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X. R. Zhao, Z. Sheng, H. Q. Shi, L. B. Weng, and Y. He

approximately a few decades and is limited to a few data sources, especially for the mesosphere and above. Radiosonde observations are only available in the lower stratosphere but do not extend to the upper stratosphere and the mesosphere. Lidar and rocketsonde measurements extend to the mesosphere but have limited spatial and temporal sampling. Temperature observations derived from satellite instruments provide temporally continuous and nearly global coverage measurements. Although the longest temperature

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Johannes Mülmenstädt, Dan Lubin, Lynn M. Russell, and Andrew M. Vogelmann

(2011) notes that several days’ persistence is not uncommon] and complicated structures ( Morrison et al. 2012 ). For example, single-layer clouds constituted only half of all mixed-phase cloud observations in Shupe et al. (2006) . These clouds are important for the regional heat budget, and current models have difficulty reproducing them. In particular, liquid water is present in clouds as cold as −40°C ( Shupe 2011 , and references therein), and modeled liquid water content at cold temperatures

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Jasmine Rémillard and George Tselioudis

region in the middle of the Atlantic Ocean. The stratocumulus deck over the Azores has a more transient nature as the region sits near the northern edge of the Canarian stratocumulus-prone area, and baroclinic systems often influence the region as their main track lies just to its north ( Hoskins and Hodges 2002 ). Using ship-based cloud observations, Klein and Hartmann (1993) determined that the yearly average stratus coverage (including stratocumulus among other low clouds) over the northern

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Erik Höjgård-Olsen, Hélène Brogniez, and Hélène Chepfer

al. 2016 ; Ceppi et al. 2017 ; Klein et al. 2017 ). This study quantifies the cloud cover evolution with SST under both strong ascent and strong descent in instantaneous observations. Previous observational studies usually either 1) only observed one or two of the three key variables (humidity, clouds, precipitation) and only ever discussed the third; 2) lacked the instantaneous covariation between them; 3) lacked the detailed vertical structure (e.g., Dewey and Goldblatt 2018 ) by focusing

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Likun Wang, Cheng-Zhi Zou, and Haifeng Qian

Service/Center for Satellite Applications and Research (STAR) web site as version 1.0 SSU dataset (available at http://www.star.nesdis.noaa.gov/smcd/emb/mscat/mscatmain.htm ). Validation work is being carried out using the GPS radio occultation observations and ground-based lidar measurements. The comparison results will be presented elsewhere in future studies. Acknowledgments The work is supported by NOAA Grant NESDISNESDISPO20092001589 (SDS0915). The authors thank the CRTM team including Yong Han

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Xianglei Huang, Xiuhong Chen, Gerald L. Potter, Lazaros Oreopoulos, Jason N. S. Cole, Dongmin Lee, and Norman G. Loeb

compared CanAM4 simulations with observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) and found that the simulated cloud tops in both polar regions are higher than observed. Figure 9 and Table 2 clearly show that, although the LW CREs of both GEOS-5 and CanAM4 are considerably lower than those observed, the reasons are different; the discrepancy is largely due to an underestimated global-mean CA eff for the GEOS-5 model and due to an overestimated

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Michael R. Gallagher, Matthew D. Shupe, and Nathaniel B. Miller

GrIS. This gap in our understanding is the focus of this paper. The analysis presented here categorizes GrIS surface observations by atmospheric state, relating daily circulation to variability in processes that impact GrIS mass balance. To construct this relationship, a sufficiently long, continuous, and detailed set of observations must be used. For temperature, moisture, cloud properties, and radiation on the GrIS, these detailed measurements are available only at Summit Station from the

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Florent Brient and Tapio Schneider

feedback are uncertain ( Zelinka et al. 2012 ; Webb et al. 2013 ). Ground- and space-based observations point toward weakening shortwave reflection by TLCs under warming and hence an amplifying feedback ( Clement et al. 2009 ; Dessler 2010 , 2013 ; Zhou et al. 2013 ; Bellomo et al. 2014 ). A number of recent studies have used the observed covariation of TLC reflection with surface temperature and with other environmental variables to evaluate how well climate models simulate interannual TLC

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Reinout Boers, Fred Bosveld, Henk Klein Baltink, Wouter Knap, Erik van Meijgaard, and Wiel Wauben

RACMO. d. Cloudnet To probe the vertical distribution of clouds we use the combination of radar and lidar observations by applying the Cloudnet procedure. Cloudnet was originally designed as a program to compare the cloud data from several current numerical weather forecast models with ground-based remote sensing observations ( Illingworth et al. 2007 ). To this end, a detection algorithm was constructed that ingested the data thermodynamic profiles from a numerical weather prediction (NWP) model

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Israel Silber, Johannes Verlinde, Sheng-Hung Wang, David H. Bromwich, Ann M. Fridlind, Maria Cadeddu, Edwin W. Eloranta, and Connor J. Flynn

( Bennartz et al. 2013 ; Nicolas et al. 2017 ), enhancement of meltwater runoff ( Van Tricht et al. 2016 ), and sea ice decline ( Francis and Hunter 2006 ). The synoptic-scale patterns of moisture advection into the polar regions are generally well captured by models (e.g., Coggins and McDonald 2015 ; Nicolas and Bromwich 2011 ; Scott et al. 2019 ; Scott and Lubin 2014 ; Sedlar and Tjernström 2017 ; Woods et al. 2013 ). However, discrepancies between observations and large-scale model

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