Abstract. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite has been observing polar stratospheric clouds (PSCs) from mid-June 2006 until the present. The spaceborne lidar profiles PSCs with unprecedented spatial (5 km horizontal × 180 m vertical) resolution and its dual-polarization capability enables classification of PSCs according to composition. Nearly coincident Aura Microwave Limb Sounder (MLS) measurements of the primary PSC condensables (HNO 3 and H 2 O) provide additional constraints on particle composition. A new CALIOP version 2 (v2) PSC detection and composition classification algorithm has been implemented that corrects known deficiencies in previous algorithms and includes additional refinements to improve composition discrimination. Major v2 enhancements include dynamic adjustment of composition boundaries to account for effects of denitrification and dehydration, explicit use of measurement uncertainties, addition of composition confidence indices, and retrieval of particulate backscatter, which enables simplified estimates of particulate surface area density (SAD) and volume density (VD). The over 11 years of CALIOP PSC observations in each v2 composition class conform to their expected thermodynamic existence regimes, which is consistent with previous analyses of data from 2006 to 2011 and underscores the robustness of the v2 composition discrimination approach.The v2 algorithm has been applied to the CALIOP dataset to produce a PSC reference data record spanning the 2006-2017 time period, which is the foundation for a new comprehensive, high-resolution climatology of PSC occurrence and composition for both the Antarctic and Arctic. Time series of daily-averaged, vortex-wide PSC areal coverage versus altitude illustrate that Antarctic PSC seasons are similar from year to year, with about 25 % relative standard deviation in Antarctic PSC spatial volume at the peak of the season in July and August. Multi-year average, monthly zonal mean cross sections depict the climatological patterns of Antarctic PSC occurrence in latitude-altitude and also equivalent-latitudepotential-temperature coordinate systems, with the latter system better capturing the microphysical processes controlling PSC existence. Polar maps of the multi-year mean geographical patterns in PSC occurrence frequency show a climatological maximum between longitudes 90 • W and 0 • , which is the preferential region for forcing by orography and upper tropospheric anticyclones. The climatological mean distributions of particulate SAD and VD also show maxima in this region due to the large enhancements from the frequent ice clouds.Stronger wave activity in the Northern Hemisphere leads to a more disturbed Arctic polar vortex, whose evolution and lifetime vary significantly from year to year. Accordingly, Arctic PSC areal coverage is distinct from year to year with no "typical" year, and the relative standard deviation in Arctic PSC sp...

there has been no obvious long-term trend.

Snowfall and snowpack are tightly coupled within the snow water cycle and careful monitoring is crucial to better understand snow’s role in Earth’s water and energy cycles. Current and future estimates of the total amount of seasonal snow on the ground are limited by the variability in the initial snowfall and uncertainties in in-situ and remote sensing observations. In this study, passive microwave remote sensing estimates of snowfall and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer (AMSR-E) instrument are used to assess the consistency in the snow products. A snow evolution model, SnowModel, is employed to simulate snow processes that occur between the initial snowfall and subsequent SWE. AMSR-E is found to have significant discrepancies in both snowfall and SWE compared to MERRA-2 reanalysis and the Canadian Meteorological Centre (CMC) snow product. It is shown AMSR-E snowfall is currently not a useful metric to estimate SWE without applying large corrections throughout the winter season. Regions of consistency in the AMSR-E snow products occur for reasons that pertain to underestimation in both snowfall and SWE. In addition to snow consistency, microwave brightness temperatures (TBs) are analyzed in response to the snowpack and snowfall physical properties. These experiments indicate significant sensitivity to regime dependent scattering characteristics that must be accounted for to accurately estimate global snow properties and provide better physical consistency in the snow products from remote sensing platforms.

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