Department of Energy Land Ice Modeling Efforts

S. Price - Los Alamos National Laboratory

& DOE PISCEES and ACME Land Ice Modeling Teams

Supported by DOE Office of Science (ASCR & BER)

Talk Goals Summarize programs funding DOE land ice work Summarize what DOE land ice efforts are working on Summarize what DOE land ice efforts are not working on

Lab SciDAC - PISCEES (BER, ASCR)

• develop & apply robust, accurate, scalable dynamical cores (dycores) for ice sheet modeling on structured and unstructured meshes

• evaluate models using new tools and data sets for verification and

validation and uncertainty quantification • Integrate models & tools into DOE-supported Earth System Models

ACME (BER)

• Could a dynamical instability in the Antarctic Ice Sheet be (have been) triggered within the next (past) 40 years?

• What is the long-term, committed sea-level rise from the Antarctic ice

sheet due to climate change in the last few decades and that expected in the next few decades?

HiLat (RGCM)

• Simulate Greenland evolution over the next 1-2 centuries to explore coupled ice-sheet & climate feedbacks during the initial stages of deglaciation in an anthropogenically-forced, high-CO2 climate (J. Fyke, LANL POC)

University SciDAC (BER, ASCR)

• Support for, and improvements in, the implementation and testing of ice sheet & ocean coupling processes in MPAS-Ocean (X. Asay-Davis, PIK)

• Fully coupled simulations of ice-sheet & climate evolution in warm past

climates (mid-Pliocene, last interglacial, future to AD 3000) to put long-term future projections of Greenland deglaciation in context (B. Otto-Bleisner, NCAR)

Other (past) DOE Projects*

… have contributed to: • Development and release of CISM2 (W. Lipscomb talk)

• Software infrastructure development and coupling of CISM to CESM (J. Fyke, W. Sacks, and M. Vizcaino talks)

* SciDAC-2, IMPACTS, ISICLES, PISCEES (early)

Summary* of DOE Land Ice Work

• MPAS-Land Ice development & testing (LANL, SNL) • Dynamical core development and application

• [MPAS] FELIX-FO / Albany (SNL, LANL) • [MPAS] FELIX-S (USC, FSU) • [CISM] BISICLES (LBNL, UOB, LANL)

• Stable ice thickness evolution methods (SNL, LANL) • Subglacial hydrology model development (FSU, SNL) • Verification & Validation (ORNL, LANL) • Uncertainty Quantification (SNL, UT, NYU, MIT, LANL) • Optimization & Initialization (SNL, UT, NYU, MIT, LANL) • Earth System Model integration (LANL, NCAR, SNL, LBNL)

* Does not include everything!

MPAS-Land Ice Development & Testing • Addition of many of CISM’s standard test cases • Prototype automated test suite in place • Fully working with PISCEES dycores (FELIX-S and FELIX-FO) • All “standard” basal and lateral boundary conditions supported • Variable resolution meshes supported • Adaptive time stepping added • Ongoing stress-testing on LCF machines for large-scale, realistic,

Greenland and Antarctic problems (~1-10 k cpus) • Robust solves & good scalability (strong & weak) (Felix-FO) • Conservation of energy (heat / enthalpy balance) (ongoing) • Integration within ACME (ongoing) • Semi-Implicit thickness evolution (ongoing) • Subglacial hydrology model development (ongoing)

FELIX-FO: strong scaling for high-resolution, realistic Greenland problem

• ILU preconditioner gives overall better strong scaling relative to AMG

• But AMG faster for lower #s of cores (e.g.,

FELIX-FO: weak scaling for high-resolution, realistic Antarctica problem

ILU AMG

16 cores

1024 cores

16 cores

1024 cores

• ILU precond. solver >10x slower than AMG due to poor convergence of ill-conditioned linear system (1024 cpus)

• Number of AMG iterations grow as problem is refined but still overall better suited to linear systems associated with Antarctica

Tezaur, Perego, Tuminaro, Salinger (SNL)

Tezaur, Perego, Tuminaro, Salinger (SNL)

FELIX-S: Validation of Marine Ice Sheet Dynamics • High resolution, “full” Stokes models taken as “truth” for idealized

simulations exploring marine ice sheet dynamics (e.g., MISMIP*) • To date, international community has used a single model for this

purpose (Elmer-Ice) • We are doing 1:1 comparisons with Elmer-Ice to provide additional

support for community intercomparisons and for validating our own (DOE) marine ice sheet simulations (e.g., with lower-order models like FELIX-FO and BISICLES)

MISMIP-3d using FELIX-S

* See talk & poster by T. Zhang, L. Ju (USC), Gunzburger (FSU)

POPSICLES*: Coupled Antarctic Ice sheet & Southern Ocean Simulations

* POPSICLES = POP2x + BISICLES Asay-Davis (PIK) and Martin (LBNL)

POPSICLES*: Coupled Antarctic Ice sheet & Southern Ocean Simulations

* POPSICLES = POP2x + BISICLES Asay-Davis (PIK) and Martin (LBNL)

Asay-Davis (PIK) and Martin (LBNL)

POPSICLES Summary

Difficult to get reasonable quasi-SS initial condition: • CORE-NY forcing too “cold” (not enough submarine melt) • CORE-IAF forcing too “warm” (too much submarine melt)

Cause - problems with mixed-layer depth:

• CORE-NY: too deep prohibiting CDW access • CORE-IAF: too shallow allowing to much CDW access

Mixed-layer depth:

• recent clue: in “cold” sims., mixed layer deepens in winter due to high SS salinity (does not happen in “warm” sims.)

Asay-Davis (PIK) and Martin (LBNL)

Semi-Implicit Methods for Thickness Evolution Problem: • Explicit advection algorithms, which are efficient for evolving ice thickness, treat flow

as hyperbolic … but ice flow can also be highly diffusive • Stable time step for diffusion is generally

Semi-Implicit Methods for Thickness Evolution

dt=5 yrs Perego (SNL)

Subglacial Hydrology Model Development

• We have experience adding evolutionary subglacial hydrology and physically-based sliding laws to CISM (FDM, explicit time stepping)

• New work will add similar capability to PISCEES dycores but using Trilinos-Albany software framework:

• Finite Element Methods (more robust, easier implementation of complex BCs)

• Implicit time stepping (faster, more stable evolution)

• Access to built-in, advanced capabilities:

• Newton solves

• Sensitivity analysis

• Optimization, initialization, and UQ

Bertagna, Gunzburger (FSU), Perego, Salinger (SNL), Hoffman, Price (LANL)

FELIX-FO (Albany) Hewitt, J. Glac. (2011)

* Poster by L. Bertagna et al., “Towards coupling ice sheet movement & subglacial hydrology”

Subglacial Hydrology Model Development

Land Ice Verification & Validation (LIVV) Kit

Example of test run data for validation from a coupled CESM 1.0 (pre-ACME) with active ice

sheet model

Objective: Automated tool to evaluate ice sheet models Release 1.0, https://github.com/LIVVkit/LIVVkit June, 2015 New Science: • Provides comprehensive comparisons for a suite of

benchmark tests of (for now) the CISM model • Tested on Titan, Hopper, Linux, and Mac platforms • Generates suite of plots and test results on a

hierarchical webpage Significance • Provides regression testing with full reproducibility

information • Post-processing of solver and code performance for

large problems detects performance changes and tests “value” of expensive new model features (i.e. provides cost-benefit analysis of code changes)

• Provides hooks to add additional tests & dycore options

Example LIVV output from

the shelf case

Kennedy, Bennet, Evans, Worley (ORNL)

https://github.com/LIVVkit/LIVVkithttps://github.com/LIVVkit/LIVVkithttps://github.com/LIVVkit/LIVVkit

V&V Continued Continued work on ice sheet model validation framework(s) for last few decades using: • Forcing: outlet glacier flux & SMB time series from obs. • Validation: elevation change maps (from ICESat) • Validation: rate of mass change maps (from GRACE) GIS mass trend (cm/yr

W.E.) for 2003-2011 from GRACE (left) and

as simulated by CISM2 (right)

Price, Hoffman (LANL), NASA-GSFC, OSU, USF

Cumulative thinning of Smith Glacier region Since 2001 (shading) and grounding line position (red contours) in a 40-year simulation following transient calibration of the state 2001-2011.

Goldberg, Heimbach et al. (in prep.)

Transient model calibration & prediction, ASE, West Antarctica Ice Sheet Model Optimization:

Smith Glacier

Uncertainty Quantification (UQ) Some text here

Figs from John here

Uncertainty Quantification (UQ)

Jackson, Heimbach (UT), Stadler (NYU), Tezaur, Salinger, Eldred, Jakeman (SNL)

Uncertainty Quantification (UQ)

basal traction parameter Log10( Pa yr/m )

Perturbation (multiplier)

Jakeman, Tezaur (SNL), Price (LANL)

Uncertainty Quantification (UQ)

Jakeman, Tezaur (SNL), Price (LANL)

Land Ice Model Integration in ACME • MPAS-LI stand alone model stress testing (ongoing) • MPAS-LI “in” ACME (builds, runs, not yet fully coupled) • Initial, moderate resolution Greenland and Antarctic grids / init.

conditions added • Base-level optimization (for ICs) tested / working • Ocean – to – ice sheet coupling mostly complete • Ice sheet – to – ocean coupling underway • Boundary layer physics work in MPAS-O ongoing • Non-downscaled SMB tested, looks reasonable • New (coupler-based) downscaling being integrated via CIME • Update CISM to CISM2 coupling (ongoing) • Integration of BISICLES (through CISM2), planned

Fyke, Hoffman, Jacobsen, Lipscomb, Ringler, Petersen, Price (LANL), Asay-Davis (PIK), Sacks (NCAR), Perego (SNL)

What’s Missing?

• DOE is supporting lots of work on land ice model development

• There are mechanisms / plans for ongoing land ice collaborations between DOE & CESM:

• CISM2 can be coupled to “advanced” DOE dycores

• CIME: ACME & CESM shared coupling infrastructure

• … but little short-term support for development, testing, and integration needed to support CISM2 in CESM2

• We would like to see past efforts of the LIWG come to fruition through a scientifically supported CISM2 in CESM2

• Given current funding & resource constraints, how do we make sure this happens?

LANL: J. Fyke, M. Hoffman, W. Lipscomb, S. Price, T. Ringler, M. Petersen, D. Jacobsen

SNL: A. Salinger, M. Perego, I. Tezaur, R. Tuminaro, M. Eldred, J. Jakeman

LBNL: D. Martin, E. Ng, S. Williams

ORNL: K. Evans, M. Norman, P. Worley, A. Bennet, J. Kennedy

NCAR: W. Sacks, M. Vertenstein

UT: C. Jackson, P. Heimbach (MIT)

NYU: G. Stadler (UT)

FSU: M. Gunzburger, L. Bertagna

USC: L. Ju, T. Zhang, W. Leng

PIK: X. Asay-Davis

Contributors

MPAS-LI + FELIX-FO FELIX-FO

MPAS-LI + FELIX-FO • run on up to ~10k cpus (Hopper)

• Scaling up to ~4k cpus with default settings (no tuning yet)

• approximately ~5x speed-up using new AMG preconditioner

• cross-section movie.

Warmwater incursion – Amery

Semi-Implicit Methods for Thickness Evolution

Land Ice Validation in the LIVV kit

Ongoing Collection of Observational/Reanalysis/Model Data: • Ice sheet data: e.g. MEaSUREs Project, T fields

needed • Atmosphere data: e.g. ASR: 30-km is complete

for January 1, 2000 - December 31, 2012, GREBIS (20km), PARCA Greenland Climate Network (GC-Net) automatic weather station (AWS) array

• Land Surface Data: e.g. ASR, MODIS • Regional Climate Model data: e.g. WRF, RACMO

(where not used for initialization)

Significance • First ever comprehensive comparison of a

continental ice sheet model to a swath of the latest available observation and renalysis data

• Using verification LIVV to create larger validation testing framework

• Develop comprehensive metrics to compare both gridded and in-situ data sources

Example LIVV output from CESM-CISM (pre-ACME) simulation from Vizcaino et al. (2013). Left % ice sheet, right, % ice caps and glaciers

Coupled ESM, Ice Sheet Model Optimization

Existing initialization methods do not couple smoothly with realistic climate forcing fields (e.g., from models) Standard “spin-up” methods problematic: (i) ice dynamic response on decadal to century timescales is

strongly dependent on initial state (ii) spin-up of ~104-105 yr probably not practical for next-gen.

models (hi-res., costly dycore solves) Standard “optimization”-based methods also problematic; while providing good match to present-day state, generally lead to unphysical “shock” when coupling to SMB from climate model.

Greenland: Standard Optimization Method

Perego, Price, Stadler (JGR Earth Surface, 2014)

Optimization Problem

Perego, Price, Stadler (JGR Earth Surface, 2014)

Perego, Price, Stadler (JGR Earth Surface, 2014)

Greenland: Improved Optimization Method

Flux Divergence (standard optim.)

Flux Divergence (improved optim.)

Target SMB (RACMO)

Implied vs. Target SMB

Transient model calibration for glacier flow prediction in the Amundsen Embayment, West Antarctica

Daniel Goldberg1, Patrick Heimbach2,3, Ian Joughin4, Ben Smith4

1: University of Edinburgh, UK 2: University of Texas at Austin, Austin, TX, USA 3: Massachusetts Institute of Technology, Cambridge, MA, USA 4: APL, University of Washington, Seattle, WA, USA

Transient ice flow model calibration Part 1: Time-resolved observations, 2001-2011

Ice flow velocity of the Pope/Smith/Kohler glacier and Dodson/Crosson ice shelf system in the Amundsen Embayment, West Antarctica

Magnitude of velocity change between 2006 and 2010 from annual InSAR observations

Cumulative surface thinning 2001-2011 from annual surface elevation data

Goldberg et al. (in prep.)

Uncertainty Quantification (UQ) 1. Deterministic inversion of basal traction parameter using formal

optimization methods (obtain “MAP point” field) 2. Assignment of uncertainties to MAP point field (“posterior”):

• “KLE”: idealized, eigenvector expansion (reasonable init. approx.) • Hessian vector expansion assuming normal distribution (better) • Bayesian calibration for true posterior using Hessian vectors to

accelerate sample acceptance (ideal) 3. Forward propagation of uncertainties using selection from posterior

distribution

• Quantity of interest (QOI): future SLR • Ideally, “small” no. of modes important to QOI, reducing samples /

fwd model runs needed for robust distributions • Steps 2 and 3 may involve emulators • Large-scale workflow operational w/ KLE (Hessian in devel.)

Slide Number 1Slide Number 2Slide Number 3Slide Number 4Slide Number 5Slide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11POPSICLES*: Coupled Antarctic Ice sheet & Southern Ocean SimulationsPOPSICLES*: Coupled Antarctic Ice sheet & Southern Ocean SimulationsSlide Number 14Slide Number 15Slide Number 16Slide Number 17Slide Number 18Slide Number 19Land Ice Verification & Validation (LIVV) KitSlide Number 21Slide Number 22Slide Number 23Slide Number 24Slide Number 25Slide Number 26Slide Number 27Slide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32Slide Number 33Slide Number 34Slide Number 35Slide Number 36Slide Number 37Warmwater incursion – AmerySlide Number 39Land Ice Validation in the LIVV kitSlide Number 41Slide Number 42Slide Number 43Slide Number 44Slide Number 45Transient model calibration for glacier flow prediction in the Amundsen Embayment, West AntarcticaTransient ice flow model calibration Part 1:�Time-resolved observations, 2001-2011Slide Number 48