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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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slide1

Grid-ENabled Integrated

Earth system model

www.genie.ac.uk

Andrew Price and Andrew Yool

Southampton e-Science Centre

Southampton Oceanography Centre

slide2

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

slide3

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)
slide4

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

slide5

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

slide6

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

challenges
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

the underlying technology
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
data management

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

grid computation

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

scripting a tuning study
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

matlab optimisation toolbox
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 )

optionsmatlab
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)

optionsmatlab1
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);
twin test experiment
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

tuning using observational data

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.

e science summary

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

slide20

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

slide22

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

slide23

Shutdown in the model

THC “On” (top left)

THC “Off” (bottom left)

Temperature consequences

(below)

slide24

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”
slide25

Atlantic drier

Atlantic wetter

Atmosphere

more diffusive

Atmosphere

less diffusive

961 member ensemble

slide26

THC “off”

[bad]

THC “on”

[good]

The initial ensemble

slide29

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

the genie team
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