Grid for Coupled Ensemble Prediction (GCEP)

1. 3. Ensemble Predictions on a Grid The Hadley Centre’s full HadCM3 climate model can now be run on PC clusters, where 64-bit processing is required. Each ensemble of around 30 such model runs will take a few days spread across PC clusters at Reading, British Antarctic Survey, Rutherford and the NGS, and will generate about 200GB of data which will be output directly to a central data store in the Reading e-Science Centre. Compute grid All transactions certificated Reading BAS RAL NGS Schedule job on appropriate resource Scheduler Write output data directly Submit jobs, monitor progress via web site Retrieve data via WS Geospatial data store (Reading) Users 2. Operational Ensemble Prediction: El Niño example Seasonal forecasts are currently made operationally based on the predictability of El Niño events. The top figure shows the TAO array in the Tropical Pacific where the top 0.5km of ocean temperatures are continuously measured. These data are collected via satellite and assimilated into climate models. The assimilation methods used at the European Centre for Medium Range Weather Forecasts (ECMWF) are based on methods developed at Reading (1,2). • The figure below shows forecasts of mean surface temperature anomaly in the “Niño3” region for 6 months ahead based on the climate models. The blue line showed what really happened in the big El Niño event of 1997. Ensemble forecasts of other quantities can be made, with more or less success. GCEP will examine predictions of: • Ocean thermohaline strength • Poleward heat transport • Sea ice extent • Niño3, NAO, monsoon indices • Precipitation • Snow cover • Storm statistics Grid for Coupled Ensemble Prediction (GCEP) Keith Haines, William Connolley, Rowan Sutton, Alan Iwi University of Reading, British Antarctic Survey, CCLRC 1. Climate Predictions Ensemble climate prediction is a revolutionary technology that uses knowledge of the current climate state to forecast its subsequent evolution months, years and even decades ahead. The predictions are obtained by integrating coupled climate models. Ensembles are used to sample known uncertainties, in particular uncertainty in knowledge of initial conditions, which can be set by data assimilation methods. Most success has been achieved in seasonal forecasting of the El Niño phenomenon, where knowledge of the ocean initial conditions in the tropical Pacific is critical. More recent work has examined the potential for decadal climate forecasts, for which knowledge of initial conditions throughout the world oceans is important. The figure below shows predictions (hindcasts) of global average temperature in 3-month periods based on a 2-year lead time using the Hadley Centre’s HadCM3 climate model. There is clearly some skill. GCEP will seek to predict other quantities and to use a wider range of data assimilation methods to initialise the predictions, including ice and land surface conditions. 4. Running HadCM3 on PC Clusters: Timings and Drift The Hadley Centre’s full HadCM3 model will run on PC clusters but the results are sensitive to the hardware and software. The figure below shows global mean surface temperature for 3 multi-decadal runs on different hardware-software combinations. The grey shading shows the model 2σ spread from the Hadley Centre run on the supercomputer. The drift from the Opteron-Portland compiler combination (in red) is clearly unacceptable. Timings: HadCM3, on athlons (ie, AMD chips, blue above), ½ yr/day, running at 64-bit on 2 processors (runs twice as fast with 32-bit but drifts). On opterons (ie, 64-bit native) runs at a little more than 2yr/day using the GNU g95 compiler, black line above. 5. Ensemble Job Management and Scheduling A Climate Ensemble Toolkit exists, which uses a single HadCM3 script as the basis for creating an ensemble, with specified parameters such as initial conditions or forcings varied between members. The ensemble is currently submitted as a single job to HPC resources. We will develop tools which allow also for ensembles to be split across multiple compute clusters. Certificating will be needed for submission across multiple resources and additional parameters to allow collation of output back to a single geospatial datastore. We will review existing specifications and available tools to achieve this. 6. Assimilation of Data from Oceans, Ice and Land The GCEPS project will carry out climate predictions based on the sensitivity of the climate model to initial conditions of the Ocean, of the Ice distribution and of soil moisture and snow cover. If any aspect of climate can be predicted out to 10 years ahead it is these slowly varying properties that will contain the necessary information. The GCEPS team has expertise in modifying the initial ocean conditions as described in box 2 for El Nino forecasting. In addition new methods will be tested to control the sea ice distributions and the land surface properties. The figure above shows sea ice distributions over the north and south poles from observations (left); from the HadCM3 climate model (middle); and from the new HadGEM1 model currently being developed at the Hadley Centre (right). The ice distributions can be altered by data assimilation and the impact of doing so on ensemble climate predictability has never before been tested. These and many other experiments will be performed during the GCEPS project in collaboration with colleagues from the Hadley Centre.