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  1. Abstract. This study presents the first full annual cycle (2019–2020) of ambient surface aerosol particle number concentration measurements (condensationnuclei > 20 nm, N20) collected at Summit Station (Summit), in the centre of the Greenland Ice Sheet (72.58∘ N, −38.45∘ E; 3250 ma.s.l.). The mean surface concentration in 2019 was 129 cm−3, with the 6 h mean ranging between 1 and 1441 cm−3. The highest monthly mean concentrations occurred during the late spring and summer, with the minimum concentrations occurring in February (mean: 18 cm−3). High-N20 events are linked to anomalous anticyclonic circulation over Greenland and the descent of free-tropospheric aerosol down to the surface, whereas low-N20 events are linked to anomalous cyclonic circulation over south-east Greenland that drives upslope flow and enhances precipitation en route to Summit. Fog strongly affects particle number concentrations, on average reducing N20 by 20 % during the first 3 h of fog formation. Extremely-low-N20 events (< 10 cm−3) occur in all seasons, and we suggest that fog, and potentially cloud formation, can be limited by low aerosol particle concentrations over central Greenland.
  2. Abstract. We use the CloudSat 2006–2016 data record to estimate snowfall over theGreenland Ice Sheet (GrIS). We first evaluate CloudSat snowfall retrievalswith respect to remaining ground-clutter issues. Comparing CloudSatobservations to the GrIS topography (obtained from airborne altimetrymeasurements during IceBridge) we find that at the edges of the GrISspurious high-snowfall retrievals caused by ground clutter occasionallyaffect the operational snowfall product. After correcting for this effect,the height of the lowest valid CloudSat observation is about 1200&thinsp;mabove the local topography as defined by IceBridge. We then use ground-based millimeter wavelength cloud radar (MMCR) observations obtained from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland (ICECAPS) experiment to devise a simple,empirical correction to account for precipitation processes occurringbetween the height of the observed CloudSat reflectivities and the snowfallnear the surface. Using the height-corrected, clutter-cleared CloudSatreflectivities we next evaluate various ZS relationships in terms ofsnowfall accumulation at Summit through comparison with weekly stake fieldobservations of snow accumulation available since 2007. Using a set of threeZS relationships that best agree with the observed accumulation at Summit,we then calculate the annual cycle snowfall over the entire GrIS as well asover different drainage areas and compare the derived meanmore »values and annualcycles of snowfall to ERA-Interim reanalysis. We find the annual meansnowfall over the GrIS inferred from CloudSat to be 34±7.5&thinsp;cm&thinsp;yr−1liquid equivalent (where the uncertainty is determined by the range invalues between the three different ZS relationships used). In comparison,the ERA-Interim reanalysis product only yields 30&thinsp;cm&thinsp;yr−1 liquid equivalentsnowfall, where the majority of the underestimation in the reanalysisappears to occur in the summer months over the higher GrIS and appears to berelated to shallow precipitation events. Comparing all available estimatesof snowfall accumulation at Summit Station, we find the annually averagedliquid equivalent snowfall from the stake field to be between 20 and 24&thinsp;cm&thinsp;yr−1, depending on the assumed snowpack density and from CloudSat 23±4.5&thinsp;cm&thinsp;yr−1. The annual cycle at Summit is generally similar betweenall data sources, with the exception of ERA-Interim reanalysis, which showsthe aforementioned underestimation during summer months.

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  3. null (Ed.)
    Abstract The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements ofmore »plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.« less
  4. The ability of in situ snowflake microphysical observations to constrain estimates of surface snowfall accumulations derived from coincident, ground-based radar observations is explored. As part of the High-Latitude Measurement of Snowfall (HiLaMS) field campaign, a Micro Rain Radar (MRR), Precipitation Imaging Package (PIP), and Multi-Angle Snow Camera (MASC) were deployed to the Haukeliseter Test Site run by the Norwegian Meteorological Institute during winter 2016/17. This measurement site lies near an elevation of 1000 m in the mountains of southern Norway and houses a double-fence automated reference (DFAR) snow gauge and a comprehensive set of meteorological observations. MASC and PIP observations provided estimates of particle size distribution (PSD), fall speed, and habit. These properties were used as input for a snowfall retrieval algorithm using coincident MRR reflectivity measurements. Retrieved surface snowfall accumulations were evaluated against DFAR observations to quantify retrieval performance as a function of meteorological conditions for the Haukeliseter site. These analyses found differences of less than 10% between DFAR- and MRR-retrieved estimates over the field season when using either PIP or MASC observations for low wind “upslope” events. Larger biases of at least 50% were found for high wind “pulsed” events likely because of sampling limitations in the inmore »situ observations used to constrain the retrieval. However, assumptions of MRR Doppler velocity for mean particle fall speed and a temperature-based PSD parameterization reduced this difference to +16% for the pulsed events. Although promising, these results ultimately depend upon selection of a snowflake particle model that is well matched to scene environmental conditions.

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