Pith. sign in

REVIEW 3 major objections 5 minor 93 references

Multifidelity Uncertainty Quantification for Ice Sheet Simulations

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Multifidelity estimators cut the cost of unbiased ice-sheet uncertainty quantification by up to 91-fold on a Greenland test case.

desk verdict Useful first head-to-head of multifidelity UQ estimators on a real Greenland model, but the headline 'two orders of magnitude' speedup excludes ~199 CPU-days of pilot covariance estimation and is not supported as stated. read the letter →

arxiv 2412.06110 v1 pith:JMJDLFV3 submitted 2024-12-08 physics.geo-ph physics.ao-phphysics.comp-phphysics.data-an

Nicole Aretz , Max Gunzburger , Mathieu Morlighem , Karen Willcox This is my paper · ORCID
classification physics.geo-phphysics.ao-phphysics.comp-phphysics.data-an MSC 65C0586A40
keywords multifidelityuncertaintyquantificationicesheetsimulationGreenlandsealevelriseMonteCarloestimationbestlinearunbiasedestimatorbasalfrictiongeothermalheatflux
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that formal uncertainty quantification for continental-scale ice-sheet simulations is achievable if the estimator is built from several models of different fidelity instead of from Monte Carlo sampling of the expensive high-fidelity model alone. It demonstrates the argument on a Greenland ice-sheet model whose basal friction and geothermal heat flux are treated as uncertain, estimating the expected 2015-2050 ice mass loss. The three multifidelity estimators---multifidelity Monte Carlo, multilevel Monte Carlo, and the multilevel best linear unbiased estimator---are all unbiased, so they target the high-fidelity answer rather than a biased surrogate answer. For a target accuracy of $\pm1$ mm sea-level equivalent at 95% confidence, the paper reports speedups of $12.6\times$, $10.2\times$, and $91.0\times$ over Monte Carlo, corresponding to about 30, 38, and 4 CPU-days instead of 381 CPU-days. The point is that unbiased uncertainty quantification, previously considered prohibitive at this scale, becomes computationally accessible.

What carries the argument

The machinery is the cross-model covariance matrix $\Sigma$, the table of variances and correlations among the 13 model outputs, which all three estimators consume to allocate samples. Multifidelity Monte Carlo uses it to set regression weights $\alpha_j = \Sigma_{1j}/\Sigma_{jj}$ and sample counts; multilevel Monte Carlo uses it to form level variances $\mathrm{Var}(s_j - s_{j+1})$ and balance a telescoping sum; the multilevel best linear unbiased estimator casts estimation as a Gauss-Markov regression over every nonempty subset of models and solves a semidefinite program for sample sizes. The paper's particular surrogate ladder---higher-order and shallow-shelf physics on fine, medium, and coarse meshes, plus extrapolation and interpolation surrogates---keeps correlations with the high-fidelity model high enough and costs low enough that the allocation shifts nearly all sampling work onto cheap models.

What would settle it

Recount all speedups with the 32 high-fidelity and 96 low-fidelity pilot runs included in each estimator's budget, and compare the resulting cost with Monte Carlo at the same 1 mm sea-level target; if the multifidelity estimates no longer cost less, the headline speedup claim fails for that accounting.

Watch

Extended reading notes

Core claim

The paper's central claim is that the expected ice mass loss of a high-fidelity ice-sheet model can be estimated to a prescribed accuracy without paying the full Monte Carlo price, by exploiting correlation with cheaper models while preserving unbiasedness. On the Greenland test case, the best linear unbiased estimator uses one high-fidelity run, many surrogate runs of several types (coarser meshes, shallow-shelf physics, extrapolated and interpolated surrogates), and a covariance matrix estimated from pilot samples, to reach the 1 mm sea-level accuracy target in 4.2 CPU-days, whereas Monte Carlo needs 381.2 CPU-days. The estimates are verified against Monte Carlo through overlapping confidence intervals, and in a fixed low budget of five high-fidelity solves the best linear unbiased estimator produces a confidence interval as tight as the one Monte Carlo would need roughly 491 high-fidelity solves to match.

Load-bearing premise

The cost of the pilot runs used to estimate the table of correlations between model predictions is excluded from every speedup calculation, so the advertised savings assume that table is already available for free.

Editorial extensions

If this is right

  • Unbiased uncertainty quantification for continental ice sheets becomes computationally accessible at the 1 mm sea-level scale: tens of CPU-days rather than roughly a CPU-year of Monte Carlo sampling.
  • The best linear unbiased estimator is guaranteed by construction to have variance no larger than the other two multifidelity estimators, and in the Greenland test it is the only method whose confidence interval at a five-high-fidelity-solve budget is comparable to hundreds of Monte Carlo runs.
  • Cheap surrogates that are poor approximations individually, such as the linear interpolation model, still pay off when they are highly correlated with the high-fidelity output, because the estimator shifts samples to them without introducing bias.
  • For a fixed small budget, all three multifidelity estimators produce tighter confidence intervals than Monte Carlo, with the best linear unbiased estimator reducing the interval width by more than a factor of nine at the tested budget.
  • The framework is agnostic to how the parametric uncertainties are characterized, so the same estimator structure applies to other ice-sheet codes, other regions, and other uncertain inputs without changing the algorithm.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: if the pilot runs used to estimate the covariance matrix (32 high-fidelity and 96 low-fidelity evaluations) were charged to the estimator budgets, the reported speedups would shrink by roughly an order of magnitude, and the best linear unbiased estimator's advantage over Monte Carlo for the target accuracy could become marginal.
  • Beyond the paper: the same covariance-allocation machinery should transfer directly to Antarctic projections and to other high-cost geophysical simulators, since the only new ingredients are a correlated surrogate family and a pilot estimate of the cross-model covariance matrix.
  • Beyond the paper: because the framework is agnostic to how uncertainties are characterized, the basal-friction and heat-flux uncertainty model could be replaced by posterior distributions from Bayesian inversions, converting the output from a prior-driven spread into a decision-relevant posterior predictive.
  • Beyond the paper: a cheap stress test of the headline claim would be to recompute the optimal sample allocations with a covariance matrix estimated from substantially more pilot samples and check whether the 91-fold speedup persists.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper applies three multifidelity Monte Carlo estimators—MFMC, MLMC, and MLBLUE—to quantify uncertainty in projected 2015–2050 Greenland ice mass loss under parametric uncertainty in basal friction and geothermal heat flux. The high-fidelity model is an ISSM higher-order ice-flow model; lower-fidelity surrogates are obtained by mesh coarsening, SSA physics reduction, time-truncation/extrapolation, and interpolation. The estimators are standard and are presented with algorithms and MSE formulas. The numerical demonstration reports that, for a target accuracy of ±1 mm SLR at 95% confidence, the multifidelity estimators require 30.1, 37.5, and 4.2 CPU-days versus 381.2 CPU-days for Monte Carlo, corresponding to speedups of 12.6, 10.2, and 91.0, which the abstract summarizes as two orders of magnitude. However, the 13×13 model covariance matrix is estimated from 32 HO pilot samples plus 96 additional SSA samples, and Section 4.1 explicitly excludes this pilot cost from all budgets; this exclusion is the main driver of the headline speedups. The paper also treats the estimated covariance as known in the MSE and confidence-interval calculations.

Significance. If the numerical claims held under a fair from-scratch cost accounting, this would be a valuable demonstration that unbiased multifidelity UQ is tractable for continental-scale ice sheet simulations. The paper's strengths are its use of a community code (ISSM), its adherence to the ISMIP6 protocol, its comparison against Monte Carlo estimates, and its provision of code and prediction data. The theoretical estimators are standard and correctly presented, and the unbiasedness guarantees are not in question. However, the headline quantitative claims currently depend on excluding a pilot cost that is comparable to the Monte Carlo target budget, so the demonstrated significance is not yet established. The methodological contribution is incremental rather than radically new, but the application is novel and potentially useful to the ice-sheet modeling community.

major comments (3)
  1. [Sections 3.1 and 4.1] Section 3.1 states that estimation of the model covariance matrix Σ is not counted toward computational costs, and Section 4.1 repeats this for the pilot computations ('we do not count these computations towards our budget'). This exclusion is load-bearing for the central speedup claim. Using the costs in Table 2, the 32 HO pilot runs (models s1–s6) cost about 193.7 CPU-days, and the additional 96 SSA runs (models s7–s12) cost about 3.9 CPU-days, for a pilot stage of roughly 199 CPU-days—about half of the 381.2 CPU-day Monte Carlo budget and about 47 times the reported 4.2 CPU-day MLBLUE budget. A from-scratch user must spend at least this amount, and even under the paper's reuse suggestion the total cost is at least about 199 CPU-days, bounding any speedup by roughly 381.2/199 ≈ 1.9. The reported speedups of 12.6, 10.2, and 91.0, and the abstract's 'two orders of magnitude,' are therefore not supported by the demonstration as accounted. The paper should either include pilot cost in a from-scratch comparison or explicitly rescope the claims to 'incremental cost given a pre-existing covariance estimate.'
  2. [Sections 3.1, 4.1, and 4.2] The covariance matrix Σ is estimated from only 32 samples for all entries involving HO models, yet the MSE formulas (11), (17), and (26), together with the optimal sample-size rules (14)–(15) and the MLBLUE SDP (27), treat this estimate as the true covariance. With n = 32, the sampling error in a correlation estimate is on the order of 0.15–0.2, and the MFMC allocation depends on differences of squared correlations while the MLBLUE optimization is known to be sensitive to approximation errors in Σ (as the authors themselves note in Section 4.4). The paper does not report a sensitivity or bootstrap analysis, nor does it verify in repeated experiments that the MSE-predicted target accuracy is achieved. Since the 'guaranteed target accuracy' is the basis for the reported speedups, this finite-sample uncertainty should be quantified.
  3. [Sections 4.2 and 4.3, Table 3] The 95% confidence intervals in Table 3 are computed from the estimator variance together with normal quantiles. For the target-accuracy runs, the high-fidelity model is sampled 7 times by MFMC, 12 times by MLMC, and only once by MLBLUE (Section 4.2 and Figure 8). The ice mass loss distribution is explicitly shown to be non-Gaussian in Section 2.2 and Figure 4, so the normal approximation for the MLBLUE estimator with a single high-fidelity sample is not justified by the CLT, and the claimed 95% confidence level may not hold. The paper should either use conservative inequalities, provide a bootstrap or coverage study, or restrict the confidence statements to settings with larger high-fidelity sample sizes.
minor comments (5)
  1. [Abstract] The abstract's claim of 'two orders of magnitude' speedup overstates the largest factor reported in the body, 91.0×; the body itself says 'between one and two orders of magnitude.' Please align the abstract with the quantitative results.
  2. [Section 2.1.2] The ocean water density is given as 'ρw = 1023 km/m3'; the unit should be kg/m3.
  3. [Section 2.1.2] The citation to Paterson appears as '[ ?, p. 97]]Paterson1994' and needs to be corrected or completed.
  4. [Section 3.1] The symbol Σ is used both for the model covariance matrix and for the diagonal covariance matrix Σα of the basal friction coefficients; consider renaming one to avoid confusion.
  5. [Section 4.4] There is a duplicated word in 'robust against against approximation bias'; it should read 'robust against approximation bias.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 2050 mass-loss estimates are computed by unbiased multifidelity estimators on model outputs, and the covariance-based speedup calculations are performance predictions, not reductions of the target quantity to the fitted inputs.

full rationale

The paper's derivation chain is: build the high-fidelity HO model and cheaper SSA/mesh/extrapolation/interpolation surrogates, estimate the 13x13 covariance matrix Sigma from pilot simulations, feed Sigma and model costs into the MFMC/MLMC/MLBLUE sample-size optimizations, draw model evaluations to form unbiased estimators of E[s1], and evaluate the resulting MSE with the analytical formulas (11), (17), and (26). The ice mass loss estimates in Table 3 are not defined in terms of Sigma; they are Monte-Carlo-type averages over model outputs, and their unbiasedness follows from the telescoping/control-variate estimator structures rather than from the fitted covariance. The reported speedup factors are computed from the same estimated Sigma, so the performance analysis is self-referential in the sense that the predicted MSE inherits any bias in Sigma, and the explicit exclusion of pilot costs in Sections 3.1 and 4.1 is a real accounting limitation that belongs in a correctness assessment. However, none of this is circular in the required sense: no equation defines the 2050 expectation as a function of the fitted covariance, and no result is imported solely through a self-citation. The citations to works by the authors, including [76], [19], [37], and [38], are references to established published methods and results, and the central claim is supported by the ISSM simulations, the Monte Carlo comparison, and the reported confidence intervals. The pilot-cost exclusion weakens the headline speedup claim, but it does not make the derivation equivalent to its inputs. Therefore no significant circularity is present.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central speedup claim rests on a covariance matrix whose pilot-sampling cost is excluded, plus several physically motivated but unverified modeling choices for the uncertainty distributions.

free parameters (4)
  • Basal friction inversion weights in cost function (4) = 180, 0.6, 8e-6
    Chosen to balance terms and via L-curve analysis; they define the reference basal friction field and thus the center of the uncertainty model.
  • Number of PCA modes and variances for basal friction uncertainty = 7 modes with diagonal covariance from log-differences of 2015-2021 velocity inversions
    The uncertainty distribution in Eq. (7) is fit to seven annual velocity products; the variance magnitudes and mode shapes control the spread of the ice mass loss ensemble.
  • Geothermal heat flux mixture weight = Xgeo ~ U(0,1) between Greve and Shapiro-Ritzwoller fields
    A modeling choice following the NASA Sea Level Portal that defines the geothermal uncertainty; no data fitting is shown.
  • Extrapolation surrogate parameters = Linear slope, intercept, and yearly adjustment learned from 9 SSA coarse reference runs
    Each extrapolation surrogate is a linear fit to the first 20 years plus learned yearly adjustments; these fitted values set the correlation structure that drives the speedups.
assumptions (5)
  • domain assumption Higher-order (HO) model is a faithful high-fidelity reference for the true Greenland ice sheet
    Section 2.1.2 uses HO instead of full Stokes; the speedup claim is relative to HO, not to physical reality.
  • domain assumption Ice is fully grounded
    Section 2.1.2 assumes all ice grounded; authors note this affects only 0.22% of the domain.
  • domain assumption The estimated covariance matrix equals the true model covariance
    Section 3.1 assumes the covariance is known; Section 4.1 approximates it from 32 and 96 pilot samples and then plugs it into the MSE formulas.
  • ad hoc to paper Pilot cost can be ignored because validation and verification data would supply the covariance
    Section 4.1 states the covariance estimation cost is not counted; this is the load-bearing accounting assumption behind the speedups.
  • ad hoc to paper Xgeo and Xalpha are independent
    Section 2.2 states this independence, noting it neglects coupling between geothermal heating and basal friction inversion.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multifidelity Uncertainty Quantification for Ice Sheet Simulations." pith.science (2026). https://pith.science/paper/JMJDLFV3

@misc{pith2026241206110,
  author       = {Pith},
  title        = {Pith review of: Multifidelity Uncertainty Quantification for Ice Sheet Simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JMJDLFV3}},
  note         = {Machine review of arXiv:2412.06110}
}
read the original abstract

Ice sheet simulations suffer from vast parametric uncertainties, such as the basal sliding boundary condition or geothermal heat flux. Quantifying the resulting uncertainties in predictions is of utmost importance to support judicious decision-making, but high-fidelity simulations are too expensive to embed within uncertainty quantification (UQ) computations. UQ methods typically employ Monte Carlo simulation to estimate statistics of interest, which requires hundreds (or more) of ice sheet simulations. Cheaper low-fidelity models are readily available (e.g., approximated physics, coarser meshes), but replacing the high-fidelity model with a lower fidelity surrogate introduces bias, which means that UQ results generated with a low-fidelity model cannot be rigorously trusted. Multifidelity UQ retains the high-fidelity model but expands the estimator to shift computations to low-fidelity models, while still guaranteeing an unbiased estimate. Through this exploitation of multiple models, multifidelity estimators guarantee a target accuracy at reduced computational cost. This paper presents a comprehensive multifidelity UQ framework for ice sheet simulations. We present three multifidelity UQ approaches -- Multifidelity Monte Carlo, Multilevel Monte Carlo, and the Best Linear Unbiased Estimator -- that enable tractable UQ for continental-scale ice sheet simulations. We demonstrate the techniques on a model of the Greenland ice sheet to estimate the 2015-2050 ice mass loss, verify their estimates through comparison with Monte Carlo simulations, and give a comparative performance analysis. For a target accuracy equivalent to 1 mm sea level rise contribution at 95% confidence, the multifidelity estimators achieve computational speedups of two orders of magnitude.

Figures

Figures reproduced from arXiv: 2412.06110 by the authors.

Figure 1
Figure 1. Multifidelity UQ framework In this paper we lay out a multifidelity uncertainty quantification (UQ) framework that addresses this challenge. Model intercomparison studies have a long tradition in glaciology, including in model validation [46, 73, 74], verification based on historical data [30, 89], and for high-fidelity projections [31, 88, 43]. Moreover, parametric uncertainties in ice sheets are studied extensivel… view at source ↗
Figure 2
Figure 2. Far left: Reference basal friction field at the parametric mean (measured in (s [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Geothermal heat flux fields from [34] (left) and [90] (right) measured in W [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Left: High-fidelity ice mass change projections for 32 samples (colored lines) of the geothermal heat [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Effect of mesh coarsening (left to right) and physical approximation (top to bottom) on the [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Left: schematic explanation of extrapolation surrogate modeling setup. Right: Interpolation-based [PITH_FULL_IMAGE:figures/full_fig_p021_6.png]
Figure 7
Figure 7. Figure 7: Comparison of root MSE of the multifidelity UQ methods MFMC, MLMC, and MLBLUE, and [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]
Figure 8
Figure 8. Figure 8: Distribution of computational budget for MFMC, MLMC, and MLBLUE for reaching a target [PITH_FULL_IMAGE:figures/full_fig_p023_8.png]
Figure 9
Figure 9. Figure 9: Confidence intervals for expected Greenland ice mass change in 2050 relative to 2015 and control [PITH_FULL_IMAGE:figures/full_fig_p024_9.png]
Figure 10
Figure 10. Figure 10: Estimates of the 2015-2050 ice mass change and 95% confidence interval (shaded) for a computa [PITH_FULL_IMAGE:figures/full_fig_p025_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

93 extracted references · 80 canonical work pages

  1. [1]

    Principal component analysis

    Herv´ e Abdi and Lynne J Williams. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics , 2(4):433–459, 2010

    2010

  2. [2]

    Lithosphere thermal thickness and geothermal heat flux in Greenland from a new thermal isostasy method

    Irina M Artemieva. Lithosphere thermal thickness and geothermal heat flux in Greenland from a new thermal isostasy method. Earth-Science Reviews, 188:469–481, 2019

    2019

  3. [3]

    Contribution of the Greenland Ice Sheet to sea level over the next millennium

    Andy Aschwanden, Mark A Fahnestock, Martin Truffer, Douglas J Brinkerhoff, Regine Hock, Con- stantine Khroulev, Ruth Mottram, and S Abbas Khan. Contribution of the Greenland Ice Sheet to sea level over the next millennium. Science advances, 5(6):eaav9396, 2019

    2019

  4. [4]

    Inferring the basal slid- ing coefficient field for the Stokes ice sheet model under rheological uncertainty

    Olalekan Babaniyi, Ruanui Nicholson, Umberto Villa, and No´ emi Petra. Inferring the basal slid- ing coefficient field for the Stokes ice sheet model under rheological uncertainty. The Cryosphere , 15(4):1731–1750, 2021

    2021

  5. [5]

    An expert judgement assessment of future sea level rise from the ice sheets

    Jonathan L Bamber and WP Aspinall. An expert judgement assessment of future sea level rise from the ice sheets. Nature Climate Change , 3(4):424–427, 2013

    2013

  6. [6]

    Jowan M Barnes and G Hilmar Gudmundsson. The predictive power of ice sheet models and the regional sensitivity of ice loss to basal sliding parameterisations: a case study of Pine Island and Thwaites glaciers, West Antarctica. The Cryosphere, 16(10):4291–4304, 2022

    2022

  7. [7]

    Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization

    Jose H Blanchet and Peter W Glynn. Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization. In 2015 Winter Simulation Conference (WSC) , pages 3656–3667. IEEE, 2015

    2015

  8. [8]

    Dynamics of thermally induced ice streams simulated with a higher-order flow model

    DJ Brinkerhoff and JV Johnson. Dynamics of thermally induced ice streams simulated with a higher-order flow model. Journal of Geophysical Research: Earth Surface , 120(9):1743–1770, 2015. 27

    2015

Show all 93 references
  1. [9]

    Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference

    Douglas Brinkerhoff, Andy Aschwanden, and Mark Fahnestock. Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference. Journal of Glaciology , 67(263):385–403, 2021

    2021

  2. [10]

    Bayesian inference of subglacial topography using mass conservation

    Douglas J Brinkerhoff, Andy Aschwanden, and Martin Truffer. Bayesian inference of subglacial topography using mass conservation. Frontiers in Earth Science , 4:8, 2016

    2016

  3. [11]

    W. F. Budd, P. L. Keage, and N. A. Blundy. Empirical studies of ice sliding. J. Glaciol., 23:157–170, 1979

    1979

  4. [12]

    Uncertainty quantification of the multi-centennial response of the Antarctic ice sheet to climate change

    Kevin Bulthuis, Maarten Arnst, Sainan Sun, and Frank Pattyn. Uncertainty quantification of the multi-centennial response of the Antarctic ice sheet to climate change. The Cryosphere, 13(4):1349– 1380, 2019

    2019

  5. [13]

    Implementation of a gaussian markov random field sampler for forward uncertainty quantification in the ice-sheet and sea-level system model v4

    Kevin Bulthuis and Eric Larour. Implementation of a gaussian markov random field sampler for forward uncertainty quantification in the ice-sheet and sea-level system model v4. 19. Geoscientific Model Development, 15(3):1195–1217, 2022

    2022

  6. [14]

    D. Chen, M. Rojas, B.H. Samset, K. Cobb, A. Diongue Niang, P. Edwards, S. Emori, S.H. Faria, E. Hawkins, P. Hope, P. Huybrechts, M. Meinshausen, S.K. Mustafa, G.-K. Plattner, and A.-M. Tr´ eguier.Framing, Context, and Methods , page 147–286. Cambridge University Press, Cambrid...

    2021

  7. [15]

    Well-posedness results for a nonlinear Stokes problem arising in glaciology

    Qingshan Chen, Max Gunzburger, and Mauro Perego. Well-posedness results for a nonlinear Stokes problem arising in glaciology. SIAM Journal on Mathematical Analysis , 45(5):2710–2733, 2013

    2013

  8. [16]

    Gong Cheng, Nina Kirchner, and Per L¨ otstedt. Sensitivity of ice sheet surface velocity and elevation to variations in basal friction and topography in the full Stokes and shallow-shelf approximation frameworks using adjoint equations. The Cryosphere, 15(2):715–742, 2021

    2021

  9. [17]

    Topographic correction of geothermal heat flux in Greenland and Antarctica

    William Colgan, Joseph A MacGregor, Kenneth D Mankoff, Ryan Haagenson, Harihar Rajaram, Yasmina M Martos, Mathieu Morlighem, Mark A Fahnestock, and Kristian K Kjeldsen. Topographic correction of geothermal heat flux in Greenland and Antarctica. Journal of Geophysical Research:...

    2021

  10. [18]

    Greenland geothermal heat flow database and map (version 1)

    William Colgan, Agnes Wansing, Kenneth Mankoff, Mareen L¨ osing, John Hopper, Keith Louden, J¨ org Ebbing, Flemming G Christiansen, Thomas Ingeman-Nielsen, Lillemor Claesson Liljedahl, et al. Greenland geothermal heat flow database and map (version 1). Earth System Science Dat...

    2021

  11. [19]

    Multi-output multilevel best linear unbiased estimators via semidefinite programming

    M Croci, K E Willcox, and S J Wright. Multi-output multilevel best linear unbiased estimators via semidefinite programming. Computer Methods in Applied Mechanics and Engineering , 413:116130, 2023

    2023

  12. [20]

    Contribution of Antarctica to past and future sea-level rise

    Robert M DeConto and David Pollard. Contribution of Antarctica to past and future sea-level rise. Nature, 531(7596):591–597, 2016

    2016

  13. [21]

    A new vertically integrated MOno-Layer Higher-Order (MOLHO) ice flow model

    Thiago Dias dos Santos, Mathieu Morlighem, and Douglas Brinkerhoff. A new vertically integrated MOno-Layer Higher-Order (MOLHO) ice flow model. The Cryosphere, 16(1):179–195, 2022

    2022

  14. [22]

    Multilevel Markov chain Monte Carlo

    Tim J Dodwell, Christian Ketelsen, Robert Scheichl, and Aretha L Teckentrup. Multilevel Markov chain Monte Carlo. Siam Review, 61(3):509–545, 2019

    2019

  15. [23]

    Revisiting Antarctic ice loss due to marine ice-cliff instability

    Tamsin L Edwards, Mark A Brandon, Gael Durand, Neil R Edwards, Nicholas R Golledge, Philip B Holden, Isabel J Nias, Antony J Payne, Catherine Ritz, and Andreas Wernecke. Revisiting Antarctic ice loss due to marine ice-cliff instability. Nature, 566(7742):58–64, 2019

    2019

  16. [24]

    Projected land ice contributions to twenty-first-century sea level rise

    Tamsin L Edwards, Sophie Nowicki, Ben Marzeion, Regine Hock, Heiko Goelzer, H´ el` ene Seroussi, Nicolas C Jourdain, Donald A Slater, Fiona E Turner, Christopher J Smith, et al. Projected land ice contributions to twenty-first-century sea level rise. Nature, 593(7857):74–82, 2021. 28

    2021

  17. [25]

    Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization

    Veronika Eyring, Sandrine Bony, Gerald A Meehl, Catherine A Senior, Bjorn Stevens, Ronald J Stouffer, and Karl E Taylor. Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization. Geoscientific Model Development , 9(5):1937–1958, 2016

    1937

  18. [26]

    Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification

    Ionut,-Gabriel Farcas,, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, and Hans-Joachim Bun- gartz. Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification. Computer Methods in Applied Mechanics and Engineering , 406:115908, 2023

    2023

  19. [27]

    Multilevel Monte Carlo path simulation

    Michael B Giles. Multilevel Monte Carlo path simulation. Operations Research, 56(3):607–617, 2008

    2008

  20. [28]

    Multilevel Monte Carlo methods

    Michael B Giles. Multilevel Monte Carlo methods. Acta Numerica, 24:259–328, 2015

    2015

  21. [29]

    Multilevel quasi-Monte Carlo path simulation

    Michael B Giles and Benjamin J Waterhouse. Multilevel quasi-Monte Carlo path simulation. Ad- vanced Financial Modelling, Radon Series on Computational and Applied Mathematics , 8:165–181, 2009

    2009

  22. [30]

    Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison

    Heiko Goelzer, Sophie Nowicki, Tamsin Edwards, Matthew Beckley, Ayako Abe-Ouchi, Andy As- chwanden, Reinhard Calov, Olivier Gagliardini, Fabien Gillet-Chaulet, Nicholas R Golledge, et al. Design and results of the ice sheet model initialisation experiments initMIP-Greenland: a...

    2018

  23. [31]

    The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6

    Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andrew Shepherd, Erika Simon, et al. The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6. The Cry...

    2020

  24. [32]

    The multi-millennial Antarctic commitment to future sea-level rise

    Nicholas R Golledge, Douglas E Kowalewski, Timothy R Naish, Richard H Levy, Christopher J Fogwill, and Edward GW Gasson. The multi-millennial Antarctic commitment to future sea-level rise. Nature, 526(7573):421–425, 2015

    2015

  25. [33]

    A generalized approxi- mate control variate framework for multifidelity uncertainty quantification.Journal of Computational Physics, 408:109257, 2020

    Alex A Gorodetsky, Gianluca Geraci, Michael S Eldred, and John D Jakeman. A generalized approxi- mate control variate framework for multifidelity uncertainty quantification.Journal of Computational Physics, 408:109257, 2020

    2020

  26. [34]

    Geothermal heat flux distribution for the Greenland ice sheet, derived by combining a global representation and information from deep ice cores

    Ralf Greve. Geothermal heat flux distribution for the Greenland ice sheet, derived by combining a global representation and information from deep ice cores. Polar Data Journal , 3:22–36, 2019

    2019

  27. [35]

    Dynamics of ice sheets and glaciers

    Ralf Greve and Heinz Blatter. Dynamics of ice sheets and glaciers . Springer Science & Business Media, 2009

    2009

  28. [36]

    The general Gauss-Markov model with possibly singular dispersion matrix

    J¨ urgen Groß. The general Gauss-Markov model with possibly singular dispersion matrix. Statistical Papers, 45:311–336, 2004

    2004

  29. [37]

    Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling

    Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang. Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling. Geoscientific Model Development, 16(4):1213–1229, 2023

    2023

  30. [38]

    A multifidelity Monte Carlo method for realistic computational budgets

    Anthony Gruber, Max Gunzburger, Lili Ju, and Zhu Wang. A multifidelity Monte Carlo method for realistic computational budgets. Journal of Scientific Computing , 94(1):2, 2023

    2023

  31. [39]

    Inferring ice thickness from a glacier dynamics model and multiple surface data sets

    Yawen Guan, Murali Haran, and David Pollard. Inferring ice thickness from a glacier dynamics model and multiple surface data sets. Environmetrics, 29(5-6):e2460, 2018

    2018

  32. [40]

    Multi-index Monte Carlo: when sparsity meets sampling

    Abdul-Lateef Haji-Ali, Fabio Nobile, and Ra´ ul Tempone. Multi-index Monte Carlo: when sparsity meets sampling. Numerische Mathematik , 132:767–806, 2016

    2016

  33. [41]

    A hy- brid deep neural operator/finite element method for ice-sheet modeling

    QiZhi He, Mauro Perego, Amanda A Howard, George Em Karniadakis, and Panos Stinis. A hy- brid deep neural operator/finite element method for ice-sheet modeling. Journal of Computational Physics, 492:112428, 2023

    2023

  34. [42]

    Multilevel Monte Carlo methods

    Stefan Heinrich. Multilevel Monte Carlo methods. In Large-Scale Scientific Computing: Third International Conference, LSSC 2001 Sozopol, Bulgaria, June 6–10, 2001 Revised Papers 3 , pages 58–67. Springer, 2001. 29

    2001

  35. [43]

    GlacierMIP–a model intercomparison of global-scale glacier mass-balance models and projections

    Regine Hock, Andrew Bliss, BEN Marzeion, Rianne H Giesen, Yukiko Hirabayashi, Matthias Huss, Valentina Radi´ c, and Aime´ e BA Slangen. GlacierMIP–a model intercomparison of global-scale glacier mass-balance models and projections. Journal of Glaciology , 65(251):453–467, 2019

    2019

  36. [44]

    Adaptive multilevel Monte Carlo simulation

    H ˚ akon Hoel, Erik Von Schwerin, Anders Szepessy, and Ra´ ul Tempone. Adaptive multilevel Monte Carlo simulation. In Numerical Analysis of Multiscale Computations: Proceedings of a Winter Work- shop at the Banff International Research Station 2009 , pages 217–234. Springer, 2011

    2009

  37. [45]

    The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets

    Ian M Howat, A Negrete, and Benjamin E Smith. The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets. The Cryosphere, 8(4):1509–1518, 2014

    2014

  38. [46]

    The EISMINT benchmarks for testing ice-sheet models

    Philippe Huybrechts, Tony Payne, et al. The EISMINT benchmarks for testing ice-sheet models. Annals of Glaciology , 23:1–12, 1996

    1996

  39. [47]

    Climate Change 2021: The Physical Science Basis

    IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change , volume In Press. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2021

    2021

  40. [48]

    Tobin Isaac, Noemi Petra, Georg Stadler, and Omar Ghattas. Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet. Journal of Computational Phy...

    2015

  41. [49]

    I. Joughin. Measures greenland annual ice sheet velocity mosaics from sar and landsat, version 4, 2022

    2022

  42. [50]

    Joughin, B

    I. Joughin, B. Smith, I. Howat, and T. Scambos. MEaSUREs Multi-year Greenland Ice Sheet Velocity Mosaic, version 1, 2016

    2016

  43. [51]

    Deep learning speeds up ice flow modelling by several orders of magnitude

    Guillaume Jouvet, Guillaume Cordonnier, Byungsoo Kim, Martin L¨ uthi, Andreas Vieli, and Andy Aschwanden. Deep learning speeds up ice flow modelling by several orders of magnitude. Journal of Glaciology, 68(270):651–664, 2022

    2022

  44. [52]

    Satellite magnetics suggest a complex geothermal heat flux pattern beneath the Greenland ice sheet

    Mick Emil Kolster, Arne Døssing, and Shfaqat Abbas Khan. Satellite magnetics suggest a complex geothermal heat flux pattern beneath the Greenland ice sheet. Remote Sensing, 15(5):1379, 2023

    2023

  45. [53]

    A preliminary investigation of the influence of basal and surface topography on supraglacial lake distribution near jakobshavn isbrae, western greenland

    DJ Lampkin and J VanderBerg. A preliminary investigation of the influence of basal and surface topography on supraglacial lake distribution near jakobshavn isbrae, western greenland. Hydrological Processes, 25(21):3347–3355, 2011

    2011

  46. [54]

    Ice flow sensitivity to geothermal heat flux of Pine Island Glacier, Antarctica.Journal of Geophysical Research: Earth Surface, 117(F4), 2012

    E Larour, M Morlighem, H Seroussi, J Schiermeier, and E Rignot. Ice flow sensitivity to geothermal heat flux of Pine Island Glacier, Antarctica.Journal of Geophysical Research: Earth Surface, 117(F4), 2012

    2012

  47. [55]

    Continental scale, high order, high spatial resolution, ice sheet modeling using the Ice Sheet System Model (ISSM)

    Eric Larour, Helene Seroussi, Mathieu Morlighem, and Eric Rignot. Continental scale, high order, high spatial resolution, ice sheet modeling using the Ice Sheet System Model (ISSM). Journal of Geophysical Research: Earth Surface , 117(F1), 2012

    2012

  48. [56]

    Glacier flow modelling: a comparison of the Shallow Ice Approximation and the full-Stokes solution

    Emmanuel Le Meur, Olivier Gagliardini, Thomas Zwinger, and Juha Ruokolainen. Glacier flow modelling: a comparison of the Shallow Ice Approximation and the full-Stokes solution. Comptes rendus. Physique, 5(7):709–722, 2004

    2004

  49. [57]

    Finite element three-dimensional Stokes ice sheet dynamics model with enhanced local mass conservation

    Wei Leng, Lili Ju, Yan Xie, Tao Cui, and Max Gunzburger. Finite element three-dimensional Stokes ice sheet dynamics model with enhanced local mass conservation. Journal of Computational Physics , 274:299–311, 2014

    2014

  50. [58]

    Upper bounds on twenty- first-century Antarctic ice loss assessed using a probabilistic framework

    Christopher M Little, Michael Oppenheimer, and Nathan M Urban. Upper bounds on twenty- first-century Antarctic ice loss assessed using a probabilistic framework. Nature Climate Change , 3(7):654–659, 2013

    2013

  51. [59]

    D. R. MacAyeal. Large-scale ice flow over a viscous basal sediment: Theory and application to Ice Stream B, Antarctica. J. Geophys. Res. , 94(B4):4071–4087, APR 10 1989. 30

    1989

  52. [60]

    Explor- ing uncertainty in glacier mass balance modelling with Monte Carlo simulation

    Horst Machguth, Ross S Purves, Johannes Oerlemans, Martin Hoelzle, and Frank Paul. Explor- ing uncertainty in glacier mass balance modelling with Monte Carlo simulation. The Cryosphere , 2(2):191–204, 2008

    2008

  53. [61]

    Geothermal heat flux reveals the Iceland hotspot track underneath Greenland

    Yasmina M Martos, Tom A Jordan, Manuel Catal´ an, Thomas M Jordan, Jonathan L Bamber, and David G Vaughan. Geothermal heat flux reveals the Iceland hotspot track underneath Greenland. Geophysical research letters, 45(16):8214–8222, 2018

    2018

  54. [62]

    A general method for debiasing a Monte Carlo estimator

    Don McLeish. A general method for debiasing a Monte Carlo estimator. Monte Carlo Methods and Applications, 17(4):301–315, 2011

    2011

  55. [63]

    Inversion of basal friction in Antarctica using exact and incomplete adjoints of a higher-order model

    M Morlighem, H Seroussi, E Larour, and E Rignot. Inversion of basal friction in Antarctica using exact and incomplete adjoints of a higher-order model. Journal of Geophysical Research: Earth Surface, 118(3):1746–1753, 2013

    2013

  56. [64]

    et al Morlighem

    M. et al Morlighem. IceBridge BedMachine Greenland, version 5, 2022

    2022

  57. [65]

    Spatial patterns of basal drag inferred using control methods from a full-Stokes and simpler models for Pine Island Glacier, West Antarctica

    Mathieu Morlighem, Eric Rignot, Helene Seroussi, Eric Larour, H Ben Dhia, and Denis Aubry. Spatial patterns of basal drag inferred using control methods from a full-Stokes and simpler models for Pine Island Glacier, West Antarctica. Geophysical Research Letters, 37(14), 2010

    2010

  58. [66]

    Bedmachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo sounding combined with mass conservation

    Mathieu Morlighem, Chris N Williams, Eric Rignot, Lu An, Jan Erik Arndt, Jonathan L Bam- ber, Ginny Catania, Nolwenn Chauch´ e, Julian A Dowdeswell, Boris Dorschel, et al. Bedmachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo soun...

    2017

  59. [67]

    Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018

    J´ er´ emie Mouginot, Eric Rignot, Anders A Bjørk, Michiel Van den Broeke, Romain Millan, Mathieu Morlighem, Brice No¨ el, Bernd Scheuchl, and Michael Wood. Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018. Proceedings of the national academy of sciences, ...

    1972

  60. [68]

    Multifidelity approaches for optimization under uncertainty

    Leo WT Ng and Karen E Willcox. Multifidelity approaches for optimization under uncertainty. International Journal for Numerical Methods in Engineering , 100(10):746–772, 2014

    2014

  61. [69]

    Experimental protocol for sealevel projections from ISMIP6 standalone ice sheet models

    Sophie Nowicki, Antony J Payne, Heiko Goelzer, Helene Seroussi, William H Lipscomb, Ayako Abe- Ouchi, C´ ecile Agosta, Patrick Alexander, Xylar S Asay-Davis, Alice Barthel, et al. Experimental protocol for sealevel projections from ISMIP6 standalone ice sheet models. The Cryos...

    2020

  62. [70]

    Ice sheet model intercom- parison project (ismip6) contribution to cmip6

    Sophie MJ Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, Heiko Goelzer, William Lip- scomb, Jonathan Gregory, Ayako Abe-Ouchi, and Andrew Shepherd. Ice sheet model intercom- parison project (ismip6) contribution to cmip6. Geoscientific model development , 9(12):4521–4545, 2016

    2016

  63. [71]

    Expert judgement and uncertainty quantification for climate change

    Michael Oppenheimer, Christopher M Little, and Roger M Cooke. Expert judgement and uncertainty quantification for climate change. Nature climate change , 6(5):445–451, 2016

    2016

  64. [72]

    Antarctic subglacial conditions inferred from a hybrid ice sheet/ice stream model

    Frank Pattyn. Antarctic subglacial conditions inferred from a hybrid ice sheet/ice stream model. Earth and Planetary Science Letters , 295(3-4):451–461, 2010

    2010

  65. [73]

    Bench- mark experiments for higher-order and full-Stokes ice sheet models (ISMIP–HOM)

    Frank Pattyn, Laura Perichon, Andy Aschwanden, Beate Breuer, Brigitte De Smedt, Olivier Gagliar- dini, G Hilmar Gudmundsson, Richard CA Hindmarsh, Alun Hubbard, Jesse V Johnson, et al. Bench- mark experiments for higher-order and full-Stokes ice sheet models (ISMIP–HOM). The C...

    2008

  66. [74]

    Results from the EISMINT model intercomparison: the effects of thermomechanical coupling

    AJ Payne, Philippe Huybrechts, A Abe-Ouchi, R Calov, JL Fastook, R Greve, SJ Marshall, I Mar- siat, C Ritz, L Tarasov, et al. Results from the EISMINT model intercomparison: the effects of thermomechanical coupling. Journal of Glaciology , 46(153):227–238, 2000

    2000

  67. [75]

    Multifidelity Monte Carlo estimation with adaptive low-fidelity models

    Benjamin Peherstorfer. Multifidelity Monte Carlo estimation with adaptive low-fidelity models. SIAM/ASA Journal on Uncertainty Quantification , 7(2):579–603, 2019. 31

    2019

  68. [76]

    Optimal model management for multifidelity Monte Carlo estimation

    Benjamin Peherstorfer, Karen Willcox, and Max Gunzburger. Optimal model management for multifidelity Monte Carlo estimation. SIAM Journal on Scientific Computing , 38(5):A3163–A3194, 2016

    2016

  69. [77]

    Heat flow from the Earth’s interior: analysis of the global data set

    Henry N Pollack, Suzanne J Hurter, and Jeffrey R Johnson. Heat flow from the Earth’s interior: analysis of the global data set. Reviews of Geophysics , 31(3):267–280, 1993

    1993

  70. [78]

    Description of a hybrid ice sheet-shelf model, and application to Antarctica

    D Pollard and RM DeConto. Description of a hybrid ice sheet-shelf model, and application to Antarctica. Geoscientific Model Development , 5(5):1273–1295, 2012

    2012

  71. [79]

    Large ensemble modeling of the last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques

    David Pollard, Won Chang, Murali Haran, Patrick Applegate, and Robert DeConto. Large ensemble modeling of the last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques. Geoscientific Model Development , 9(5):1697–1723, 2016

    2016

  72. [80]

    Multifidelity Monte Carlo estimation of variance and sensitivity indices

    Elizabeth Qian, Benjamin Peherstorfer, Daniel O’Malley, Velimir V Vesselinov, and Karen Willcox. Multifidelity Monte Carlo estimation of variance and sensitivity indices. SIAM/ASA Journal on Uncertainty Quantification, 6(2):683–706, 2018

    2018

  73. [81]

    Predicting the geothermal heat flux in Greenland: A machine learning approach

    Soroush Rezvanbehbahani, Leigh A Stearns, Amir Kadivar, J Doug Walker, and Cornelis J van der Veen. Predicting the geothermal heat flux in Greenland: A machine learning approach. Geophysical Research Letters, 44(24):12–271, 2017

    2017

  74. [82]

    Variational inference of ice shelf rheology with physics-informed machine learning

    Bryan Riel and Brent Minchew. Variational inference of ice shelf rheology with physics-informed machine learning. Journal of Glaciology , 69(277):1167–1186, 2023

    2023

  75. [83]

    Changes in the velocity structure of the Greenland Ice Sheet

    Eric Rignot and Pannir Kanagaratnam. Changes in the velocity structure of the Greenland Ice Sheet. Science, 311(5763):986–990, 2006

    2006

  76. [84]

    Potential sea-level rise from Antarctic ice-sheet instability constrained by observations

    Catherine Ritz, Tamsin L Edwards, Ga¨ el Durand, Antony J Payne, Vincent Peyaud, and Richard CA Hindmarsh. Potential sea-level rise from Antarctic ice-sheet instability constrained by observations. Nature, 528(7580):115–118, 2015

    2015

  77. [85]

    Effects of uncertainties in the geothermal heat flux distribution on the Greenland Ice Sheet: An assessment of existing heat flow models

    Irina Rogozhina, JM Hagedoorn, Z Martinec, Kevin Fleming, O Soucek, R Greve, and Maik Thomas. Effects of uncertainties in the geothermal heat flux distribution on the Greenland Ice Sheet: An assessment of existing heat flow models. Journal of Geophysical Research: Earth Surfac...

    2012

  78. [86]

    On multilevel best linear unbiased estimators

    Daniel Schaden and Elisabeth Ullmann. On multilevel best linear unbiased estimators. SIAM/ASA Journal on Uncertainty Quantification , 8(2):601–635, 2020

    2020

  79. [87]

    Asymptotic analysis of multilevel best linear unbiased estimators

    Daniel Schaden and Elisabeth Ullmann. Asymptotic analysis of multilevel best linear unbiased estimators. SIAM/ASA Journal on Uncertainty Quantification , 9(3):953–978, 2021

    2021

  80. [88]

    ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21 st century

    H´ el` ene Seroussi, Sophie Nowicki, Antony J Payne, Heiko Goelzer, William H Lipscomb, Ayako Abe Ouchi, C´ ecile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, et al. ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21 st cen...

    2020

  81. [89]

    initMIP- Antarctica: an ice sheet model initialization experiment of ISMIP6

    H´ el` ene Seroussi, Sophie Nowicki, Erika Simon, Ayako Abe-Ouchi, Torsten Albrecht, Julien Bron- dex, Stephen Cornford, Christophe Dumas, Fabien Gillet-Chaulet, Heiko Goelzer, et al. initMIP- Antarctica: an ice sheet model initialization experiment of ISMIP6. The Cryosphere ,...

    2019

  82. [90]

    Inferring surface heat flux distributions guided by a global seismic model: particular application to Antarctica

    Nikolai M Shapiro and Michael H Ritzwoller. Inferring surface heat flux distributions guided by a global seismic model: particular application to Antarctica. Earth and Planetary Science Letters , 223(1-2):213–224, 2004

    2004

  83. [91]

    Sensitivity of Greenland Ice Sheet surface mass balance to surface albedo parameterization: a study with a regional climate model

    JH Van Angelen, JTM Lenaerts, Stef Lhermitte, Xavier Fettweis, P Kuipers Munneke, MR Van den Broeke, E Van Meijgaard, and CJPP Smeets. Sensitivity of Greenland Ice Sheet surface mass balance to surface albedo parameterization: a study with a regional climate model. The Cryosph...

    2012

  84. [92]

    Cnrm-cerfacs cnrm-cm6-1 model output prepared for cmip6 cmip

    Aurore Voldoire. Cnrm-cerfacs cnrm-cm6-1 model output prepared for cmip6 cmip. 2018

    2018

  85. [93]

    Basal friction of Fleming Glacier, Antarctica–part 1: Sensitivity of inversion to temperature and bedrock uncertainty

    Chen Zhao, Rupert M Gladstone, Roland C Warner, Matt A King, Thomas Zwinger, and Math- ieu Morlighem. Basal friction of Fleming Glacier, Antarctica–part 1: Sensitivity of inversion to temperature and bedrock uncertainty. The Cryosphere, 12(8):2637–2652, 2018. 33

    2018

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.