Introduction to climate modeling

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Introduction toclimate modeling

• Peter Guttorp
• University of Washington
• peter_at_stat.washington.edu
• http//www.stat.washington.edu/peter

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Acknowledgements

• ASA climate consensus workshop
• Kevin Trenberth
• Ben Santer
• Myles Allen
• IPCC Fourth Assessment Reports
• Steve Sain
• NCAR IMAGe/GSP

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Weather and climate

• Climate is
• average weather
• WMO 30 years (1961-1990)
• marginal distribution of weather
• temperature
• wind
• precipitation
• classification of weather type
• state of the climate system
• Weather is
• current activity in troposphere

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Models of climate and weather

• Numerical weather prediction
• Initial state is critical
• Dont care about entire distribution, just most
  likely event
• Need not conserve mass and energy
• Climate models
• Independent of initial state
• Need to get distribution of weather right
• Critical to conserve mass and energy

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The heat engine
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Greenhouse effect
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A simple climate model

• What comes in
• must go out

Solar constant 1367 W/m2
Earths albedo 0.3
Stefans constant 5.6710-8 W/(K4m2)
Effective emissivity (greenhouse, clouds) 0.64
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Solution

• Average earth temperature is T285K (12C)
• One degree Celsius change in average earth
  temperature is obtained by changing
• solar constant by 1.4
• Earths albedo by 3.3
• effective emissivity by 1.4

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But in reality

• The solar constant is not constant
• The albedo changes with land use changes, ice
  melting and cloudiness
• The emissivity changes with greenhouse gas
  changes and cloudiness
• Need to model the three-dimensional (at least)
  atmosphere
• But the atmosphere interacts with land surfaces
• and with oceans!

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Historically

• mid 70s Atmosphere models
• mid-80s Interactions with land
• early 90s Coupled with sea ice
• late 90s Added sulphur aerosols
• 2000 Other aerosols and carbon cycle
• 2005 Dynamic vegetation and atmospheric chemistry

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The climate engine I

• If Earth did not rotate
• tropics get higher solar radiation
• hot air rises, reducing surface pressure
• and increasing pressure higher up
• forces air towards poles
• lower surface pressure at poles makes air sink
• moves back towards tropics

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The climate engine II

• Since earth does rotate, air packets do not
  follow longitude lines (Coriolis effect)
• Speed of rotation highest at equator
• Winds travelling polewards get a bigger and
  bigger westerly speed (jet streams)
• Air becomes unstable
• Waves develop in the westerly flow (low pressure
  systems over Northern Europe)
• Mixes warm tropical air with cold polar air
• Net transport of heat polewards

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Modeling the atmosphere

• Coupled partial differential equations describing
• Conservation of mass
• Conservation of momentum
• Conservation of water
• Thermodynamics
• Hydrostatic equilibrium
• Boundary values
• Radiative forcings

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The effect of gridding
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Parameterization

• Some important processes happen on scales below
  the discretization
• Typically expressed in terms of resolved
  processes (statistically) or data
• Examples
• 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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Can data force parametrizations?

• Experiment with simple climate model
• Realistic priors on forcings
• Using several data sets on
• hemispheric annual mean temperature
• oceanic heat content
• Markov chain Monte Carlo analysis
• Goal Estimate climate sensitivity (temperature
  response to CO2 doubling)

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Hemispheric model

• Schlesinger, Jiang Charlson 1992

NH atmosphere
SH atmosphere
NH mixed layer NH interior
ocean NH bottom
SH mixed layer SH interior ocean
SH bottom
NH polar ocean
SH polar ocean
Vertical heat transport by upwelling and
diffusion Atmosphere in equilibrium with ocean
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Stochastic model

• Observation Y
• Model output
• Truth Z
• SOI E
• Missing data treated as additional parameters to
  be estimated

parameters
forcings
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Vertical heat diffusivity
Mixed layer
Polar parameter
Ocean hemispheric exchange
Upwelling velocity
Air-ocean exchange
SOI coeff, SH
SOI coeff, NH
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Comparison of Mean Simulation Properties
Simulated Land Temp
Difference Sim- Observed
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Sources of uncertainty

• Forcings
• Sea surface temperature is uncertain, especially
  for early years
• Greenhouse gases vague estimates for early part
• Data
• Global mean temperature is not measured
• Uncertainty in estimates may be as big as 1C

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Greenhouse gases

• Anthropogenic CO2 from fossil fuel and land use
  change
• Methane from agriculture and fossil fuels
• 1/3 of NOx from agricultural sources

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Historical data
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Sensitivity

• Reasonable climate models must reproduce
• El Niño
• Pacific Decadal Oscillation
• Dust bowl, Sahel drought etc.

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El Niño simulations
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El Niño simulations
simulations
obs
temp
precip
slp
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Cloud (OLR) Anomalies and ENSO
Observed
Simulated
Hack (1998)
More Cloud Less Cloud
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Regional models

• Dynamic downscaling Higher resolution models
  driven by lower resolution global models
• Statistical downscaling Regression model using
  global model, terrain etc.
• Stochastic downscaling Stochastic model for
  subgridscale processes driven by global model

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Dynamic downscaling of a GCM
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Comparing RCM to data

• Regional climate model RCM3 from SMHI
• Forced by ERA40
• Need to compare distributions
• Data observed minimum daily temperatures at
  Stockholm Observatory

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How well does the climate model reproduce data?
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Resolution in a regional climate model
50 x 50 km
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Where is the problem?

• Regional model corresponds to
• grid square average
• average over land cover type
• 3 hr resolution
• Data correspond to
• point measurement
• open air
• continuous time
• Model
• problems with cloud representation
• constrain to lower resolution model?

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Data issues

• Need for high quality climate data repository
  (Exeter workshop)
• Reanalysis not only needed for met data
• Lots of satellites are deterioratingmany are not
  being replaced
• Some countries will not make data available to
  the international community
• Homogenization

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Historical SST data issues

• Ocean surface temperature record
• Data from buoys, ships, satellites, floats

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Arctic ice pack