1. 1 Managed by UT-Battellefor the Department of EnergyWhat is a model and how do we use themto understand Earths climateDanny McKennaOak Ridge National LaboratoryThanks to: Dept. of Energy, Office of Science, BER program, ASCR Program,and SciDAC (and National Science Foundation for CESM support)Special Thanks to Dr Kate Evans for many slides

2. 2 Managed by UT-Battellefor the Department of EnergyCalving of the Holgate GlacierPresentation_name 3. 3 Managed by UT-Battellefor the Department of EnergyWhat will happen when the Ice melts?Will sea level rise?Will sea level stay the same?Will sea leave fall?What we need is a model to answer thatquestion.Lets do an experiment?Lets try a physical model.Need two volunteersPresentation_nameAudience Participation! 4. 4 Managed by UT-Battellefor the Department of EnergyDifferent types of modelsPhysical Model Will the water level rise?Mental Models Need a volunteer Can we predict where the ball will be?Simplified Models The sieve is a 3D object but can be treated as 2-D.Statistical Models Audience participation We can predict on average when something will happen.Presentation_name 5. 5 Managed by UT-Battellefor the Department of EnergyWhat is Climate Science?Weather follows thechanges in theatmosphere over timeClimate is the study ofthe statistics of theatmosphere over longtime periodsClimate is independentof the weather at agiven time 6. 6 Managed by UT-Battellefor the Department of EnergyDifferent types of modelsPhysical Model Will the water level riseMental Models We can predict where the ball will beSimplified Models The sieve is a 3D object but canStatistical Models We can predict on Average when something will happenPresentation_name 7. 7 Managed by UT-Battellefor the Department of EnergyNear term goals for understandingclimate change:Earths global temperatureHotCold 2010 2050Blue line: Observed global temperature distribution for 2010Orange line: Possible global temperature distributions in 2050What we are really asking is, what is the probability function of futuretemperature, precipitation, etc. ?Number of days 8. 8 Managed by UT-Battellefor the Department of EnergyNear term goals for understandingclimate change:Earths global temperatureHotCold 2010 2050Blue line: Observed global temperature distribution for 2010Orange line: Possible global temperature distributions in 2050What we are really asking is, what is the probability function of futuretemperature, precipitation, etc. ?Number of days 9. 9 Managed by UT-Battellefor the Department of EnergyNear term goals for understandingclimate change:Earths global temperatureHotCold 2010 2050Blue line: Observed global temperature distribution for 2010Orange line: Possible global temperature distributions in 2050What we are really asking is, what is the probability function of futuretemperature, precipitation, etc. ?Number of days 10. 10 Managed by UT-Battellefor the Department of EnergyWhat do we know fromobservations?Ice core samples taken from Arctic and Antarctic Ice Sheets. 11. 11 Managed by UT-Battellefor the Department of EnergyWhat do we know fromobservations?Carbon dioxide levels are rising due toanthropogenic emissions. 12. 12 Managed by UT-Battellefor the Department of EnergyGlobal warming is observed directly andindirectly in many components of the EarthSystem.Slowdown of the North Atlantic circulation 13. 13 Managed by UT-Battellefor the Department of EnergyDifferent types of modelsPhysical Model Will the water level riseMental Models We can predict where the ball will beSimplified Models The sieve is a 3D object but canStatistical Models We can predict on Average when something will happenTheoretical Models Why does something happenNumerical Model We want a very detail description of very complicatedprocessesPresentation_name 14. 14 Managed by UT-Battellefor the Department of EnergyThe Earth system is complicated.KEY: Energy Balance 15. 15 Managed by UT-Battellefor the Department of EnergyClimate Models are a collection ofcomponents, each with budgetsAtmosphereOceanSea IceLandLand IceSolar radiation,cloud physics BGC, overflowregions Ice age, aerosoldepositionUrban vs. rural,root depth Ice streams,subglaciallakes 16. 16 Managed by UT-Battellefor the Department of EnergyUnder the hood of each modelcomponent:1. Equations for the balance of mass,momentum, energy, and more!2. Discretized on a grid3. Solve for the unknown variables (e.g. velocity, temperature, waterquantities) at each time level and march forward 17. 17 Managed by UT-Battellefor the Department of EnergyMPAS-OceanUnstructured GridKinetic Energy snapshot, Global 30km resolution ocean, courtesy Todd Ringler 18. 18 Managed by UT-Battellefor the Department of EnergyCan also map the Earth with anIcosahedral gridNow its your turnPresentation_name 19. 19 Managed by UT-Battellefor the Department of EnergyModels provide us with possiblefuture climate scenariosWe run them many times to create ensembles, which give information aboutmeans and extremes 20. 20 Managed by UT-Battellefor the Department of EnergyCloud and Precipitation SimulationPresentation_name 21. 21 Managed by UT-Battellefor the Department of EnergyExample: Stott et al(2000) modeled theEarth system withoutthe observedincrease in CO2 andglobally avgdtemperatures alsodid not increase.High fidelitymodels canperformexperimentsto help usdetermine thelink.Is there a link between rising observedtemperatures and increased carbon dioxide? 22. 22 Managed by UT-Battellefor the Department of EnergyMultiple Emission SenariosScience Saturday 23. 23 Managed by UT-Battellefor the Department of EnergyThe coupled CESM run on the entireJaguar xk5 systemLayouts for regularand high-resolutionCESM configurationsNew xk6 systemAt ORNL TitanA modern super computer is made upof many many many ordinary processorsLike the ones you find in a smart phone 24. 24 Managed by UT-Battellefor the Department of EnergyOne WargorRadagasts Racing Rhosgobel RabbitsPresentation_name 25. 25 Managed by UT-Battellefor the Department of EnergySimulation Sea IceNeed to check if oursimulations are correct.Now it you turn to makelike a Rhosgobel Rabbit!Courtesy Julie McClean, Scripps 26. 26 Managed by UT-Battellefor the Department of EnergyMake like a processor and Integrate.Start plonking!Presentation_name 27. 27 Managed by UT-Battellefor the Department of Energy Presentation_nameWhat do the models predict?Sea Ice is going away more quickly than models predict! 28. 28 Managed by UT-Battellefor the Department of EnergySo there might be missing processes.Not all ice and snow is white.Melt ponds form on thesurfaceSometimes the Arctic isvery dirty!Presentation_name 29. 29 Managed by UT-Battellefor the Department of EnergyBut lets not get ahead of ourselves