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

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 www.ukca.ac.uk 2

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

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

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

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

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

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

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

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

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.

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

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

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.

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: --- 3 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

Pinatubo Emulation Study 6 uncertain parameters to perturb: Mass emitted (~12 to ~24 Tg reasonable range) Injection height-range (from single level ~19km to 19-29km 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