An assessment of key model parametric uncertainties in projections of Greenland Ice Sheet behavior

Abstract. Lack of knowledge about the values of ice sheet model input parameters introduces substantial uncertainty into projections of Greenland Ice Sheet contributions to future sea level rise. Computer models of ice sheet behavior provide one of several means of estimating future sea level rise due to mass loss from ice sheets. Such models have many input parameters whose values are not well known. Recent studies have investigated the effects of these parameters on model output, but the range of potential future sea level increases due to model parametric uncertainty has not been characterized. Here, we demonstrate that this range is large, using a 100-member perturbed-physics ensemble with the SICOPOLIS ice sheet model. Each model run is spun up over 125 000 yr using geological forcings and subsequently driven into the future using an asymptotically increasing air temperature anomaly curve. All modeled ice sheets lose mass after 2005 AD. Parameters controlling surface melt dominate the model response to temperature change. After culling the ensemble to include only members that give reasonable ice volumes in 2005 AD, the range of projected sea level rise values in 2100 AD is ~40 % or more of the median. Data on past ice sheet behavior can help reduce this uncertainty, but none of our ensemble members produces a reasonable ice volume change during the mid-Holocene, relative to the present. This problem suggests that the model's exponential relation between temperature and precipitation does not hold during the Holocene, or that the central-Greenland temperature forcing curve used to drive the model is not representative of conditions around the ice margin at this time (among other possibilities). Our simulations also lack certain observed physical processes that may tend to enhance the real ice sheet's response. Regardless, this work has implications for other studies that use ice sheet models to project or hindcast the behavior of the Greenland Ice Sheet.

Download Full-text

Preliminary assessment of model parametric uncertainty in projections of Greenland Ice Sheet behavior

The Cryosphere Discussions ◽

10.5194/tcd-5-3175-2011 ◽

2011 ◽

Vol 5 (6) ◽

pp. 3175-3205 ◽

Cited By ~ 3

Author(s):

P. J. Applegate ◽

N. Kirchner ◽

E. J. Stone ◽

K. Keller ◽

R. Greve

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Ice Sheets ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Parametric Uncertainty ◽

Temperature And Precipitation ◽

Input Parameters ◽

Air Temperature Anomaly ◽

Model Parametric Uncertainty

Abstract. Lack of knowledge about the values of ice sheet model input parameters introduces substantial uncertainty into projections of Greenland Ice Sheet contributions to future sea level rise. Computer models of ice sheet behavior provide one of several means of estimating future sea level rise due to mass loss from ice sheets. Such models have many input parameters whose values are not well known. Recent studies have investigated the effects of these parameters on model output, but the range of potential future sea level increases due to model parametric uncertainty has not been characterized. Here, we demonstrate that this range is large, using a 100-member perturbed-physics ensemble with the SICOPOLIS ice sheet model. Each model run is spun up over 125 000 yr using geological forcings, and subsequently driven into the future using an asymptotically increasing air temperature anomaly curve. All modeled ice sheets lose mass after 2005 AD. After culling the ensemble to include only members that give reasonable ice volumes in 2005 AD, the range of projected sea level rise values in 2100 AD is 30 % or more of the median. Data on past ice sheet behavior can help reduce this uncertainty, but none of our ensemble members produces a reasonable ice volume change during the mid-Holocene, relative to the present. This problem suggests that the model's exponential relation between temperature and precipitation does not hold during the Holocene, or that the central-Greenland temperature forcing curve used to drive the model is not representative of conditions around the ice margin at this time (among other possibilities). Our simulations also lack certain observed physical processes that may tend to enhance the real ice sheet's response. Regardless, this work has implications for other studies that use ice sheet models to project or hindcast the behavior of the Greenland ice sheet.

Download Full-text

Trends and projections in ice sheet mass balance

10.5194/egusphere-egu2020-20660 ◽

2020 ◽

Author(s):

Andrew Shepherd ◽

Keyword(s):

Mass Balance ◽

Sea Level Rise ◽

Sea Level ◽

Ice Sheets ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Ice Dynamics ◽

Surface Mass ◽

Sea Levels ◽

Global Sea Level

In recent decades, the Antarctic and Greenland Ice Sheets have been major contributors to global sea-level rise and are expected to be so in the future. Although increases in glacier flow and surface melting have been driven by oceanic and atmospheric warming, the degree and trajectory of today&#8217;s imbalance remain uncertain. Here we compare and combine 26 individual satellite records of changes in polar ice sheet volume, flow and gravitational potential to produce a reconciled estimate of their mass balance. Since the early 1990&#8217;s, ice losses from Antarctica and Greenland have caused global sea-levels to rise by 18.4 millimetres, on average, and there has been a sixfold increase in the volume of ice loss over time. Of this total, 41 % (7.6 millimetres) originates from Antarctica and 59 % (10.8 millimetres) is from Greenland. In this presentation, we compare our reconciled estimates of Antarctic and Greenland ice sheet mass change to IPCC projection of sea level rise to assess the model skill in predicting changes in ice dynamics and surface mass balance. &#160;Cumulative ice losses from both ice sheets have been close to the IPCC&#8217;s predicted rates for their high-end climate warming scenario, which forecast an additional 170 millimetres of global sea-level rise by 2100 when compared to their central estimate.

Download Full-text

Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties

Geoscientific Model Development ◽

10.5194/gmd-7-1933-2014 ◽

2014 ◽

Vol 7 (5) ◽

pp. 1933-1943 ◽

Cited By ~ 9

Author(s):

W. Chang ◽

P. J. Applegate ◽

M. Haran ◽

K. Keller

Keyword(s):

Mass Loss ◽

Sea Level Rise ◽

Sea Level ◽

Data Model ◽

Spatial Information ◽

Full Range ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Parametric Uncertainty ◽

Model Parameters

Abstract. Computer models of ice sheet behavior are important tools for projecting future sea level rise. The simulated modern ice sheets generated by these models differ markedly as input parameters are varied. To ensure accurate ice sheet mass loss projections, these parameters must be constrained using observational data. Which model parameter combinations make sense, given observations? Our method assigns probabilities to parameter combinations based on how well the model reproduces the Greenland Ice Sheet profile. We improve on the previous state of the art by accounting for spatial information and by carefully sampling the full range of realistic parameter combinations, using statistically rigorous methods. Specifically, we estimate the joint posterior probability density function of model parameters using Gaussian process-based emulation and calibration. This method is an important step toward calibrated probabilistic projections of ice sheet contributions to sea level rise, in that it uses data–model fusion to learn about parameter values. This information can, in turn, be used to make projections while taking into account various sources of uncertainty, including parametric uncertainty, data–model discrepancy, and spatial correlation in the error structure. We demonstrate the utility of our method using a perfect model experiment, which shows that many different parameter combinations can generate similar modern ice sheet profiles. This result suggests that the large divergence of projections from different ice sheet models is partly due to parametric uncertainty. Moreover, our method enables insight into ice sheet processes represented by parameter interactions in the model.

Download Full-text

Asynchronous Antarctic and Greenland ice-volume contributions to the last interglacial sea-level highstand

10.5194/egusphere-egu2020-1513 ◽

2020 ◽

Author(s):

Eelco Rohling ◽

Fiona Hibbert

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Global Climate ◽

Ice Sheets ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Climate Forcing ◽

Potential Contribution ◽

Last Interglacial ◽

Ice Shelf

Sea-level rise is among the greatest risks that arise from anthropogenic global climate change. It is receiving a lot of attention, among others in the IPCC reports, but major questions remain as to the potential contribution from the great continental ice sheets. In recent years, some modelling work has suggested that the ice-component of sea-level rise may be much faster than previously thought, but the rapidity of rise seen in these results depends on inclusion of scientifically debated mechanisms of ice-shelf decay and associated ice-sheet instability. The processes have not been active during historical times, so data are needed from previous warm periods to evaluate whether the suggested rates of sea-level rise are supported by observations or not. Also, we then need to assess which of the ice sheets was most sensitive, and why. The last interglacial (LIG; ~130,000 to ~118,000 years ago, ka) was the last time global sea level rose well above its present level, reaching a highstand of +6 to +9 m or more. Because Greenland Ice Sheet (GrIS) contributions were smaller than that, this implies substantial Antarctic Ice Sheet (AIS) contributions. However, this still leaves the timings, magnitudes, and drivers of GrIS and AIS reductions open to debate. I will discuss recently published sea-level reconstructions for the LIG highstand, which reveal that AIS and GrIS contributions were distinctly asynchronous, and that rates of rise to values above 0 m (present-day sea level) reached up to 3.5 m per century. Such high pre-anthropogenic rates of sea-level rise lend credibility to high rates inferred by ice modelling under certain ice-shelf instability parameterisations, for both the past and future. Climate forcing was distinctly asynchronous between the southern and northern hemispheres as well during the LIG, explaining the asynchronous sea-level contributions from AIS and GrIS. Today, climate forcing is synchronous between the two hemispheres, and also faster and greater than during the LIG. Therefore, LIG rates of sea-level rise should likely be considered minimum estimates for the future.

Download Full-text

fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models

Geoscientific Model Development ◽

10.5194/gmd-14-5843-2021 ◽

2021 ◽

Vol 14 (9) ◽

pp. 5843-5861

Author(s):

Conrad P. Koziol ◽

Joe A. Todd ◽

Daniel N. Goldberg ◽

James R. Maddison

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Ice Sheets ◽

Ice Sheet ◽

Parametric Uncertainty ◽

Time Dependent ◽

Algorithmic Differentiation ◽

Dependent Model ◽

Computationally Intensive ◽

Projection Error

Abstract. Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise is and hence crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions often involve tens or hundreds of thousands of parameters, with unknown uncertainties and dependencies. The computationally intensive nature of inversions along with their high number of parameters mean traditional methods such as Monte Carlo are expensive for uncertainty quantification. Here we develop a framework to estimate the posterior uncertainty of inversions and project them onto sea level change projections over the decadal timescale. The framework treats parametric uncertainty as multivariate Gaussian and exploits the equivalence between the Hessian of the model and the inverse covariance of the parameter set. The former is computed efficiently via algorithmic differentiation, and the posterior covariance is propagated in time using a time-dependent model adjoint to produce projection error bars. This work represents an important step in quantifying the internal uncertainty of projections of ice sheet models.

Download Full-text

Probabilistic calibration of a Greenland Ice Sheet model using spatially-resolved synthetic observations: toward projections of ice mass loss with uncertainties

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-1905-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 1905-1931 ◽

Cited By ~ 1

Author(s):

W. Chang ◽

P. J. Applegate ◽

M. Haran ◽

K. Keller

Keyword(s):

Mass Loss ◽

Sea Level Rise ◽

Sea Level ◽

Observational Data ◽

Spatial Information ◽

Full Range ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Parametric Uncertainty ◽

Model Parameters

Abstract. Computer models of ice sheet behavior are important tools for projecting future sea level rise. The simulated modern ice sheets generated by these models differ markedly as input parameters are varied. To ensure accurate ice sheet mass loss projections, these parameters must be constrained using observational data. Which model parameter combinations make sense, given observations? Our method assigns probabilities to parameter combinations based on how well the model reproduces the Greenland Ice Sheet profile. We improve on the previous state of the art by accounting for spatial information, and by carefully sampling the full range of realistic parameter combinations, using statistically rigorous methods. Specifically, we estimate the joint posterior probability density function of model parameters using Gaussian process-based emulation and calibration. This method is an important step toward probabilistic projections of ice sheet contributions to sea level rise, in that it uses observational data to learn about parameter values. This information can, in turn, be used to make projections while taking into account various sources of uncertainty, including parametric uncertainty, data–model discrepancy, and spatial correlation in the error structure. We demonstrate the utility of our method using a perfect model experiment, which shows that many different parameter combinations can generate similar modern ice sheet profiles. This result suggests that the large divergence of projections from different ice sheet models is partly due to parametric uncertainty. Moreover, our method enables insight into ice sheet processes represented by parameter interactions in the model.

Download Full-text

fenics_ice 1.0: A framework for quantifying initialisation uncertainty fortime-dependent ice-sheet models

10.5194/gmd-2021-90 ◽

2021 ◽

Author(s):

Conrad P. Koziol ◽

Joe A. Todd ◽

Daniel N. Goldberg ◽

James R. Maddison

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Ice Sheets ◽

Ice Sheet ◽

Parametric Uncertainty ◽

Algorithmic Differentiation ◽

Dependent Model ◽

Key Variables ◽

Computationally Intensive ◽

Projection Error

Abstract. Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise, and hence is crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions often involve tens or hundreds of thousands of parameters, with unknown uncertainties and dependencies. The computationally intensive nature of inversions along with their high number of parameters mean traditional methods such as Monte Carlo are expensive for uncertainty quantification. Here we develop a framework to estimate the posterior uncertainty of inversions, and project them onto sea level change projections over the decadal timescale. The framework treats parametric uncertainty as multivariate Gaussian, and exploits the equivalence between the Hessian of the model and the inverse covariance of the parameter set. The former is computed efficiently via algorithmic differentiation, and the posterior covariance is propagated in time using a time-dependent model adjoint to produce projection error bars. This work represents an important step in quantifying the internal uncertainty of projections of ice-sheet models.

Download Full-text

Could climate engineering save the Greenland Ice Sheet?

Climanosco Research Articles - https://www.climanosco.org/collection/climate-change-and-its-impacts/ ◽

10.37207/cra.1.10 ◽

2016 ◽

Author(s):

Patrick J. Applegate ◽

K. Keller

Keyword(s):

Sea Level Rise ◽

Greenhouse Gas Emissions ◽

Greenhouse Gas ◽

Sea Level ◽

Computer Model ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Upper Atmosphere ◽

Climate Engineering ◽

Air Temperatures

Engineering the climate through albedo modification (AM) could slow, but probably would not stop, melting of the Greenland Ice Sheet. Albedo modification is a technology that could reduce surface air temperatures through putting reflective particles into the upper atmosphere. AM has never been tested, but it might reduce surface air temperatures faster and more cheaply than reducing greenhouse gas emissions. Some scientists claim that AM would also prevent or reverse sea-level rise. But, are these claims true? The Greenland Ice Sheet will melt faster at higher temperatures, adding to sea-level rise. However, it's not clear that reducing temperatures through AM will stop or reverse sea-level rise due to Greenland Ice Sheet melting. We used a computer model of the Greenland Ice Sheet to examine its contributions to future sea level rise, with and without AM. Our results show that AM would probably reduce the rate of sea-level rise from the Greenland Ice Sheet. However, sea-level rise would likely continue even with AM, and the ice sheet would not regrow quickly. Albedo modification might buy time to prepare for sea-level rise, but problems could arise if policymakers assume that AM will stop sea-level rise completely.

Download Full-text

Relative control of bedrock roughness versus topography on global glacier bed friction

10.5194/egusphere-egu21-9719 ◽

2021 ◽

Author(s):

Olivier Gagliardini ◽

Fabien Gillet-Chaulet ◽

Florent Gimbert

Keyword(s):

Sea Level Rise ◽

Sea Level ◽

Inverse Method ◽

Ad Hoc ◽

Ice Sheets ◽

Ice Sheet ◽

Grid Resolution ◽

Classical Approach ◽

Bed Topography ◽

Bed Friction

Friction at the base of ice-sheets has been shown to be one of the largest uncertainty of model projections for the contribution of ice-sheet to future sea level rise. On hard beds, most of the apparent friction is the result of ice flowing over the bumps that have a size smaller than described by the grid resolution of ice-sheet models. To account for this friction, the classical approach is to replace this under resolved roughness by an ad-hoc friction law. In an imaginary world of unlimited computing resource and highly resolved bedrock DEM, one should solve for all bed roughnesses assuming pure sliding at the bedrock-ice interface. If such solutions are not affordable at the scale of an ice-sheet or even at the scale of a glacier, the effect of small bumps can be inferred using synthetical periodic geometry. In this presentation,&#160; beds are constructed using the superposition of up to five bed geometries made of sinusoidal bumps of decreasing wavelength and amplitudes. The contribution to the total friction of all five beds is evaluated by inverse methods using the most resolved solution as observation. It is shown that small features of few meters can contribute up to almost half of the total friction, depending on the wavelengths and amplitudes distribution. This work also confirms that the basal friction inferred using inverse method&#160; is very sensitive to how the bed topography is described by the model grid, and therefore depends on the size of the model grid itself.&#160;

Download Full-text

Simulation of the future sea level contribution of Greenland with a new glacial system model

The Cryosphere ◽

10.5194/tc-12-3097-2018 ◽

2018 ◽

Vol 12 (10) ◽

pp. 3097-3121 ◽

Cited By ~ 17

Author(s):

Reinhard Calov ◽

Sebastian Beyer ◽

Ralf Greve ◽

Johanna Beckmann ◽

Matteo Willeit ◽

...

Keyword(s):

Boundary Conditions ◽

Surface Temperature ◽

Mass Balance ◽

Sea Level Rise ◽

Sea Level ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

System Model ◽

Surface Mass Balance ◽

Surface Mass

Abstract. We introduce the coupled model of the Greenland glacial system IGLOO 1.0, including the polythermal ice sheet model SICOPOLIS (version 3.3) with hybrid dynamics, the model of basal hydrology HYDRO and a parameterization of submarine melt for marine-terminated outlet glaciers. The aim of this glacial system model is to gain a better understanding of the processes important for the future contribution of the Greenland ice sheet to sea level rise under future climate change scenarios. The ice sheet is initialized via a relaxation towards observed surface elevation, imposing the palaeo-surface temperature over the last glacial cycle. As a present-day reference, we use the 1961–1990 standard climatology derived from simulations of the regional atmosphere model MAR with ERA reanalysis boundary conditions. For the palaeo-part of the spin-up, we add the temperature anomaly derived from the GRIP ice core to the years 1961–1990 average surface temperature field. For our projections, we apply surface temperature and surface mass balance anomalies derived from RCP 4.5 and RCP 8.5 scenarios created by MAR with boundary conditions from simulations with three CMIP5 models. The hybrid ice sheet model is fully coupled with the model of basal hydrology. With this model and the MAR scenarios, we perform simulations to estimate the contribution of the Greenland ice sheet to future sea level rise until the end of the 21st and 23rd centuries. Further on, the impact of elevation–surface mass balance feedback, introduced via the MAR data, on future sea level rise is inspected. In our projections, we found the Greenland ice sheet to contribute between 1.9 and 13.0 cm to global sea level rise until the year 2100 and between 3.5 and 76.4 cm until the year 2300, including our simulated additional sea level rise due to elevation–surface mass balance feedback. Translated into additional sea level rise, the strength of this feedback in the year 2100 varies from 0.4 to 1.7 cm, and in the year 2300 it ranges from 1.7 to 21.8 cm. Additionally, taking the Helheim and Store glaciers as examples, we investigate the role of ocean warming and surface runoff change for the melting of outlet glaciers. It shows that ocean temperature and subglacial discharge are about equally important for the melting of the examined outlet glaciers.

Download Full-text