Transcript Document

An Introduction to
Climate Modeling
Andrew Gettelman
National Center for Atmospheric Research
Boulder, Colorado USA
Assistance from: J. J. Hack (NCAR)
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A. Gettelman& J. Hack
Real NCAR Scientists
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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
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Question 1: What is Climate?
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D.
Average/Expected ‘Weather’
The temperature & precipitation range
Distribution of all possible weather
Record of Extreme events
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(1) What is Climate?
Climate change
and its manifestation
in terms of weather
(climate extremes)
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Climate change
and its manifestation
in terms of weather
(climate extremes)
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Climate change
and its manifestation
in terms of weather
(climate extremes)
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Impacts of Climate Change
Observed Change 1950-1997
Snowpack
Temperature
(- +)
Mote et al 2005
(- +)
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Observed Temperature Records
IPCC, 3rd Assessment, Summary For Policymakers
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Radiative Forcing (Wm-2)
‘Anthropogenic’ Changes
1000
1200
1400
1600
1800
2000
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‘Anthropogenic’ Changes (2)
560ppmv CO2
~2060
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Question 2
• What is the difference between Numerical Weather
Prediction and Climate prediction?
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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
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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)
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Question 3
How can we predict Climate (50 yrs)
if we can’t predict Weather (10 days)?
Statistics!
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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
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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)
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Physical processes regulating climate
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Earth System Model ‘Evolution’
2000
2005
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Modeling the Atmospheric General Circulation
Requires understanding of :
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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
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Meteorological Primitive Equations
• Applicable to wide scale of motions; > 1hour, >100km
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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)
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Grid Discretizations
Equations are distributed on a sphere
• Different grid approaches:
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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
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Terrain following (sigma)
Pressure
Isentropic
Hybrid Sigma-pressure (most common)
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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
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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)
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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
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dry and moist convection
cloud amount/cloud optical properties
radiative transfer
planetary boundary layer transports
surface energy exchanges
horizontal and vertical dissipation processes
...
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F
F
Sq
Sq
Q
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Atmospheric Energy Transport
Synoptic-scale mechanisms
• hurricanes
http://www.earth.nasa.gov
• extratropical storms
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Process Models and Parameterization
•Boundary Layer
•Clouds
Stratiform
Convective
•Microphysics
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Radiation
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Other Energy Budget Impacts From Clouds
http://www.earth.nasa.gov
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Energy Budget Impacts of Atmospheric Aerosol
http://www.earth.nasa.gov
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Scales of Atmospheric Motions/Processes
Resolved Scales
Global Models
Future Global Models
Cloud/Mesoscale/Turbulence Models
Cloud Drops
Microphysics
CHEMISTRY
Anthes et al. (1975)
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Global Modeling and Horizontal Resolution
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Examples of Global Model Resolution
~300km
Typical Climate Application
50-100km
Next Generation Climate
Applications
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High Resolution Art Global Model Simulation
100km x 100km Global Model Precipitation
NCAR CCM3 run on Earth Simulator, Japan
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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
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Marine Stratus: Low Clouds over the Ocean
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Parameterization of Clouds
Cloud amount (fraction) as simulated by 25 atmospheric GCMs
Weare and Mokhov (1995)
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Low Clouds Over the Ocean
Change in low cloud
with 2xCO2
2 Models: Changes
are OPPOSITE!
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Key Uncertainties for Climate (2):
2. High Clouds:
Dominant effect is that they Trap heat (warm)
More Clouds=Warming
Fewer Clouds=Cooling
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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
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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
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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
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Comparison of Mean Simulation Properties 1
Simulated
Precipitation
Observed
Precipitation
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Comparison of Mean Simulation Properties 1
Simulated
Precipitation
Difference:
Sim- Observed
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Comparison of Mean Simulation Properties 2
Simulated
Land Temp
Observed
Land Temp
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Comparison of Mean Simulation Properties 2
Simulated
Land Temp
Difference:
Sim- Observed
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Testing AGCM Sensitivity
Cloud (OLR) Anomalies and ENSO
Observed
Simulated
Hack (1998)
More Cloud
Less Cloud
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Turning The Crank: Results
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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
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Kicking the System: Radiative Forcing
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Observations: 20th Century Warming
Model Solutions with Human Forcing
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Surface Temperature Variations 1000-2100
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CCSM Past: Last Millennium to 2100
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CCSM Future: Next 100+ years
Atmospheric CO2 (input)
Temperature (output)
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CMIP 2001: Temperature and Precipitation
Covey et al. (2001)
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Impacts of Climate Change
Observed Change 1950-1997
Snowpack
Temperature
(- +)
Mote et al 2005
(- +)
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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
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Example of State of the Art Global Model Simulation
10 X 10 km Global Model Precipitation
NEIS AGCM for the Earth Simulator, Japan
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Example of State of the Art Global Model Simulation
10 X 10 km Global Model Precipitation: Mid Latitude Cyclone over Japan
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‘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
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The End
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