The cryosphere and the processes that force its evolution have profound and permanent effects on the global climate. In particular, Arctic amplification leads to extreme mid-latitude weather and glacier and ice sheet retreat is increasing global mean sea level causing the ocean to encroach onto coastal communities. Despite potentially devastating impacts, accurate predictions of future dynamics and rigorous characterizations of the associated uncertainty remain elusive. Misunderstood physics and computational limitations require complex physical processes to be parameterized and calibrated using noisy data that is sparse in both space and time. However, collecting data in remote polar regions is difficult, dangerous, and expensive. Therefore, we must leverage remote sensing techniques and wisely allocate limited resources. Finally, predictive uncertainties must be quantified to give meaningful error bounds on quantities of interest, such as future mean sea level. This session discusses recent advancements trying to understand the dynamic processes governing the cryosphere given observations and/or models as well as techniques to obtain and analyze data.
14:00
Uncertainty quantification of sea-level projections informed by the Ice and Sea-level System Model
Eric Larour | Jet Propulsion Laboratory | United States
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Eric Larour | Jet Propulsion Laboratory | United States
We present a new investigation of the complex interactions between relative sea-level (RSL) changes around the globe, and local ice-sheet dynamics in Antarctica and Greenland, as well as corresponding solid-Earth deformation. Such interactions have not been well represented in probabilistic frameworks currently used to project sea-level rise in the 21st century. Inclusion of such feedback introduces biases that need to be accounted for in sampling algorithms used to recombine different signals coming from: 1) the dynamic steric ocean effects, 2) sea-level from melting of ice; and 3) sea-level decrease as the solid-Earth uplifts. Here, we rely on the Ice and Sea-level System Model (ISSM, JPL/NASA, http://issm.jpl.nasa.gov) to explore such feedback and to understand how current projections of sea-level rise need to be adjusted accordingly.
This work was performed at the California Institute of Technology's Jet Propulsion Laboratory under a contract with the National Aeronautics and Space Administration's Cryosphere Science Program.
14:30
- CANCELED - Using Gaussian Process Emulators to reduce uncertainty in sea level projections with ice sheet models
Andy Aschwanden | University of Alaska Fairbanks | United States
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Andy Aschwanden | University of Alaska Fairbanks | United States
Uncertainty Quantification is becoming an integral tool of estimating future sea level rise. Using a numerical ice sheet model, Aschwanden et al (2019) performed a 500 member ensemble simulation and projected that by 2100 Greenland could contribute 5-19 cm (RCP 2.6), 8-23 cm (RCP 4.5), and 14-33 cm (RCP 8.5) to sea level on the 16/84th percentile confidence interval. High-resolution numerical ice sheet model simulations are computationally expensive, and thus the number of ensemble members is often limited by the availability of high-performance computing resources. To investigate how well the 500 member ensemble of the numerical ice sheet model characterize the sea level rise probability distributions (PDFs), we performed a second, much larger (>2k) member ensemble for RCP 4.5 and compared the their respective PDFs using the Kullback-Leibler divergence. Our goal is to provide guidance on how many simulations with the ice sheet model are necessary to accurately characterize the sea-level contribution PDF. We then fit Gaussian Process (GP) emulators to the results of Aschwanden et al (2019). We conclude that statistical models can help reducing uncertainties in sea level projections by complementing numerical computer models.
15:00
Stochastic Modeling of Uncertainty in Ice Sheet Models, with application to the Antarctic Ice Sheet response to Climate Change
Kevin Bulthuis | Université de Liège | Belgium
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Kevin Bulthuis | Université de Liège | Belgium
Predicting the contribution of the Antarctic ice sheet (AIS) to sea level rise is recognised as a source of deep uncertainty in assessing climate-change consequences (SROCC). Recently, efforts have shift towards providing sea-level rise projections with quantified uncertainty. Accounting for uncertainty in ice-sheet models (ISMs) remains challenging due to data scarcity, limitations in the representations of physical processes and the computational cost of ISMs.
In this talk, we address methods based on Monte Carlo sampling, surrogate models, and probabilistic learning for the propagation of uncertainty and global sensitivity analysis in ISMs. We apply these methods to investigate the influence of several sources of uncertainty on the AIS response to climate change. We show that the sensitivity of the projections to uncertainty increases and the contribution of the uncertainty in sub-shelf melting to the uncertainty in the projections becomes more and more dominant as the scenario gets warmer. We show that the significance of the contribution to sea-level rise is controlled by instabilities in marine basins, especially in the West Antarctic ice sheet (WAIS). We find that, irrespective of parametric uncertainty, the RCP 2.6 scenario prevents the WAIS collapse, that in both RCP 4.5 and RCP 6.0 the occurrence of instabilities in marine basins is more sensitive to parametric uncertainty and that, almost irrespective of parametric uncertainty, RCP 8.5 triggers the WAIS collapse.
15:30
Quantifying the uncertainties of climate model predictions and ice sheet contributions to sea-level rise
Tamsin Edwards | King’s College London | United Kingdom
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Tamsin Edwards | King’s College London | United Kingdom
Predicting the Antarctic contribution to future sea level rise has been a fast-moving and contentious topic in recent years, with rapid advances in computer models, observations and the adoption of uncertainty quantification methods to bring them together. In 2015 a new hypothesis was also proposed – Marine Ice Cliff Instability (MICI), a sustained collapse of coastal ice cliffs leading to extremely rapid sea level rise towards the end of the century and beyond. I will show results from the first application of emulation to Antarctic ice sheet model projections, which show MICI is not required to reproduce three periods of past sea level change, and estimating probabilities of sea level contribution with and without this instability. I will also show new results emulating many ice sheet models at once, using the “super-ensemble” designed for the next Assessment Report of the Intergovernmental Panel on Climate Change. The aim is to estimate robust probability distributions for the Antarctic contribution to sea level rise as a function of future global mean temperature change, incorporating structural and parametric uncertainties from both global climate models and ice sheet models at once.