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An Introduction to Climate Modeling Andrew Gettelman National Center for Atmospheric Research Boulder, Colorado USA Assistance from: J. J. Hack (NCAR) Gettelman: November 2006 A. Gettelman& J. Hack Real NCAR Scientists Gettelman: November 2006 Outline • What is Climate – Why is climate different from weather and forecasting • Hierarchy of atmospheric modeling strategies – Focus on 3D General Circulation models (GCMs) • Conceptual Framework for General Circulation Models • Parameterization of physical processes – concept of resolvable and unresolvable scales of motion – approaches rooted in budgets of conserved variables • Model Validation and Model Solutions Gettelman: November 2006 Question 1: What is Climate? A. B. C. D. Average/Expected ‘Weather’ The temperature & precipitation range Distribution of all possible weather Record of Extreme events Gettelman: November 2006 (1) What is Climate? Climate change and its manifestation in terms of weather (climate extremes) Gettelman: November 2006 Climate change and its manifestation in terms of weather (climate extremes) Gettelman: November 2006 Climate change and its manifestation in terms of weather (climate extremes) Gettelman: November 2006 Impacts of Climate Change Observed Change 1950-1997 Snowpack Temperature (- +) Mote et al 2005 (- +) Gettelman: November 2006 Observed Temperature Records IPCC, 3rd Assessment, Summary For Policymakers Gettelman: November 2006 Radiative Forcing (Wm-2) ‘Anthropogenic’ Changes 1000 1200 1400 1600 1800 2000 Gettelman: November 2006 ‘Anthropogenic’ Changes (2) 560ppmv CO2 ~2060 Gettelman: November 2006 Question 2 • What is the difference between Numerical Weather Prediction and Climate prediction? Gettelman: November 2006 Climate v. Numerical Weather Prediction • NWP: – Initial state is CRITICAL – Don’t really care about whole PDF, just probable phase space – Non-conservation of mass/energy to match observed state • Climate – – – – – Get rid of any dependence on initial state Conservation of mass & energy critical Want to know the PDF of all possible states Don’t really care where we are on the PDF Really want to know tails (extreme events) Gettelman: November 2006 Question 3 How can we predict Climate (50 yrs) if we can’t predict Weather (10 days)? Statistics! Gettelman: November 2006 Conceptual Framework for Modeling • Can’t resolve all scales, so have to represent them • Energy Balance / Reduced Models – Mean State of the System – Energy Budget, conservation, Radiative transfer • Dynamical Models – – – – – Finite element representation of system Fluid Dynamics on a rotating sphere Basic equations of motion Advection of mass, trace species Physical Parameterizations for moving energy • Scales: Cloud Resolving/Mesoscale/Regional/Global – Global= General Circulation Models (GCM’s) Gettelman: November 2006 Physical processes regulating climate Gettelman: November 2006 Earth System Model ‘Evolution’ 2000 2005 Gettelman: November 2006 Modeling the Atmospheric General Circulation Requires understanding of : – – – – – – atmospheric predictability/basic fluid dynamics physics/dynamics of phase change radiative transfer (aerosols, chemical constituents, etc.) interactions between the atmosphere and ocean (El Nino, etc.) solar physics (solar-terrestrial interactions, solar dynamics, etc.) impacts of anthropogenic and other biological activity Basic Process: – iterate finite element versions of dynamics on a rotating sphere – Incorporate representation of physical processes Gettelman: November 2006 Meteorological Primitive Equations • Applicable to wide scale of motions; > 1hour, >100km Gettelman: November 2006 Global Climate Model Physics Terms F, Q, and Sq represent physical processes • Equations of motion, F – turbulent transport, generation, and dissipation of momentum • Thermodynamic energy equation, Q – convective-scale transport of heat – convective-scale sources/sinks of heat (phase change) – radiative sources/sinks of heat • Water vapor mass continuity equation – convective-scale transport of water substance – convective-scale water sources/sinks (phase change) Gettelman: November 2006 Grid Discretizations Equations are distributed on a sphere • Different grid approaches: – – – – Rectilinear (lat-lon) Reduced grids ‘equal area grids’: icosahedral, cubed sphere Spectral transforms • Different numerical methods for solution: – Spectral Transforms – Finite element – Lagrangian (semi-lagrangian) • Vertical Discretization – – – – Terrain following (sigma) Pressure Isentropic Hybrid Sigma-pressure (most common) Gettelman: November 2006 Model Physical Parameterizations Physical processes breakdown: • Moist Processes – Moist convection, shallow convection, large scale condensation • Radiation and Clouds – Cloud parameterization, radiation • Surface Fluxes – Fluxes from land, ocean and sea ice (from data or models) • Turbulent mixing – Planetary boundary layer parameterization, vertical diffusion, gravity wave drag Gettelman: November 2006 Basic Logic in a GCM (Time-step Loop) For a grid of atmospheric columns: 1. ‘Dynamics’: Iterate Basic Equations Horizontal momentum, Thermodynamic energy, Mass conservation, Hydrostatic equilibrium, Water vapor mass conservation 2. Transport ‘constituents’ (water vapor, aerosol, etc) 3. Calculate forcing terms (“Physics”) for each column Clouds & Precipitation, Radiation, etc 4. Update dynamics fields with physics forcings 5. Gravity Waves, Diffusion (fastest last) 6. Next time step (repeat) Gettelman: November 2006 Physical Parameterization To close the governing equations, it is necessary to incorporate the effects of physical processes that occur on scales below the numerical truncation limit • Physical parameterization – express unresolved physical processes in terms of resolved processes – generally empirical techniques • Examples of parameterized physics – – – – – – – dry and moist convection cloud amount/cloud optical properties radiative transfer planetary boundary layer transports surface energy exchanges horizontal and vertical dissipation processes ... Gettelman: November 2006 F F Sq Sq Q Gettelman: November 2006 Atmospheric Energy Transport Synoptic-scale mechanisms • hurricanes http://www.earth.nasa.gov • extratropical storms Gettelman: November 2006 Process Models and Parameterization •Boundary Layer •Clouds Stratiform Convective •Microphysics Gettelman: November 2006 Radiation Gettelman: November 2006 Other Energy Budget Impacts From Clouds http://www.earth.nasa.gov Gettelman: November 2006 Energy Budget Impacts of Atmospheric Aerosol http://www.earth.nasa.gov Gettelman: November 2006 Scales of Atmospheric Motions/Processes Resolved Scales Global Models Future Global Models Cloud/Mesoscale/Turbulence Models Cloud Drops Microphysics CHEMISTRY Anthes et al. (1975) Gettelman: November 2006 Global Modeling and Horizontal Resolution Gettelman: November 2006 Examples of Global Model Resolution ~300km Typical Climate Application 50-100km Next Generation Climate Applications Gettelman: November 2006 High Resolution Art Global Model Simulation 100km x 100km Global Model Precipitation NCAR CCM3 run on Earth Simulator, Japan Gettelman: November 2006 Key Uncertainties for Climate (1): 1. Low Clouds over the ocean: Reflect Sunlight (cool) : Dominant Effect Trap heat (warm) More Clouds=Cooling Fewer Clouds=Warming Gettelman: November 2006 Marine Stratus: Low Clouds over the Ocean Gettelman: November 2006 Parameterization of Clouds Cloud amount (fraction) as simulated by 25 atmospheric GCMs Weare and Mokhov (1995) Gettelman: November 2006 Low Clouds Over the Ocean Change in low cloud with 2xCO2 2 Models: Changes are OPPOSITE! Gettelman: November 2006 Key Uncertainties for Climate (2): 2. High Clouds: Dominant effect is that they Trap heat (warm) More Clouds=Warming Fewer Clouds=Cooling Gettelman: November 2006 Key Uncertainties for Climate (3): 3. Water Vapor: largest greenhouse gas Increasing Temp=Increasing water Vapor (more greenhouse) Effect is expected to ‘amplify’ warming through a ‘feedback’ 1D Radiative-Convective Model: Higher humidity=>warmer surface Gettelman: November 2006 Summary • Global Climate Modeling – complex and evolving scientific problem – parameterization of physical processes pacing progress – observational limitations pacing process understanding • Parameterization of physical processes – opportunities to explore alternative formulations – exploit higher-order statistical relationships? – exploration of scale interactions using modeling and observation – high-resolution process modeling to supplement observations – e.g., identify optimal truncation strategies for capturing major scale interactions – better characterize statistical relationships between resolved and unresolved scales Gettelman: November 2006 How can we evaluate simulation quality? • Compare long term mean climatology – average mass, energy, and momentum balances – tells you where the physical approximations take you – but you don’t necessarily know how you get there! • Consider dominant modes of variability – provides the opportunity to evaluate climate sensitivity – response of the climate system to a specific forcing factor – exploit natural forcing factors to test model response – diurnal and seasonal cycles, El Niño Southern Oscillation (ENSO), solar variability Gettelman: November 2006 Comparison of Mean Simulation Properties 1 Simulated Precipitation Observed Precipitation Gettelman: November 2006 Comparison of Mean Simulation Properties 1 Simulated Precipitation Difference: Sim- Observed Gettelman: November 2006 Comparison of Mean Simulation Properties 2 Simulated Land Temp Observed Land Temp Gettelman: November 2006 Comparison of Mean Simulation Properties 2 Simulated Land Temp Difference: Sim- Observed Gettelman: November 2006 Testing AGCM Sensitivity Cloud (OLR) Anomalies and ENSO Observed Simulated Hack (1998) More Cloud Less Cloud Gettelman: November 2006 Turning The Crank: Results • • • • • Simulations of Atmospheric Model Coupled to Ocean Present Day Climate Simulations into the future with ‘Scenarios’ Different Models=Different ‘Sensitivity’ Potential Changes in Temp, Precip Gettelman: November 2006 Kicking the System: Radiative Forcing Gettelman: November 2006 Observations: 20th Century Warming Model Solutions with Human Forcing Gettelman: November 2006 Surface Temperature Variations 1000-2100 Gettelman: November 2006 CCSM Past: Last Millennium to 2100 Gettelman: November 2006 CCSM Future: Next 100+ years Atmospheric CO2 (input) Temperature (output) Gettelman: November 2006 CMIP 2001: Temperature and Precipitation Covey et al. (2001) Gettelman: November 2006 Impacts of Climate Change Observed Change 1950-1997 Snowpack Temperature (- +) Mote et al 2005 (- +) Gettelman: November 2006 The Future Regardless of Scale: Still need parameterizations for most things Goal: get interactions right (Mesoscale). Also extreme events Resolved Scales Global Models Future Global Models Cloud/Mesoscale/Turbulence Models Gettelman: November 2006 Example of State of the Art Global Model Simulation 10 X 10 km Global Model Precipitation NEIS AGCM for the Earth Simulator, Japan Gettelman: November 2006 Example of State of the Art Global Model Simulation 10 X 10 km Global Model Precipitation: Mid Latitude Cyclone over Japan Gettelman: November 2006 ‘Nested’ Models inside a GCM Another Approach: Nested Modeling (GCM forces Cloud or Mesoscale Model) NCAR NRCM: Outgoing Longwave Radiation, Jan1: 36km QuickTime™ and a PNG decompressor are needed to see this picture. Recall Scales: Still need parameterizations for most things (Radiation, Convection, Microphysics). Goal is to do small scale interactions better Gettelman: November 2006 The End Gettelman: November 2006