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Climate Models and Their Evaluation

Climate Models and Their Evaluation. What is a Model?. What is a Model?. Substitute for reality Closely mimics some essential elements Omits or poorly mimics non-essential elements. What is a Model?.

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Climate Models and Their Evaluation

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  1. Climate Models and Their Evaluation

  2. What is a Model?

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

  4. 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

  5. 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?

  6. 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

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

  8. Example of a Model: Earth’s Energy Balance Solar Radiation S = 1380 Wm-2 (plane, parallel) 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 Planetary Emission

  9. 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

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

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

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

  13. Ultimate: all physico-biogeochemical Earth System

  14. 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

  15. 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)

  16. SST along the equator Annual Mean Difference from obs. (a) (b) (c) (d) Jin et al. 2008 Climate Dynamics

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

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

  19. Standard Deviation Difference from obs. (a) (b) (c) (d) Jin et al. 2008 Climate Dynamics

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

  21. Correlation Forecast lead month Jin et al. 2008 Climate Dynamics

  22. Correlation Forecast lead month Jin et al. 2008 Climate Dynamics

  23. Correlation Forecast lead month Jin et al. 2008 Climate Dynamics

  24. Evaluating the IPCC Models

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

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

  27. Center of Ocean-Land-Atmosphere studies 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

  28. RMS error w.r.t. ERBE Figure 8.4 mean error in SWTOA mean error in OLR

  29. Annual Mean Precipitation 1980-1999 Figure 8.5 OBS MME

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