High-Resolution Ocean and Ice Models for Forecasting and Climate Projection Albert J. Semtner Naval Postgraduate School, Monterey, CA 93943, USA This talk describes ocean and ice models that are capable of reproducing the observed mean states and variability of the global ocean and its sea ice. It is necessary to use horizontal grid spacing less than 10 km for both ocean and ice, as indicated by a comparison of model statistics with the observational statistics. As a result, the most advanced computing systems are required to run the models. Systems that deliver multiple teraflops of sustained performance can be used to project climatic conditions out for many centuries, with highly realistic ocean and ice interactions in terms of spatial and temporal evolution. On sub-teraflop systems, ensemble forecasting of ocean and ice for optimal ship routing and other marine applications can be done for time scales of months. Specific results will be shown from running the Parallel Ocean Program and the Sea Ice Model developed at Los Alamos Laboratory. The output from a number of simulations conducted by investigators at the Naval Postgraduate School and their collaborators will be evaluated against observations. The simulations were conducted on large IBM, NEC, and Cray machines. Ongoing research to continue improving the models will be described.

Navy Prediction Vision A high-resolution global coupled air/ocean/ice prediction system (via NOGAPS - Navy Operational Global Atmospheric Prediction System, plus suitable ocean and ice models not yet finalized) Very-high resolution regional coupled models nested into the global system at strategic locations.(via COAMPS - Coupled Ocean Atmosphere Mesoscale Prediction System) A coupled Pan-Arctic ice-ocean model that provides operational forecasts of sea ice and ocean conditions. (via PIPS - Polar Ice Prediction System)

Parallel Ocean Program (POP) Primitive equation z-level ocean model with active thermodynamics A modern descendent of the “GFDL Bryan-Cox ocean model”, developed and supported by a group at Los Alamos National Laboratory 3-D “baroclinic” variables are explicitly timestepped; however, 2-D equations for the vertically averaged “barotropic” flow and free surface elevation are implicitly treated (leading to global sums in solving for the latter). Fortran90 Designed to run on multi-processor machines using domain decomposition in latitude and longitude MPI for inter-processor communications on distributed memory machines and SHMEM on shared memory machines

0.1  40-level global POP SST SSH McClean (NPS) and Maltrud (LANL)

Grid size of an earlier global simulation by Maltrud, Smith, Semtner, and Malone,1998) following Grid size for this study

North Atlantic 0.1 degree simulation with Navy wind forcing

High resolution is needed for proper jet separation

Principal standard deviation ellipses (cm/sec) from 2  x2  binned North Atlantic surface drifter velocity data (green), 0.28  (blue), and 0.1  POP (red) velocity output for High resolution is needed for correct mean path of Gulf Stream.

Times series of SSHA (cm) from NA 0.1  POP (red) and tide gauges for Courtesy, Robin Tokmakian

Fully Global Displaced North Pole Grid Pole is rotated into Hudson Bay to avoid polar singularity. In the northern hemisphere mid-latitudes the highest horizontal resolution is off east and west coasts of the U.S Average grid spacing over all ocean points is ~6.5 km.

Performance Statistics One year of simulation requires 8 DAYS on 500 IBM SP3 processors at DOD’s NAVO site (McClean and Maltrud). One year of simulation requires 8 HOURS on 960 NEC SX6 processors of the Earth Simulator (F. Bryan and CRIEPI collaborators)

Instantaneous strong currents

Near Surface Speed May have to resolve the complex transfers of heat across the Circumpolar Current toward sea ice and ice shelves

Sea Surface Height May need to resolve eddy transport of heat northward in South Atlantic part of the global “Conveyor Belt”

Sea surface height variability (cm)

High resolution is needed for Kuroshio/Oyashio dynamics.

Ongoing POP Improvements (underway at LANL) More scalable barotropic solver Hybrid OpenMP/MPI with better cache performance Hybrid vertical coordinate Partial bottom cells Better subgrid closure schemes Bottom boundary layer Parallel optimization Numerical accuracy Physics

Polar Ice Prediction System (PIP3.0) Ocean model: POP (1280x720x45) has ~9 km near- constant grid spacing over the pan-Arctic region Finite-difference sea-ice model equations for momentum, compactness, and thickness (same grid) Viscous-plastic sea-ice rheology and simple thermodynamic heat transfer through ice Developed and integrated as a DOD “grand challenge” project on the Cray T3E at the Arctic Region Supercomputing Center W. Maslowski et al.

Sea Surface Height (cm) - August/Year13 PIPS3.0 Spinup

LABRADOR SEA EDDY KINETIC ENERGY(cm 2 /s 2 ) 18 KM VS 9 KM, August 1980 Snapshot LABRADOR SEA EDDY KINETIC ENERGY(cm 2 /s 2 ) 18 KM VS 9 KM, August 1980 Snapshot 0-43 m (levels 1-2)0-45 m (levels 1-7) Order of magnitude increase in EKE from 18 km to 9 km 9 km values approaching observed ones (not shown) 9 km values approaching observed ones (not shown) Eddy activity preconditions deep convection here and in the Greenland/Norwegian Sea => high resolution needed Eddy activity preconditions deep convection here and in the Greenland/Norwegian Sea => high resolution needed

Sea ice concentration (%) in (a) winter and (b) summer

(a) (b) (d) (c) A snapshot of (a) ice area and drift, (b) divergence, (c) shear, and (d) vorticity- Spinup 08/01/79 Inclusion of leads, polynyas, and ridges requires high resolution.

Los Alamos Sea Ice Model:CICE Hunke, Bitz, Lipscomb Multicategory ice thickness, presently 4 ice layers plus snow Elastic-Viscous-Plastic dynamics (for better parallelism) 2-D re-mapping scheme for improved horizontal ice transport Ridging parameterization for updating the thickness distribution

Multi-category Sea Ice Concentrations (%) – September 1, 1982 (Maslowski and Lipscomb) Allows proper treatment of the non-linear dependence of both dynamics and thermodynamics on ice thickness distribution. Total (%) and Drft (m/s) Category I ( m)Category II ( m) Category III ( m)Category IV ( m)Category V ( m)

Conclusions High-resolution models of the Global Ocean, the Arctic Ocean and sea ice: - correctly represent critical aspects of the mean features and their eddy dynamics, many of which may not be representable in coarse-grid models - have the potential to make predictions on short to long time scales when used with atmospheric models - require large blocks of supercomputer time, especially for extended integrations

The End