Whole atmosphere aerosolmicrophysics 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)

UK Chemistry and Aerosol projecto Collaboration between NCAS (Leeds Uni &

Cambridge Uni) & UK Met Office since 2005

o Aerosol-chemistry sub-model in the Unified Model environment for a range of applications (climate, air quality, Earth system science, weather)

o Fully coupled tropospheric and stratospheric chemistry schemes

o Multi-component aerosol microphysics (GLOMAP)

o Cloud drop concentrations

o Direct & indirect radiative effects for fully coupled composition-climate simulations

Carslaw & Karcher (SPARC ASAP report, 2006)

Lifecycle of stratospheric aerosol (e.g. Hamill et al., 1997)

4

Stratospheric dynamics

Dhomse et al. (in prep.)HadGEM-UKCA N48L60 CheS+GLOMAP

Pinatubo case study: inject 20 Tg of SO2 (10TgS) on 15th June 1991.Source divided among levels between 19 and 27kmInjection 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.

5

Stratospheric aerosol optical properties

Dhomse et al. (in prep.)

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.

HadGEM-UKCA N48L60 CheS+GLOMAP

Pinatubo Case StudyInject 20 Tg of SO2

between 19-27km 5S-15N (match initial observed dispersion)

6

Stratospheric aerosol: particle size

Dhomse et al. (in prep.)HadGEM-UKCA N48L60 CheS+GLOMAP

Contour lines = model, Colours = Bauman et al. (2003): SAGE-II & CLAES

7

Stratospheric aerosol microphysics

Dhomse et al. (in prep.)

Oct 1991

August 1991

Sept 1991

Nov 1991

HadGEM-UKCA N48L60 CheS+GLOMAP

Balloon-borne CPC & OPC measurements (Deshler et al., 2003)

Laramie,Wyomng,USA(41N)

Although satelliteshows strat-AOD perturbation confined to tropics until ~Oct 1991,OPC balloonobservations show lower-most stratosphere (15-18km) perturbed in Aug/Sep

8

Stratospheric aerosol microphysics

Dhomse et al. (in prep.)

March 1993

March 1991

March 1992

March 1994

HadGEM-UKCA N48L60 CheS+GLOMAP

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?

Balloon-borne CPC & OPC measurements (Deshler et al., 2003)

Laramie,Wyomng,USA(41N)

9

New particle formation during Pinatubo

Dhomse et al. (in prep.)HadGEM-UKCA N48L60 CheS+GLOMAP

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.

Model Intercomparisons tend to focus on diversityInforms on inter-model range of forcings. Quantifying sensitivity to uncertain model parameters or emissions assumptions would give valuable extra information.

mean

+1

-1

Emission sizeWet scav rate

SOA burden

Nucleation rate

Best model“Real” discrepancy

ObservationsDevelopment path

From model diversity to quantifying/attributing uncertainty

Objectives:

1.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

2.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

Experimental design

(select points in parameter space).

e.g. Latin Hypercube

Run perturbed parameter ensemble

Build emulator for

each grid box and output

variable

Test emulator against

simulator(additional

validation runs)

Expert elicitation(choose parameters

and their ranges)

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.Lee L.A. et al., The magnitude and causes of uncertainty in global models of CCN, ACP, 2013.

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.

Applying techniques to quantify sensitivity to uncertain parameters

Using the approach to quantify the magnitude and causes of uncertainty in indirect radiative forcing predicted by global aerosol microphysics model

Collaborating with UK Met Office to feed into uncertainty in decadal projections using HadGEM3-UKCA composition-climate model

1- uncertainty in indirect forcing due to 28 aerosol processes and emissions in the GLOMAP model

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

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

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.

Range of model uncertainties around the Pinatubo eruption.

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:1) Mass emitted (~12 to ~24 Tg reasonable range)2) Injection height-range (from single level ~19km to 19-29km deep)3) Spreading out injections (from 15N local to range 15N-10S)4) Sub-grid particle formation (range 0 to 5%?)5) Nucleation rate scaling (factor 10 or more either way?)6) 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