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Climate Models and Their Evaluation

Climate Models and Their Evaluation. What is a Model?. Substitute for reality Closely mimics some essential elements Omits or poorly mimics non-essential elements. What is a Model?. Quantitative and/or qualitative representation of natural processes (may be physical or mathematical)

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Climate Models and Their Evaluation

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  1. Climate Models and Their Evaluation

  2. What is a Model? • Substitute for reality • Closely mimics some essential elements • Omits or poorly mimics non-essential elements

  3. What is a Model? • Quantitative and/or qualitative representation of natural processes (may be physical or mathematical) • Based on theory • Suitable for testing “What if…?” hypotheses • Capable of making predictions

  4. What is a Model? Input Data Model Output Data What input data might we consider for a typical climate model? What output data might we consider for a typical climate model? Tunable Parameters What are the tunable parameters of interest?

  5. S, , a, g, Ω O3 H2O CO2 Ω CLIMATE DYNAMICS OF THE PLANET EARTH g a  (albedo) Gases: H2O, CO2, O3 S T4 h*: mountains, oceans (SST) w*: forest, desert (soil wetness) CLIMATE . stationary waves (Q, h*), monsoons WEATHER hydrodynamic instabilities of shear flows; stratification & rotation; moist thermodynamics day-to-day weather fluctuations; wavelike motions: wavelength, period, amplitude

  6. Example of a Model: Earth’s Energy Balance THEORY: Energy conservation: Change in energy due to difference in fluxes Solar Radiation S = 1380 Wm-2 (plane, parallel) Planetary Emission

  7. Example of a Model: Earth’s Energy Balance THEORY: Energy conservation: Change in energy due to difference in fluxes MODEL: Assume radiative equilibrium INCOMING FLUX = OUTGOING FLUX (1 - ) S ( a2) = Qe (4  a2) Qe = 1/4 (1 - ) S Measured albedo () = 0.31 Measured planetary Qe = 237 Wm-2 Blackbody temperature (T4 law): 254 K Measured surface Qes = 390 Wm-2 Blackbody temperature (T4 law): 288 K Atmosphere absorbs 153 Wm-2 Greenhouse effect: 34 K Solar Radiation S = 1380 Wm-2 (plane, parallel) Planetary Emission

  8. What is a Climate Model? • Equations of motions and laws of thermodynamics to predict rate of change of: • T, P, V, q, etc. (A, O, L, CO2, etc.) • 10 Million Equations: • 100,000 Points X 100 Levels X 10 Variables • With Time Steps of:~ 10 Minutes • Use Supercomputers

  9. Model Complexity:Development of Climate/Earth System Models

  10. Model Complexity:Development of Climate/Earth System Models

  11. Model Complexity:Development of Climate/Earth System Models

  12. Ultimate: all physico-biogeochemical Earth System

  13. What is Model Evaluation? • Validation • Confirmation that formulation of model conforms to intent (equations, algorithms, units, specified parameters etc.) • Confirmation that outputs are, within tolerable limits, as expected for given inputs • Verification • Comparison with known, measured (observed) quantities • Means, variability (frequency, amplitude, phase) • Spatial structure (scale, shape, amplitude) • Simulation: confirms theory for specified circumstances (e.g. specified boundary conditions) • Prediction: accurately reproduces time series of observed evolution from specified initial conditions • (Inter-)Comparison • Comparison among different models’ outputs for identical inputs

  14. What is Model Evaluation? • Example: ENSO Prediction • Comparison of many salient characteristics of ENSO with observations and among models • Coupled ocean-atmosphere models with specified, observed initial conditions and external forcing (e.g. GHG concentrations)

  15. SST along the equator Annual Mean Difference from Observations Jin et al. 2008 Climate Dynamics

  16. SST along the equator in the Pacific (mean annual cycle) - lead time 1-3 months Jin et al. 2008 Climate Dynamics

  17. SST along the equator in the Pacific (mean annual cycle) - lead time 4-6 months Jin et al. 2008 Climate Dynamics

  18. Standard Deviation Difference from Observations Jin et al. 2008 Climate Dynamics

  19. (a) (c) Intra-ensemble Variability Annual Cycle Error Interannual Variability RMSE (b) Intra-ensemble Variability Interannual Variability Jin et al. 2008 Climate Dynamics

  20. Correlation Forecast Lead (months) Jin et al. 2008 Climate Dynamics

  21. Correlation Forecast Lead (months) Jin et al. 2008 Climate Dynamics

  22. Correlation Forecast Lead (months) Jin et al. 2008 Climate Dynamics

  23. Evaluating the IPCC Models

  24. SST (1980-1999) SAT (1961-1990) Figure 8.2 OBS (contours) & mean MME error (shades) MME RMS error

  25. SST & SAT st. dev. Figure 8.3 OBS (contours) & mean MME error (shades)

  26. RMS error w.r.t. ERBE mean error in SWTOA mean error in OLR

  27. Annual Mean Precipitation 1980-1999 OBS MME

  28. Climate Model Fidelity and Projections of Climate Change J. Shukla, T. DelSole, M. Fennessy, J. Kinter and D. Paolino Geophys. Research Letters, 33, doi10.1029/2005GL025579, 2006

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