skip to main content

Explore Research Products in the NSF-PAR

Title: The transferability of adjoint inversion products between different ice flow models
Abstract. Among the most important challenges faced by ice flow models is how to represent basal and rheological conditions, which are challenging to obtain from direct observations. A common practice is to use numerical inversions to calculate estimates for the unknown properties, but there are many possible methods and not one standardised approach. As such, every ice flow model has a unique initialisation procedure. Here we compare the outputs of inversions from three different ice flow models, each employing a variant of adjoint-based optimisation to calculate basal sliding coefficients and flow rate factors using the same observed surface velocities and ice thickness distribution. The region we focus on is the Amundsen Sea Embayment in West Antarctica, the subject of much investigation due to rapid changes in the area over recent decades. We find that our inversions produce similar distributions of basal sliding across all models, despite using different techniques, implying that the methods used are highly robust and represent the physical equations without much influence by individual model behaviours. Transferring the products of inversions between models results in time-dependent simulations displaying variability on the order of or lower than existing model intercomparisons. Focusing on contributions to sea level, the highest variability we find in simulations run in the same model with different inversion products is 32 %, over a 40-year period, a difference of 3.67 mm. There is potential for this to be improved with further standardisation of modelling processes, and the lowest variability within a single model is 13 %, or 1.82 mm over 40 years. While the successful transfer of inversion outputs from one model to another requires some extra effort and technical knowledge of the particular models involved, it is certainly possible and could indeed be useful for future intercomparison projects.  more » « less
Award ID(s):
2152622
NSF-PAR ID:
10319977
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
The Cryosphere
Volume:
15
Issue:
4
ISSN:
1994-0424
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al ( , The Cryosphere)
    null (Ed.)
    Abstract. Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution inresponse to different climate scenarios and assess the mass loss that would contribute tofuture sea level rise. However, there is currently no consensus on estimates of the future massbalance of the ice sheet, primarily because of differences in the representation of physicalprocesses, forcings employed and initial states of ice sheet models. This study presentsresults from ice flow model simulations from 13 international groups focusing on the evolutionof the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet ModelIntercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from theCoupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climatemodel results. Simulations of the Antarctic ice sheet contribution to sea level rise in responseto increased warming during this period varies between −7.8 and 30.0 cm of sea level equivalent(SLE) under Representative ConcentrationPathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment withconstant climate conditions and should therefore be added to the mass loss contribution underclimate conditions similar to present-day conditions over the same period. The simulated evolution of theWest Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica mass change varies between −6.1 and8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighingthe increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelfcollapse, here assumed to be caused by large amounts of liquid water ponding at the surface ofice shelves, yields an additional simulated mass loss of 28 mm compared to simulations without iceshelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, thecalibration of these melt rates based on oceanic conditions taken outside of ice shelf cavitiesand the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario basedon two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared tosimulations done under present-day conditions for the two CMIP5 forcings used and displaylimited mass gain in East Antarctica. 
    more » « less
  2. ; ; ; ; ; ( , Geoscientific Model Development)

    Abstract. Geoscientific models are facing increasing challenges to exploit growing datasets coming from remote sensing. Universal differential equations (UDEs), aided by differentiable programming, provide a new scientific modelling paradigm enabling both complex functional inversions to potentially discover new physical laws and data assimilation from heterogeneous and sparse observations. We demonstrate an application of UDEs as a proof of concept to learn the creep component of ice flow, i.e. a nonlinear diffusivity differential equation, of a glacier evolution model. By combining a mechanistic model based on a two-dimensional shallow-ice approximation partial differential equation with an embedded neural network, i.e. a UDE, we can learn parts of an equation as nonlinear functions that then can be translated into mathematical expressions. We implemented this modelling framework as ODINN.jl, a package in the Julia programming language, providing high performance, source-to-source automatic differentiation (AD) and seamless integration with tools and global datasets from the Open Global Glacier Model in Python. We demonstrate this concept for 17 different glaciers around the world, for which we successfully recover a prescribed artificial law describing ice creep variability by solving ∼ 500 000 ordinary differential equations in parallel. Furthermore, we investigate which are the best tools in the scientific machine learning ecosystem in Julia to differentiate and optimize large nonlinear diffusivity UDEs. This study represents a proof of concept for a new modelling framework aiming at discovering empirical laws for large-scale glacier processes, such as the variability in ice creep and basal sliding for ice flow, and new hybrid surface mass balance models.

     
    more » « less
  3. ; ; ; ; ( , Journal of Climate)
    Abstract

    Arctic sea ice loss in response to a warming climate is assessed in 42 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Sea ice observations show a significant acceleration in the rate of decline commencing near the turn of the twenty-first century. It is our assertion that state-of-the-art climate models should qualitatively reflect this accelerated trend within the limitations of internal variability and observational uncertainty. Our analysis shows that individual CMIP6 simulations of sea ice depict a wide range of model spread on biases and anomaly trends both across models and among their ensemble members. While the CMIP6 multimodel mean captures the observed sea ice area (SIA) decline relatively well, an individual model’s ability to represent the acceleration in sea ice decline remains a challenge. Seventeen (40%) out of 42 CMIP6 models and 37 (13%) out of the total 286 ensemble members reasonably capture the observed trends and acceleration in SIA decline. In addition, a larger ensemble size appears to increase the odds for a model to include at least one ensemble member skillfully representing the accelerated SIA trends. Simulations of sea ice volume (SIV) show much larger spread and uncertainty than SIA; however, due to limited availability of sea ice thickness data, these are not as well constrained by observations. Finally, we find that models with more ocean heat transport simulate larger sea ice declines, which suggests an emergent constraint in CMIP6 ensembles. This relationship points to the need for better understanding and modeling of ice–ocean interactions, especially with respect to frazil ice growth.

     
    more » « less
  4. ; ; ; ; ; ; ; ; ( , Geophysical Journal International)
    null (Ed.)
    SUMMARY We determine crustal shear wave velocity structure and crustal thickness at recently deployed seismic stations across West Antarctica, using a joint inversion of receiver functions and fundamental mode Rayleigh wave phase velocity dispersion. The stations are from both the UK Antarctic Network (UKANET) and Polar Earth Observing Network/Antarctic Network (POLENET/ANET). The former include, for the first time, four stations along the spine of the Antarctic Peninsula, three in the Ellsworth Land and five stations in the vicinity of the Pine Island Rift. Within the West Antarctic Rift System (WARS) we model a crustal thickness range of 18–28 km, and show that the thinnest crust (∼18 km) is in the vicinity of the Byrd Subglacial Basin and Bentley Subglacial Trench. In these regions we also find the highest ratio of fast (Vs = 4.0–4.3 km s–1, likely mafic) lower crust to felsic/intermediate upper crust. The thickest mafic lower crust we model is in Ellsworth Land, a critical area for constraining the eastern limits of the WARS. Although we find thinner crust in this region (∼30 km) than in the neighbouring Antarctic Peninsula and Haag-Ellsworth Whitmore block (HEW), the Ellsworth Land crust has not undergone as much extension as the central WARS. This suggests that the WARS does not link with the Weddell Sea Rift System through Ellsworth Land, and instead has progressed during its formation towards the Bellingshausen and Amundsen Sea Embayments. We also find that the thin WARS crust extends towards the Pine Island Rift, suggesting that the boundary between the WARS and the Thurston Island block lies in this region, ∼200 km north of its previously accepted position. The thickest crust (38–40 km) we model in this study is in the Ellsworth Mountain section of the HEW block. We find thinner crust (30–33 km) in the Whitmore Mountains and Haag Nunatak sectors of the HEW, consistent with the composite nature of the block. In the Antarctic Peninsula we find a crustal thickness range of 30–38 km and a likely dominantly felsic/intermediate crustal composition. By forward modelling high frequency receiver functions we also assess if any thick, low velocity subglacial sediment accumulations are present, and find a 0.1–0.8-km-thick layer at 10 stations within the WARS, Thurston Island and Ellsworth Land. We suggest that these units of subglacial sediment could provide a source region for the soft basal till layers found beneath numerous outlet glaciers, and may act to accelerate ice flow. 
    more » « less
  5. ; ; ; ( , The Cryosphere)
    Abstract. Antarctic ice shelves are vulnerable to warming ocean temperatures, and some have already begun thinning in response to increased basal melt rates.Sea level is therefore expected to rise due to Antarctic contributions, but uncertainties in its amount and timing remain largely unquantified. In particular, there is substantial uncertainty in future basal melt rates arising from multi-model differences in thermal forcing and how melt rates depend on that thermal forcing. To facilitate uncertainty quantification in sea level rise projections, we build, validate, and demonstrate projections from a computationally efficient statistical emulator of a high-resolution (4 km) Antarctic ice sheet model, the Community Ice Sheet Model version 2.1. The emulator is trained to a large (500-member) ensemble of 200-year-long 4 km resolution transient ice sheet simulations, whereby regional basal melt rates are perturbed by idealized (yet physically informed) trajectories. The main advantage of our emulation approach is that by sampling a wide range of possible basal melt trajectories, the emulator can be used to (1) produce probabilistic sea level rise projections over much larger Monte Carlo ensembles than are possible by direct numerical simulation alone, thereby providing better statistical characterization of uncertainties, and (2) predict the simulated ice sheet response under differing assumptions about basal melt characteristics as new oceanographic studies are published, without having to run additional numerical ice sheet simulations. As a proof of concept, we propagate uncertainties about future basal melt rate trajectories, derived from regional ocean models, to generate probabilistic sea level rise estimates for 100 and 200 years into the future. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email