Sensitivity analysis and calibration of a global aerosol model

Transcription

1 School of Earth and Environment Sensitivity analysis and calibration of a global aerosol model Lindsay Lee l.a.lee@leeds.ac.uk Ken Carslaw 1, Carly Reddington 1, Kirsty Pringle 1, Jeff Pierce 3, Graham Mann 1, Jill Johnson 1, Dominick Spracklen 1, Philip Stier 2 1. University of Leeds 2. University of Oxford 3. Colorado State University

2 1. Motivation

3 2. GLOMAP We use the global aerosol model GLOMAP (Mann et al. 2010) A microphysical modal model simulating the evolution of global aerosol including sulphate, sea-salt, dust and black carbon

4 3. Uncertainty in modelling Structural uncertainty Compare multiple models of the same type Compare model to observations AEROCOM Aerosol Comparisons between Observations and Models >14 models with the same emissions for 2000 and pre-industrial

5 4. GLOMAP in AEROCOM 5

6 4. GLOMAP in AEROCOM 6

7 4. GLOMAP in AEROCOM 7

8 4. GLOMAP in AEROCOM 8

9 5. Parametric uncertainty More complex models = more uncertain parameters How do these uncertain parameters affect model predictions? Which processes in the model are dominating the prediction? Together with AEROCOM find the most important uncertainties 9

10 6. Parametric uncertainty in a complex computer model Ideally want to carry out sensitivity analysis Need a good estimation of the output distribution given uncertain inputs Consider GLOMAP output Y = η(x) X is uncertain so Y is uncertain Want to know f(y) given G(X) Monte Carlo sampling impossible Have to use f ˆ( Y ) 10

11 7. Bayesian statistics f ˆ( Y ) found using Bayesian statistics Use a limited number of model runs training data Make some assumptions about the model behaviour prior distribution Find the posterior distribution of the model and use the mean as f ˆ( Y ) 11

12 8. The procedure

13 9. The model output Focus on Cloud condensation nuclei (CCN) concentration Soluble aerosol (>50nm) form cloud droplets at a given supersaturation Uncertainty in CCN concentration due to: uncertainty in the emission, nucleation of new aerosol, growth to 50nm, solubility, loss of aerosol.

14 10. Expert elicitation Elicitation: Ask the experts We think these are the uncertain parameters and their values are very unlikely to fall outside of these ranges

15 11. Statistical design Need maximum information in fewest runs Space-filling in 28-dimensions Maximin Latin Hypercube used good marginal coverage good space-filling properties Number of runs validation Emulator validation to highlight design issues

16 X2 12. Filling in the gaps - emulation Interpolate well-spaced model runs to estimate at untried points Gaussian Process - conditional probability Non-parametric Linear Model GP mean True Function X1 Key assumption is that parameter settings give information about model behaviour close by in parameter space

17 Output X2 13. Filling in the gaps - emulation Emulated output distribution Emulator mean Marginal functions X1 X2 X1 Input

18 14. Emulator validation Is the emulator output a good approximate of the model output? YES use the emulator mean instead

19 15. Emulator validation I

20 16. Emulator validation II

21 17. Estimated CCN and its uncertainty concentration in every surface grid box January July

22 18. Variance-based sensitivity analysis Variance decomposition: Variance due to each parameter: V i Var( Y ) Var Main effect sensitivity: Main effects + Interactions: i 1 X S i i i 1 V ( E( Y Xi)), i j S S i i ij V ij i j V ij Var V i Var(Y) V 12 X ij S p ( E( Y X ij)) 12 p 1

23 Output 19. Variance-based sensitivity analysis Emulated output distribution Marginal functions V i Var X i ( E( Y Xi)), X1 X2 Var(Y) Input

24 20. Contributions to uncertainty in every grid box January CCN 24

25 20. Contributions to uncertainty in every grid box January CCN BB_EMS DMS_FLUX AIT_WIDTH PRIM_SO4_DIAM ACT_DIAM ANTH_SOA DRYDEP_ACC BB_DIAM SO2O3_CLEAN 25

26 21. Contributions to uncertainty in every grid box Surface parameter sensitivities January July January July σ CCN /µ CCN σ CCN

27 22. Summarising global maps

28 23. Seasonal parameter sensitivities

29 24. Multi-parameter analysis

30 24. Multi-parameter analysis BL_NUC BIO_SOA

31 25. Towards structural uncertainty and calibration Structural uncertainty Considering the model uncertainty is any model configuration near to observations

32 26. AEROCOM versus AEROS ACT_DIAM BIO_SOA ANTH_SOA

33 27. Reduce global problem Jan CN Jul CCN PCA and cluster analysis Similar patterns seen in both

34 28. Cluster analysis ACT_DIAM SO2O3_CLEAN Model parameters Structure and cloud processing AIT_WIDTH BB_DIAM BB_EMS Fossil fuel emissions

35 29. Summary Sensitivity analysis provides deeper understanding of model behaviour Quantifying parametric uncertainty can help identify structural errors/model errors/modeller errors Can help direct research areas/observation strategies Will help calibration

36 30. Calibration discussion points Calibration of global means not satisfactory How do we calibrate the global model with known structural errors? Do we expect one set of calibrated parameters given we know model behaviour is variable? How can we use the sensitivity information? to reduce dimensions regional and temporal information identify active parameters How do we specify discrepancy? Use AEROCOM Use grid box variability

37 References Lee, L. A., Carslaw, K. S., Pringle, K. J., Mann, G. W., and Spracklen, D. V.: Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters, Atmos. Chem. Phys., 11, , doi: /acp , Lee, L. A., Carslaw, K. S., Pringle, K. J., and Mann, G. W.: Mapping the uncertainty in global CCN using emulation, Atmos. Chem. Phys., 12, , doi: /acp , Lee, L. A., Pringle, K. J., Reddington, C. L., Mann, G. W., Stier, P., Spracklen, D. V., Pierce, J. R., and Carslaw, K. S.: The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei, Atmos. Chem. Phys. Discuss., 13, , doi: /acpd , Mann, GW; Carslaw, KS; Spracklen, DV; Ridley, DA; Manktelow, PT; Chipperfield, MP; Pickering, SJ; Johnson, CE (2010) Description and evaluation of GLOMAP-mode: a modal global aerosol microphysics model for the UKCA composition-climate model, GEOSCI MODEL DEV, 3, pp doi: /gmd Partridge, D. G., Vrugt, J. A., Tunved, P., Ekman, A. M. L., Struthers, H., and Sorooshian, A.: Inverse modelling of cloud-aerosol interactions Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach, Atmos. Chem. Phys., 12, , doi: /acp , Roustant, O., Ginsbourger, D., and Deville, Y. (2010). DiceKriging: Kriging methods for computer experiments. R package version Bastos, L. S. and O'Hagan, A. (2009). Diagnostics for Gaussian process emulators. Technometrics 51, Pujol, G. (2008). sensitivity: Sensitivity Analysis. R package version Saltelli, A., Chan, K., Scott, M. Editors, 2000, Sensitivity Analysis, John Wiley & Sons publishers, Probability and Statistics series.

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