Articles | Volume 14, issue 12
https://doi.org/10.5194/tc-14-4719-2020
© 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-14-4719-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Dec 2020
Research article |
| 23 Dec 2020
| 23 Dec 2020
Scoring Antarctic surface mass balance in climate models to refine future projections
Tessa Gorte
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado, USA
Jan T. M. Lenaerts
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado, USA
Brooke Medley
Cryospheric Sciences Laboratory, National Aeronautics and Space Administration's Goddard Space Flight Center, Greenbelt, Maryland, USA
Related authors
Devon Dunmire, Jan T. M. Lenaerts, Rajashree Tri Datta, and Tessa Gorte
The Cryosphere, 16, 4163–4184, https://doi.org/10.5194/tc-16-4163-2022, https://doi.org/10.5194/tc-16-4163-2022, 2022
Short summary
Short summary
Earth system models (ESMs) are used to model the climate system and the interactions of its components (atmosphere, ocean, etc.) both historically and into the future under different assumptions of human activity. The representation of Antarctica in ESMs is important because it can inform projections of the ice sheet's contribution to sea level rise. Here, we compare output of Antarctica's surface climate from an ESM with observations to understand strengths and weaknesses within the model.
Andrew O. Hoffman, Michelle L. Maclennan, Jan Lenaerts, Kristine M. Larson, and Knut Christianson
The Cryosphere, 19, 713–730, https://doi.org/10.5194/tc-19-713-2025, https://doi.org/10.5194/tc-19-713-2025, 2025
Short summary
Short summary
Traditionally, glaciologists use global navigation satellite systems (GNSSs) to measure the surface elevation and velocity of glaciers to understand processes associated with ice flow. Using the interference of GNSS signals that bounce off of the ice sheet surface, we measure the surface height change near GNSS receivers in the Amundsen Sea Embayment (ASE). From surface height change, we infer daily accumulation rates that we use to understand the drivers of extreme precipitation in the ASE.
Haokui Xu, Leung Tsang, Julie Miller, Brooke Medley, and Jeol Johnson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2395, https://doi.org/10.5194/egusphere-2024-2395, 2025
Short summary
Short summary
This paper provides a physical model to analyze the brightness temperature time series over the firn aquifer in Greenland and Antarctica. The model can match the V and H SMAP brightness temperature time series well. This model provides a potential to study the aquifer liquid water content with radiometry.
Marissa E. Dattler, Brooke Medley, and C. Max Stevens
The Cryosphere, 18, 3613–3631, https://doi.org/10.5194/tc-18-3613-2024, https://doi.org/10.5194/tc-18-3613-2024, 2024
Short summary
Short summary
We developed an algorithm based on combining models and satellite observations to identify the presence of surface melt on the Antarctic Ice Sheet. We find that this method works similarly to previous methods by assessing 13 sites and the Larsen C ice shelf. Unlike previous methods, this algorithm is based on physical parameters, and updates to this method could allow the meltwater present on the Antarctic Ice Sheet to be quantified instead of simply detected.
Rajashree Tri Datta, Adam Herrington, Jan T. M. Lenaerts, David P. Schneider, Luke Trusel, Ziqi Yin, and Devon Dunmire
The Cryosphere, 17, 3847–3866, https://doi.org/10.5194/tc-17-3847-2023, https://doi.org/10.5194/tc-17-3847-2023, 2023
Short summary
Short summary
Precipitation over Antarctica is one of the greatest sources of uncertainty in sea level rise estimates. Earth system models (ESMs) are a valuable tool for these estimates but typically run at coarse spatial resolutions. Here, we present an evaluation of the variable-resolution CESM2 (VR-CESM2) for the first time with a grid designed for enhanced spatial resolution over Antarctica to achieve the high resolution of regional climate models while preserving the two-way interactions of ESMs.
Haokui Xu, Brooke Medley, Leung Tsang, Joel T. Johnson, Kenneth C. Jezek, Marco Brogioni, and Lars Kaleschke
The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, https://doi.org/10.5194/tc-17-2793-2023, 2023
Short summary
Short summary
The density profile of polar ice sheets is a major unknown in estimating the mass loss using lidar tomography methods. In this paper, we show that combing the active radar data and passive radiometer data can provide an estimation of density properties using the new model we implemented in this paper. The new model includes the short and long timescale variations in the firn and also the refrozen layers which are not included in the previous modeling work.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
Short summary
Short summary
Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Megan Thompson-Munson, Nander Wever, C. Max Stevens, Jan T. M. Lenaerts, and Brooke Medley
The Cryosphere, 17, 2185–2209, https://doi.org/10.5194/tc-17-2185-2023, https://doi.org/10.5194/tc-17-2185-2023, 2023
Short summary
Short summary
To better understand the Greenland Ice Sheet’s firn layer and its ability to buffer sea level rise by storing meltwater, we analyze firn density observations and output from two firn models. We find that both models, one physics-based and one semi-empirical, simulate realistic density and firn air content when compared to observations. The models differ in their representation of firn air content, highlighting the uncertainty in physical processes and the paucity of deep-firn measurements.
Michelle L. Maclennan, Jan T. M. Lenaerts, Christine A. Shields, Andrew O. Hoffman, Nander Wever, Megan Thompson-Munson, Andrew C. Winters, Erin C. Pettit, Theodore A. Scambos, and Jonathan D. Wille
The Cryosphere, 17, 865–881, https://doi.org/10.5194/tc-17-865-2023, https://doi.org/10.5194/tc-17-865-2023, 2023
Short summary
Short summary
Atmospheric rivers are air masses that transport large amounts of moisture and heat towards the poles. Here, we use a combination of weather observations and models to quantify the amount of snowfall caused by atmospheric rivers in West Antarctica which is about 10 % of the total snowfall each year. We then examine a unique event that occurred in early February 2020, when three atmospheric rivers made landfall over West Antarctica in rapid succession, leading to heavy snowfall and surface melt.
Benjamin E. Smith, Brooke Medley, Xavier Fettweis, Tyler Sutterley, Patrick Alexander, David Porter, and Marco Tedesco
The Cryosphere, 17, 789–808, https://doi.org/10.5194/tc-17-789-2023, https://doi.org/10.5194/tc-17-789-2023, 2023
Short summary
Short summary
We use repeated satellite measurements of the height of the Greenland ice sheet to learn about how three computational models of snowfall, melt, and snow compaction represent actual changes in the ice sheet. We find that the models do a good job of estimating how the parts of the ice sheet near the coast have changed but that two of the models have trouble representing surface melt for the highest part of the ice sheet. This work provides suggestions for how to better model snowmelt.
Devon Dunmire, Jan T. M. Lenaerts, Rajashree Tri Datta, and Tessa Gorte
The Cryosphere, 16, 4163–4184, https://doi.org/10.5194/tc-16-4163-2022, https://doi.org/10.5194/tc-16-4163-2022, 2022
Short summary
Short summary
Earth system models (ESMs) are used to model the climate system and the interactions of its components (atmosphere, ocean, etc.) both historically and into the future under different assumptions of human activity. The representation of Antarctica in ESMs is important because it can inform projections of the ice sheet's contribution to sea level rise. Here, we compare output of Antarctica's surface climate from an ESM with observations to understand strengths and weaknesses within the model.
Brooke Medley, Thomas A. Neumann, H. Jay Zwally, Benjamin E. Smith, and C. Max Stevens
The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, https://doi.org/10.5194/tc-16-3971-2022, 2022
Short summary
Short summary
Satellite altimeters measure the height or volume change over Earth's ice sheets, but in order to understand how that change translates into ice mass, we must account for various processes at the surface. Specifically, snowfall events generate large, transient increases in surface height, yet snow fall has a relatively low density, which means much of that height change is composed of air. This air signal must be removed from the observed height changes before we can assess ice mass change.
Karen E. Alley, Christian T. Wild, Adrian Luckman, Ted A. Scambos, Martin Truffer, Erin C. Pettit, Atsuhiro Muto, Bruce Wallin, Marin Klinger, Tyler Sutterley, Sarah F. Child, Cyrus Hulen, Jan T. M. Lenaerts, Michelle Maclennan, Eric Keenan, and Devon Dunmire
The Cryosphere, 15, 5187–5203, https://doi.org/10.5194/tc-15-5187-2021, https://doi.org/10.5194/tc-15-5187-2021, 2021
Short summary
Short summary
We present a 20-year, satellite-based record of velocity and thickness change on the Thwaites Eastern Ice Shelf (TEIS), the largest remaining floating extension of Thwaites Glacier (TG). TG holds the single greatest control on sea-level rise over the next few centuries, so it is important to understand changes on the TEIS, which controls much of TG's flow into the ocean. Our results suggest that the TEIS is progressively destabilizing and is likely to disintegrate over the next few decades.
Madison L. Ghiz, Ryan C. Scott, Andrew M. Vogelmann, Jan T. M. Lenaerts, Matthew Lazzara, and Dan Lubin
The Cryosphere, 15, 3459–3494, https://doi.org/10.5194/tc-15-3459-2021, https://doi.org/10.5194/tc-15-3459-2021, 2021
Short summary
Short summary
We investigate how melt occurs over the vulnerable ice shelves of West Antarctica and determine that the three primary mechanisms can be evaluated using archived numerical weather prediction model data and satellite imagery. We find examples of each mechanism: thermal blanketing by a warm atmosphere, radiative heating by thin clouds, and downslope winds. Our results signify the potential to make a multi-decadal assessment of atmospheric stress on West Antarctic ice shelves in a warming climate.
Devon Dunmire, Alison F. Banwell, Nander Wever, Jan T. M. Lenaerts, and Rajashree Tri Datta
The Cryosphere, 15, 2983–3005, https://doi.org/10.5194/tc-15-2983-2021, https://doi.org/10.5194/tc-15-2983-2021, 2021
Short summary
Short summary
Here, we automatically detect buried lakes (meltwater lakes buried below layers of snow) across the Greenland Ice Sheet, providing insight into a poorly studied meltwater feature. For 2018 and 2019, we compare areal extent of buried lakes. We find greater buried lake extent in 2019, especially in northern Greenland, which we attribute to late-summer surface melt and high autumn temperatures. We also provide evidence that buried lakes form via different processes across Greenland.
Eric Keenan, Nander Wever, Marissa Dattler, Jan T. M. Lenaerts, Brooke Medley, Peter Kuipers Munneke, and Carleen Reijmer
The Cryosphere, 15, 1065–1085, https://doi.org/10.5194/tc-15-1065-2021, https://doi.org/10.5194/tc-15-1065-2021, 2021
Short summary
Short summary
Snow density is required to convert observed changes in ice sheet volume into mass, which ultimately drives ice sheet contribution to sea level rise. However, snow properties respond dynamically to wind-driven redistribution. Here we include a new wind-driven snow density scheme into an existing snow model. Our results demonstrate an improved representation of snow density when compared to observations and can therefore be used to improve retrievals of ice sheet mass balance.
Marie G. P. Cavitte, Quentin Dalaiden, Hugues Goosse, Jan T. M. Lenaerts, and Elizabeth R. Thomas
The Cryosphere, 14, 4083–4102, https://doi.org/10.5194/tc-14-4083-2020, https://doi.org/10.5194/tc-14-4083-2020, 2020
Short summary
Short summary
Surface mass balance (SMB) and surface air temperature (SAT) are correlated at the regional scale for most of Antarctica, SMB and δ18O. Areas with low/no correlation are where wind processes (foehn, katabatic wind warming, and erosion) are sufficiently active to overwhelm the synoptic-scale snow accumulation. Measured in ice cores, the link between SMB, SAT, and δ18O is much weaker. Random noise can be removed by core record averaging but local processes perturb the correlation systematically.
Xavier Fettweis, Stefan Hofer, Uta Krebs-Kanzow, Charles Amory, Teruo Aoki, Constantijn J. Berends, Andreas Born, Jason E. Box, Alison Delhasse, Koji Fujita, Paul Gierz, Heiko Goelzer, Edward Hanna, Akihiro Hashimoto, Philippe Huybrechts, Marie-Luise Kapsch, Michalea D. King, Christoph Kittel, Charlotte Lang, Peter L. Langen, Jan T. M. Lenaerts, Glen E. Liston, Gerrit Lohmann, Sebastian H. Mernild, Uwe Mikolajewicz, Kameswarrao Modali, Ruth H. Mottram, Masashi Niwano, Brice Noël, Jonathan C. Ryan, Amy Smith, Jan Streffing, Marco Tedesco, Willem Jan van de Berg, Michiel van den Broeke, Roderik S. W. van de Wal, Leo van Kampenhout, David Wilton, Bert Wouters, Florian Ziemen, and Tobias Zolles
The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, https://doi.org/10.5194/tc-14-3935-2020, 2020
Short summary
Short summary
We evaluated simulated Greenland Ice Sheet surface mass balance from 5 kinds of models. While the most complex (but expensive to compute) models remain the best, the faster/simpler models also compare reliably with observations and have biases of the same order as the regional models. Discrepancies in the trend over 2000–2012, however, suggest that large uncertainties remain in the modelled future SMB changes as they are highly impacted by the meltwater runoff biases over the current climate.
Thore Kausch, Stef Lhermitte, Jan T. M. Lenaerts, Nander Wever, Mana Inoue, Frank Pattyn, Sainan Sun, Sarah Wauthy, Jean-Louis Tison, and Willem Jan van de Berg
The Cryosphere, 14, 3367–3380, https://doi.org/10.5194/tc-14-3367-2020, https://doi.org/10.5194/tc-14-3367-2020, 2020
Short summary
Short summary
Ice rises are elevated parts of the otherwise flat ice shelf. Here we study the impact of an Antarctic ice rise on the surrounding snow accumulation by combining field data and modeling. Our results show a clear difference in average yearly snow accumulation between the windward side, the leeward side and the peak of the ice rise due to differences in snowfall and wind erosion. This is relevant for the interpretation of ice core records, which are often drilled on the peak of an ice rise.
Michael Studinger, Brooke C. Medley, Kelly M. Brunt, Kimberly A. Casey, Nathan T. Kurtz, Serdar S. Manizade, Thomas A. Neumann, and Thomas B. Overly
The Cryosphere, 14, 3287–3308, https://doi.org/10.5194/tc-14-3287-2020, https://doi.org/10.5194/tc-14-3287-2020, 2020
Short summary
Short summary
We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S. We find small–scale variability in snow accumulation based on the snow radar subsurface layering, indicating areas of strong wind redistribution are prevalent at 88° S. There is no slope–independent relationship between surface roughness and accumulation.
Cited articles
Agosta, C., Fettweis, X., and Datta, R.: Evaluation of the CMIP5 models in the aim of regional modelling of the Antarctic surface mass balance, The Cryosphere, 9, 2311–2321, https://doi.org/10.5194/tc-9-2311-2015, 2015. a
Agosta, C., Amory, C., Kittel, C., Orsi, A., Favier, V., Gallée, H., van den Broeke, M. R., Lenaerts, J. T. M., van Wessem, J. M., van de Berg, W. J., and Fettweis, X.: Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes, The Cryosphere, 13, 281–296, https://doi.org/10.5194/tc-13-281-2019, 2019. a, b, c
Barthel, A., Agosta, C., Little, C. M., Hattermann, T., Jourdain, N. C., Goelzer, H., Nowicki, S., Seroussi, H., Straneo, F., and Bracegirdle, T. J.: CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica, The Cryosphere, 14, 855–879, https://doi.org/10.5194/tc-14-855-2020, 2020. a, b, c, d
Beaumet, J., Déqué, M., Krinner, G., Agosta, C., and Alias, A.: Effect of prescribed sea surface conditions on the modern and future Antarctic surface climate simulated by the ARPEGE atmosphere general circulation model, The Cryosphere, 13, 3023–3043, https://doi.org/10.5194/tc-13-3023-2019, 2019. a
Bromwich, D. H., Nicolas, J. P., and Monaghan, A. J.: An Assessment of
precipitation changes over antarctica and the southern ocean since 1989 in
contemporary global reanalyses, J. Climate, 24, 4189–4209,
https://doi.org/10.1175/2011JCLI4074.1, 2011. a
Burgener, L., Rupper, S., Koenig, L., Forster, R., Christensen, W. F.,
Williams, J., Koutnik, M., Miège, C., Steig, E. J., Tingey, D., Keeler,
D., and Riley, L.: An observed negative trend in West Antarctic accumulation
rates from 1975 to 2010: Evidence from new observed and simulated records,
J. Geophys. Res.-Atmos., 118, 4205–4216,
https://doi.org/10.1002/jgrd.50362, 2013. a
Frezzotti, M., Scarchilli, C., Becagli, S., Proposito, M., and Urbini, S.: A
synthesis of the Antarctic surface mass balance during the last 800 yr,
Cryosphere, 7, 303–319, https://doi.org/10.5194/tc-7-303-2013, 2013. a
Fyke, J., Lenaerts, J. T., and Wang, H.: Basin-scale heterogeneity in
Antarctic precipitation and its impact on surface mass variability, Nat.
Snow Ice Data Center, 558, 2595–2609, https://doi.org/10.1175/2011JCLI4074.1, 2017. a
Gallée, H., Trouvilliez, A., Agosta, C., Genthon, C., Favier, V., and
Naaim-Bouvet, F.: Transport of Snow by the Wind: A Comparison Between
Observations in Adélie Land, Antarctica, and Simulations Made with the
Regional Climate Model MAR, Bound.-Layer Meteorol., 146, 133–147,
https://doi.org/10.1007/s10546-012-9764-z, 2013. a
Genthon, C., Krinner, C., and Castebrunet, H.: Antarctic precipitation and
climate-change predictions: Horizontal resolution and margin vs plateau
issues, Ann. Glaciol., 50, 55–60, https://doi.org/10.3189/172756409787769681,
2009. a
Grieger, J., Leckebusch, G. C., and Ulbrich, U.: Net precipitation of
Antarctica: Thermodynamical and dynamical parts of the climate change
signal, J. Climate, 29, 907–924, https://doi.org/10.1175/JCLI-D-14-00787.1,
2016. a
Hosking, J. S., Orr, A., Marshall, G. J., Turner, J., and Phillips, T.: The
Influence of the Amundsen–Bellingshausen Seas Low on the Climate of West
Antarctica and Its Representation in Coupled Climate Model Simulations,
J. Climate, 26, 6633–6648, https://doi.org/10.1175/JCLI-D-12-00813.1,
2013. a
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) Large Ensemble Project: A Community
Resource for Studying Climate Change in the Presence of Internal Climate
Variability, B. Am. Meteorol. Soc., 96,
1333–1349, https://doi.org/10.1175/BAMS-D-13-00255.1, 2015. a
Kittel, C., Amory, C., Agosta, C., Delhasse, A., Doutreloup, S., Huot, P.-V., Wyard, C., Fichefet, T., and Fettweis, X.: Sensitivity of the current Antarctic surface mass balance to sea surface conditions using MAR, The Cryosphere, 12, 3827–3839, https://doi.org/10.5194/tc-12-3827-2018, 2018. a
Krinner, G., Largeron, C., Ménégoz, M., Agosta, C., and
Brutel-Vuilmet, C.: Oceanic Forcing of Antarctic Climate Change: A Study
Using a Stretched-Grid Atmospheric General Circulation Model, J.
Climate, 27, 5786–5800, https://doi.org/10.1175/JCLI-D-13-00367.1,
2014. a, b
Lenaerts, J. T., Van Den Broeke, M. R., Van De Berg, W. J., Van Meijgaard, E.,
and Kuipers Munneke, P.: A new, high-resolution surface mass balance map of
Antarctica (1979–2010) based on regional atmospheric climate modeling,
Geophys. Res. Lett., 39, 1–5, https://doi.org/10.1029/2011GL050713, 2012. a, b
Lenaerts, J. T., Vizcaino, M., Fyke, J., van Kampenhout, L., and van den
Broeke, M. R.: Present-day and future Antarctic ice sheet climate and
surface mass balance in the Community Earth System Model, Clim. Dynam.,
47, 1367–1381, https://doi.org/10.1007/s00382-015-2907-4, 2016. a
Lenaerts, J. T., Medley, B., van den Broeke, M. R., and Wouters, B.: Observing
and Modeling Ice Sheet Surface Mass Balance, Rev. Geophys., 57, 376–420,
https://doi.org/10.1029/2018RG000622, 2019. a
Marshall, G. J., Thompson, D. W., and van den Broeke, M. R.: The Signature of
Southern Hemisphere Atmospheric Circulation Patterns in Antarctic
Precipitation, Geophys. Res. Lett., 44, 580–11,
https://doi.org/10.1002/2017GL075998, 2017. a
Monaghan, A. and Bromwich, D.: Global warming at the poles, Nat.
Geosci., 1, 728, https://doi.org/10.1038/ngeo346, 2008. a
Monaghan, A. J., Bromwich, D., Fogt, R., Wang, S.-H., Mayewski, P., Dixon, D.,
Ekaykin, A., Frezzotti, M., Goodwin, I., Isaksson, E., Kaspari, S., Morgan,
V., Oerter, H., Van Ommen, T., Van der Veen, C., and Wen, J.: Insignificant
Change in Antarctic Snowfall Since the International Geophysical Year
Andrew, Science, 313, 827–831, https://doi.org/10.5061/dryad.5t110.Supplementary,
2006. 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, b
Palerme, C., Genthon, C., Claud, C., Kay, J. E., Wood, N. B., 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,
2017. a, b
Philippe, M., Tison, J.-L., Fjøsne, K., Hubbard, B., Kjær, H. A., Lenaerts, J. T. M., Drews, R., Sheldon, S. G., De Bondt, K., Claeys, P., and Pattyn, F.: Ice core evidence for a 20th century increase in surface mass balance in coastal Dronning Maud Land, East Antarctica, The Cryosphere, 10, 2501–2516, https://doi.org/10.5194/tc-10-2501-2016, 2016. a
Previdi, M. and Polvani, L. M.: Anthropogenic impact on Antarctic surface mass
balance, currently masked by natural variability, to emerge by mid-century,
Environ. Res. Lett, 11, 94001, https://doi.org/10.1088/1748-9326/11/9/094001, 2016. a
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp,
A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S.,
Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F.,
Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D.,
Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G.,
Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M.,
Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A.,
and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land
use, and greenhouse gas emissions implications: An overview, Global
Environm. Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009,
2017. a
Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019. a
Shepherd, A., Ivins, E., Rignot, E., Smith, B., Van Den Broeke, M., Velicogna,
I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne,
T., Scambos, T., Schlegel, N., Geruo, A., Agosta, C., Ahlstrøm, A.,
Babonis, G., Barletta, V., Blazquez, A., Bonin, J., Csatho, B., Cullather,
R., Felikson, D., Fettweis, X., Forsberg, R., Gallee, H., Gardner, A.,
Gilbert, L., Groh, A., Gunter, B., Hanna, E., Harig, C., Helm, V., Horvath,
A., Horwath, M., Khan, S., Kjeldsen, K. K., Konrad, H., Langen, P.,
Lecavalier, B., Loomis, B., Luthcke, S., McMillan, M., Melini, D., Mernild,
S., Mohajerani, Y., Moore, P., Mouginot, J., Moyano, G., Muir, A., Nagler,
T., Nield, G., Nilsson, J., Noel, B., Otosaka, I., Pattle, M. E., Peltier,
W. R., Pie, N., Rietbroek, R., Rott, H., Sandberg-Sørensen, L., Sasgen,
I., Save, H., Scheuchl, B., Schrama, E., Schröder, L., Seo, K. W.,
Simonsen, S., Slater, T., Spada, G., Sutterley, T., Talpe, M., Tarasov, L.,
Van De Berg, W. J., Van Der Wal, W., Van Wessem, M., Vishwakarma, B. D.,
Wiese, D., and Wouters, B.: Mass balance of the Antarctic Ice Sheet from
1992 to 2017, Nature, 558, 219–222, https://doi.org/10.1038/s41586-018-0179-y, 2018. a
Thomas, E. R., Hosking, J. S., Tuckwell, R. R., Warren, R. A., and Ludlow,
E. C.: Twentieth century increase in snowfall in coastal West Antarctica,
Geophys. Res. Lett., 42, 9387–9393, https://doi.org/10.1002/2015GL065750,
2015. a
Thomas, E. R., van Wessem, J. M., Roberts, J., Isaksson, E., Schlosser, E., Fudge, T. J., Vallelonga, P., Medley, B., Lenaerts, J., Bertler, N., van den Broeke, M. R., Dixon, D. A., Frezzotti, M., Stenni, B., Curran, M., and Ekaykin, A. A.: Regional Antarctic snow accumulation over the past 1000 years, Clim. Past, 13, 1491–1513, https://doi.org/10.5194/cp-13-1491-2017, 2017.
a, b, c, d
Tokarska, K. B., Hegerl, G. C., Schurer, A. P., Forster, P. M., and Marvel, K.:
Observational constraints on the effective climate sensitivity from the
historical period, Environ. Res. Lett., 15, 034043,
https://doi.org/10.1088/1748-9326/ab738f, 2020. a
Turner, J., Phillips, T., Hosking, J. S., Marshall, G. J., and Orr, A.: The
amundsen sea low, Int. J. Climatol., 33, 1818–1829,
https://doi.org/10.1002/joc.3558, 2013. a, b, c
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J. F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: An overview, Clim. Change, 109,
5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011. a
van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018. a
Short summary
In this paper, we analyze several spatial and temporal criteria to assess the ability of models in the CMIP5 and CMIP6 frameworks to recreate past Antarctic surface mass balance. We then compared a subset of the top performing models to all remaining models to refine future surface mass balance predictions under different forcing scenarios. We found that the top performing models predict lower surface mass balance by 2100, indicating less buffering than otherwise expected of sea level rise.
In this paper, we analyze several spatial and temporal criteria to assess the ability of models...
