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Introduction to climate modeling

Introduction to climate modeling. Peter Guttorp University of Washington peter@stat.washington.edu http://www.stat.washington.edu/peter. Acknowledgements. ASA climate consensus workshop Kevin Trenberth Ben Santer Myles Allen IPCC Fourth Assessment Reports Steve Sain NCAR IMAGe/GSP.

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Introduction to climate modeling

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  1. Introduction toclimate modeling Peter Guttorp University of Washington peter@stat.washington.edu http://www.stat.washington.edu/peter

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

  3. 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

  4. 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

  5. The heat engine

  6. Greenhouse effect

  7. A simple climate model • What comes in • must go out Solar constant 1367 W/m2 Earth’s albedo 0.3 Stefan’s constant 5.67×10-8 W/(K4·m2) Effective emissivity (greenhouse, clouds) 0.64

  8. 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%

  9. 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!

  10. 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

  11. 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

  12. 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

  13. Modeling the atmosphere • Coupled partial differential equations describing • Conservation of mass • Conservation of momentum • Conservation of water • Thermodynamics • Hydrostatic equilibrium • Boundary values • Radiative forcings

  14. The effect of gridding

  15. 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

  16. 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)

  17. 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

  18. Stochastic model • Observation Y • Model output • Truth Z • SOI E • Missing data treated as additional parameters to be estimated parameters forcings

  19. Vertical heat diffusivity Mixed layer Polar parameter Ocean hemispheric exchange Upwelling velocity Air-ocean exchange SOI coeff, SH SOI coeff, NH

  20. Comparison of Mean Simulation Properties Simulated Land Temp Difference: Sim- Observed

  21. 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 1°C

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

  23. Historical data

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

  25. El Niño simulations

  26. El Niño simulations simulations “obs” temp precip slp

  27. Cloud (OLR) Anomalies and ENSO Observed Simulated Hack (1998) More Cloud Less Cloud

  28. 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

  29. Dynamic downscaling of a GCM

  30. 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

  31. How well does the climate model reproduce data?

  32. Resolution in a regional climate model 50 x 50 km

  33. 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?

  34. 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

  35. Historical SST data issues • Ocean surface temperature record Data from buoys, ships, satellites, floats

  36. Arctic ice pack

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