding ridges. Upvalley wind persists throughout the day only on a few days during the warm season. We link differences in valley wind evolution to wind direction at upper levels at and above ridge height and propose forced channeling mechanisms to describe coupling between valley and upper-level wind when the convective boundary layer grows above ridge height. The frequency distribution of upper-level wind direction is such that channeling in the downvalley direction is favored, which explains the predominance of days with short upvalley wind. The deep convective boundary layer is supported by the presence of a deep weakly stably stratified residual layer with high aerosol content, which is regularly present over the mountain range during the warm season. On days when the convective boundary layer does not grow above ridge height, for example when the valley floor is covered by snow, thermally-driven upvalley wind is able to persist throughout the day independent of upper-level wind direction.

Reliable boundary-layer turbulence parametrizations for polar conditions are needed to reduce uncertainty in projections of Arctic sea ice melting rate and its potential global repercussions. Surface turbulent fluxes of sensible and latent heat are typically represented in weather/climate models using bulk formulae based on the Monin-Obukhov Similarity Theory (MOST), sometimes finely tuned to high stability conditions and the potential presence of sea ice. In this study, we test the performance of new, machine-learning (ML) flux parametrizations, using an advanced polar-specific bulk algorithm as a baseline. Neural networks, trained on observations from previous Arctic campaigns, are used to predict surface turbulent fluxes measured over sea ice as part of the recent MOSAiC expedition. The ML parametrizations outperform the bulk at the MOSAiC sites, with RMSE reductions of up to 70 percent. We provide a plug-in Fortran implementation of the neural networks for use in models.

The amount of snow on Arctic sea ice impacts the ice mass budget. Wind redistribution of snow into open water in leads is hypothesized to cause significant wintertime snow loss. However, there are no direct measurements of snow loss into Arctic leads. We measured the snow lost in four leads in the Central Arctic in winter 2020. We find, contrary to the general consensus, that under typical winter conditions, minimal snow was lost into leads. However, during a cyclone that delivered warm air temperatures, high winds, and snowfall, 35.0 ± 1.1 cm snow water equivalent (SWE) was lost into a lead (per unit lead area). This corresponded to a removal of 0.7–1.1 cm SWE from the entire surface—∼6–10% of this site’s annual snow precipitation. Warm air temperatures, which increase the length of time that wintertime leads remain unfrozen, may be an underappreciated factor in snow loss into leads.

Supercooled fogs can have an important radiative impact at the surface of the Greenland Ice Sheet, but they are difficult to detect and our understanding of the factors that control their lifetime and radiative properties is limited by a lack of observations. This study demonstrates that spectrally resolved measurements of downwelling longwave radiation can be used to generate retrievals of fog microphysical properties (phase and particle effective radius) when the fog visible optical depth is greater than ~0.25. For twelve cases of fog under otherwise clear skies between June and September 2019 at Summit Station in central Greenland, nine cases were mixed-phase. The mean ice particle (optically-equivalent sphere) effective radius was 24.0±7.8 µm, and the mean liquid droplet effective radius was 14.0±2.7 µm. These results, combined with measurements of aerosol particle number concentrations, provide observational evidence supporting the hypotheses that (a) low surface aerosol particle number concentrations can limit fog liquid water path, (b) fog can act to increase near-surface aerosol particle number concentrations through enhanced mixing, and (c) multiple fog events in quiescent periods gradually deplete near-surface aerosol particle number concentrations.

This paper focuses on the accuracy of longwave radiation flux retrievals at the top and bottom of the atmosphere at Eureka station, Canada, in the high Arctic. We report comparisons between seven products derived from (1) calculations based on a combination of ground-based and space-based lidar and radar observations, (2) standard radiometric observations from the CERES satellite, (3) direct observations at the surface from a broadband radiation station and (4) the ERA-Interim and ERA5 reanalyses. Statistical, independent analyses are first performed to look at recurring bias and trends in fluxes at Top and Bottom of the Atmosphere. The analysis is further refined comparing fluxes derived from coincident observations decomposed by scene types. Results show that radiative transfer calculations using ground-based lidar-radar profiles derived at Eureka agree well with TOA LW fluxes observed by CERES and with BOA LW fluxes reference. CloudSat-CALIPSO also show good agreement with calculations from ground-based sensor observations, with a relatively small bias. This bias is shown to be largely due to low and thick cloud occurrences that the satellites are insensitive to owing to attenuation from clouds above and surface clutter. These conditions of opaque low clouds, cause an even more pronounced bias for CERES BOA flux calculation in winter, due to the deficit of low clouds identified by MODIS. ERA-I and ERA5 fluxes behave differently, the large positive bias observed with ERA-I is much reduced in ERA5. ERA5 is closer to reference observations due to a better behaviour of low and mid-level clouds.

An improved understanding of the seasonality of the Arctic snowpack properties related to the timing and intensity of snowmelt processes is the key driver to better quantify atmosphere-ice-ocean interactions, and in particular the seasonal energy and mass budgets of the ice-covered polar oceans. Various satellite data products over the last decades have shown a trend towards an earlier snowmelt onset in the Arctic, thus contributing to Arctic amplification and sea-ice decline, underlining the need to better understand these processes. We present here the physical snow properties from spring 2020 examined during the “Multidisciplinary drifting Observatory for the Study of Arctic Climate” (MOSAiC). We focus on southerly air mass advection events in mid-April that were associated with near-surface air temperatures near freezing at the MOSAiC floe. In doing so, we emphasize a single sampling site that was revisited daily-to-weekly throughout the spring. At the sampling site, snow depth ranged from 10 to 14 cm with the bulk density varying between 200 to 350 kg m-3, mainly driven by freshly fallen snow. The vertical snow structure prior to the warm event was characterized by large pores with distinct snow crystal structures and widespread depth hoar crystals, both related to the strong temperature gradient in the snowpack. During the warm air intrusion, increasing temperatures temporarily reversed the thermal gradient in the snow. The warm snow surface, now above a relatively cold snow/ice interface, resulted temporary negative vertical heat flux values observed to be up to -12 Wm-2. Because the snow/ice interface is close to freezing, the negative flux is an indicator that melt may have occurred. Once temperatures dropped again, the vertical temperature and heat flux gradients returned back to the previous patterns. However, the decreased snow grain sizes throughout the snowpack due to the warming and the associated compacted lower layers now dominated the snowpack. Such a temporary warm spell event has decisive impacts on the sea-ice energy and mass budget of the MOSAiC floe. Understanding this effect on a local scale will help to transfer that knowledge to larger spatial scales, and thus to quantify the influence of warm air intrusions during winter and/or spring in the ice-covered Arctic basin.