Gettelman: November 2006 An Introduction to Climate Modeling Andrew Gettelman National Center for Atmospheric Research Boulder, Colorado USA Assistance from: J. J. Hack (NCAR)

Gettelman: November 2006 Climate Modeling A. Gettelman& J. Hack Real NCAR Scientists Science, Statistics, Parameterization, Results Its all in here! Simulate the future on your desktop

Gettelman: November 2006 Outline What is ClimateWhat is Climate –Why is climate different from weather and forecasting Hierarchy of atmospheric modeling strategiesHierarchy of atmospheric modeling strategies –Focus on 3D General Circulation models (GCMs) Conceptual Framework for General Circulation ModelsConceptual Framework for General Circulation Models Parameterization of physical processesParameterization of physical processes –concept of resolvable and unresolvable scales of motion –approaches rooted in budgets of conserved variables Model Validation and Model SolutionsModel Validation and Model Solutions

Gettelman: November 2006 Question 1: What is Climate? A.Average/Expected Weather B.The temperature & precipitation range C.Distribution of all possible weather D.Record of Extreme events

Gettelman: November 2006 Climate change and its manifestation in terms of weather (climate extremes) (1) What is Climate?

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 Mote et al 2005 Observed Change SnowpackTemperature (- +)(- +) (- +)

Gettelman: November 2006 Observed Temperature Records IPCC, 3rd Assessment, Summary For Policymakers

Gettelman: November 2006 Anthropogenic Changes Radiative Forcing (Wm -2 )

Gettelman: November 2006 Anthropogenic Changes (2) 560ppmv CO 2 ~2060

Gettelman: November 2006 Question 2 What is the difference between Numerical Weather Prediction and Climate prediction?What is the difference between Numerical Weather Prediction and Climate prediction?

Gettelman: November 2006 Climate v. Numerical Weather Prediction NWP:NWP: –Initial state is CRITICAL –Dont really care about whole PDF, just probable phase space –Non-conservation of mass/energy to match observed state ClimateClimate –Get rid of any dependence on initial state –Conservation of mass & energy critical –Want to know the PDF of all possible states –Dont 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 cant predict Weather (10 days)? Statistics!

Gettelman: November 2006 Conceptual Framework for Modeling Cant resolve all scales, so have to represent themCant resolve all scales, so have to represent them Energy Balance / Reduced ModelsEnergy Balance / Reduced Models –Mean State of the System –Energy Budget, conservation, Radiative transfer Dynamical ModelsDynamical 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/GlobalScales: Cloud Resolving/Mesoscale/Regional/Global –Global= General Circulation Models (GCMs)

Gettelman: November 2006 Physical processes regulating climate

Gettelman: November Earth System Model Evolution

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: 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, >100kmApplicable to wide scale of motions; > 1hour, >100km

Gettelman: November 2006 Global Climate Model Physics Terms F, Q, and S q represent physical processes Equations of motion, FEquations of motion, F –turbulent transport, generation, and dissipation of momentum Thermodynamic energy equation, QThermodynamic 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 equationWater 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:Different grid approaches: –Rectilinear (lat-lon) –Reduced grids –equal area grids: icosahedral, cubed sphere –Spectral transforms Different numerical methods for solution:Different numerical methods for solution: –Spectral Transforms –Finite element –Lagrangian (semi-lagrangian) Vertical DiscretizationVertical Discretization –Terrain following (sigma) –Pressure –Isentropic –Hybrid Sigma-pressure (most common)

Gettelman: November 2006 Model Physical Parameterizations Physical processes breakdown: Moist ProcessesMoist Processes –Moist convection, shallow convection, large scale condensation Radiation and CloudsRadiation and Clouds –Cloud parameterization, radiation Surface FluxesSurface Fluxes –Fluxes from land, ocean and sea ice (from data or models) Turbulent mixingTurbulent 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 Physical parameterizationPhysical parameterization –express unresolved physical processes in terms of resolved processes –generally empirical techniques Examples of parameterized physicsExamples 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 –... To close the governing equations, it is necessary to incorporate the effects of physical processes that occur on scales below the numerical truncation limit

Gettelman: November 2006 Q F F SqSq SqSq

Atmospheric Energy Transport Synoptic-scale mechanisms hurricanes extratropical storms

Gettelman: November 2006 Process Models and Parameterization Boundary Layer Clouds Stratiform Convective Microphysics

Gettelman: November 2006Radiation

Other Energy Budget Impacts From Clouds

Gettelman: November 2006 Energy Budget Impacts of Atmospheric Aerosol

Gettelman: November 2006 Scales of Atmospheric Motions/Processes Anthes et al. (1975) Resolved Scales Global Models Future Global Models Cloud/Mesoscale/Turbulence Models Cloud Drops MicrophysicsCHEMISTRY

Gettelman: November 2006 Global Modeling and Horizontal Resolution

Gettelman: November 2006 Examples of Global Model Resolution Typical Climate Application Next Generation Climate Applications ~300km50-100km

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=CoolingFewer Clouds=Warming

Gettelman: November 2006 Marine Stratus: Low Clouds over the Ocean

Gettelman: November 2006 Parameterization of Clouds Weare and Mokhov (1995) Cloud amount (fraction) as simulated by 25 atmospheric GCMs

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=WarmingFewer 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 ModelingGlobal Climate Modeling –complex and evolving scientific problem –parameterization of physical processes pacing progress –observational limitations pacing process understanding Parameterization of physical processesParameterization 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 climatologyCompare long term mean climatology –average mass, energy, and momentum balances –tells you where the physical approximations take you –but you dont necessarily know how you get there! Consider dominant modes of variabilityConsider 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 Observed Precipitation Simulated Precipitation

Gettelman: November 2006 Comparison of Mean Simulation Properties 1 Difference: Sim- Observed Simulated Precipitation

Gettelman: November 2006 Comparison of Mean Simulation Properties 2 Observed Land Temp Simulated 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 Hack (1998) Observed Simulated More CloudLess Cloud

Gettelman: November 2006 Turning The Crank: Results Simulations of Atmospheric Model Coupled to OceanSimulations of Atmospheric Model Coupled to Ocean Present Day ClimatePresent Day Climate Simulations into the future with ScenariosSimulations into the future with Scenarios Different Models=Different SensitivityDifferent Models=Different Sensitivity Potential Changes in Temp, PrecipPotential Changes in Temp, Precip

Gettelman: November 2006 Kicking the System: Radiative Forcing

Gettelman: November 2006 Observations: 20 th Century Warming Model Solutions with Human Forcing

Gettelman: November 2006 Surface Temperature Variations

Gettelman: November 2006 CCSM Past: Last Millennium to 2100

Gettelman: November 2006 Atmospheric CO 2 (input)Temperature (output) CCSM Future: Next 100+ years

Gettelman: November 2006 CMIP 2001: Temperature and Precipitation Covey et al. (2001)

Gettelman: November 2006 Impacts of Climate Change Mote et al 2005 Observed Change SnowpackTemperature (- +)(- +) (- +)

Gettelman: November 2006 The Future Regardless of Scale: Still need parameterizations for most things Resolved Scales Global Models Future Global Models Cloud/Mesoscale/Turbulence Models Goal: get interactions right (Mesoscale). Also extreme events

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 Recall Scales: Still need parameterizations for most things (Radiation, Convection, Microphysics). Goal is to do small scale interactions better

Gettelman: November 2006 The End