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Andrew Price and Andrew Yool Southampton e-Science Centre Southampton Oceanography Centre

Grid-ENabled Integrated Earth system model . www.genie.ac.uk. Andrew Price and Andrew Yool Southampton e-Science Centre Southampton Oceanography Centre. In order to predict the future ….

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Andrew Price and Andrew Yool Southampton e-Science Centre Southampton Oceanography Centre

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  1. Grid-ENabled Integrated Earth system model www.genie.ac.uk Andrew Price and Andrew Yool Southampton e-Science Centre Southampton Oceanography Centre

  2. In order to predict the future … The central scientific goal of the GENIE project is to study the forcing and feedbacks driving the glacial-interglacial cycles that dominated the Earth’s climate over the last 1 million years By better understanding the processes (physical and biogeochemical) which regulated these cycles in the past, we can be more confident about the predictions climate models make for the future

  3. ice-age ice-age ice-age ice-age • Orbital parameters affect incident radiation and climate • Biological and geological processes interact with, and feedback upon, the climate (via, for instance, CO2)

  4. How GENIE fits in GENIE intends to study these climate cycles by building a new Earth system Model of Intermediate Complexity (an EMIC) A key component for the success of GENIE will be its harnessing of new e-Science techniques For example : making use of Grid resources for large ensemble simulations; data management and analysis; adopting new programming techniques to facilitate model construction and Grid-based execution

  5. 3D atmosphere Atmospheric CO2 3D ice sheets 2D sea ice 2D land surface 3D ocean Land vegetation Ocean biogeochemistry Ocean sediments Atmosphere – Bristol’s IGCM3 Ocean – SOC’s GOLDSTEIN Land – Met. Office’s TRIFFID Land ice – Bristol’s GLIMMER Ocean biogeochemistry and sediments – UEA’s BioGEM The (final) GENIE model

  6. 2D atmosphere EMBM Atmospheric CO2 2D sea ice 3D ocean GOLDSTEIN Runoff The current GENIE model Simpler, but fast - ~1000 y per ~1 h CPU time on a PC

  7. c-GOLDSTEIN grid and bathymetry

  8. Challenges How to … ? … integrate state-of-the-art Earth system modules repeatably and flexibly Collaborative grid-based component programming … improve model composition tools Modern development environment Management of distributed compute and data resources … integrate large-scale hardware systems in a flexible way Data archives & visualisation of simulation runs … share, post-process, archive, re-use modeling results Inter-comparison of alternative modules/ models … test hypotheses about Earth System Modeling

  9. The underlying technology • Wrapping of component models • XML schema, Java, .NET, Web Services technology • Scripting environment • E.g. Matlab, Python (Jython) • Portal • Web-based • Repositories for components and data • Database system • Computational Grid infrastructure • Condor pools, Beowulf clusters, linked by middleware • Meta-scheduler • Monitors the Grid, runs model on best platform/s

  10. Globus Server Database Web Services Jython Location Service Authorisation Service Portal Metadata Archive & Query Services Data Management Client Grid Geodise Database Toolbox Jython Functions Java Client Code SOAP Apache Axis Matlab Functions Metadata Database CoG GridFTP XML Schema

  11. Computational Toolbox

  12. Jython Portal Local Resources (GT2) Southampton Condor Pool Imperial Condor Pool Grid Computation National Grid Service (GT2) Oxford Leeds WS Client Java CoG RAL Manchester SOAP GRAM GridFTP Flocked Condor Pools WS

  13. Scripting a Tuning Study MATLAB function RMS_Error = cgoldstein(params) optimum = fminsearch( … @cgoldstein, params, … ) GENIE Database gd_query(results) Grid Resource gd_putfile(CG binary) CG binary gd_putfile(config file) config file gd_jobsubmit(RSL) gd_getfile(results file) results file gd_archive(results) return RMS_Error

  14. Matlab Optimisation Toolbox % ************************ % Specify a starting point % ************************ parameters = [ 0.5 ]; % ************************ % Perform the minimisation % ************************ optimum = fminsearch( @cgoldstein_1D, parameters, optimisation_parameters ) % ************************ % Specify a starting point % ************************ parameters = [ 420 5000000 ]; % ************************ % Perform the minimisation % ************************ optimum = fminsearch( @cgoldstein_2D, parameters, optimisation_parameters )

  15. OptionsMatlab • Matlab interface to the Options design exploration system • http://www.soton.ac.uk/~ajk/options/welcome.html • State of the art design search and optimisation algorithms • Design of Experiment methods • Response Surface Modelling • Over 30 search methods including: Adaptive Random Search (ADRANS), Powell's Direct Search (PDS), Simplex Method (SIMP), Genetic Algorithm (GA), Simulated Annealing (SA), Evolutionary Programming (EP)

  16. OptionsMatlab Available Optimisation Methods: 1.1 for OPTIVAR routine ADRANS 1.2 for OPTIVAR routine DAVID 1.3 for OPTIVAR routine FLETCH 1.4 for OPTIVAR routine JO 1.5 for OPTIVAR routine PDS 1.6 for OPTIVAR routine SEEK 1.7 for OPTIVAR routine SIMPLX 1.8 for OPTIVAR routine APPROX 1.9 for OPTIVAR routine RANDOM 2.1 for user specified routine OPTUM1 2.2 for user specified routine OPTUM2 2.3 for NAG routine E04UCF 2.4 for bit climbing 2.5 for dynamic hill climbing 2.6 for population based incremental learning 2.7 for numerical recipes routines 2.8 for design of experiment based routines 3.11 for Schwefel library Fibonacci search 3.12 for Schwefel library Golden section search 3.13 for Schwefel library Lagrange interval search 3.2 for Schwefel library Hooke and Jeeves search 3.3 for Schwefel library Rosenbrock search 3.41 for Schwefel library DSCG search 3.42 for Schwefel library DSCP search 3.5 for Schwefel library Powell search 3.6 for Schwefel library DFPS search 3.7 for Schwefel library Simplexsearch 3.8 for Schwefel library Complexsearch 3.91 for Schwefel library two­membered evolution strategy 3.92 for Schwefel library multi­membered evolution strategy 4 for genetic algorithm search 5 for simulated annealing 6 for evolutionary programming 7 for evolution strategy • >> OptionsInput = createOptionsStructure(4.0) • DNULL: -777 • OLEVEL: 2 • MAXJOBS: 100 • NVRS: 12 • VNAM: {'SCLTAU' 'INVDRAG' 'OCNHORZDF' ... } • LVARS: [1.3000 2.0000 2500 ... ] • UVARS: [2.1000 4.8000 5700 ... ] • VARS: [1.7000 3.4000 4100 ... ] • ONAM: 'RMSERROR' • OMETHD: 4.0000 • DIRCTN: -1 • NITERS: 1000 • OPTFUN: 'cgoldstein_12D' • OPTJOB: 'optjobparallel' • GA_NPOP: 100 • >> OptionsOutput = OptionsMatlab(OptionsInput);

  17. Twin-Test Experiment Attempt to recover a known state of the model using a Genetic Algorithm. Performed 10 generations of a 100 member population. Then applied a local Simplex search of the best candidate. Population too small to find optimal solution – suitable for finding local minima

  18. Model Sea Surface Temperatures Model Air Temperatures NCEP Sea Surface Temperatures NCEP Air Temperatures Tuning using Observational Data Apply the same method but calculate the RMS error statistic by comparing the model state with NCEP observational data.

  19. Grid Computation Optimisation Tools Data Management System Optimization toolbox OPTIONS ICENI Web based portal Portal Scripting environments Jython Geodise Toolboxes e-Science Summary Environmental Scientist Application Middleware Grid

  20. Fusing science and e-science One successful use of e-science so far has been a large-scale study of the bistability of the ocean’s thermohaline circulation (THC) The THC is responsible for large-scale distribution of heat and salt throughout the ocean, and has climatic consequences such as the warming of western Europe (via the “Gulf Stream”) Changes to the budgets of heat and freshwater caused by global warming may have important consequences for its future behaviour

  21. After W. S. Broecker, modified by E. Maier-Reimer The Thermohaline Circulation

  22. Consequences? Some of the less likely consequences of a shut-down of the THC … [‘The Day After Tomorrow’] While western Europe and north America may cool, globally the earth will warm, and a new ice-age is unlikely

  23. Shutdown in the model THC “On” (top left) THC “Off” (bottom left) Temperature consequences (below)

  24. Study design • We identified two parameters affecting the freshwater budget of the Atlantic … • Atlantic-to-Pacific zonal transport • Atmospheric diffusivity (meridional transport) • Using a portal to a Condor pool, simulations using different values of these parameters were varied • The results of these simulations were then used to feed new simulations to examine “classic bistability”

  25. Atlantic drier Atlantic wetter Atmosphere more diffusive Atmosphere less diffusive 961 member ensemble

  26. THC “off” [bad] THC “on” [good] The initial ensemble

  27. 9 x 961member ensemble

  28. Identifying bistable region

  29. Scientific and e-Scientific conclusions The work has allowed us to determine the region of parameter space over which the model THC is bistable With this information, we have been able to study climate feedbacks in more detail, and work out the minimum duration of Greenland icesheet melting that can shutdown the THC (88 years!) The use of e-Science allowed us unprecedented total simulation duration (42 million years) with time-efficiency of ~1 order of magnitude

  30. Coordinator: Tim Lenton –CEH Edinburgh Principal investigator: Paul Valdes – Bristol Research Team and Collaborators: James Annan –FRSGC, Japan Chris Brockwell – UEA Norwich David Cameron –CEH Edinburgh Peter Cox –Hadley Centre (UKMO) Neil Edwards –Bern, Switzerland Murtaza Gulamali– London e-Science Centre Julia Hargreaves –FRSGC, Japan Phil Harris – CEH Wallingford Dan Lunt – Bristol Bob Marsh–SOC Andrew Price – Southampton e-Science Centre Andy Ridgwell –UBC, Canada Ian Rutt – Bristol Gang Xue – Southampton e-Science Centre Andrew Yool – SOC Management Team: Melvin Cannell – CEH Edinburgh Trevor Cooper-Chadwick – Southampton e-Sci. Centre Simon Cox – Southampton e-Sci. Centre John Darlington – London e-Science Centre Richard Harding – CEH Wallingford Steven Newhouse – London e-Science Centre Tony Payne – Bristol John Shepherd – SOC Andrew Watson – UEA Norwich The GENIE Team

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