School of Earth and Environment INSTITUTE FOR CLIMATE & ATMOSPHERIC SCIENCE Is the Arctic the most uncertain aerosol environment in the world? Ken Carslaw, Jo Browse, Lindsay Lee, Graham Mann Kirsty Pringle, Carly Reddington, Graham Mann, Dom Spracklen, Alex Rap, Piers Forster, Ben Drummond University of Leeds, UK Jeff Pierce Colorado State University Funding: NERC AEROS, GASSP and ACCACIA projects, EU PEGASOS, the National Centre for Atmospheric Science

Model predictions of Arctic aerosol Black Carbon Sulfate CO Shindell et al., 2008

Annual mean sulfate Mean concentration and diversity of the “central two- thirds” of 12 AEROCOM microphysics model AEROCOM microphysics intercomparison: Sulfate diversity SO 4 concentration SO 4 diversity Mann et al., ACPD, 2013

AEROCOM microphysics intercomparison: BC diversity Annual mean BC Mean concentration and diversity of the “central two- thirds” of 12 AEROCOM microphysics model Mann et al., ACPD, 2013 BC concentration BC diversity

AEROCOM microphysics intercomparison: Particle concentration diversity Mann et al., ACPD, 2013 N30 concentrationN100 concentration N30 diversity N100 diversity

Two “aerosol seasons” with very different processes Svalbard observations show a transition from “Arctic haze” in winter to very clean summer Korhonen et al., ACP, 2008

Browse et al., ACP, 2012 December-AprilJune-August Garrett et al (2010): “In the Arctic, the freezing point appears to serve as a ‘scavenging point’ that is only passed in the mid-summer months

A meteorological “scavenging point” temperature Observed ratio of winter/summer BC WinterSpring Fraction of precip as snow Browse et al., ACP, 2012

“Fixing” the inverse seasonal cycle problem Switching off scavenging at -15C, adding summer drizzle Browse et al., ACP, 2012

More comprehensive sampling of the model uncertainty Model response surface in one grid box Model runs Essentially using the interpolated parameter space to enable a very dense Monte Carlo sampling of the model Parameter 1 Parameter 2 Output (e.g., CCN) Extend to 28 dimensions with 168 runs Lee, L.A. et al., Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters, ACP Lee, L.A. et al., The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei, ACP 2013.

28 simultaneously perturbed parameters (1/2) ParameterLowerUpper BCOC mass emission rate (fossil fuel) BCOC mass emission rate (biomass burning) BCOC mass emission rate (biofuel) Sea spray mass flux (coarse/acc)0.2x5.0x SO2 emission flux (anthropogenic)0.6x1.5x SO2 emission flux (volcanic)0.5x2.0x Biogenic monoterpene production of SOA5 Tg/a360Tg/a Anthropogenic VOC production of SOA2Tg/a112Tg/a DMS mass flux0.5x3.0x BCOC mode diameter (fossil fuel)30 nm80 nm BCOC mode diameter (biomass burning)50 nm200 nm BCOC mode diameter (biofuel)50 nm200 nm Subgrid conversion of SO2 to SO4 ("primary SO4“)0%1% Mode diameter of "primary SO4"20 nm100 nm Particle and precursor gas emission rates Properties of emitted particles A probability distribution for each parameter was also elicited

ParameterLowerUpper BL nucleation rate k[H2SO4]4E-72E-04 FT nucleation rate (BHN)x0.01X10 Ageing "rate" from insol to sol (monolayer)0.35 Modal width (accumulation) Modal width (Aitken) Mode separation diameter (nucleation/Aitken)9 nm20 nm Mode separation diameter (Aitken/accumulation)x1.5x3 Microphysical rates Model “structural choices” Cloud drop activation dry diameter30100 Reaction SO2 + O3 in cloud water (clean)pH=4pH=6.5 Reaction SO2 + O3 in cloud water (polluted)pH=3.5pH=5 Nucleation scavenging dry D (above activation)0 nm100 nm Nucleation scavenging fraction (T> -15C) Dry deposition velocity (Aitken)x0.5X2.0 Dry deposition velocity (accumulation)X0.1X10.0 Cloud processing Dry and wet deposition 28 simultaneously perturbed parameters (2/2) Current projects extending to host model physics parameters

PDFs of mean CCN concentration in every grid box (July)

Parametric uncertainty in BC and sulfate in one model Standard deviation divided by the mean (December) Uncertainty due to 28 parameters Crude comparison with multi-model ensemble: Parametric +  /-  ~ 4 compared to +MME/-MME ~ 20 Arctic dominated by model structural uncertainty? BC uncertaintySO4 uncertainty

BC fraction of variance (January)

BC fraction of variance (July)

CCN fraction of variance (July) BL nuc Dry dep Aitken width BB ems BB diam

indirect forcing fraction of variance (July) BB diam DMS ems SO2 ems CCN and indirect forcing uncertainties are not caused by the same factors See Carslaw et al., Nature, 2013

Response of marine emissions to loss of sea ice Present day DMSDMS (no ice) Sea spray (no ice)Sea spray Browse et al., The complex response of Arctic cloud condensation nuclei to retreat of sea ice, ACPD 2013.

Response of Arctic aerosol to loss of sea ice Change in CCNChange in N3 Browse et al., The complex response of Arctic cloud condensation nuclei to retreat of sea ice, ACPD In a scavenging dominated environment, the response of CCN to changes in emissions can be complex!

Minimal impact of future Arctic shipping Future Arctic shipping could contribute locally 10% to BC deposition on snow and ice There are likely to be much larger changes due to meteorology Browse et al., Impact of future Arctic shipping on high-latitude black carbon deposition, GRL 2013.

Conclusions The AEROCOM global aerosol microphysics multi-model ensemble shows huge diversity in Arctic BC, SO 4 and CCN (factor 30 between central 2/3 of models versus <factor 4 elsewhere) The parametric uncertainty of aerosol in a single model (GLOMAP) peaks in the Arctic (factor 4 versus <factor 2 elsewhere) Structural uncertainty dominates? (although correction of some gross model errors would reduce this substantially) The factors controlling the uncertainty in Arctic CCN are different to the factors that control indirect forcing Retreat of Arctic ice seems to cause a complex response in CCN Changes in Arctic shipping not a major factor for future BC The answer is YES: the Arctic is the most uncertain aerosol environment on Earth

Confronting the model with observations See gassp.org.uk