Revisiting Antarctic ice loss due to marine ice-cliff instability

Edwards, Tamsin; Brandon, Mark A.; Durand, Gaël; Edwards, Neil R.; Golledge, Nicholas R.; Holden, Philip B.; Nias, Isabel; Payne, Antony J.; Ritz, Catherine; Wernecke, Andreas

doi:10.1038/s41586-019-0901-4

Cited by 294 publications

(367 citation statements)

References 50 publications

Supporting

Mentioning

349

Contrasting

Order By: Relevance

“…Here we do not consider this difference as a necessary condition for opening up an additional group of studies. Very low confidence : Third, we combine the probability distributions of studies based on DP16, notably Kopp et al () and Le Bars et al (). We assign a very low confidence to these studies due to their results being conditional on a single and observationally poorly constrained line of evidence (i.e., a single ice sheet model), and the much lower projections from the most recent studies (Edwards et al, ; Golledge et al, ).…”

Section: Meeting the Identified Needsmentioning

confidence: 97%

Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

et al. 2019

View full text Add to dashboard Cite

Despite widespread efforts to implement climate services, there is almost no literature that systematically analyzes users' needs. This paper addresses this gap by applying a decision analysis perspective to identify what kind of mean sea level rise (SLR) information is needed for local coastal adaptation decisions. We first characterize these decisions, then identify suitable decision analysis approaches and the sea level information required, and finally discuss if and how these information needs can be met given the state of the art of sea level science. We find that four types of information are needed: (i) probabilistic predictions for short‐term decisions when users are uncertainty tolerant; (ii) high‐end and low‐end SLR scenarios chosen for different levels of uncertainty tolerance; (iii) upper bounds of SLR for users with a low uncertainty tolerance; and (iv) learning scenarios derived from estimating what knowledge will plausibly emerge about SLR over time. Probabilistic predictions can only be attained for the near term (i.e., 2030–2050) before SLR significantly diverges between low and high emission scenarios, for locations for which modes of climate variability are well understood and the vertical land movement contribution to local sea levels is small. Meaningful SLR upper bounds cannot be defined unambiguously from a physical perspective. Low‐ to high‐end scenarios for different levels of uncertainty tolerance and learning scenarios can be produced, but this involves both expert and user judgments. The decision analysis procedure elaborated here can be applied to other types of climate information that are required for mitigation and adaptation purposes.

show abstract

Section: Meeting the Identified Needsmentioning

confidence: 97%

Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

et al. 2019

View full text Add to dashboard Cite

show abstract

“…By dominating GrIS mass loss through increased runoff and partly mitigating dynamic AIS mass loss by increased snowfall, the future evolution of ice sheet SMB will continue to play a pivotal role in ice sheet mass changes and associated changes in sea level. Ice sheet SMB also influences past, present, and future ice sheet mass change by controlling ice sheet dynamical processes, such as basal hydrology, fjord circulation by basal runoff and determining meltwater input for Antarctic ice shelf hydrofracture and ice cliff instability (DeConto & Pollard, ; Edwards et al, ).…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Observing and Modeling Ice Sheet Surface Mass Balance

Lenaerts

Medley

Broeke

et al. 2019

Reviews of Geophysics

164

152

View full text Add to dashboard Cite

Surface mass balance (SMB) provides mass input to the surface of the Antarctic and Greenland Ice Sheets and therefore comprises an important control on ice sheet mass balance and resulting contribution to global sea level change. As ice sheet SMB varies highly across multiple scales of space (meters to hundreds of kilometers) and time (hourly to decadal), it is notoriously challenging to observe and represent in models. In addition, SMB consists of multiple components, all of which depend on complex interactions between the atmosphere and the snow/ice surface, large‐scale atmospheric circulation and ocean conditions, and ice sheet topography. In this review, we present the state‐of‐the‐art knowledge and recent advances in ice sheet SMB observations and models, highlight current shortcomings, and propose future directions. Novel observational methods allow mapping SMB across larger areas, longer time periods, and/or at very high (subdaily) temporal frequency. As a recent observational breakthrough, cosmic ray counters provide direct estimates of SMB, circumventing the need for accurate snow density observations upon which many other techniques rely. Regional atmospheric climate models have drastically improved their simulation of ice sheet SMB in the last decade, thanks to the inclusion or improved representation of essential processes (e.g., clouds, blowing snow, and snow albedo), and by enhancing horizontal resolution (5–30 km). Future modeling efforts are required in improving Earth system models to match regional atmospheric climate model performance in simulating ice sheet SMB, and in reinforcing the efforts in developing statistical and dynamic downscaling to represent smaller‐scale SMB processes.

show abstract

“…Quantification of uncertainty has been an integral part of global climate model projection for a number of years and features heavily in the Intergovernmental Panel on Climate Change assessment reports (Collins et al, 2013). However, only in the last few years has the ice sheet modeling community begun to formally consider uncertainty when estimating future contributions to sea level (Applegate et al, 2012;Chang et al, 2014;Edwards et al, 2014aEdwards et al, , 2014bEdwards et al, , 2019Gladstone et al, 2012;Levermann et al, 2014;Little et al, 2013;Ritz et al, 2015;Ruckert et al, 2017;Schlegel et al, 2018;Tsai et al, 2017). This delay is due, in part, to computational issues making it difficult to produce sufficiently large ensembles of simulations to investigate parameter uncertainty with available computational resources (Chang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Assessing Uncertainty in the Dynamical Ice Response to Ocean Warming in the Amundsen Sea Embayment, West Antarctica

Nias

Cornford

Edwards

et al. 2019

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Ice mass loss from the Amundsen Sea Embayment ice streams in West Antarctica is a major source of uncertainty in projections of future sea level rise. Physically based ice flow models rely on a number of parameters that represent unobservable quantities and processes, and accounting for uncertainty in these parameters can lead to a wide range of dynamic responses. Here we perform a Bayesian calibration of a perturbed parameter ensemble, in which we score each ensemble member on its ability to match the magnitude and broad spatial pattern of present-day observations of ice sheet surface elevation change. We apply an idealized melt rate forcing to extend the most likely simulations forward to 2200. We find that diverging grounding line response between ensemble members drives an exaggeration in the upper tail of the distribution of sea level rise by 2200, demonstrating that extreme future outcomes cannot be excluded.

show abstract

Revisiting Antarctic ice loss due to marine ice-cliff instability

Cited by 294 publications

References 50 publications

Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

Meeting User Needs for Sea Level Rise Information: A Decision Analysis Perspective

Observing and Modeling Ice Sheet Surface Mass Balance

Assessing Uncertainty in the Dynamical Ice Response to Ocean Warming in the Amundsen Sea Embayment, West Antarctica

Contact Info

Product

Resources

About