Willem A. Landman Asmerom Beraki Francois Engelbrecht Stephanie Landman Supercomputing for weather and climate modelling: convenience or necessity

11 June 2009 Cut-off low over central SA

New Multi-Model Short-Range Ensemble System (precipitation) 24hour totals for: –day1 (14 members) and –day2 (6 members) Unified Model (different configurations and resolutions) –10 members: 12km (xaana/ng/nj) 15km (xaaha/hc) WRF model –2 members: 12km Non-hydrostatic mesoscale core –2 members: 15km Advanced Research WRF core In Test Phase –1º NCEP model (15 members)

Probability Maps Day 1Day2 >5mm >10mm

Are AGCMs useful? “trend” hits=27/33=82% The best model is the ECHAM4.5 AGCM

Uncertainty in initial atmospheric state Uncertainty in future atmospheric state Ensemble forecast from model 1 explores part of the future uncertainty Ensemble forecast from model 2, run from (even the) same set of initial states, typically explores additional future uncertainties Uncertainty in SST state ??? Will uncertainties in forcing SST fields better estimate the probability of each outcome? Ensemble forecast from model 3, run from different ocean states may explore additional future uncertainties

Shaded areas: forecast uncertainty as reflected by forecast ensemble; black line: ensemble mean; red line: model climatology

The multi-models: Skill Differences 3 AGCM configurations: –Forced with ca_sst, ECMWFem and ECMWFsc 2 AGCM configurations –Forced with ECMWFem and ECMWFsc Positive values where MM is better than best single model (ECMWFem) By considering (some of) the certainties in forcing SST fields the probability of forecast outcomes is better estimated (over some areas)

Coupled model on CHPC… ECHAM4.5-MOM3 on CHPC using 8 processors, i.e., 4 processors for each model. Simulation for one month (May 1982) Time needed to fish the coupled run was sec (1.49 hrs) Similar run for uncoupled ECHAM4.5 using 4 processors took sec (19 min) Seems that coupled run is slower than expected – usually double the time is assumed for coupled run (here, just one case) Total rainfall (mm; shaded) and 500 hPa geopotential height (m; contour)

Resolution over southern Africa is about 60 km ARC-CSIR-CHPC-UP-CSIRO Meraka Institute, C4-cluster High-resolution regional climate modelling Exp1: 60 km resolution over southern Africa Forcing (wind nudging) from NCEP reanalysis data Period simulated Time step 20 min Data set size: 300 GB High-resolution panel: 40 S to 10 S 10 E to 40 E

Resolution over Australia is about 60 km High-resolution regional climate modelling: Exp2: 8 km resolution over the southwestern Cape Forcing (wind nudging) from 60 km simulation Period simulated Time step 3 min Data set size: 600 GB High-resolution panel: 35.5 S to 31.5 S 17.5 E to 21.5 E ARC-CSIR-CHPC-UP-CSIRO Meraka Institute, C4-cluster

Resolution over Australia is about 60 km Forcing (wind nudging) from 8 km simulation Period simulated Time step 30 sek Data set size: 1.8 TB High-resolution regional climate modelling: Exp3: 1 km resolution over a portion of the southwestern Cape High-resolution panel: S to S E to E ARC-CSIR-CHPC-UP-CSIRO Meraka Institute, C4-cluster

Resolution over Australia is about 60 km High-resolution regional climate modelling: Exp4: 200 m resolution over the Stellenbosch region Forcing (wind nudging) from 1 km simulation Period simulated Time step 6 sek Data set size: 1.8 TB High-resolution panel: S to S E to E ARC-CSIR-CHPC-UP-CSIRO Meraka Institute, C4-cluster

A few machines...

System Configuration The ES is a highly parallel vector supercomputer system of the distributed-memory type, and consisted of 160 processor nodes connected by Fat-Tree Network. Each Processor node is a system with a shared memory, consisting of 8 vector-type arithmetic processors, a 128-GB main memory system. The peak performance of each Arithmetic processors is 102.4Gflops. The ES as a whole thus consists of 1280 arithmetic processors with 20 TB of main memory and the theoretical performance of 131Tflops.

Global Atmosphere Simulation with MSSG-A 03-08AUG2003, Horizontal resolution: 1.9 km, 32 vertical layers

The Northern Pacific Ocean Horizontal Resolution: 2.78km, Vertical Layers: 40 layers, 15 years integration Boundary condition: monthly data from NCAR monthly data from OFES simulation( 10km global simulation) Ocean Component of Multi-Scale Simulator for the Geoenvironment: MSSG-O

Conclusion High-resolution, large ensemble, many models, various configuration –All require dedicated high-speed computer for Operational forecasts/projections Research to better understand the weather/climate system