Articles | Volume 14, issue 12
https://doi.org/10.5194/tc-14-4379-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-4379-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Dec 2020
Research article |
| 04 Dec 2020
| 04 Dec 2020
Satellite observations of snowfall regimes over the Greenland Ice Sheet
Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
Claire Pettersen
Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
Norman B. Wood
Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
Tristan S. L'Ecuyer
Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
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Chanyoung Park, Brian J. Soden, Ryan J. Kramer, Tristan S. L'Ecuyer, and Haozhe He
Atmos. Chem. Phys., 25, 7299–7313, https://doi.org/10.5194/acp-25-7299-2025, https://doi.org/10.5194/acp-25-7299-2025, 2025
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This study addresses the long-standing challenge of quantifying the impact of aerosol–cloud interactions. Using satellite observations, reanalysis data, and a "perfect-model" cross-validation, we show that explicitly accounting for aerosol–cloud droplet activation rates is key to accurately estimating ERFaci (effective radiative forcing due to aerosol–cloud interactions). Our results indicate a smaller and less uncertain ERFaci than previously assessed, implying the reduced role of aerosol–cloud interactions in shaping climate sensitivity.
Natasha Vos, Tristan S. L'Ecuyer, and Tim Michaels
EGUsphere, https://doi.org/10.5194/egusphere-2024-2040, https://doi.org/10.5194/egusphere-2024-2040, 2024
Preprint withdrawn
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PREFIRE uses two CubeSats to make novel measurements of outgoing energy. The CubeSats will frequently resample regions, forming orbit “intersections” that reveal how polar processes impact thermal emissions. This study develops new methods to characterize orbit intersections and applies them to simulated PREFIRE orbits to assess the hypothetical resampling distribution. Generalizing our results informs future missions that two CubeSats at different altitudes greatly enhance resampling coverage.
Brian Kahn, Cameron Bertossa, Xiuhong Chen, Brian Drouin, Erin Hokanson, Xianglei Huang, Tristan L'Ecuyer, Kyle Mattingly, Aronne Merrelli, Tim Michaels, Nate Miller, Federico Donat, Tiziano Maestri, and Michele Martinazzo
EGUsphere, https://doi.org/10.5194/egusphere-2023-2463, https://doi.org/10.5194/egusphere-2023-2463, 2023
Preprint archived
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A cloud detection mask algorithm is developed for the upcoming Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) satellite mission to be launched by NASA in May 2024. The cloud mask is compared to "truth" and is capable of detecting over 90 % of all clouds globally tested with simulated data, and about 87 % of all clouds in the Arctic region.
Charles Nelson Helms, Stephen Joseph Munchak, Ali Tokay, and Claire Pettersen
Atmos. Meas. Tech., 15, 6545–6561, https://doi.org/10.5194/amt-15-6545-2022, https://doi.org/10.5194/amt-15-6545-2022, 2022
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This study compares the techniques used to measure snowflake shape by three instruments: PIP, MASC, and 2DVD. Our findings indicate that the MASC technique produces reliable shape measurements; the 2DVD technique performs better than expected considering the instrument was designed to measure raindrops; and the PIP technique does not produce reliable snowflake shape measurements. We also demonstrate that the PIP images can be reprocessed to correct the shape measurement issues.
Fraser King, George Duffy, Lisa Milani, Christopher G. Fletcher, Claire Pettersen, and Kerstin Ebell
Atmos. Meas. Tech., 15, 6035–6050, https://doi.org/10.5194/amt-15-6035-2022, https://doi.org/10.5194/amt-15-6035-2022, 2022
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Under warmer global temperatures, precipitation patterns are expected to shift substantially, with critical impact on the global water-energy budget. In this work, we develop a deep learning model for predicting snow and rain accumulation based on surface radar observations of the lower atmosphere. Our model demonstrates improved skill over traditional methods and provides new insights into the regions of the atmosphere that provide the most significant contributions to high model accuracy.
Alyson Rose Douglas and Tristan L'Ecuyer
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-688, https://doi.org/10.5194/acp-2022-688, 2022
Revised manuscript not accepted
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Aerosol, or small particles released by human activities, enter the atmosphere and eventually interact with clouds in what we term aerosol-cloud interactions. As more aerosol enter a cloud, they act as cloud droplet nuclei, increasing the number of cloud droplets in a cloud and delaying rain formation, leading to a larger cloud. We use machine learning and found that these interactions lead to 1.27 % more cloudiness on Earth and offset ~1/4 of the warming due to CO2.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
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We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Michael R. Gallagher, Matthew D. Shupe, Hélène Chepfer, and Tristan L'Ecuyer
The Cryosphere, 16, 435–450, https://doi.org/10.5194/tc-16-435-2022, https://doi.org/10.5194/tc-16-435-2022, 2022
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By using direct observations of snowfall and mass changes, the variability of daily snowfall mass input to the Greenland ice sheet is quantified for the first time. With new methods we conclude that cyclones west of Greenland in summer contribute the most snowfall, with 1.66 Gt per occurrence. These cyclones are contextualized in the broader Greenland climate, and snowfall is validated against mass changes to verify the results. Snowfall and mass change observations are shown to agree well.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
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We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Alyson Douglas and Tristan L'Ecuyer
Atmos. Chem. Phys., 21, 15103–15114, https://doi.org/10.5194/acp-21-15103-2021, https://doi.org/10.5194/acp-21-15103-2021, 2021
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When aerosols enter the atmosphere, they interact with the clouds above in what we term aerosol–cloud interactions and lead to a series of reactions which delay the onset of rain. This delay may lead to increased rain rates, or invigoration, when the cloud eventually rains. We show that aerosol leads to invigoration in certain environments. The strength of the invigoration depends on how large the cloud is, which suggests that it is highly tied to the organization of the cloud system.
Erik Johansson, Abhay Devasthale, Michael Tjernström, Annica M. L. Ekman, Klaus Wyser, and Tristan L'Ecuyer
Geosci. Model Dev., 14, 4087–4101, https://doi.org/10.5194/gmd-14-4087-2021, https://doi.org/10.5194/gmd-14-4087-2021, 2021
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Understanding the coupling of clouds to large-scale circulation is a grand challenge for the climate community. Cloud radiative heating (CRH) is a key parameter in this coupling and is therefore essential to model realistically. We, therefore, evaluate a climate model against satellite observations. Our findings indicate good agreement in the seasonal pattern of CRH even if the magnitude differs. We also find that increasing the horizontal resolution in the model has little effect on the CRH.
Andrew M. Dzambo, Tristan L'Ecuyer, Kenneth Sinclair, Bastiaan van Diedenhoven, Siddhant Gupta, Greg McFarquhar, Joseph R. O'Brien, Brian Cairns, Andrzej P. Wasilewski, and Mikhail Alexandrov
Atmos. Chem. Phys., 21, 5513–5532, https://doi.org/10.5194/acp-21-5513-2021, https://doi.org/10.5194/acp-21-5513-2021, 2021
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This work highlights a new algorithm using data collected from the 2016–2018 NASA ORACLES field campaign. This algorithm synthesizes cloud and rain measurements to attain estimates of cloud and precipitation properties over the southeast Atlantic Ocean. Estimates produced by this algorithm compare well against in situ estimates. Increased rain fractions and rain rates are found in regions of atmospheric instability. This dataset can be used to explore aerosol–cloud–precipitation interactions.
Jens Redemann, Robert Wood, Paquita Zuidema, Sarah J. Doherty, Bernadette Luna, Samuel E. LeBlanc, Michael S. Diamond, Yohei Shinozuka, Ian Y. Chang, Rei Ueyama, Leonhard Pfister, Ju-Mee Ryoo, Amie N. Dobracki, Arlindo M. da Silva, Karla M. Longo, Meloë S. Kacenelenbogen, Connor J. Flynn, Kristina Pistone, Nichola M. Knox, Stuart J. Piketh, James M. Haywood, Paola Formenti, Marc Mallet, Philip Stier, Andrew S. Ackerman, Susanne E. Bauer, Ann M. Fridlind, Gregory R. Carmichael, Pablo E. Saide, Gonzalo A. Ferrada, Steven G. Howell, Steffen Freitag, Brian Cairns, Brent N. Holben, Kirk D. Knobelspiesse, Simone Tanelli, Tristan S. L'Ecuyer, Andrew M. Dzambo, Ousmane O. Sy, Greg M. McFarquhar, Michael R. Poellot, Siddhant Gupta, Joseph R. O'Brien, Athanasios Nenes, Mary Kacarab, Jenny P. S. Wong, Jennifer D. Small-Griswold, Kenneth L. Thornhill, David Noone, James R. Podolske, K. Sebastian Schmidt, Peter Pilewskie, Hong Chen, Sabrina P. Cochrane, Arthur J. Sedlacek, Timothy J. Lang, Eric Stith, Michal Segal-Rozenhaimer, Richard A. Ferrare, Sharon P. Burton, Chris A. Hostetler, David J. Diner, Felix C. Seidel, Steven E. Platnick, Jeffrey S. Myers, Kerry G. Meyer, Douglas A. Spangenberg, Hal Maring, and Lan Gao
Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, https://doi.org/10.5194/acp-21-1507-2021, 2021
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Southern Africa produces significant biomass burning emissions whose impacts on regional and global climate are poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA investigation designed to study the key processes that determine these climate impacts. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project, the dataset it produced, and the most important initial findings.
Norman B. Wood and Tristan S. L'Ecuyer
Atmos. Meas. Tech., 14, 869–888, https://doi.org/10.5194/amt-14-869-2021, https://doi.org/10.5194/amt-14-869-2021, 2021
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Although millimeter-wavelength radar reflectivity observations are used to investigate snowfall properties, their ability to constrain specific properties has not been well-quantified. An information-focused retrieval
method shows how well snowfall properties, including rate and size distribution, are constrained by reflectivity. Sources of uncertainty in snowfall rate are dominated by uncertainties in the retrieved size distribution properties rather than by other retrieval assumptions.
Kai-Wei Chang and Tristan L'Ecuyer
Atmos. Chem. Phys., 20, 12499–12514, https://doi.org/10.5194/acp-20-12499-2020, https://doi.org/10.5194/acp-20-12499-2020, 2020
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High-altitude clouds in the tropics that reside in the transition layer between the troposphere and stratosphere are important as they influence the amount of water vapor going into the stratosphere. Waves in the atmosphere can influence the temperature and form these high-altitude cirrus clouds. We use satellite observations to explore the connection between atmospheric waves and clouds and show that cirrus clouds occurrence and properties are closely correlated with waves.
Anne Sophie Daloz, Marian Mateling, Tristan L'Ecuyer, Mark Kulie, Norm B. Wood, Mikael Durand, Melissa Wrzesien, Camilla W. Stjern, and Ashok P. Dimri
The Cryosphere, 14, 3195–3207, https://doi.org/10.5194/tc-14-3195-2020, https://doi.org/10.5194/tc-14-3195-2020, 2020
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The total of snow that falls globally is a critical factor governing freshwater availability. To better understand how this resource is impacted by climate change, we need to know how reliable the current observational datasets for snow are. Here, we compare five datasets looking at the snow falling over the mountains versus the other continents. We show that there is a large consensus when looking at fractional contributions but strong dissimilarities when comparing magnitudes.
Cited articles
Alley, R. B., Meese, D. A., Shuman, C. A., Gow, A. J., Taylor, K. C., Grootes,
P. M., White, J. W. C., Ram, M., Waddington, E. D., Mayewski, P. A., and
Zielinski, G. A.: Abrupt increase in Greenland snow accumulation at the end
of the Younger Dryas event, Nature, 362, 527–529, https://doi.org/10.1038/362527a0,
1993. a
Bamber, J. L., Griggs, J. A., Hurkmans, R. T. W. L., Dowdeswell, J. A., Gogineni, S. P., Howat, I., Mouginot, J., Paden, J., Palmer, S., Rignot, E., and Steinhage, D.: A new bed elevation dataset for Greenland, The Cryosphere, 7, 499–510, https://doi.org/10.5194/tc-7-499-2013, 2013. a
Battaglia, A. and Delanoë, J.: Synergies and complementarities of
cloudsat-calipso snow observations, J. Geophys. Res.-Atmos., 118, 721–731, https://doi.org/10.1029/2012JD018092, 2013. a
Berdahl, M., Rennermalm, A., Hammann, A., Mioduszweski, J., Hameed, S.,
Tedesco, M., Stroeve, J., Mote, T., Koyama, T., and McConnell, J. R.:
Southeast Greenland Winter Precipitation Strongly Linked to the Icelandic Low Position, J. Climate, 31, 4483–4500, https://doi.org/10.1175/JCLI-D-17-0622.1,
2018. a, b, c, d, e, f, g, h
Boening, C., Lebsock, M., Landerer, F., and Stephens, G.: Snowfall-driven mass change on the East Antarctic ice sheet, Geophys. Res. Lett., 39, L21501,
https://doi.org/10.1029/2012GL053316,
2012. a
Box, J. E., Fettweis, X., Stroeve, J. C., Tedesco, M., Hall, D. K., and Steffen, K.: Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers, The Cryosphere, 6, 821–839, https://doi.org/10.5194/tc-6-821-2012, 2012. a
Bring, A., Fedorova, I., Dibike, Y., Hinzman, L., Mård, J., Mernild, S. H.,
Prowse, T., Semenova, O., Stuefer, S. L., and Woo, M.-K.: Arctic terrestrial
hydrology: A synthesis of processes, regional effects, and research
challenges, J. Geophys. Res.-Biogeo., 121, 621–649,
https://doi.org/10.1002/2015JG003131, 2016. a
Bromwich, D. H., Wilson, A. B., Bai, L.-S., Moore, G. W. K., and Bauer, P.: A
comparison of the regional Arctic System Reanalysis and the global
ERA-Interim Reanalysis for the Arctic, Q. J. Roy. Meteor. Soc., 142, 644–658, https://doi.org/10.1002/qj.2527, 2016. a, b, c
C3S: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global
climate, Copernicus Climate Change Service Climate Data Store (CDS), https://doi.org/10.24381/cds.adbb2d47, 2017. a, b
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P.,
Stubenrauch, C. J., and Zeng, S.: The GCM-Oriented CALIPSO Cloud Product
(CALIPSO-GOCCP), J. Geophys. Res.-Atmos., 115, D00H16,
https://doi.org/10.1029/2009JD012251, 2010. a
CloudSat Data Processing Center: CloudSat Standard Data Products, available at: https://cloudsat.atmos.colostate.edu/data, last access: 7 May 2019. a
Cullather, R. I., Nowicki, S. M. J., Zhao, B., and Suarez, M. J.: Evaluation of
the Surface Representation of the Greenland Ice Sheet in a General
Circulation Model, J. Climate, 27, 4835–4856,
https://doi.org/10.1175/JCLI-D-13-00635.1, 2014. a, b
Ettema, J., van den Broeke, M. R., van Meijgaard, E., van de Berg, W. J.,
Bamber, J. L., Box, J. E., and Bales, R. C.: Higher surface mass balance of
the Greenland ice sheet revealed by high-resolution climate modeling,
Geophys. Res. Lett., 36, L12501, https://doi.org/10.1029/2009GL038110, 2009. a
Frei, A., Tedesco, M., Lee, S., Foster, J., Hall, D. K., Kelly, R., and
Robinson, D. A.: A review of global satellite-derived snow products, Adv. Space Res., 50, 1007–1029,
https://doi.org/10.1016/j.asr.2011.12.021, 2012. a
Hakuba, M. Z., Folini, D., Wild, M., and Schär, C.: Impact of Greenland's
topographic height on precipitation and snow accumulation in idealized
simulations, J. Geophys. Res.-Atmos., 117, D09107, https://doi.org/10.1029/2011JD017052, 2012. a, b
Hanna, E., Cropper, T. E., Hall, R. J., and Cappelen, J.: Greenland Blocking
Index 1851–2015: a regional climate change signal, Int. J. Climatol., 36, 4847–4861, https://doi.org/10.1002/joc.4673, 2016. a, b, c
Henderson, D. S., L’Ecuyer, T., Stephens, G., Partain, P., and Sekiguchi, M.:
A Multisensor Perspective on the Radiative Impacts of Clouds and Aerosols,
J. Appl. Meteorol. Clim., 52, 853–871,
https://doi.org/10.1175/JAMC-D-12-025.1, 2013. a
Jakobson, E. and Vihma, T.: Atmospheric moisture budget in the Arctic based on
the ERA-40 reanalysis, Int. J. Climatol., 30, 2175–2194,
https://doi.org/10.1002/joc.2039, 2010. a, b
Kapsner, W. R., Alley, R. B., Shuman, C. A., Anandakrishnan, S., and Grootes,
P. M.: Dominant influence of atmospheric circulation on snow accumulation in
Greenland over the past 18,000 years, Nature, 373, 52–54,
https://doi.org/10.1038/373052a0, 1995. a, b
Koyama, T. and Stroeve, J.: Greenland monthly precipitation analysis from the
Arctic System Reanalysis (ASR): 2000–2012, Polar Sci., 19, 1–12,
https://doi.org/10.1016/j.polar.2018.09.001,
2019. a, b, c
Kulie, M. S., Bennartz, R., Greenwald, T. J., Chen, Y., and Weng, F.:
Uncertainties in Microwave Properties of Frozen Precipitation: Implications
for Remote Sensing and Data Assimilation, J. Atmos. Sci., 67, 3471–3487, https://doi.org/10.1175/2010JAS3520.1, 2010. a, b, c
L'Ecuyer, T. and Jiang, J.: Touring the Atmosphere Aboard the A-Train, Phys. Today, 63, 36–41, https://doi.org/10.1063/1.3653856, 2010. a
Lenaerts, J. T. M., Camron, M. D., Wyburn-Powell, C. R., and Kay, J. E.: Present-day and future Greenland Ice Sheet precipitation frequency from CloudSat observations and the Community Earth System Model, The Cryosphere, 14, 2253–2265, https://doi.org/10.5194/tc-14-2253-2020, 2020. a, b
Lewis, G., Osterberg, E., Hawley, R., Whitmore, B., Marshall, H. P., and Box, J.: Regional Greenland accumulation variability from Operation IceBridge airborne accumulation radar, The Cryosphere, 11, 773–788, https://doi.org/10.5194/tc-11-773-2017, 2017. a
Liu, G. and Curry, J.: Precipitation characteristics in
Greenland-Iceland-Norwegian Seas determined by using satellite microwave
data, J. Geophys. Res.-Atmos., 102, 987–997,
https://doi.org/10.1029/96JD03090, 1997. a
Maahn, M., Burgard, C., Crewell, S., Gorodetskaya, I. V., Kneifel, S.,
Lhermitte, S., Van Tricht, K., and Van Lipzig, N. P. M.: How does the
spaceborne radar blind zone affect derived surface snowfall statistics in
polar regions?, J. Geophys. Res.-Atmos., 119, 604–620, https://doi.org/10.1002/2014JD022079, 2014. a, b
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor
detection using Cloudsat – An earth-orbiting 94-GHz cloud radar, J. Atmos. Oceanic Tech., 25, 519–533,
https://doi.org/10.1175/2007JTECHA1006.1, 2008. a
Matus, A. V. and L'Ecuyer, T. S.: The role of cloud phase in Earth's radiation budget, J. Geophys. Res.-Atmos., 122, 2559–2578, https://doi.org/10.1002/2016JD025951, 2017. a
McIlhattan, E. A., L'Ecuyer, T. S., and Miller, N. B.: Observational Evidence
Linking Arctic Supercooled Liquid Cloud Biases in CESM to Snowfall Processes, J. Climate, 30, 4477–4495, https://doi.org/10.1175/JCLI-D-16-0666.1, 2017. a, b, c, d
Miège, C., Forster, R. R., Box, J. E., Burgess, E. W., McConnell, J. R.,
Pasteris, D. R., and Spikes, V. B.: Southeast Greenland high accumulation
rates derived from firn cores and ground-penetrating radar, Ann. Glaciol., 54, 322–332, https://doi.org/10.3189/2013AoG63A358, 2013. a
Milani, L., Porcù, F., Casella, D., Dietrich, S., Panegrossi, G., Petracca, M., and Sanò, P.: Analysis of long-term precipitation pattern over Antarctica derived from satellite-borne radar, The Cryosphere Discuss., 9, 141–182, https://doi.org/10.5194/tcd-9-141-2015, 2015. a
Milani, L., Kulie, M. S., Casella, D., Dietrich, S., L'Ecuyer, T. S.,
Panegrossi, G., Porcù, F., Sanò, P., and Wood, N. B.: CloudSat
snowfall estimates over Antarctica and the Southern Ocean: An assessment of
independent retrieval methodologies and multi-year snowfall analysis,
Atmos. Res., 213, 121–135, https://doi.org/10.1016/j.atmosres.2018.05.015,
2018. a, b, c
Miller, N. B., Shupe, M. D., Cox, C. J., Walden, V. P., Turner, D. D., and
Steffen, K.: Cloud Radiative Forcing at Summit, Greenland, J. Climate, 28, 6267–6280, https://doi.org/10.1175/JCLI-D-15-0076.1, 2015. a, b
Moran, K. P., Martner, B. E., Post, M. J., Kropfli, R. A., Welsh, D. C., and
Widener, K. B.: An Unattended Cloud-Profiling Radar for Use in Climate
Research, B. Am. Meteorol. Soc., 79, 443–456,
https://doi.org/10.1175/1520-0477(1998)079<0443:AUCPRF>2.0.CO;2, 1998. a
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and
Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012. a, b, c
Morrison, A. L., Kay, J. E., Chepfer, H., Guzman, R., and Yettella, V.:
Isolating the Liquid Cloud Response to Recent Arctic Sea Ice Variability
Using Spaceborne Lidar Observations, J. Geophys. Res.-Atmos., 123, 473–490, https://doi.org/10.1002/2017JD027248, 2018. a
Mottram, R., Simonsen, S. B., Hø yer Svendsen, S., Barletta, V. R., Sandberg Sø rensen, L., Nagler, T., Wuite, J., Groh, A., Horwath, M., Rosier, J., Solgaard, A., Hvidberg, C. S., and Forsberg, R.: An Integrated View of Greenland Ice Sheet Mass Changes Based on Models and Satellite Observations, Remote Sens., 11, 1407, https://doi.org/10.3390/rs11121407, 2019. a, b
Mouginot, J., Rignot, E., Bjørk, A. A., van den Broeke, M., Millan, R.,
Morlighem, M., Noël, B., Scheuchl, B., and Wood, M.: Forty-six years of
Greenland Ice Sheet mass balance from 1972 to 2018, P. Natl. Acad. Sci. USA, 116, 9239–9244, https://doi.org/10.1073/pnas.1904242116, 2019. a, b, c, d
Noël, B., van de Berg, W. J., van Meijgaard, E., Kuipers Munneke, P., van de Wal, R. S. W., and van den Broeke, M. R.: Evaluation of the updated regional climate model RACMO2.3: summer snowfall impact on the Greenland Ice Sheet, The Cryosphere, 9, 1831–1844, https://doi.org/10.5194/tc-9-1831-2015, 2015. a
Norin, L., Devasthale, A., L'Ecuyer, T. S., Wood, N. B., and Smalley, M.: Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden, Atmos. Meas. Tech., 8, 5009–5021, https://doi.org/10.5194/amt-8-5009-2015, 2015. a, b
Palerme, C., Kay, J. E., Genthon, C., L'Ecuyer, T., Wood, N. B., and Claud, C.: How much snow falls on the Antarctic ice sheet?, The Cryosphere, 8, 1577–1587, https://doi.org/10.5194/tc-8-1577-2014, 2014. a
Palerme, C., Genthon, C., Claud, C., Kay, J., Wood, B. N., and L'Ecuyer, T.:
Evaluation of current and projected Antarctic precipitation in CMIP5 models,
Clim. Dynam., 48, 225–239, https://doi.org/10.1007/s00382-016-3071-1, 2016. a, b
Pettersen, C. and Merrelli, A.: Microwave radiometer snow categorization tool
for Summit, Greenland, 2010–2015, Arctic Data Center,
https://doi.org/10.18739/A2R28Q, 2018. a, b, c
Pettersen, C., Bennartz, R., Kulie, M. S., Merrelli, A. J., Shupe, M. D., and Turner, D. D.: Microwave signatures of ice hydrometeors from ground-based observations above Summit, Greenland, Atmos. Chem. Phys., 16, 4743–4756, https://doi.org/10.5194/acp-16-4743-2016, 2016. a
Pettersen, C., Bennartz, R., Merrelli, A. J., Shupe, M. D., Turner, D. D., and Walden, V. P.: Precipitation regimes over central Greenland inferred from 5 years of ICECAPS observations, Atmos. Chem. Phys., 18, 4715–4735, https://doi.org/10.5194/acp-18-4715-2018, 2018. a
Petty, G. W.: A First Course in Atmospheric Radiation, Sundog Publishing,
Madison, USA, 2006. a
Rogers, J. C., Bathke, D. J., Mosley-Thompson, E., and Wang, S.-H.: Atmospheric
circulation and cyclone frequency variations linked to the primary modes of
Greenland snow accumulation, Geophys. Res. Lett., 31, L23208, https://doi.org/10.1029/2004GL021048, 2004. a, b
Ryan, J. C., Smith, L. C., van As, D., Cooley, S. W., Cooper, M. G., Pitcher,
L. H., and Hubbard, A.: Greenland Ice Sheet surface melt amplified by
snowline migration and bare ice exposure, Science Advances, 5, eaav3738,
https://doi.org/10.1126/sciadv.aav3738, 2019. a
Ryan, J. C., Smith, L. C., Wu, M., Cooley, S. W., Miège, C., Montgomery,
L. N., Koenig, L. S., Fettweis, X., Noel, B. P. Y., and van den Broeke,
M. R.: Evaluation of CloudSat's Cloud-Profiling Radar for Mapping Snowfall
Rates Across the Greenland Ice Sheet, J. Geophys. Res.-Atmos., 125, e2019JD031411, https://doi.org/10.1029/2019JD031411, 2020. a, b
Serreze, M. C. and Barrett, A. P.: The Summer Cyclone Maximum over the Central
Arctic Ocean, J. Climate, 21, 1048–1065,
https://doi.org/10.1175/2007JCLI1810.1, 2008. a, b
Shupe, M.: Millimeter Cloud Radar measurements taken at Summit Station,
Greenland – Arctic Observing Network program, Arctic Data Center,
https://doi.org/10.18739/A20G3GZ8B., 2010. a
Shupe, M. D., Turner, D. D., Walden, V. P., Bennartz, R., Cadeddu, M. P.,
Castellani, B. B., Cox, C. J., Hudak, D. R., Kulie, M. S., Miller, N. B.,
Neely, R. R., Neff, W. D., and Rowe, P. M.: HIGH AND DRY: New Observations of
Tropospheric and Cloud Properties above the Greenland Ice Sheet, B. Am. Meteorol. Soc., 94, 169–186,
http://www.jstor.org/stable/26219494, 2013. a, b, c, d, e
Skofronick-Jackson, G., Kulie, M., Milani, L., Munchak, S. J., Wood, N. B., and
Levizzani, V.: Satellite Estimation of Falling Snow: A Global Precipitation
Measurement (GPM) Core Observatory Perspective, J. Appl. Meteorol. Clim., 58, 1429–1448, https://doi.org/10.1175/JAMC-D-18-0124.1,
2019. a
Souverijns, N., Gossart, A., Lhermitte, S., Gorodetskaya, I. V., Grazioli, J., Berne, A., Duran-Alarcon, C., Boudevillain, B., Genthon, C., Scarchilli, C., and van Lipzig, N. P. M.: Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars, The Cryosphere, 12, 3775–3789, https://doi.org/10.5194/tc-12-3775-2018, 2018. a
Steffen, K. and Box, J.: Surface climatology of the Greenland Ice Sheet:
Greenland Climate Network 1995–1999, J. Geophys. Res., 106,
33951, https://doi.org/10.1029/2001JD900161, 2001. a
Tanelli, S., Durden, S. L., Im, E., Pak, K. S., Reinke, D. G.,
Partain, P., Haynes, J. M., and Marchand, R. T.: CloudSat's Cloud
Profiling Radar After Two Years in Orbit: Performance, Calibration, and
Processing, IEEE T. Geosci. Remote, 46,
3560–3573, https://doi.org/10.1109/TGRS.2008.2002030, 2008. a
van den Broeke, M. R., Enderlin, E. M., Howat, I. M., Kuipers Munneke, P., Noël, B. P. Y., van de Berg, W. J., van Meijgaard, E., and Wouters, B.: On the recent contribution of the Greenland ice sheet to sea level change, The Cryosphere, 10, 1933–1946, https://doi.org/10.5194/tc-10-1933-2016, 2016. a, b
Van Tricht, K., Lhermitte, S., Lenaerts, J. T. M., Gorodetskaya, I. V.,
L/'Ecuyer, T. S., Noel, B., van den Broeke, M. R., Turner, D. D., and van
Lipzig, N. P. M.: Clouds enhance Greenland ice sheet meltwater runoff, Nat.
Commun., 7, 10266, https://doi.org/10.1038/ncomms10266, 2016. a, b
Vaughan, D., Comiso, J., Allison, I., Carrasco, J., Kaser, G., Kwok, R., Mote,
P., Murray, T., Paul, F., Ren, J., Rignot, E., Solomina, O., Steffen, K., and Zhang, T.: Observations: Cryosphere, in: Climate Change 2013 – The Physical Science Basis, The Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge, United Kingdom and New York, USA, 317–382, https://doi.org/10.1017/CBO9781107415324.012, 2013. a
Vernon, C. L., Bamber, J. L., Box, J. E., van den Broeke, M. R., Fettweis, X., Hanna, E., and Huybrechts, P.: Surface mass balance model intercomparison for the Greenland ice sheet, The Cryosphere, 7, 599–614, https://doi.org/10.5194/tc-7-599-2013, 2013. a
Vihma, T., Screen, J., Tjernström, M., Newton, B., Zhang, X., Popova, V.,
Deser, C., Holland, M., and Prowse, T.: The atmospheric role in the Arctic
water cycle: A review on processes, past and future changes, and their
impacts, J. Geophys. Res.-Biogeo., 121, 586–620,
https://doi.org/10.1002/2015JG003132, 2016. a, b, c
Wood, N. B. and L'Ecuyer, T. S.: Level 2C Snow Profile Process Description and Interface Control Document, Product Version, P1R05, NASA JPL CloudSat project document revision 0, 26 pp., available at: http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2C-SNOW-PROFILE_PDICD.P1_R05.rev0_.pdf, last access: 28 June 2018. a
Zappa, G., Shaffrey, L. C., Hodges, K. I., Sansom, P. G., and Stephenson,
D. B.: A Multimodel Assessment of Future Projections of North Atlantic and
European Extratropical Cyclones in the CMIP5 Climate Models, J. Climate, 26, 5846–5862, https://doi.org/10.1175/JCLI-D-12-00573.1, 2013.
a, b
Zhang, X., Walsh, J. E., Zhang, J., Bhatt, U. S., and Ikeda, M.: Climatology
and Interannual Variability of Arctic Cyclone Activity: 1948–2002, J. Climate, 17, 2300–2317,
https://doi.org/10.1175/1520-0442(2004)017<2300:CAIVOA>2.0.CO;2, 2004. a, b, c
Zwally, H. J., Li, J., Brenner, A. C., Beckley, M., Cornejo, H. G., Marzio,
J. D., Giovinetto, M. B., Neumann, T. A., Robbins, J., Saba, J. L., Yi, D.,
and Wang, W.: Greenland ice sheet mass balance: Distribution of increased
mass loss with climate warming; 2003–07 versus 1992–2002, J. Glaciol., 57, 88–102, https://doi.org/10.3189/002214311795306682, 2011. a, b
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Short summary
Snowfall builds the mass of the Greenland Ice Sheet (GrIS) and reduces melt by brightening the surface. We present satellite observations of GrIS snowfall events divided into two regimes: those coincident with ice clouds and those coincident with mixed-phase clouds. Snowfall from ice clouds plays the dominant role in building the GrIS, producing ~ 80 % of total accumulation. The two regimes have similar snowfall frequency in summer, brightening the surface when solar insolation is at its peak.
Snowfall builds the mass of the Greenland Ice Sheet (GrIS) and reduces melt by brightening the...
