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Introduction toclimate modeling
Peter Guttorp
University of Washington
[email protected]://www.stat.washington.edu/peter
Acknowledgements
ASA climate consensus workshopKevin Trenberth
Ben Santer
Myles Allen
IPCC Fourth Assessment Reports
Steve Sain
NCAR IMAGe/GSP
Weather and climate
Climate is –average weather
WMO 30 years (1961-1990)
–marginal distribution of weathertemperature
wind
precipitation
–classification of weather typestate of the climate system
Weather is–current activity in troposphere
Models of climate and weather
Numerical weather prediction:–Initial state is critical–Don’t 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
The heat engine
Greenhouse effect
A simple climate model
What comes in
must go out
Sπr2 (1−a)
4πr2εσT4
Solar constant1367 W/m2
Earth’s albedo0.3
Effective emissivity(greenhouse, clouds)0.64
Stefan’s constant5.67×10-8 W/(K4·m2)
Solution
Average earth temperature is T=285K (12°C)
One degree Celsius change in average earth temperature is obtained by changing
solar constant by 1.4%
Earth’s albedo by 3.3%
effective emissivity by 1.4%
But in reality…
The solar constant is not constantThe albedo changes with land use changes, ice melting and cloudinessThe emissivity changes with greenhouse gas changes and cloudinessNeed to model the three-dimensional (at least) atmosphereBut the atmosphere interacts with land surfaces……and with oceans!
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
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
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
Modeling the atmosphere
Coupled partial differential equations describing
Conservation of massConservation of momentumConservation of waterThermodynamicsHydrostatic equilibrium
Boundary valuesRadiative forcings
The effect of gridding
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
Can data force parametrizations?
Experiment with simple climate model
Realistic priors on forcings
Using several data sets onhemispheric annual mean temperature
oceanic heat content
Markov chain Monte Carlo analysis
Goal: Estimate climate sensitivity (temperature response to CO2 doubling)
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
Vertical heat transport by upwelling and diffusionAtmosphere in equilibrium with ocean
Stochastic model
Observation Y
Model output
Truth Z
SOI E
Missing data treated as additional parameters to be estimated
M(Θ,Φ)
Θ,Φ Y⎡⎣ ⎤⎦∝ Y Z⎡⎣ ⎤⎦ Z M,Θ,Φ,E⎡⎣ ⎤⎦ Θ[ ] Φ[ ]
parameters forcings
Mixed layerVertical heatdiffusivity Polar parameter
Upwellingvelocity
Air-oceanexchange
Ocean hemisphericexchange
SOI coeff, SH
SOI coeff, NH
Comparison of Mean Comparison of Mean Simulation PropertiesSimulation Properties
SimulatedLand Temp
Difference:Sim- Observed
Sources of uncertainty
ForcingsSea surface temperature is uncertain, especially for early years
Greenhouse gases vague estimates for early part
DataGlobal mean temperature is not measured
Uncertainty in estimates may be as big as 1°C
Greenhouse gases
Anthropogenic CO2 from fossil fuel and land use change
Methane from agriculture and fossil fuels
1/3 of NOx from agricultural sources
Historical data
Sensitivity
Reasonable climate models must reproduce
El Niño
Pacific Decadal Oscillation
Dust bowl, Sahel drought etc.
El Niño simulations
El Niño simulations
“obs” simulations
temp
precip
slp
Cloud (OLR) Anomalies and ENSOCloud (OLR) Anomalies and ENSO
Hack (1998)
Observed
Simulated
More Cloud Less Cloud
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
Dynamic downscaling of a GCM
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
How well does the climate model reproduce data?
Resolution in a regional climate model
50 x 50 km
Where is the problem?
Regional model corresponds to grid square average
average over land cover type
3 hr resolution
Data correspond topoint measurement
open air
continuous time
Model
problems with cloud representation
constrain to lower resolution model?
Data issues
Need for high quality climate data repository (Exeter workshop)
Reanalysis not only needed for met data
Lots of satellites are deteriorating–many are not being replaced
Some countries will not make data available to the international community
Homogenization
Historical SST data issues
Ocean surface temperature recordData from buoys, ships, satellites, floats
Arctic ice pack
