1. Acknowledgements to others in the UKCA team
Whole atmosphere aerosol microphysics simulations of the Mount Pinatubo eruption.
Graham Mann (NCAS, School of Earth & Environment, Univ. of Leeds) Sandip Dhomse, Ken Carslaw, Lindsay Lee (School of Earth & Environment, Univ. of Leeds) Kathryn Emmerson (CSIRO, Melbourne, Australia)
Acknowledgements to others in the UKCA team
Coin Johnson, Mohit Dalvi Nicolas Bellouin (Hadley Centre, UK Met Office) (University of Reading)
Luke Abraham, Paul Telford, Peter Braesicke, Alex Archibald, John Pyle (University of Cambridge) 1

2. UK Chemistry and Aerosol project
Collaboration between NCAS (Leeds Uni & Cambridge Uni) & UK Met Office since 2005
Aerosol-chemistry sub-model in the Unified Model environment for a range of applications (climate, air quality, Earth system science, weather)
Fully coupled tropospheric and
stratospheric chemistry schemes
Multi-component aerosol microphysics (GLOMAP)
Cloud drop concentrations
Direct & indirect radiative effects for fully coupled composition-climate simulations 2

3. Lifecycle of stratospheric aerosol (e.g. Hamill et al., 1997)
Carslaw & Karcher (SPARC ASAP report, 2006)

4. Stratospheric dynamics
Pinatubo case study: inject 20 Tg of SO2 (10TgS) on 15th June 1991.
Source divided among levels between 19 and 27km
Injection spread over 5S to 15N to match initial plume dispersion
Use double-call configuration (radiative effects diagnosed only). Initialised to ensure QBO phase is Easterly at time of eruption.
Dhomse et al. (in prep.) 4
HadGEM-UKCA N48L60 CheS+GLOMAP 4

5. Stratospheric aerosol optical properties
Pinatubo Case Study Inject 20 Tg of SO2 between 19-27km 5S-15N (match initial observed dispersion)
Stratospheric AOD diagnosed online in the run via RADAER (Bellouin et al., 2010)
Model captures general spatial & temporal evolution of the Pinatubo plume
Over-predicts AOD in the tropics against satellite observations in the initial phase of the eruption. 5
HadGEM-UKCA N48L60 CheS+GLOMAP
Dhomse et al. (in prep.) 5

6. Stratospheric aerosol: particle size
Contour lines = model, Colours = Bauman et al. (2003): SAGE-II & CLAES 6
HadGEM-UKCA N48L60 CheS+GLOMAP
Dhomse et al. (in prep.) 6

7. Stratospheric aerosol microphysics
Laramie, Wyomng, USA (41N)
August 1991
Sept 1991
Although satellite shows strat-AOD perturbation confined to tropics until ~Oct 1991, OPC balloon observations show lower-most stratosphere (15-18km) perturbed in Aug/Sep
Oct 1991
Nov 1991
Balloon-borne CPC & OPC measurements (Deshler et al., 2003) 7
Dhomse et al. (in prep.)
HadGEM-UKCA N48L60 CheS+GLOMAP 7

8. Stratospheric aerosol microphysics
Laramie, Wyomng, USA (41N)
March 1991
March 1992
General good agreement re: size distribution evolution.
But too many of the very smallest particles.
Propogates to N150
Nucleation too strong in high SO2 conditions?
March 1993
March 1994
Balloon-borne CPC & OPC measurements (Deshler et al., 2003) 8
HadGEM-UKCA N48L60 CheS+GLOMAP
Dhomse et al. (in prep.) 8

9. New particle formation during Pinatubo
Strong nucleation in the tropics during the first month after eruption as gas phase H2SO4 being produced from SO2 oxidation.
Through August and September nucleation begins to subside as SO2 has been used up and much enhanced Surface Area Density acts as sink for elevated H2SO4.
Nucleation is “quenched” and returned to background levels by October 1991. 9
HadGEM-UKCA N48L60 CheS+GLOMAP
Dhomse et al. (in prep.) 9

10. From model diversity to quantifying/attributing uncertainty
Model Intercomparisons tend to focus on diversity
Informs on inter-model range of forcings.
Quantifying sensitivity to uncertain model parameters or emissions assumptions would give valuable extra information.
Objectives:
Do better than simple “one at a time” model tests. Quantify a “proper error bar”, i.e., standard deviation by generating a full pdf of the model output which accounts for all model uncertainties
Attribute the uncertainty to each parameter (variance decomposition)
This is only possible with many thousands of model runs that fill the uncertainty space – i.e., Monte Carlo Simulation
mean
+1s
-1s
Emission size
Wet scav rate
SOA burden
Nucleation rate
Development path
Best model
“Real” discrepancy
Observations

11. Applying techniques to quantify sensitivity to uncertain parameters
Use Bayesian emulators conditioned on an ensemble of perturbed-parameter model runs.
Run emulator as full Monte Carlo to enable full variance-based sensitivity analysis.
Expert elicitation
(choose parameters and their ranges)
Experimental design
(select points in parameter space). e.g. Latin Hypercube
Build emulator for each grid box and output variable
Test emulator against simulator
(additional validation runs)
Run perturbed parameter ensemble
Full variance based sensitivity analysis
(Monte Carlo using emulator)
Lee L.A. et al., The magnitude and causes of uncertainty in global models of CCN, ACP, 2013.

12. Carslaw et al., (in press, 2013, Nature).
Using the approach to quantify the magnitude and causes of uncertainty in indirect radiative forcing predicted by global aerosol microphysics model
1-s uncertainty in indirect forcing due to 28 aerosol processes and emissions in the GLOMAP model
Carslaw et al., (in press, 2013, Nature).
Collaborating with UK Met Office to feed into uncertainty in decadal projections using HadGEM3-UKCA composition-climate model

13. Carslaw et al., (in press, 2013, Nature).

14. Range of model uncertainties around the Pinatubo eruption.
Emissions Uncertainties -- total SO2 injected remains quite uncertain range of estimates (14-20Tg of SO2) -- depth of injection (19-21km, 19-26km) -- initial dispersion of the plume some models inject local, others spread over range of latitudes
Model predictions may be sensitive to several processes -- chemical conversion of SO2 to sulphuric acid -- sedimentation -- new particle formation during initial phase of eruption -- sub-grid particle formation & growth – “primary sulphate”
Range of model assumptions for particle size mass-based – fixed mean radius and mode width two-moment modal – varying size but fixed mode width two-moment sectional – freely evolving size distribution.

15. Proposal: For the SSiRC/AeroCom model intercomparison, Leeds offer to analyse ensemble of Pinatubo runs from each model.
Need to decide what’s feasible in terms of number of runs.
Also consider range of model sophistication -- some won’t have some microphysical processes to perturb (e.g. nucleation, primary emission)
All models able to include perturbations to: emissions uncertainties (SO2, vertical extent, latitude-spread) --- sedimentation velocity
Microphysics models could also perturb nucleation rate and carry out runs with fraction of SO2 emission as “primary sulphate”
Propose 30 5-year Pinatubo runs -- 6 parameters at 5 values with modellers submitting AeroCom-2 3D-monthly-mean diagnostics.
Leeds will run software to “train” emulator on results. Carry out full Monte Carlo with emulator  pdf for each model

16. Pinatubo Emulation Study
6 uncertain parameters to perturb:
Mass emitted (~12 to ~24 Tg reasonable range)
Injection height-range (from single level ~19km to km deep)
Spreading out injections (from 15N local to range 15N-10S)
Sub-grid particle formation (range 0 to 5%?)
Nucleation rate scaling (factor 10 or more either way?)
Sedimentation velocity (scale by factor 3 either way?)
Start: emulate monthly-mean 550/1020nm extinction. Better understand influences on peak simulated AOD & decay timescale
For microphysics models also examine effective radius evolution. Understand dominant source of uncertainty
Repeat to allow to quantify uncertainty in other simulated quantities