1. Coupled ocean-atmosphere modeling on horizontally icosahedral and vertically hybrid- isentropic/isopycnic grids Rainer Bleck, Shan Sun, Haiqin Li, Stan Benjamin NOAA Earth System Research Laboratory Boulder, Colorado April 2016

2. Design goals: (some reached, some pending) Global domain No grid mismatch at ocean-atmo interface Same horizontal resolution as medium-range NWP models (=> eddy-permitting ocean) Envisioned forecast time range: 1 day - 1 month Intrinsic model climate & variability similar to observed – Deemed important for reducing biases even in simulations as short as 1 month. – Key variability indicators: ENSO, MJO, wintertime stratospheric warming, tropospheric blocking freq. – Steadiness of meridional overturning circulations (Hadley, Brewer-Dobson, thermohaline circ.)

3. The coupled model Atmosphere: FIM (F=flow-following, I=icosahedral) – Horizontal grid: icosahedral; resolution choices 240, 120, 60, 30, 15 km (10 4 - 2.5x10 6 grid cells) – Vertical grid: isentropic aloft, reverting to terrain-following near the ground. Adaptive, 64 layers. Ocean: i-HYCOM (icosahedral version of Hybrid Coordinate Ocean Model) – Horizontal grid: same as atmosphere – Vertical grid: isopycnic at depth, reverting to p near the sea surface. Adaptive, 32 layers. Flux Coupler: a non-issue (matching grid cells!) – important: no coastline ambiguities – Atmosphere in charge of computing surface fluxes – No flux adjustment or post-mortem bias removal

4. Additional model details -- atmosphere Designed and used at NOAA-ESRL as medium-range NWP model (http://fim.noaa.gov) Hydrostatic. Arakawa-A grid (all variables carried in grid cell center). Finite-volume numerics (2 nd order). Column physics imported from GFS (NOAA-NCEP). Rudimentary river-routing scheme – Essential for closing freshwater cycle (=> ocean salinity). Indirect addressing (table lookup) for lateral neighbor interactions. Efficiently parallelized.

5. Additional model details -- ocean Essentially HYCOM, but rewritten for icosahedral mesh. Vertical coordinate: potential density (  pot ) – referenced to 1km depth. d  pot /dt=0 in adiab.flow (i.e. incompressible w.r.t.  pot ) Prognostic variables: T, S, u, v, dp (layer thickness). Basic 1-layer ice model, free drift. Simplified barotropic-baroclinic mode splitting – 3D equations for u,v,dp solved on “barotropic” time steps. – Bonus: no need to express freshwater fluxes as virtual salt fluxes. Software engineering innovations (parallelization etc.) inherited from FIM. Tightly coupled to atmosphere on “baroclinic” time steps.

6. 1. Short-range application: Hurricane Chosen example: Hurricane Sandy, October 2012 Initial state (ocean and atmosphere) from NOAA-NCEP Climate Forecast System v.2 Model resolution ~15km. Ocean is eddy-permitting, but no realistic eddies in initial state.

7. Hurrican e Sandy Wind stress (left) and surface currents (right) off the U.S. east coast in 5.5 day forecast starting at 12GMT, 23 Oct 2012. Note: ocean response dominated by inertial waves. No hurricane-induced vortex. Sample results 1 triangle=0.5Pa1 triangle=25cm/s

8. Surface temperature (left) and salinity (right) in 5.5 day forecast starting at 12GMT, 23 Oct 2012. Note: SST appears to be mainly controlled by cloudiness/precip, not wind stress. Sample results

9. Zonal vertical section at 37 0 N showing temperature and coordinate layer interfaces. Top section is blowup of bottom section. Note shallowness of srfc. cold layer. Gulf Stream front 1 st layer thickness: 2m

10. 2. Medium-range application: Does intrinsic model variability change over time?

11. Frequency of blocking episodes (Pelly-Hoskins) in 600 30-day coupled simulations spread over years 1999-2010. Mesh size 30km

12. 3. Long-range application: Does the model support the leading coupled mode in the ocean- atmosphere system?

13. Top: Nino3 index from final 50-yr period of coupled simulations. Mesh size 120km. Bottom: observed index. Grell-Freitas cloud parameterization: mod 3 Grell-Freitas cloud parameterization: mod 2

14. Is model drift due to shortcomings in convective cloud/radiation parameterization tolerable?

15. (show time series of global/ocean- only net heat fluxes, explain measures taken to reduce drift)

16. Does the model maintain a reasonably steady Atlantic meridional overturning circulation?

17. Mass flux diagnosis is tedious on icosahedral grids…

18. Time series of oceanic mass flux (Atlantic overturning, Drake Passage, Lombok Strait) in multi-decadal coupled simulation. Mesh size 120km. Note low AMOC (25% of observed strength) Units: 1 Sverdrup = 10 6 m 3 /s

19. Which component of the coupled model is responsible for the low AMOC? Shown here are two ocean-only simulations, forced by observed atmosphere (CORE-II). AMOC is acceptable in this case. low/high vertical diffusivity

20. Salinity trends in “concentration basins” result from precip/evaporation imbalances. Are they tolerable?

21. Sea surface temperature in concentration basins Year 20 Year 10 Year 0

22. Sea surface salinity in concentration basins Year 0 Year 10 Year 20 Capping of salinity required here, other basins OK.

23. Conclusions and Outlook Converting a weather model into a GCM is a challenge, especially when coupling it to an ocean – Surface fluxes “thrown away” in atmo-only applications suddenly become important – Column physics codes written for NWP are not necessarily conservative (energy, freshwater cycle) – Convective cloud parameterization requires intensive tuning to achieve zero net radiation balance Simulation of ENSO variability moderately successful. Subtle decrease in blocking frequency with forecast duration on monthly time scale. Model maintains weak AMOC on decadal time scales. By using a unique combination of nonstandard 3D grids (icosahedral + isentropic/isopycnic) we strive to contribute to the genetic diversity in coupled multi- model ensemble applications.