Atmospheric measurements were made over Arctic sea ice north of Svalbard from winter to early summer (January–June) 2015 during the Norwegian Young Sea Ice (N‐ICE2015) expedition. These measurements, which are available publicly, represent a comprehensive meteorological data set covering the seasonal transition in the Arctic Basin over the new, thinner sea ice regime. Winter was characterized by a succession of storms that produced short‐lived (less than 48 h) temperature increases of 20 to 30 K at the surface. These storms were driven by the hemispheric scale circulation pattern with a large meridional component of the polar jet stream steering North Atlantic storms into the high Arctic. Nonstorm periods during winter were characterized by strong surface temperature inversions due to strong radiative cooling (“radiatively clear state”). The strength and depth of these inversions were similar to those during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign. In contrast, atmospheric profiles during the “opaquely cloudy state” were different to those from SHEBA due to differences in the synoptic conditions and location within the ice pack. Storm events observed during spring/summer were the result of synoptic systems located in the Barents Sea and the Arctic Basin rather than passing directly over N‐ICE2015. These synoptic systems were driven by a large‐scale circulation pattern typical of recent years, with an Arctic Dipole pattern developing during June. Surface temperatures became near‐constant 0°C on 1 June marking the beginning of summer. Atmospheric profiles during the spring and early summer show persistent lifted temperature and moisture inversions that are indicative of clouds and cloud processes.

Reanalysis datasets from atmospheric models and satellite products are often used for Arctic surface shortwave (SW) and longwave (LW) radiative budget analyses, but they suffer from limitations and require validation against local‐scale observations. These are rare in the high Arctic, especially for longer periods that include seasonal transitions. In this study, radiation and meteorological observations acquired during the Norwegian Young Sea Ice Cruise (N‐ICE2015) campaign over sea ice north of Svalbard (80–83°N, 5–25°E) from January–June 2015, cloud lidar observations from the Ice‐Atmosphere‐Ocean Observing System and the Cloud and Aerosol Lidar with Orthogonal Polarization are compared to daily and monthly satellite retrievals from the Clouds and the Earth's Radiant Energy System (CERES) and ERA‐Interim and ERA5 reanalysis. Results indicate that surface temperature is a significant driver for winter LW radiation biases in both satellite and reanalysis data, along with cloud optical depth in CERES. In May, the SW and LW downwelling irradiances are close to observations and cloud properties are well captured (except for ERA‐Interim), while SW upward irradiances are biased low due to surface albedo biases in all datasets. Net SW and LW radiation biases are comparable (∼20–30 Wm⁻²) but opposite in sign for ERA‐Interim and CERES in May, which allows for error compensation. Biases reduce to ±10 Wm⁻² in ERA5. In June downward LW remains biased low (8–10 Wm⁻²) in all datasets suggesting unsettled cloud representation issues. Surface albedo always differs by more than 0.1 between datasets, leading to significant SW and total flux differences.

The Norwegian young sea ice campaign obtained the first measurements of the surface energy budget over young, thin Arctic sea ice through the seasonal transition from winter to summer. This campaign was the first of its kind in the North Atlantic sector of the Arctic. This study describes the atmospheric and surface conditions and the radiative and turbulent heat fluxes over young, thin sea ice. The shortwave albedo of the snow surface ranged from about 0.85 in winter to 0.72–0.80 in early summer. The near‐surface atmosphere was typically stable in winter, unstable in spring, and near neutral in summer once the surface skin temperature reached 0°C. The daily average radiative and turbulent heat fluxes typically sum to negative values (−40 to 0 W m⁻²) in winter but then transition toward positive values of up to nearly +60 W m⁻² as solar radiation contributes significantly to the surface energy budget. The sensible heat flux typically ranges from +20–30 W m⁻² in winter (into the surface) to negative values between 0 and −20 W m⁻² in spring and summer. A winter case study highlights the significant effect of synoptic storms and demonstrates the complex interplay of wind, clouds, and heat and moisture advection on the surface energy components over sea ice in winter. A spring case study contrasts a rare period of 24 h of clear‐sky conditions with typical overcast conditions and highlights the impact of clouds on the surface radiation and energy budgets over young, thin sea ice.

A novel method for classifying Arctic precipitation using ground based remote sensors is presented. Using differences in the spectral variation of microwave absorption and scattering properties of cloud liquid water and ice, this method can distinguish between different types of snowfall events depending on the presence or absence of condensed liquid water in the clouds that generate the precipitation. The classification reveals two distinct, primary regimes of precipitation over the Greenland Ice Sheet (GIS): one originating from fully glaciated ice clouds and the other from mixed-phase clouds. Five years of co-located, multi-instrument data from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) are used to examine cloud and meteorological properties and patterns associated with each precipitation regime. The occurrence and accumulation of the precipitation regimes are identified and quantified. Cloud and precipitation observations from additional ICECAPS instruments illustrate distinct characteristics for each regime. Additionally, reanalysis products and back-trajectory analysis show different synoptic-scale forcings associated with each regime. Precipitation over the central GIS exhibits unique microphysical characteristics due to the high surface elevations as well as connections to specific large-scale flow patterns. Snowfall originating from the ice clouds is coupled to deep, frontal cloud systems advecting up and over the southeast Greenland coast to the central GIS. These events appear to be associated with individual storm systems generated by low pressure over Baffin Bay and Greenland lee cyclogenesis. Snowfall originating from mixed-phase clouds is shallower and has characteristics typical of supercooled cloud liquid water layers, and slowly propagates from the south and southwest of Greenland along a quiescent flow above the GIS.

In the coming decades, as we experience global population growth and global aging issues, there will be corresponding concerns about the quality of the air we experience inside and outside buildings. Because we can anticipate that there will be behavioral changes that accompany population growth and aging, we examine the relationship between home occupant behavior and indoor air quality. To do this, we collect both sensor-based behavior data and chemical indoor air quality measurements in smart home environments. We introduce a novel machine learning-based approach to quantify the correlation between smart home features and chemical measurements of air quality, and evaluate the approach using two smart homes. The findings may help us understand the types of behavior that measurably impact indoor air quality. This information could help us plan for the future by developing an automated building system that would be used as part of a smart city.