Integrating Aerosol Cooling Within Climate Change Mitigation Policy

Confronting the abatement paradox Integrating aerosol cooling within climate change mitigation policy

Justin Wood BSc, BAppSc, MSc

is thesis is presented for the degree of Doctor of Philosophy of Murdoch University

2013

Discipline of Physics and Energy Studies School of Engineering and Information Technology I declare that this thesis is my own account of my research and contains, as its main content, work that has not been previously submied for a degree at any tertiary education institution.

Supervisors: Prof. Philip Jennings and Adam McHugh, with assistance from Dr August Schläpfer.

Produced in the open-source LYX editor and Zotero bibliographic management soware. Typeset in LATEX and XƎTEX with the open-source fonts Linux Bolinum, Linux Libertine, and Fontin Sans. My sincere gratitude to the LYX user community, Jürgen Spitzmüller in particular, and to Petr Šimon for LyZ, his invaluable Zotero–BIBTEX–LYX integration tool.

e author’s original work is licensed under Creative Commons. All other material remains copyright of the authors and their publishers. Acknowledgements

My sincere gratitude and appreciation goes to my principal supervisors, Philip and Adam, for their wisdom and their forbearance. August provided welcome advice and enthusi- asm. I thank Prof. Tom Lyons for early review of the physical science material (remaining mistakes or omissions are mine alone). I can scarcely thank Dr Cecily Scu enough, for without her support, guidance, concern, and when needed, cajoling, this thesis would simply never have been completed. And my dear friend Dr Beth Gouldthorp, for her unending encouragement and understanding. I wish to recognise those that led or helped me to begin this doctorate, oen unbe- knownst to them: David Spra, whose book Climate code red seeded the idea that we need to talk about aerosols; James Hansen, for his indefatigable courage, determination, and insight; RealClimate.org, for teaching us all; and to my friend Glenn, who once told me that he had never regreed any decision to just take the leap.

And ﬁnally, to my friends and family, for puing up with me thus far.

Contents

Abstract 1

1 Introduction and overview 5 1.1 e aerosol dilemma ...... 5 1.2 On avoiding dangerous interference ...... 8 1.3 Research questions ...... 14 1.4 Methodology ...... 16 1.4.1 Research approach ...... 16 1.4.2 esis structure ...... 17 1.4.3 Nomenclature ...... 18

2 Aerosol effects on climate 19 2.1 Physical characteristics ...... 20 2.1.1 Formation and composition ...... 20 2.1.2 Mixing state ...... 20 2.1.3 Atmospheric residence time and emissions ...... 21 2.1.4 Atmospheric distribution ...... 22 2.1.5 Perturbation baseline ...... 23 2.2 Optical and radiative properties ...... 24 2.2.1 Particle effective radius ...... 24 2.2.2 Absorption, scaering, and extinction coefficients ...... 25 2.2.3 Asymmetry parameter ...... 26 2.2.4 Single scaering albedo ...... 26 2.2.5 Aerosol optical depth ...... 27 2.3 Radiative forcing ...... 29 2.3.1 Forcing efficacy ...... 33 2.4 Species ...... 34 2.4.1 Natural aerosols ...... 35 2.4.2 Anthropogenic aerosols ...... 35 2.5 Climatic effects and influence ...... 46 2.5.1 Direct radiative effect ...... 47 2.5.2 Indirect cloud albedo effect ...... 51 2.5.3 Indirect cloud lifetime effect ...... 54

i Contents

2.5.4 Semi-direct effect ...... 57 2.5.5 ermodynamic effect ...... 62 2.5.6 Glaciation effect ...... 63 2.5.7 Biomass fertilisation effect ...... 64 2.5.8 in Arctic cloud ...... 65 2.5.9 Longwave absorption effect ...... 66 2.5.10 Further effects on climatic processes ...... 66 2.5.11 Effects on surface irradiance ...... 68 2.5.12 Summary of effects ...... 73 2.6 Synthesis and implications ...... 75 2.7 Observation and modelling ...... 79 2.7.1 Observation and measurement ...... 79 2.7.2 Aerosol modelling ...... 83

3 Climate response and associated risk 95 3.1 Climate response to forcing ...... 95 3.1.1 Radiative forcing revisited ...... 96 3.1.2 Equilibrium climate sensitivity ...... 98 3.2 Planetary energy imbalance and climate response ...... 99 3.2.1 antitative estimates of energy imbalance and aerosol forcing . 103 3.3 Commied warming ...... 109 3.3.1 Derived climate response function ...... 110 3.4 Dangerous anthropogenic interference ...... 113 3.4.1 Aerosol-induced temperature change ...... 114 3.5 Toward appropriate metrics ...... 118 3.5.1 e problem with GWP and CO2-e ...... 119 3.5.2 Alternative methods ...... 122 3.6 Aerosol emission rates ...... 130 3.6.1 Aggregate emissions ...... 131 3.6.2 Emissions by source ...... 134 3.6.3 Future emissions ...... 136 3.7 Policy implications ...... 141

4 Comparative analysis of mitigation policy 143 4.1 Pollution taxonomy ...... 143 4.1.1 Emission and assimilation ...... 144 4.1.2 Atmospheric mixing ...... 147 4.1.3 Environmental impact ...... 148 4.1.4 Applying the taxonomy ...... 150 4.1.5 A new term for emission sources ...... 151

ii Contents

4.2 Policy ontology and form ...... 152 4.2.1 Less is beer ...... 152 4.2.2 Less is worse ...... 154 4.2.3 e policy challenge ...... 156 4.3 A revised ontology ...... 159 4.3.1 Clarifying effect and impact ...... 159 4.3.2 Expanded classification schema ...... 161 4.4 Accounting for aerosols in climate change policy ...... 166 4.4.1 Linkage synergy and co-benefits ...... 166 4.4.2 Linkage barriers and conflict ...... 167 4.4.3 Are they included currently? ...... 170 4.4.4 Could they be? ...... 174 4.5 Confounding complexities ...... 178 4.5.1 e problem of CO2 non-equivalence ...... 178 4.5.2 Correlated externality feedbacks ...... 179 4.6 Unintended consequences: the abatement paradox ...... 184 4.7 Dangerous omissions ...... 186

5 Aerosol-integrated mitigation policy criteria 193 5.1 Policy objective ...... 194 5.2 Discussion and scope ...... 196 5.2.1 Deﬁnition of terms ...... 197 5.2.2 Plausible compensative measures ...... 198 5.2.3 Which emissions, which sources? ...... 203 5.2.4 Measurement and reporting ...... 206 5.2.5 How much and for how long? ...... 208 5.2.6 Temporal and spatial considerations ...... 210 5.2.7 Compensative masking services ...... 212 5.2.8 e question of accounting ...... 213 5.2.9 Domestic implementation ...... 216 5.3 Implementation model evaluation ...... 218 5.3.1 Loss-liability model ...... 219 5.3.2 Cost-neutrality model ...... 223 5.3.3 Funding and ultimate liability ...... 226 5.3.4 Central authority model ...... 228 5.3.5 Conferred value model ...... 230 5.4 Ramiﬁcations for abatement pathways ...... 236

6 Scheme implementation: the balancing market 241 6.1 Restricted direct value model ...... 241 6.1.1 Overview and rationale ...... 242

iii Contents

6.1.2 Carbon price mechanism integration ...... 243 6.2 Scheme implementation ...... 244 6.2.1 Licensed compensative measures ...... 246 6.2.2 Coverage and reporting liabilities ...... 247 6.2.3 antification metric ...... 249 6.2.4 NFA emission baseline ...... 250 6.2.5 e residual masking license ...... 252 6.2.6 Market demand ...... 253 6.2.7 Reverse auction mechanism ...... 255 6.2.8 Contract funding ...... 260 6.3 Market dynamics ...... 265 6.3.1 Price determinants ...... 265 6.3.2 Supply shortfalls ...... 271 6.3.3 Effect on carbon price and abatement pathway ...... 275 6.3.4 Supply excess ...... 291 6.4 Interaction with air pollution regulation ...... 295 6.4.1 Apparent conflicts ...... 296 6.4.2 Operational price dynamics ...... 298 6.4.3 Targeted regulation ...... 301 6.5 Model evaluation ...... 302

7 Conclusions 305 7.1 Research questions revisited ...... 305 7.2 Summary of key ﬁndings ...... 306 7.3 Limitations and further research ...... 310 7.4 A ﬁnal statement ...... 312

References 315

Glossary 331

iv List of Tables

2.1 DRF (Fa) by anthropogenic species reported by AR4 ...... 49 2.2 Summary of aerosol eﬀects on climate ...... 74

3.1 Estimates of planetary energy imbalance and inferred aerosol forcing .. 108 3.2 Comparative GWP and GTPP at time horizon H years for BC, OC, and SO2 125 3.3 Speciﬁc forcing pulse for black carbon and organic maer ...... 128 3.4 Comparative RF and 20 & 100 year GWPs for BC and OM, derived from SFP ...... 129 3.5 Comparison of selected characteristics of alternative metrics ...... 130 3.6 Proportional anthropogenic emissions of major aerosol species by sector 135

4.1 Comparative dominant pollutant characteristics ...... 150 4.2 Comparative pollutant effects ...... 155 4.3 Co-benefits in climate and air quality policy linkage ...... 168 4.4 Conflicts in climate and air quality policy linkage ...... 169

5.1 Categories of plausible compensative measures ...... 199

6.1 Sealed-bid ﬁrst-price reverse auction types ...... 257 6.2 Two balancing market funding Options ...... 262

v List of Figures

1.1 Global CO2 emissions and carbon intensity ...... 10

2.1 Characteristic AOD for combined anthropogenic aerosol loading ..... 28 2.2 Illustration of RF calculation methodologies ...... 32 2.3 Range of forcing efficacies reported in AR4 ...... 34 2.4 Illustrations of aerosol influence on climate ...... 47 2.5 AR4 DRF at TOA by anthropogenic species ...... 50 2.6 Indirect cloud albedo effect ...... 52 2.7 Aerosol atmospheric forcing at altitude ...... 58 2.8 Semi-direct effect ...... 59 2.9 Aerosol effects on mixed-phase clouds ...... 63 2.10 Surface dimming due to all anthropogenic aerosols ...... 70 2.11 AR4 climate forcings by major component ...... 76 2.12 Aerosol and GHG forcing PDF ...... 77 2.13 Time dependence of aerosol optical thickness and effective forcing .... 91 2.14 Temporal evolution of surface and radiative forcing ...... 91

3.1 Illustrative sketch of the response to positive energy imbalance ..... 100 3.2 Cumulative planetary energy budget since 1950 ...... 103 3.3 Indicative planetary energy imbalance over time ...... 107 3.4 Dangerous anthropogenic interference for a range of tipping elements . 115 3.5 Illustrative response to air pollution and climate change mitigation policies 119 3.6 Modelled aerosol burden over time ...... 132 3.7 Total annual emissions of major aerosol species since 1850 ...... 133 3.8 Global SO2 emissions since 1850 by source and by end-use sector .... 136 3.9 Projected anthropogenic aerosol emissions 2000 to 2100 under all RCP scenarios ...... 139

4.1 Illustrative pollution classification: aerosols as local air pollutants .... 164 4.2 Illustrative pollution classification: GHG effects on climate ...... 164 4.3 Illustrative pollution classification: GHG effects on climate and oceans . 165 4.4 Illustrative pollution classification: aerosol effects on forcing ...... 165 4.5 Illustrative pollution classification: aerosol effects on forcing and hydrological cycle ...... 165

vi Contents

4.6 Illustrative pollution classiﬁcation: all forcing agent eﬀects on climate .. 166

6.1 Restricted direct value model ...... 259 6.2 Illustrative carbon price tolerance of two representative facilities ..... 279 6.3 Shutdown curve for two representative technology groupings, period t=0 280 6.4 Shutdown curve for two representative technology groupings, period t=x 281 6.5 Shutdown curve for two representative technology groupings, period t=y 281 6.6 Economy-wide shutdown curve ...... 283 6.7 Illustrative carbon price tolerance of two representative facilities, shied by balancing market revenue under funding Option 1 ...... 284 6.8 Shutdown curve for coupled NFA (A) versus non-NFA emiing (B) technology groupings under funding Option 1, period t=y ...... 285 6.9 Relative tolerances are reordered by balancing market revenue under funding Option 1 ...... 286 6.10 Balancing market revenue causes a shi to a new abatement pathway under funding Option 1 ...... 286 6.11 Illustrative carbon price tolerance of two representative facilities, shied by levy costs under funding Option 2 ...... 288 6.12 Shutdown curve for coupled NFA (A) versus non-NFA emiing (B) technology groupings under funding Option 2, period t=y ...... 289 6.13 Relative tolerances are reordered by levy costs under funding Option 2 . 290 6.14 Levy costs causes a shi to a new abatement pathway under funding Option 2 ...... 290 6.15 Minimum required oﬀer price coupled emier shutdown curve ...... 294 6.16 Scheme interaction with air pollution regulation through price dynamics 300

vii List of Boxes

2.1 e role of SO2 pollution regulations ...... 37 2.2 Aerosol eﬀects on the hydrological cycle ...... 60 2.3 Missing satellite observations ...... 80

3.1 Is climate response faster than previously thought? ...... 111 3.2 Australian aerosol emissions ...... 137

4.1 Perverse incentives as an impediment to discourse? ...... 172 4.2 Beware Black Swans ...... 192

5.1 Geoengineering can never substitute for real GHG abatement ...... 200 5.2 e polluter pays principle ...... 219 5.3 e ethical dilemma of aerosol cooling ...... 238

6.1 Ethics and environmental eﬀectiveness ...... 254 6.2 e risk of Scheme collapse ...... 275

viii Abstract

Anthropogenic climate change is a problem more confounding than commonly appreciated. For while greenhouse gas (GHG) emissions lead to a positive climate forcing and hence rising global mean surface temperature, parallel emission of particulate aerosol species does much the opposite, in parallel to their deleterious effects as local air pollutants. rough direct radiative interaction with incoming sunlight and complex indirect microphysical effects on cloud, anthropogenic aerosols exert a significant negative forcing — a counteracting cooling influence. Global temperature rise to date is thereby substantially less than would have occurred due to past GHG emissions alone. In fact, painstaking investigations find that aerosols mask between about 35 % and as much as 50 % of current GHG warming.

What is more, where GHGs remain in the atmosphere for decades to centuries aer emission, aerosols are washed out within days to weeks. is large residence time asymmetry means that the ‘protection’ aﬀorded by aerosol masking depends entirely on their continual emission. But that masking comes at the expense of well-documented damage to natural systems, physical infrastructure, and human health. Crucially, many activities that produce GHGs also emit aerosols and their precursor gases, fossil fuel combustion prominent among them. Climate change mitigation policies that abate GHG emissions therefore simultaneously reduce aerosols — not only their emissions, but actual atmospheric loading. Yet due to the residence time asymmetry, the positive forcing of existing GHG concentrations is lile aﬀected in the short term, while the aerosol mask weakens

1 in direct consequence of those lost emissions. is weakened mask constitutes a ‘negative abatement feedback’ of mitigation eﬀorts.

If unmasking is large enough, near the full effect of existing GHG forcing will be exposed, leading to potentially strong and rapid increase in temperature. As thresholds for dangerous climate change are quickly approaching, including a range of climatic tipping points, a lost aerosol mask as an unintended side effect of genuine GHG abatement may hence, paradoxically, exacerbate rather than mitigate against dangerous anthropogenic interference. e current trajectory of ever-rising global GHG emissions and unambitious, ineffective international mitigation efforts therefore suggests that a policy response to lost aerosol cooling will be needed when abatement finally begins in earnest, as it must. However, critical assessment of current policy making and instrument design finds that this systemic risk is largely unrecognised, and that normative ontological bias in the conception of pollutant effects hinders adequate recognition of aerosols’ full role.

e objective of this thesis is then to investigate policy approaches by which that risk may be contained.

To do so, I construct a set of policy design criteria that must be met by any eﬀective mitigation framework that explicitly confronts the aerosol abatement paradox. Chief among these is the need to avoid distortion of underlying real GHG abatement; to account for aerosol emissions via metrics that accurately represent their negative forcing character; to properly recognise temporal constraints on their removal; and to ensure that withdrawal of aerosol masking is ‘managed’. Analysing a range of conceivable implementation models uncovers further critical insights for eﬀective design, in accordance with the physical science.

2 From these foundations, I propose the ‘balancing market’ mechanism, a prototype policy instrument compliant with those criteria, integrated with a pre-existing carbon price mechanism under an assumed, and necessary, future international agreement. e central feature of the underlying implementation model is to prioritise abatement pathways in which GHG-emission sources without coupled aerosol emissions are shutdown first. e balancing market does this by paying for the quantity of negative forcing deemed required until mitigation efforts restore a ‘safe’ atmospheric configuration. In a stark illustration of the abatement paradox’s unprecedented challenge, this translates as payment for continued aerosol emissions during a transitionary period while alternative compensative technologies are developed — likely including carbon dioxide removal, and forms of geoengineering.

It is my hope that this proposal and the analysis on which it is founded may assist in expanding the policy discourse so as to take full and unvarnished account of all components of anthropogenic interference in the climate system — and the danger they represent. e ethical implications are enormously confronting, but such is the nature of the age of consequences.

1 Introduction and overview

1.1 The aerosol dilemma

Anthropogenic climate change is a physical reality in the twenty-ﬁrst century (eg, IPCC

2007a). Greenhouse gases (GHGs) such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and the halocarbons, generated by a range of human activities, absorb and re-radiate thermal energy emied by the Earth’s surface. In doing so, these gases increase retention of energy within the Earth-atmosphere system, perturbing planetary energy balance. e global mean surface temperature rises in response until energy balance is restored at a new radiative equilibrium. Anthropogenic greenhouse gases thereby warm our planet. As the Intergovernmental Panel on Climate Change (IPCC) state clearly in their 2007 Fourth Assessment Report (AR4), the evidence of that warming is now ‘unequivocal’ (IPCC 2007b, 5).

Yet there exists another class of climatically active substances emied by human activity in parallel to greenhouse gases. Aerosols also exert significant influence on the climate system, though less well-known than greenhouse gases — and far less understood. While greenhouse gases heat our planet, in short, aerosols are cooling it. e cooling effect of these microscopic solid and liquid –phase atmospheric particles is caused by a complex array of direct and indirect processes that, on balance,1 enhance the effective planetary

1 Black carbon complicates this picture by exerting a signiﬁcant net warming inﬂuence (see section 2.4.2.3).

5 Chapter 1 Introduction and overview albedo, reducing the quantity of solar radiation absorbed at the surface (see Chapter 2). is reduction results in a negative planetary energy imbalance. e continuing net positive imbalance is the sum of the negative aerosol term and the larger positive term of GHGs. Aerosols thereby mask as much as half of GHG-induced warming, though the true strength is far from certain.

e countervailing aerosol mask means that global temperature rise to date is markedly less than would have occurred in the absence of anthropogenic aerosols.2 Nonetheless, a signiﬁcant positive transient energy imbalance remains, and the inexorable rise of atmospheric GHG concentrations continues to strengthen it — further global warming is hence ‘in the pipeline’.

at aerosols have slowed the onset of climate impacts can rightly be regarded as a blessing. Yet these same species are simultaneously major local air pollutants, causing well-documented damage to natural ecosystems, human infrastructure, and human health. eir conﬂicting roles in climate (masking warming) and local air and water quality (damaging pollutants) then gives rise to a dilemma: actions that target these aerosol species for abatement as pollutants will also lead to increased global warming (Crutzen 2006; Ramanathan and Feng 2009).

is framing is the common expression of the aerosol dilemma, but it does not give the full picture. For beyond the negative consequences of air pollution regulations, the reality of aerosol masking also constitutes a potential foil for any eventual serious coordinated climate change mitigation eﬀort, for two fundamental reasons.

First, anthropogenic aerosols are emied by a range of activities, including industrial practises, biomass burning, agriculture and land use change, and in particular the combustion of fossil fuels.3 Many of these same activities, especially fossil fuel combustion,

2 Other effects of increased GHG emissions are not offset by aerosols, ocean acidification chief among them. 3 is applies overwhelmingly to coal and oil; natural gas combustion produces few aerosols (Ramanathan and Feng 2008).

6 1.1 e aerosol dilemma are also the principal anthropogenic GHG emission sources — aerosols and GHGs are oen emied simultaneously from a single underlying source. Aempts to curtail GHG emissions from these shared-source activities will then necessarily also curtail aerosol emissions.

Second, critically, while most GHGs have atmospheric residence times of decades to centuries, aerosol lifetimes are orders of magnitude smaller, measured in days to weeks. Atmospheric aerosol loading is therefore directly proportional to their emission rates at any point in time, hence so is their cooling eﬀect. Conversely, atmospheric GHG concentrations are a function of cumulative emissions, the particular gas species’ properties, and the response of the Earth-atmosphere cycles which remove them over time.4 Indeed, the changes already wrought by existing atmospheric carbon dioxide concentrations will likely persist for a millennium (Solomon et al. 2009) — even immediate cessation of all anthropogenic emissions would not ‘undo’ those changes, quite apart from the role of aerosols.

is residence time asymmetry means that abatement of fossil fuel GHG emissions will in general simultaneously diminish anthropogenic aerosol loading, not simply their emission rates. Aerosol cooling will thereby reduce immediately in response to any fall in emissions, while the GHG perturbation of climate continues, subject to the removal rate of the carbon and other cycles. Any loss of aerosol cooling therefore induces a matching increase in positive planetary energy imbalance: eﬀectively an instant increase in solar radiation incident at the planetary surface. Global mean surface temperature responds rapidly to such sudden changes in perturbation (Brasseur and Roeckner 2005; Mahews and Caldeira 2007; Ross and Mahews 2009); a precipitous removal of aerosols may therefore induce a rate of temperature rise beyond any yet experienced (see Chapter 3).

e dilemma posed by anthropogenic aerosols is hence more perverse than ﬁrst stated

4 Principally the carbon cycle, but also others such as that of nitrogen.

7 Chapter 1 Introduction and overview above. In achieving transformative action to signiﬁcantly reduce anthropogenic GHG emissions, aerosol masking is simultaneously diminished; yet the rates of natural atmospheric GHG removal processes are orders of magnitude slower than of aerosols, so near the full warming eﬀect of existing GHG concentrations is exposed. In seeking to arrest GHG warming, we may in fact strengthen it for a decade or more, with potentially dire climatic consequences for human and natural systems. e aerosol dilemma hence renders anthropogenic climate change a threat more complex and confounding than indicated by consideration of temperature trends and GHG emissions alone. Hansen (2009, 98) describes the situation starkly as ‘the Faustian climate bargain that humanity inadvertently entered into’.5

e aerosol cooling dilemma, properly understood, has profound implications for climate policy. It is the underlying contention of this thesis that those implications are to date largely unrecognised in the public and political discourse regarding anthropogenic climate change, and the policy mechanisms designed to address it. e role of anthropogenic aerosols may ultimately come to dramatically undermine mitigation policy mechanisms that remain blind to them.

1.2 On avoiding dangerous interference

e ultimate objective of this Convention … is to achieve … stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system. Such a level should be achieved within a time frame suﬃcient to allow ecosystems to adapt naturally to climate change, to ensure that food production is not threatened and to enable economic development to proceed in a sustainable manner. (United Nations 1992, Article 2)

e need for systemic abatement of anthropogenic GHG emissions is now long known.

5 Hansen explains that no maer what action humanity may take, fossil fuels must eventually run out, and at that point our payment comes due as aerosols disappear.

8 1.2 On avoiding dangerous interference

e ever-strengthening scientiﬁc understanding of climate change and the danger it poses has been periodically reviewed and articulated by the IPCC since their First As- sessment Report in 1990. e United Nations Framework Convention on Climate Change (UNFCCC) was adopted in 1992, with its o-cited objective of avoiding dangerous anthropogenic interference, reproduced above.

Aerosols’ role in anthropogenic climate change was broadly understood by the early 1990s, if not the finer details or full magnitude. e IPCC’s First Assessment Report discussed aerosols along with GHGs, but noted their net effect on climate forcing was as yet unclear. By 2001, the IPCC’s 2001 ird Assessment Report (TAR) devoted a full chapter to the effects of aerosols, and this work was substantially updated in AR4.6 In general however, aerosol cooling is not readily apparent as a factor in the evolution of climate change mitigation policy over the past two decades. Given the state of climate science from the start of that period, GHG emission rates, and perceived time available to properly enact changes to socioeconomic systems, that omission is understandable and not unreason- able. ough in a 1991 Nature article, Wigley raised the potential for increased warming as an unintended side effect of declining sulphate7 aerosol emissions as a consequence of fossil fuel abatement. His warning that ’[r]ather than acting as a panacea, the possible effects of fossil-fuel-derived sulphate aerosols should be seen as further reason for implementing controls on fossil-fuel use’ (p. 506) has evidently gone unheeded.

Continued omission of aerosol cooling from mitigation policy discourse is no longer reasonable — all of the factors listed above have now changed dramatically.

GHG emission rates have surged since the UNFCCC was ratiﬁed, accelerating in particular from 2000 to grow at about 3.4% per year8 (Allison et al. 2009; Richardson et al.

6 Indeed, AR4 clearly aributes the substantial uncertainties surrounding aerosols as the principal cause for the widening of the net anthropogenic forcing probability distribution compared to that of GHGs alone (see section 2.6). 7 Sulphate is an important aerosol species; see section 2.4.2.1. 8 Fossil fuel derived CO2 emissions were some 40% higher in 2008 than in 1990.

9 Chapter 1 Introduction and overview

2009); in fact, emission trends continue to track the upper bound of the most fossil fuel intensive scenario considered by the IPCC in the TAR or AR4, A1FI, though the airborne fraction appears to be remaining steady. Even the demand destruction eﬀects of the global ﬁnancial crisis of 2007 to 2009 did lile more than momentarily suppress GHG emissions, as shown in Figure 1.1.9

Figure 1.1: Global CO2 emissions and carbon intensity (Fig. 1a, Peters et al. 2012). CO2 emissions from fossil fuel combustion and cement production on le axis (black line), carbon intensity of world GDP in US$ (2000) on right axis (red line). Reprinted by permission from Macmillan Publishers Ltd: Nature Climate Change, copyright 2012.

Global CO2 emissions from fossil fuel combustion and cement production recovered in 2010, growing 5.9 % (compared to the 2009 fall of only 1.4 %), an increase of 0.59 Pg C to 9.1 ±0.5 Pg C — the highest ever annual jump, beating the previous records set in 2003 and 1979 (Peters et al. 2012). e IEA reported a further, though smaller, 3.2 % rise in

9 Peters et al. (2012, 2) state that ‘For recent decades, the growth in global CO2 emissions can be explained mainly by the growth in economic activity corrected for decreases in the fossil-fuel carbon intensity (FFCI) of the global economy’. However, the downward trend in FFCI between 1980 to 2000 of 1.4 %/yr shrunk to 0.9 %/yr from 2000, which the authors interpret as indicating ‘the positive trend of improvements in carbon intensity reversed’ (p. 2).

10 1.2 On avoiding dangerous interference

fossil fuel CO2 emissions in 2011 (International Energy Agency 2012).

Knowledge of the climate system has also increased enormously in the past two decades. Improved understanding and ongoing observations increasingly indicate climatic impacts more severe and more rapid than expected even a few years earlier, and with remaining uncertainties surrounding some important aspects of climate science pointing only toward more deleterious outcomes (Allison et al. 2009; Steﬀen 2009; Richardson et al. 2009; Garnaut 2011). e time frame for eﬀective mitigation action contracts due to both the persistent lack of any decline in emissions, and continual downward revisions of emission levels at which truly dangerous climate changes will occur. Perhaps most importantly of all, critical and far-reaching shis in climate science and in observations have occurred since the literature reviewed in AR4 was published. Prominent examples include:

• e precipitous decline in Arctic sea ice extent and volume, far exceeding worst- case AR4 projections, with consequent feedback reduction of ice albedo and markedly enhanced regional warming (Lindsay et al. 2009; Eisenman and Welaufer 2009; Allison et al. 2009; Comiso et al. 2008; Kwok and Rothrock 2009).

• Accelerating rates of mass loss in both the Greenland and Antarctic ice sheets (Rignot et al. 2011; Svendsen et al. 2013), beer understanding of ice sheet dynamics, and strong upwards revisions of projected sea level rise (Rahmstorf 2010; Vermeer and Rahmstorf 2009).

• Firming consensus that cumulative emission budgets are more deterministic of eventual climate changes than year-on-year emission rates, or even atmospheric GHG concentrations (Zickfeld et al. 2009; Meinshausen et al. 2009; England et al. 2009; Allen et al. 2009).

• e paleoclimatic evidence suggesting that ‘safe’ atmospheric GHG levels have already been surpassed (Hansen et al. 2008), and the related concept of tightly-

11 Chapter 1 Introduction and overview

coupled planetary boundaries within which humanity must operate (Rockström et al. 2009).

e implications of these shiing dynamics for the continued relevance of the o-cited 2 ℃ target are emblematic. As a ‘guard rail’ against dangerous anthropogenic interference in the climate system, limiting global warming to an increase of 2 ℃ beyond preindustrial levels10 has become a widely-nominated goal in international mitigation discourse in recent years (Richardson et al. 2009; WBGU 2009; Anderson and Bows 2011). Yet the progression of climate science makes that goal now decidedly problematic. Not only does a 2 ℃ limit realistically require serious mitigative action with net declines beginning by 2020 at the latest11 (WBGU 2009; Meinshausen et al. 2009; England et al. 2009), the target itself ‘now more appropriately represents the threshold between “dangerous” and “extremely dangerous” climate change’ (Anderson and Bows 2011, 20).

e looming risks of triggering multiple points of nonlinearity underscore re-framing of the problem. An array of ‘tipping points’ within the Earth system exist as probability distributions along a continuum of temperature increases, and some may be triggered before the 2 ℃ threshold is breached (Schellnhuber 2009; Lenton et al. 2008; Kriegler et al. 2009; Rockström et al. 2009; Ramanathan and Feng 2008). Smith et al. (2009) updated the iconic ‘burning embers’ illustration of assessed dangerous interference from the TAR, concluding that comparatively smaller increases in global temperature now result in ‘significant or substantial consequences’ in all five of the assessment framework’s ‘reasons for concern’. A further example of these seemingly constant revisions toward impacts occurring both faster and with greater severity is given by Robinson et al. (2012). eir recent fully coupled model simulation of Greenland ice sheet stability finds previous assessments systematically understate its sensitivity to climate change:

10 Global mean surface air temperature has risen by ≈0.8 ℃ Hansen et al. (2010); AR4 reported the increase from 1850–1899 to 2001–2005 is 0.76 ℃ (IPCC 2007b). 11 And even then with probabilities around 66%.

12 1.2 On avoiding dangerous interference an ice-free state may be initiated at global mean temperature rise of 1.6 ℃.12

Indeed, climate changes so far manifest with a rise of ‘only’ 0.8 ℃ are already damaging human and natural systems. Human health and mortality is increasingly being affected by anthropogenic climate change, especially by extreme weather and disruption to water supplies (Richardson et al. 2009). A wide range of natural ecosystems are negatively affected, and by the rate of climate changes as well as their magnitude. Ocean acidification is a major threat, now progressing at a rate beyond any comparable extinction period in Earth’s history (Richardson et al. 2009). e intensity and frequency13 of extreme weather events is growing (Richardson et al. 2009; Smith et al. 2009), and anthropogenic climate change has almost certainly substantially enhanced their risk (Schiermeier 2011; Garnaut 2011). Assessing the past decade of extreme events, Coumou and Rahm- storf (2012, 5) conclude that ‘anthropogenic, unprecedented heat and rainfall extremes are here — and are causing intense human suffering’. e extreme and unprecedented weather conditions in south east Australia on February 7, 2009, coincided with the ‘Black Saturday’ bush fires, the country’s worst natural disaster in a century (Karoly 2009).14 e scenario-based speculative investigation of Dyer (2010) into the consequences for international security and human welfare — and human warfare — throughout this century as climate change progresses makes for ominous, distressing reading.15

International negotiations under UNFCCC auspices to cra a successor to the modest 1997 Kyoto Protocol foundered badly in Copenhagen in 2009 and fared lile beer in Cancún in 2010, nor since. ere are few indications that growth in global GHG emissions will be arrested in the near future; an ultimate emissions peak before 2020 is far from assured. Negotiations also suﬀer from a growing disconnect with the underlying 12 is is the best estimate of Robinson et al. (2012), but with a 95 % CI range from 3.2 ℃ to as lile as 0.8 ℃. 13 ough not necessarily both in all instances. 14 On that day, parts of the region recorded ﬁre danger indications of 190 or more, on a scale intended to have a maximum of 100, calibrated for the previous most deadly event of 1939. 15 It should be noted that Dyer’s material oen cites military planners and strategists, including the Pentagon.

13 Chapter 1 Introduction and overview science: talks continue to frame policy decisions in terms of distant emission reduction targets, despite the science pointing to cumulative emission budgets as the appropriate framework (Anderson and Bows 2011). Assessment by the UN Environment Program showed that the sum of international abatement commitments under the Copenhagen Accord have effectively zero probability of holding to the 2 ℃ limit (den Elzen et al. 2010). Updating that work, den Elzen et al. (2012) find that for a ‘likely chance’ of restraining temperature rise to 2 ℃, the total abatement pledges confirmed at Cancún leave an emission gap in 2020 of between 7 Gt CO2 to 11 Gt CO2 above the approximately 46 Gt CO2 maximum required — 2.5 Gt CO2 greater than their earlier estimate. In the words of New et al. (2011, 6), a 2 ℃ target is now ‘extremely difficult, arguably impossible’. In fact, recent modelling of the A1FI scenario finds a likely temperature rise of 4 ℃ by the 2070s, and possibly by the early 2060s if carbon-cycle feedbacks turn out to be at the higher end of uncertainty (Bes et al. 2011). e geophysical realities of a world 4 ℃ warmer than the preindustrial16 would be truly catastrophic (Garnaut 2011).

GHG emissions continue without relent. International mitigation negotiations have to date produced lile of consequence. Further warming is in the pipeline due to the current planetary energy imbalance, regardless of any emission reductions. Damaging climate change impacts are already apparent, and worsening. And climate science advances rapidly, new understanding consistently raising the level of threat and shortening the time remaining to forestall dangerous anthropogenic interference. It is in this context that the aerosol mask must be considered.

1.3 Research questions

e principal objective of this research is to answer the overarching question, ‘Do anthropogenic aerosols represent a threat to climate change mitigation eﬀorts, and if so

16 Which involves signiﬁcant regional temperature variations beyond that mark.

14 1.3 Research questions what is an appropriate policy response?’ I hold that the missing account of anthropogenic aerosols in mitigation policy needs to be addressed. It may be that their likely role under advancing mitigation efforts is sufficiently benign that recognition alone is satis- factory. However, if a full understanding of the role of anthropogenic aerosols necessit- ates material changes to the policy response, those changes must be clearly identified. A further set of research questions therefore commend themselves:

1. What are atmospheric aerosols, what are their sources, and what is their role in anthropogenic climate change, both qualitatively and, to the extent it is possible to determine with current knowledge, quantitatively?

2. How might aerosol climatic eﬀects be measured in a policy-relevant manner, analogous to global warming potential and carbon dioxide equivalence?

3. To what degree do existing major climate change mitigation policy instruments account for aerosol eﬀects?

4. Is there a distinct need for climate change mitigation policy instruments to separately and directly account for aerosol eﬀects?

a) Given that model-derived projections of unmitigated future climate change have commonly been driven by SRES scenarios, and that these scenarios include broadly declining aerosol emissions, is there a need for aerosol emissions to be explicitly considered by policy instruments?

b) Do explicit mitigation scenarios, such as those based on cumulative emission budgets, similarly include declining aerosol emissions, and to what extent?

c) Do such scenarios adequately reﬂect the current state of both climate science and emission trends, especially the pace and impact of climate changes already underway?

5. What design criteria should aerosol-aware mitigation policy instruments meet to be eﬀective?

15 Chapter 1 Introduction and overview

a) Is it possible to amend existing policy instruments to address these criteria?

b) Are there particular circumstances or consequences pertaining to aerosol eﬀects that must be identiﬁed and managed within such mitigation instruments?

6. What novel mitigation policy instruments or policy frameworks, plausibly meet- ing these criteria, can be identiﬁed?

a) What are the pros, cons, and tradeoﬀs of multiple approaches?

1.4 Methodology

1.4.1 Research approach

My research approach consists of three components:

1. Review and synthesis of existing literature deﬁning the physical science of aerosols’ role in anthropogenic climate change. is synthesis aims to establish the geophysical basis of the subsequent implications for mitigation policy, including the relation between aerosol cooling and dangerous anthropogenic interference.

2. Critical analysis of contemporary mitigation policy approaches in light of these implications, seeking to assess their capacity to incorporate anthropogenic aerosol eﬀects, and any complications that may arise.

3. Development of novel insights into the necessary form of a valid policy response to aerosol cooling. is component is inherently based on my underlying initial hypothesis that existing policy frameworks do not have capacity adequate to the task.

e ﬁrst two components of this thesis are therefore predominantly qualitative in nature; however, importantly, the particular climatic properties and impacts of anthropogenic

16 1.4 Methodology aerosols are expressed and considered quantitatively where appropriate and to the extent possible. e third component is informed by policy analysis frameworks such as that articulated by Oikonomou and Jepma (2008), and employs relatively simple microeco- nomic constructs in developing the prototype policy response model given in Chapter 6.

1.4.2 Thesis structure

is Introduction deﬁnes the broad scope of the aerosol dilemma, siting it within the current geophysical and sociopolitical realities of anthropogenic climate change and its impacts. Chapter 2 establishes the physical science basis of aerosols’ role in climate, identifying and explaining their mechanisms of action; the current limitations and persistent uncertainties in forward modelling quantitative estimates are highlighted. Chapter 3 examines alternative quantitative approaches based on planetary energy imbalance made possible by recent advances in measurement of ocean heat content, laying out the implications for dangerous anthropogenic interference. Chapter 3 also investigates alternative metrics that are more suited to measuring aerosol eﬀects, and reviews current and projected aerosol emission data.

From this physical science basis, Chapter 4 presents a critical analysis of mitigation policy and the challenges posed by a full recognition of aerosol cooling. Chapter 5 confronts these challenges and establishes a revised and expanded aerosol-integrated mitigation policy objective, specifying a set of design criteria that any implementing policy instruments must satisfy. Further investigation of possible implementation models in the second half of Chapter 5 generates additional principles that help frame a successful, workable policy solution. Chapter 6 then builds on these principles to develop a prototype model, analysing its implementation in detail.

Chapter 7 concludes the thesis, re-visiting the research questions given in section 1.3 and discussing potential further research, as well as the limitations of this work.

17 Chapter 1 Introduction and overview

1.4.3 Nomenclature

Relevant technical terms found in the literature are typeset in sans serif font of slightly larger size than the surrounding text when ﬁrst used. New custom terms introduced in the thesis are similarly typeset and additionally italicised. A Glossary of acronyms and key terms is given at the end of the document, following the References list.

18 2 Aerosol eﬀects on climate

Aerosols form an ‘integral part’ of the atmosphere’s radiation budget and hydrological cycle (Denman et al. 2007). eir effects on the climate system are wonderfully complex; the role of greenhouse gases in anthropogenic climate change are almost simple by comparison. Indeed, Andreae et al. (2005) remark that the ‘tremendous growth’ in scientific understanding of aerosol effects and parallel emergence of likely positive carbon cycle feedbacks have ‘transformed the orderly picture of climate change of the early 1990s, dominated by GHG warming, into a complex mix of opposing effects’ (p. 1187). is complexity manifests in the diversity of aerosol species and emission sources; their atmospheric interactions and removal processes; their breadth of optical and radiative properties, sometimes conflicting and opposing one from the other; and the number of distinct climatic effects that have been identified.

is chapter summarises current scientific understanding of aerosols based on a review of the literature. Aerosol physical and optical properties are first examined in a general sense, leading into a detailed examination of radiative forcing, the central measure of climatic influence. Individual aerosol species are then catalogued, giving an overview of their physical influences and relative importance. e array of radiative and microphysical mechanisms generating these effects is then explained, including available quantitative assessments. e chapter concludes with discussion of the observational and modelling challenges posed by aerosols, and their consequent role as key sources of uncertainty in understanding climate response. A sequential order of these topics is

19 Chapter 2 Aerosol eﬀects on climate necessary, but of course some aspects rely on knowledge of others, so cross-referencing is used to make these links clear.

2.1 Physical characteristics

Aerosols are solid or liquid –phase particles with an effective diameter of about 0.05 to 10 µm, though some sea salt or mineral dust particles may near 20 µm (CCSP 2009; Penner 2000). Most aerosol particles influence climate during their short lifetimes suspended alo in the atmospheric column, but some types such as black carbon have significant effects when seled on surface snow and ice. Unless otherwise stated, this thesis is concerned with anthropogenic aerosol emissions to the troposphere; periodic natural volcanic emissions to the stratosphere have significant effects, but anthropogenic inputs here are relatively minor (emied by aircra).

2.1.1 Formation and composition

A fundamental distinction in aerosol species relates to their composition and form of emission. Primary aerosols are those emied at source in particulate form directly. Whereas secondary aerosols are emied at source as precursor gases, then undergo secondary chemical and photochemical reactions in the atmosphere to form particulates — sulphates formed from gaseous sulphur dioxide are a prominent example of this. In- terestingly, processing of gaseous emissions within clouds (section 2.5.2 and 2.5.3) is an important pathway for aerosol gas to particle conversion.

2.1.2 Mixing state

Aerosol composition is also distinguished by the chemical particle mixing state. In- ternally mixed aerosols occur when various species undergo further chemical reac-

20 2.1 Physical characteristics tions to form new particles as some combination of their constituents. For example, precursor gases may condense around organic carbon particulates, creating an external layer which then aﬀects the optical and radiative properties of the resultant aerosol.

Externally mixed aerosols occur when various species co-exist within the same location but remain unadulterated in their original form. at is, the distinct separate aerosol species do not undergo further chemical reactions.

2.1.3 Atmospheric residence time and emissions

e atmospheric residence time of aerosols — the time period within which they remain climatically active within the atmosphere — is dramatically diﬀerent to that of most GHGs. At this point it is important to distinguish the subset of GHG species that are characterised as well-mixed and long-lived (LLGHGs), such as CO2, CH4,N2O, and the halocarbons. While these LLGHGs are most commonly the focus of discussion, other GHG species are also a concern. A prominent such short-lived species is tropospheric ozone (O3), a potent GHG but with a residence time of at most a few months and concentrations localised near the site of atmospheric formation.1 Note that while water vapour is also a powerful GHG, vital to the natural greenhouse eﬀect, it has a residence time of around 10 days and is a temperature-controlled feedback process rather than a forcing agent (Schmidt et al. 2010).

e atmospheric residence times of the LLGHGs are on the order of decades to centuries.2 In stark contrast, tropospheric aerosol lifetimes are measured in days and most persist less than a week before being removed by meteorological processes such as wet or dry deposition (eg, Textor et al. 2006; Schulz et al. 2006). Wet deposition is the primary

1 Ozone forms via photochemical reaction of precursor gases such as carbon monoxide, non-methane volatile organic compounds, and nitrogen oxides (Rypdal et al. 2005). 2 Around 20 % of any carbon dioxide slug emied to the atmosphere remains aer a thousand years, and indeed equilibrium climate response is still only 90 % at that time (Hansen et al. 2007a; Solomon et al. 2009).

21 Chapter 2 Aerosol effects on climate mechanism for removal, and so its efficiency depends on aerosols’ proximity to cloud (CCSP 2009). Aerosols resident within the stratosphere are ‘above the weather’ and so not subject to removal by wet deposition; they may survive a few years.3 is residence time asymmetry between aerosols and LLGHGs is a central complicating factor for effective climate mitigation policy, as examined in Chapter 4 onwards.

Partly as a consequence of their short residence times and partly due to their inherent optical and radiative characteristics, the physical location of emission is an important factor in aerosol effects on climate — they exhibit large spatial and temporal heterogeneity. Aerosol forcing is significantly greater in areas of high emission concentration, reflecting their lack of mixing in the atmosphere compared to the LLGHGs. Most importantly, atmospheric aerosol loading is fundamentally a function of emissions at any point in time (Andreae et al. 2005); in fact, historical cumulative emissions are of lile real consequence, so instantaneous emissions are generally the only useful metric (Andronova and Schlesinger 2004).4 Composites of model studies indicate that aerosol burden5 is nearly 50 % greater over land areas, as expected (CCSP 2009).

Regionality is then crucial for understanding and quantifying aerosol climatic impacts — global means may well obscure signiﬁcant changes occurring in diﬀerent regions (CCSP 2009). In contrast, LLGHGs are well-mixed throughout the atmosphere and global means are far more appropriate.

2.1.4 Atmospheric distribution

Aerosol mass loading, M, is a measure of the atmospheric burden, determined by the combination of emission proﬁle, atmospheric transport, and removal processes (CCSP

3 Stratospheric aerosols of volcanic origin have an e-folding decay time of approximately 1 year (Hansen et al. 2007b). 4 ese physical characteristics are important for mitigation policy formation, as detailed in section 4.1. 5 Measured as aerosol optical depth, deﬁned in section 2.2.5.

22 2.1 Physical characteristics

2009). It is simply the aerosol mass quantity per unit area, g m−2. Related terms include the aerosol particle number concentration, a count of particles per unit volume (Spracklen et al. 2010), or simply particle number, the count per unit length (eg, in a plume column). Particle number concentration, and the size distribution of particles for a given mass (see section 2.2.1), are an important factor for indirect eﬀects on cloud through their relation to the cloud droplet number concentration, Nd (see section 2.5.2) (Spracklen et al. 2010; CCSP 2009).

e vertical profile of aerosol particle injection and dispersion in the atmospheric column can significantly affect resultant radiative properties. Altitude, and presence above, below, or within clouds are important observational parameters. AR4 notes that the vertical structure of aerosols and other forcing agents influences forcing efficacy (section 2.3.1), and other aspects of climate response such as regional and altitudinal temperature profiles and changes to the hydrological cycle (Forster et al. 2007).

2.1.5 Perturbation baseline

As forcing of climate (see section 2.3) is calculated with reference to some baseline con- ﬁguration of the Earth-atmosphere system, the choice of that baseline is important for assessing the eﬀects of aerosols on climate. In particular, is the baseline to be a total absence of aerosols, an absence of anthropogenic species only, or calculated relative to preindustrial loading? e preindustrial is the most commonly chosen baseline, commencing around 1750, though estimating preindustrial atmospheric aerosol loading is far from simple (see section 2.7.2). Comparisons to a hypothetical totally unperturbed atmosphere may also be useful in certain contexts.

23 Chapter 2 Aerosol eﬀects on climate

2.2 Optical and radiative properties

e subsections that follow explain important aerosol optical and radiative properties. To a first order though, aerosols are characterised by their relative propensity for scattering or absorption of incoming solar shortwave radiation (SW), that portion of the solar spectrum occurring in the visible and near-visible wavelengths.6 e specific effects are generally wavelength dependent, with the strongest aerosol interactions occurring with wavelengths similar to their diameter (Penner 2000). at is, involving Mie scattering,7 which is predominantly in the forward direction, especially when particle radius approaches light wavelength (Sturman and Tapper 2005). Reflection is a particular form of scaering caused by particles or surfaces of larger size where the incident radiation is scaered backward.

Generally, scaering aerosols exert a negative forcing — a cooling inﬂuence. Absorbing aerosols are more complex, however — they may exert either positive or negative forcing, depending on local conditions such as the presence of cloud cover, or their location alo above bright reﬂective surfaces.

2.2.1 Particle eﬀective radius

Aerosol particles will not have a uniform size in a given atmospheric loading. Particle size is represented by the eﬀective radius of the lognormal aerosol radius distribution

(essentially a mean), denoted re (Tsigaridis et al. 2008; Hansen and Travis 1974). ´ r2n(r)rdr r = ´ (2.1) e r2n(r)dr

6 e terms sunlight, solar radiation, shortwave radiation, and insolation are used fairly interchangeably; any diﬀerences will be clear by context. See the shortwave radiation Glossary deﬁnition. 7 Contrasted with Rayleigh scaering, where the particle, such as atmospheric gas molecules, is much smaller than the incident light wavelength (an order of magnitude) and scaering occurs in all directions.

24 2.2 Optical and radiative properties

Where r is the monodisperse8 particle radius and n(r) is the particle radius distribution; integration is over the whole lognormal distribution range. Aerosol indirect eﬀects on cloud are principally related to changes in aerosol re and particle number concentration.

2.2.2 Absorption, scaering, and extinction coeﬀicients

ree related parameters characterise the fraction of radiant energy aenuated by absorption and scaering; all are wavelength speciﬁc. Aenuation of incoming solar radiation in a direct beam to the surface by either absorption or scaering is referred to as extinction (Sturman and Tapper 2005). ough as discussed in section 2.5.11, scaered radiation may still reach the surface and aﬀect climate.

e extinction coeﬃcient is the total fraction of incident radiation extinguished by both absorption and scaering, with reference to path length through the atmosphere measured per unit distance (m−1, km−1, even Mm−1 ). e absorption and scattering coeﬃcients are the fraction of incident radiation that is, respectively, only absorbed or scaered.

ese coefficients are also defined as specific quantities with reference to unit aerosol mass, expressed as unit area per mass (m2 g−1). e specific extinction coefficient kext is defined as (aer Haywood and Shine 1997; Tsigaridis et al. 2008).

3qext kext = (2.2) 4ρre

Where qext is the dimensionless specific extinction efficiency of the mixed aerosol (com- puted through Mie calculations across the lognormal particle size distribution), ρ is the −3 aerosol density (g m ), and re is the effective particle radius as above. e specific

8 Meaning, uniform.

25 Chapter 2 Aerosol effects on climate extinction coefficient is the sum of the specific scaering and absorption coefficients.

kext = ksca + kabs (2.3)

2.2.3 Asymmetry parameter

e asymmetry parameter, denoted g, indicates the angular character of aerosol particle scaering. It is the average cosine of the scaering angle weighted by the scaered light intensity, derived by integrating over the complete scaering phase function (the angular distribution of scaered light) (Andrews et al. 2006; CCSP 2009). ˆ 1 π g = cos θP (θ) sin θdθ (2.4) 2 0

Where θ is the angle between incident light and scaering direction and P (θ) is the phase function. A value of –1 is entirely backscaering, 0 is isotropic (uniform scaering in all directions), and a value of +1 is entirely forward scaering. At a wavelength of 0.50 µm, dry aerosol particles have an indicative range for g between 0.64 and 0.83, and an average of 0.72 for all species; under conditions of high relative humidity, g increases to an average of 0.79 and a range 0.64 to 0.82 (Andrews et al. 2006).

e aerosol asymmetry parameter is used within climate radiative transfer models (ie, as a parameterisation) as computing the actual phase function is relatively ineﬃcient and computationally expensive.

2.2.4 Single scaering albedo

Single scattering albedo (SSA; also denoted ωo) is a dimensionless property expressing the fraction of incident radiation scaered by aerosols, deﬁned as the ratio of scaering to total radiation extinction (Tsigaridis et al. 2008). ‘Single’ refers to ‘small volume

26 2.2 Optical and radiative properties elements’ rather than the atmosphere as a whole (Hansen and Travis 1974).

ksca ωo = (2.5) kext

Where ksca is the wavelength-dependent mass-specific scaering coefficient. An SSA of 0 is purely absorbing, while purely scaering aerosols have a value of 1. Most natural aerosols have an SSA greater than or equal to about 0.75 at peak visible wavelength, 0.55 µm. Scaering aerosol species have SSA values around 0.9 or higher; strongly reflective aerosol loadings have values above 0.95, and sulphates may be as high as 0.99 (CCSP 2009; Ramanathan et al. 2001).

2.2.5 Aerosol optical depth

Aerosol optical depth (AOD; also denoted τaer) is a key measure of aerosol optical properties. AOD indicates the optical ‘thickness’ of the atmosphere, integrated throughout the vertical column; AOD is hence also commonly referred to as aerosol optical thickness (eg, Yu et al. 2006). Optical depth is a wavelength-speciﬁc dimensionless value indicating the degree to which aerosols prevent the transmission of incoming sunlight; ie, the fraction of total light extinguished by scaering or absorption (Tsigaridis et al. 2008; CCSP 2009).

AOD = kextM (2.6)

Where M is the aerosol mass loading (section 2.1.4) and kext is the mass speciﬁc extinction coeﬃcient (section 2.2.2). An AOD value near 0 represents clear skies, while polluted or ‘thick’ skies have values approaching 1.15.

Importantly, light wavelengths are aﬀected in diﬀerent ways by the combined aerosol burden. Unless otherwise stated, AOD is assumed to refer to a wavelength of 0.55 µm,

27 Chapter 2 Aerosol eﬀects on climate peak visible light. AR4 found good agreement between models and observations for an anthropogenic AOD fraction of approximately 21 % to 26 % (Forster et al. 2007); the later CCSP (2009) review similarly reported a fraction of 25 %, noting that this varies signiﬁcantly over populated land masses.

Figure 2.1 illustrates the characteristic contemporary AOD for all anthropogenic aerosol species, as derived from model studies assessed in AR4 — the strong spatial heterogeneity and concentrations over land areas of high industrial (or biomass burning) activity are clearly evident.

Figure 2.1: Characteristic AOD for combined anthropogenic aerosol loading (Fig. 2.12 panel (a), Forster et al. 2007). Data are derived as the mean result for the nine AeroCom models assessed in original reference. Copyright the Intergovernmental Panel on Climate Change, 2007.

28 2.3 Radiative forcing

2.3 Radiative forcing

e most commonly employed means to quantify the climatic effects of an agent is radiative forcing. It is important, therefore, to clearly define this term and examine its particular methodologies before continuing further, basing that definition on the discussion of physical and optical properties laid out above.

A radiative forcing (RF) is a perturbation to the Earth’s radiative energy budget relative to some base configuration of atmospheric conditions. at is, a change in the Earth-atmosphere system’s boundary conditions. Radiative forcing is a change in the net downwelling minus upwelling irradiance, of both shortwave plus longwave energy fluxes, measured in W m−2 averaged over the Earth’s surface area (Forster et al. 2007; Ramaswamy et al. 2001). Longwave radiation (LW) is that portion of the spectrum with wavelengths in the thermal infrared and larger; shortwave radiation was defined earlier (see Glossary). A positive radiative forcing indicates that additional energy is retained within the Earth-atmosphere system, either as increased incoming shortwave radiation or decreased outgoing longwave radiation (OLR) emied from the planetary surface and atmosphere. A negative radiative forcing is the opposite, where incoming shortwave radiation received at the surface is reduced or outgoing radiation exceeds energy absorbed.

e Earth-atmosphere system is in radiative equilibrium in the absence of such perturbations: energy received and absorbed at the planetary surface as shortwave solar radiation — total incoming radiation minus the fraction reﬂected back to space by planetary albedo — is balanced by emied outgoing longwave radiation (Hansen et al. 2005a;

Pierrehumbert 2011). OLR is in turn a function of global mean surface temperature, TS, and the emissivity of the Earth, determined in large part by atmospheric composition. A radiative forcing is a disruption to this equilibrium. A positive RF causes a warming eﬀect: additional energy absorbed into the land and sea surfaces and the atmosphere

29 Chapter 2 Aerosol eﬀects on climate

9 causes TS to rise until OLR (which is initially suppressed ) is again suﬃcient to balance absorbed surface radiation. Conversely, a negative RF has a cooling eﬀect due to the net reduction in energy.

Increased atmospheric greenhouse gas concentrations exert a positive RF through enhanced atmospheric absorption of OLR, which thereby reduces radiation to space — emissivity of the Earth is reduced. TS will rise accordingly via a series of feedback processes until reaching a temperature at which OLR is again in balance, given the mod- iﬁed emissivity. Conversely, increased atmospheric aerosol loading, through an array of mechanisms described in section 2.5, generally serves to enhance planetary albedo and hence exerts a negative RF by increasing shortwave reﬂection back to space. TS will fall in response, reducing OLR until it again equals the decreased shortwave radiation absorbed at the planetary surface.

Radiative forcing (RF) is linearly related to the change in global mean surface temperature (TS) by the equilibrium climate sensitivity parameter, denoted λ, as per eq. 2.7.

Climate sensitivity is hence deﬁned as the resultant change in TS in kelvin per unit forcing, giving units of kelvin per was per square metre (K(Wm−2)−1).

∆TS = λ RF (2.7)

To provide a more intuitively meaningful indication of Earth-atmosphere response, climate sensitivity is also commonly expressed as equilibrium ∆TS in ℃ (or kelvin) caused 10 by a doubling of atmospheric carbon dioxide concentrations (2×CO2), which equates to an RF of approximately 4 W m−2. e value of climate sensitivity under current geologic conditions is a key area of uncertainty, in part due to the role of natural aerosols and dynamic cloud processes. A widely reported estimate is 0.75 ±0.25 K/W m−2, which equates to 3 ±1 ℃ for a doubling of CO2 (eg, Hansen et al. 2005a). AR4 cites a best es- 9 See detailed explanation in section 3.2. 10 Temperature responds to rising CO2 concentrations logarithmically.

30 2.3 Radiative forcing timate of 3 K, with an asymmetrical likely range of 2 K to 4.5 K (IPCC 2007b). In their review of the literature, Knui and Hegerl (2008) note that reducing uncertainty in climate sensitivity is a serious challenge and that the range has not narrowed substantially since the ﬁrst estimates of the late 1970s.

A perhaps surprising range of particular RF calculation methodologies exist, and definitions vary significantly. Major methodologies are illustrated in Figure 2.2. RF is defined with reference to related but distinct degrees of included temperature and atmospheric response, and to the location within the atmosphere at which the forcing is calculated — at the top of the atmosphere (TOA), at the tropopause,11 and at the planetary surface (see Hansen et al. 2005b, for detailed discussion). Given the importance of IPCC assessment reports to climate change policy, the methodology usually applied therein12 is worth making explicit: radiative forcing is the net change in irradiance at the tropopause aer allowing stratospheric temperature to adjust to equilibrium13 while holding surface and tropospheric state fixed at their initial, unperturbed values (Forster et al. 2007).

is is the stratospherically-adjusted RF,Fa in Figure 2.2. Stratospheric temperature adjustment occurs on the order of months, whereas adjustment of the tightly coupled ocean-troposphere requires several decades — tropospheric temperature change, calculated at the tropopause, is hence the principal indicator of the ultimate climate response

(Hansen et al. 1997, 2005b) and Fa is employed for this reason.

However, Forster et al. (2007) recognised that Hansen et al. (2005b) and other studies argued that for some aerosol eﬀects that are not initially radiative — because they involve feedback responses — the ‘zero-surface-temperature-change RF’ is a beer predictor of the equilibrium surface temperature response than is Fa (pp. 134, 196). is is

11 e boundary of the troposphere and stratosphere. 12 e Fourth Assessment Report continued the same deﬁnition adopted in the ird (and earlier). 13 ough for most aerosols stratospheric temperature adjustment makes lile diﬀerence to the resultant RF (Forster et al. 2007, 134).

31 Chapter 2 Aerosol eﬀects on climate

Figure 2.2: Illustration of RF calculation methodologies (Fig. 2, Hansen et al. 2005b). Reproduced with kind permission from John Wiley & Sons, Inc.

the ﬁxed-ground-temperature methodology (Fg in Figure 2.2), which speciﬁcally permits the necessary changes in tropospheric state, including water vapour and temperature.

For absorbing aerosols in particular, Forster et al. (2007, 196) concede Fa could ‘fail’ as a useful predictor because it does not readily incorporate the semi-direct (section 2.5.4) and cloud lifetime eﬀects (section 2.5.3). In fact, in their seminal study, Hansen et al.

(2005b) concluded that the fixed-sea-surface-temperature forcing (Fs in Figure 2.2) is the best predictor of ultimate climate change for all forcings.14 Nonetheless, Forster et al. (2007, 137) conclude that although the definition of RF employed by AR4 could have been modified to accommodate these processes, the stratospherically-adjusted RF (Fa) was retained for reasons of pragmatism and simplicity — relating to climate modelling response and their uncertainties, and cloud changes in particular — and these indirect effects are consequently not considered RFs.

A further crucial distinction concerns perturbations to the surface radiative energy budget. ese changes — a prominent feature of some aerosol eﬀects (see section 2.5.4) — are termed surface forcing and should not be directly compared with radiative forcings as they ‘have quite diﬀerent properties’ (Forster et al. 2007, 133). See section 2.5.11 for full discussion of surface forcing.

14 See also the Fg and Fs Glossary deﬁnitions.

32 2.3 Radiative forcing

2.3.1 Forcing eﬀicacy

e concept of forcing efficacy, developed aer the TAR, allows comparison of the ultimate temperature response (equilibrium ∆TS) for a given forcing agent in relation to carbon dioxide, thereby addressing some of the ambiguity in RF definition. Efficacy, E, is defined as the ratio of the climate sensitivity for the specific forcing agent i to that of

CO2 (Hansen et al. 2005b; Forster et al. 2007).

λi Ei = /λCO2 (2.8)

Efficacy can in turn be used to define an effective RF for a given agent:

RF = Ei RFi (2.9)

Because efficacy is derived from climate sensitivity — which inherently encompasses all elements of the ultimate climate response — calculations of E for stratospherically- adjusted RF are able to incorporate cloud-aerosol interactions beyond that of the indirect cloud albedo effect, explained in section 2.5.2. Forcing efficacy depends primarily on the spatial structure of the forcing agent (refer to section 2.1.4) and its projection onto climatic feedback mechanisms (Forster et al. 2007). e geographical and vertical distributions are of most significance: studies have found that high-latitude forcings have higher E values than those in the tropics.

33 Chapter 2 Aerosol eﬀects on climate

Figure 2.3: Range of forcing eﬃcacies reported in AR4 (Fig. 2.19, Forster et al. 2007). Copyright the Intergovernmental Panel on Climate Change, 2007.

Forcing efficacies in the literature were surveyed in AR4 and are summarised in Fig- ure 2.3, where each leer represents a specific study. e Hansen et al. (2005b) study is a prominent work in the field, represented by leer a (see Fig. 2.19 of Forster et al. (2007) for full details). e efficacies for direct and indirect aerosol effects are discussed in section 2.5.

2.4 Species

A diversity of individual aerosol species exists, with marked variation in optical and radiative properties, emied by a wide range of sources. Natural aerosols are initially outlined below, followed by a detailed examination of anthropogenic species, the particular focus of this thesis.

34 2.4 Species

2.4.1 Natural aerosols

Naturally occurring aerosols are a fundamental component of the Earth-atmosphere system, acting as part of planetary albedo directly and, perhaps most importantly, as condensation nuclei for cloud formation (see section 2.5.2). ough anthropogenic aerosol emissions have had signiﬁcant impact on climate since the industrial revolution (and some such as black carbon and biomass burning, long before), they constitute around 10 % of the current overall atmospheric loading (CCSP 2009) — natural aerosol species remain the clear majority. e principal natural aerosol species are listed below (Den- man et al. 2007):

• Particulate sea salt is the most prevalent natural aerosol, ejected into the marine troposphere by breaking ocean waves. It plays a key role in the formation of clouds.

• Windborne dust, ejected from land surfaces, especially deserts — the Sahara is an important source.

• Sulphates are formed naturally from volcanic emission of gaseous sulphur dioxide and, in particular, biological emission of dimethylsulphide (DMS) by marine phytoplankton.

• Organic and inorganic carbon compounds are produced by natural wildﬁres and other biogenic sources, including secondary formation from volatile organic compounds.

Natural aerosols tend to have larger particle size than anthropogenic species and shorter residence times, and are hence comparatively less radiatively active (CCSP 2009).

2.4.2 Anthropogenic aerosols

e most important anthropogenic species are examined in the subsections to follow.

35 Chapter 2 Aerosol eﬀects on climate

2.4.2.1 Sulphate

Sulphate is a secondary aerosol formed from precursor gaseous sulphur dioxide (SO2).

15 Sulphate aerosols consist of sulphuric acid (H2SO4) particles present as liquid droplets or in partly crystallised form, as well as various solid phase SO4 compounds. Cloud processing is a key activation pathway for sulphate aerosol through aqueous phase reactions with cloud water droplets, oxidation of SO2 via gaseous phase reactions, and by growth via condensation around pre-existing particles (Denman et al. 2007; Forster et al. 2007, p. 160). is cloud processing causes the particle size distribution to shi toward larger sizes, which enables easier subsequent aerosol activation.

AR4 reported proportional sulphate emissions of precursor SO2 by source as approximately 72 % from fossil fuel combustion, 19 % DMS from phytoplankton, 7 % volcanic

SO2, and 2 % from biomass burning (Forster et al. 2007). Of fossil fuels, coal and oil are the predominant sources, accounting for around 55 % and 25 % of global SO2 respectively in 2002, while natural gas contributed less than 1 % (Ramanathan and Carmichael 2008).16 Strong regional variation exists, with emissions in the US and Europe decreasing — largely driven by air pollution controls, examined in Box 2.1 — while those in Asia have increased in concert with rapid industrialisation over recent decades. Total global anthropogenic SO2 emissions (measured by mass of sulphur, S) decreased from approximately 73 TgS yr−1 in 1980 to 54 TgS yr−1 in 2000; Northern Hemisphere emissions declining from 64 TgS yr−1 to 43 TgS yr−1 over that period were partly oﬀset by emissions rising from 9 TgS yr−1 to 11 TgS yr−1 in the Southern Hemisphere (Stern 2005, cited by Forster et al. 2007).17

15 Either partly or totally neutralised by ammonia. 16 55 % + 25 % gives 80 % of total, compared to the 72 % reported in AR4. 17 See section 3.6 for additional detail.

36 2.4 Species

Box 2.1: e role of SO2 pollution regulations

Sulphur dioxide emissions have another eﬀect on the atmosphere, quite apart from any change to radiative ﬂux: industrial emission of SO2 and nitrogen oxides (NOx) are also the ultimate cause of acid rain, or, technically, acid deposition (Schmalensee et al. 1998). Atmospheric reactions of these gaseous precursors produce sulphuric and nitric acid, which eventually precipitate as acid rain (or through dry deposition), causing harm to natural systems and human selements alike. Of the two, sulphuric acid deposition dominated across Europe and North America (Likens et al. 1996).

Historical SO2 emissions increased roughly by a factor of four from 1880 to the 1920s, and then a further 3.5 from the 1920s to the 1970s, primarily in the Northern Hemisphere mid latitudes (Shindell and Faluvegi 2009); ie, Europe and North America. e negative forcing in Northern Hemisphere mid latitudes over the 1931–1975 period is consistent with large increases in sulphate aerosol stemming from these emissions. In fact, the strong increase in aerosol loading aer WW II is likely an important factor in the slight cooling from the late 1940s to 1970s (eg, Wild et al. 2007). e combined effects of aerosols and GHGs therefore ‘likely explain the observed temperature evolution over mainland Europe since 1950’ (Philipona et al. 2009, 4). Aerosol loading was then ‘drastically decreased’ from the mid 1980s as emission control regulations were implemented across the industrialised world in an effort to deal with acid rain and other air pollution damages (Philipona et al. 2009, 1). e US Clean Air Act was promul- gated in 1970, leading to curbs on SO2 emissions to redress acid rain (eg, Likens et al. 1996). In Europe, the Convention on Long-Range Transboundary Air Pollution (CLRTAP), established in 1979, implemented a protocol targeting SO2 from 1985 (EEA 2004). Regulatory activity in the US culminated with amendments to the Clean Air Act that established the world’s first large- scale emissions trading scheme commencing in 1990, aimed at cuing SO2 emissions from the electricity generation sector, the major source of acid rain precursors (Schmalensee et al. 1998). Damage from acid rain is now substantially lessened in North America and western Europe through these clean air policies, though far from eliminated; nitrogen deposition in the United States remains a problem, as one example (Talhelm et al. 2011). An unintended consequence of simultaneous reductions in sulphate and other aerosol species emissions across the Northern Hemisphere, however, was a diminished cooling influence — at the same time as emissions of carbon dioxide and other GHGs continued to accelerate. e result of this reduced masking effect are global in scope but particularly felt in the Arctic region, where sulphate decline may account for much of the estimated 1.09 ±0.81 ℃ aerosol contribution to a 1.48 ±0.28 ℃ increase in surface temperature during 1976–2007 (around 74 %) (Shindell and Faluvegi 2009). Indeed, the OECD countries can be regarded as the ‘dominant contributor’ to recent temperature increases from the combination of historical GHG emissions and strongly curtailed SO2 emissions post-1980 (Andronova and Schlesinger 2004). e net result of a reduced Northern Hemisphere aerosol cooling and ever-increasing atmospheric LLGHG levels is the strong increase in global mean temperature from the 1980s onwards, as the combined forcing of anthropogenic GHGs surpassed all other forcing agents (Forster et al. 2007). While the parallel surge in SO2 emissions in recent decades from countries such as India and especially China, discussed in section 3.6, may have reduced the net fall in aerosol loading, this has come at great cost to lives and infrastructure, as is now so starkly evident.

37 Chapter 2 Aerosol eﬀects on climate

As is typical for aerosols generally, the paern of sulphate radiative effects is directly influenced by the geographic location of SO2 emissions; ie, sulphate aerosols manifest these effects principally in the location where they are emied. is is in part a function of their residence time: sulphates simply do not persist in the atmosphere long enough to become widely geographically dispersed.

Sulphate aerosol strongly scaers radiation across the whole solar spectrum, having an eﬀective SSA18 of 1, though some minor absorption occurs at near-infrared wavelengths (Forster et al. 2007). is propensity for scaering is a function of sulphate’s common particle size distribution (refer to section 2.2.1): sulphate accounts for a signiﬁcant fraction of the overall sub-micrometre aerosol mass, and is the largest anthropogenic component of AOD. However, sulphate is also ‘invariably mixed’ with other aerosol species — both internally and externally (refer to section 2.1.2) — commonly forming a ‘composite aerosol state’ (Forster et al. 2007, 161).

2.4.2.2 Organic carbon

Organic carbon (OC) describes aerosols consisting of a ‘complex mixture of chemical compounds containing carbon-carbon bonds’ (Forster et al. 2007, 161). ese compounds may derive from primary particulates or form by condensation of organic gases of low to semi volatility, such as terpene. Sources include fossil fuel combustion, industrial processes and fugitive emissions, biofuel burning, and natural biogenic emissions (Pen- ner 1994). OC particles continue to change aer emission due to atmospheric chemical processing, further modifying their hygroscopic,19 chemical, and optical properties. In fact, hundreds of such OC types exist, making modelling of their climate effects a difficult challenge. A subset of OC is termed contained burning, approximately the sum of fossil fuel and biofuel emissions; ie, effectively excluding emissions from open burn-

18 Deﬁned in section 2.2.4. 19 Aﬃnity for water.

38 2.4 Species ing of biomass. Organic carbon is included with other organic element compounds (see section 2.4.2.7) and collectively referred to as particulate organic matter (POM) (eg, Dentener et al. 2006). As a whole, OC is the second largest contributor to total AOD aer sulphate (Forster et al. 2007).

Fossil fuel OC primarily scaers shortwave radiation, but does absorb more strongly in the ultraviolet and some parts of the visible spectrum (Forster et al. 2007). e speciﬁc absorption characteristics of fossil fuel OC aerosol are aﬀected by source combustion temperature, with those formed at higher temperatures tending to be less absorbing (more scaering).

Fossil fuel OC is in effect always internally and externally mixed with other fossil fuel combustion products such as sulphate and black carbon (Forster et al. 2007). Coatings of OC on other aerosol species can alter the resultant particles’ radiative and hygroscopic properties. e optical and hygroscopic properties of secondary OC differ with originating combustion process, ambient aerosol loads, and atmospheric chemical processing, and its representation in climate models has thus been ‘highly simplified’ with substantial uncertainty remaining (Forster et al. 2007).

AR4 quantiﬁed OC emissions as approximately 5 TgC yr−1 to 17 TgC yr−1 from biofuels versus 2.4 TgC yr−1 from fossil fuels (Forster et al. 2007).

2.4.2.3 Black carbon

Black carbon (BC), the absorbing component of soot, is a complex graphitic substance emied directly as primary aerosol from incomplete combustion of fossil fuel or biomass, much of anthropogenic origin (Forster et al. 2007; Conant et al. 2002; Ramanathan and Carmichael 2008). e complex chemical chain structures of BC tend to collapse as the particles age, which modiﬁes their optical properties. Black carbon and organic carbon (section 2.4.2.2) are collectively termed carbonaceous aerosols (Penner et al. 2001);

39 Chapter 2 Aerosol effects on climate though Bond et al. (2004) caution that classification of carbonaceous aerosol depends on measurement method. e climatic influence of black carbon is perhaps the most complicated of the anthropogenic aerosol species — this section is somewhat complex as a result, and necessarily involves cross-referencing to a number of later sections for explanation of the radiative and physical mechanisms.

Unlike most other aerosol species which tend to scaer, black carbon strongly absorbs solar radiation. Because this absorption occurs alo, BC increases energy in the atmosphere (the semi-direct effect, section 2.5.4) while reducing it at the surface (decreased irradiance, section 2.5.11)(Ramanathan and Carmichael 2008). e localised atmospheric heating can in turn modify the thermal structure of the atmosphere and surface (Con- ant et al. 2002). e net positive forcing at TOA is the sum of this positive atmospheric heating and the smaller negative surface forcing (Bond et al. 2011). BC thereby affects atmospheric energy distribution, removing energy at the surface but absorbing a greater amount in the atmosphere. For these reasons, BC’s vertical profile in the atmospheric column is particularly important among anthropogenic aerosols.

e microphysical and meteorological properties of BC particles govern their horizontal and vertical distribution, dictating their residence time. When emied, BC particles tend to be largely hydrophobic, around 80 % (Conant et al. 2002; Cooke et al. 2002). As they age, these particles tend to ‘acquire a hydrophilic coating’, which eventually allows them to act as cloud condensation nuclei (CCN, detailed in section 2.5.2), forming cloud droplets on activation under supersaturated conditions (Conant et al. 2002). e cloud processing of sulphate aerosol also inﬂuences BC atmospheric residence time by enhancing the conversion of hydrophobic carbon to a hydrophilic state (Denman et al. 2007). e enhanced hydrophilic fraction then increases the rate of wet deposition through precipitation (Cooke et al. 2002); black carbon would therefore be removed less rapidly if that conversion is dampened by a reduction in SO2 emissions.

Emissions of BC are subject to large uncertainties. AR4 reported one global fossil fuel

40 2.4 Species

BC estimate of 5.8 TgC yr−1 to 8.0 TgC yr−1; another puts the total at 8.0 TgC yr−1 but with an uncertainty factor of two, comprising 4.6 TgC yr−1 from fossil fuel sources and 3.3 TgC yr−1 from open biomass burning (Forster et al. 2007). Emissions of BC decreased in the second half of the twentieth century in the UK, US, Germany, and ex Soviet states, but increased in Asia — India and China account for some 25 % to 35 % of current global BC emissions (Ramanathan and Carmichael 2008).

antification of BC forcing is best established for the direct effects of absorption. Here it should be noted that many studies refer to the mechanisms described in section 2.5.4 as direct effects rather than semi-direct. at is, those studies treat the atmospheric heating influence of BC explained in section 2.5.4 under the single category of direct effects (section 2.5.1), as absorption occurs through direct interaction with solar radiation. AR4 reported a best estimate of +0.20 ±0.15 W m−2 for BC direct RF at TOA (see section 2.5.1). A later review study of BC’s effects on climate by Ramanathan and Carmi- chael (2008) concluded a markedly higher TOA BC direct forcing of +0.9 W m−2, with a −2 −2 range of +0.4 W m to +1.2 W m , placing it second only to CO2 in warming influence

−2 20 (as much as 55 % of CO2’s 2005-level RF of +1.66 W m ). e model study of Bond et al. (2011) found net anthropogenic TOA RF of +0.40 ±0.18 W m−2, which includes enhanced absorption due to internal mixing with other aerosol species.

As fossil fuel combustion is a major anthropogenic emission source for both BC and sulphate, which have in general opposing inﬂuences (warming versus cooling), the ratio of BC-to-sulphate mass concentration aﬀects their combined interaction with shortwave radiation (Ramana et al. 2010). A higher ratio will tend to produce a more absorbing aerosol mixture than a lower ratio.21 Ramana et al. (2010) infer from their observational

20 Ramanathan and Carmichael include biomass burning emissions of BC (see section 2.4.2.5), stating these account for about 40 % of the total, so the comparison to AR4 is not entirely clear cut. 21 us the usually low-quality fuels used in commercial shipping (high sulphur content) tend to have low BC:sulphate ratios, making their aerosol emissions relatively less absorbing (Ramana et al. 2010, citing Bond et al. 2004). ese factors will be relevant if shipping is eventually brought under internationally- agreed mitigation frameworks.

41 Chapter 2 Aerosol eﬀects on climate study that fossil fuel derived BC is more strongly absorbing than that of biofuel origin for the same BC:sulphate ratio, supporting the conclusion from model studies that fossil fuel BC is a more eﬀective warming agent. e implication for mitigation policy, the authors note, is that the fossil fuel BC:sulphate ratio should itself be a target for reduction, along with total individual species emissions.

Although BC is regarded as a strong net positive forcer through shortwave radiation absorption and hence suggested as a target for mitigation action, studies by Chen et al. (2010) and Spracklen et al. (2011) caution that its role as CCN in cloud formation (outlined above) is not well understood, nor evaluated on a global scale (see section 2.5.2 and 2.5.3). In fact, the model-based study of Chen et al. (2010) found that abatement of BC aerosol actually led to a net positive increase in TOA RF due to reduction in global CCN concentration and subsequently cloud droplet number concentration; ie, cooling caused by BC effects on cloud cover was found to be greater than its warming effects. In their study based on observations of CCN combined with a global aerosol model, Spracklen et al. (2011) find that anthropogenic carbonaceous aerosol generates a negative forcing of –0.23 W m−2 through the first indirect effect on cloud (section 2.5.2).22 e authors note that while the anthropogenic contribution is about one third of the total by mass,23 their small size means that they account for two thirds of the total indirect effect. Spracklen et al. (2011) suggest that studies that propose reductions of BC relative to other aerosol compounds by virtue of their direct warming effect (section 2.5.1 and 2.5.4), such as that of Ramana et al. (2010) discussed above, do not consider the importance of particle size distribution for the first indirect effect. While these two assessments do not fully accord with the majority of BC studies, they have important implications for mitigation policy24 that should be extensively researched.

22 No uncertainty range is given for this mean estimate. 23 at is, sources such as wildfires account for ⅔ by mass. 24 Chen et al. (2010) also cite other studies that indicate that the specific type of emission control technology used strongly affects the resultant emission particle number and size distribution — potentially both increasing or decreasing them.

42 2.4 Species

e recent major study of Bond et al. (2013) aims to provide a comprehensive and quantitative assessment of BC’s inﬂuence.25 Bond et al. (2013) conclude that the net eﬀect of

26 BC is a strong warming, second only to CO2 in the present-day atmosphere. ey find an updated best estimate of BC forcing in the current industrial era of +1.1 W m−2 with a 90 % uncertainty range of +0.17 W m−2 to +2.1 W m−2. is assessment captures all known forcing mechanisms, including indirect cloud effects and cryosphere melting (see p. 61 in section 2.5.4). is estimate is substantially larger than those given above, though in part the difference may be due to omission of indirect effects in those previous studies.

2.4.2.4 Nitrates

Nitrate aerosol consists predominantly of ammonium nitrate particles (NH4NO3), formed from nitrogen radicals when sulphate is fully neutralised and excess ammonia (NH3) is available (Forster et al. 2007; Feng and Penner 2007). Nitrate is therefore sensitive both to atmospheric concentrations of ammonia and to emissions of NOx precursors, the laer subject to photochemical reactions (oxidation of NOx to nitric acid, HNO3)(Skeie et al. 2011). As nitrate and ammonium aerosol are highly hygroscopic, they aﬀect resultant aerosol radiative and optical properties by modifying the amount of water uptake for a given relative humidity (Feng and Penner 2007). e highest nitrate aerosol concentrations are found in highly industrialised locations; concentrations are correspondingly low in rural areas (Forster et al. 2007).

Nitrate aerosol scaers in the visible spectrum (Forster et al. 2007). Feng and Penner (2007) note that model and observational studies indicate nitrate may exert a similar

25 However, as Bond et al. (2013) ﬁrst became available as this thesis was nearing completion, only a limited examination of its principal conclusions is possible here, in part due to its substantial length (online pre-publication is 232 pages). 26 It should be noted that the studies of Chen et al. (2010) and Spracklen et al. (2011) were considered in this eﬀort.

43 Chapter 2 Aerosol effects on climate forcing to that of sulphate on a regional basis. However, a decrease in sulphur emissions relative to NOx would increase the nitrate aerosol burden and consequent forcing — a declining sulphate forcing could be partially offset by a simultaneous increase in nitrates. Additionally, via a similar influence on cloud processes to that described for BC aerosol in section 2.4.2.3, sulphates also suppress nitrate aerosol residence time (Denman et al. 2007), further enhancing the nitrate forcing if sulphates decline.

AR4 noted that estimates of nitrate direct radiative effect (section 2.5.1) are subject to large uncertainty due to a lack of observational studies and significant uncertainty in assessments of nitrate optical depth (Forster et al. 2007). eir efficient scaering characteristic means that nitrates exhibit a similar negative forcing at the TOA and surface.

2.4.2.5 Biomass burning

Biomass burning aerosol (BB) is a combination of scaering organic carbon and inorganic compounds (such as nitrates and sulphates), and absorbing black carbon (Forster et al. 2007). ese aerosol species are usually grouped together and assigned a single RF as they result from the effectively uncontrolled combustion of biomass material. Much of the BB aerosol burden is natural in origin,27 with no significant change evident over the industrial era. Importantly though, climate change may affect BB emissions through modifying the intensity and frequency of wildfires.

e single scaering albedo (see section 2.2.4) of BB aerosol is in part dependent on the particular source biomass,28 and tends to increase as the particles age (on the order of hours), rising from 0.85 to 0.9 at 0.55 µm. is increase in SSA is aributed to condensation of non-absorbing organic gases onto existing particles, thereby diminishing the magnitude of BC absorption within the mixture (Forster et al. 2007). e partially

27 Exemplifying the complexities and uncertainties of aerosol observation, CCSP (2009) note that satellite-based aerosol measurements may assume that all BB aerosol is actually anthropogenic, and all dust natural. 28 Boreal forest ﬁres have a high SSA from the outset, greater than 0.9 at 0.55 µm.

44 2.4 Species absorbing nature of BB means that forcing at the surface and within the atmospheric column is greater than that at TOA.

BB aerosol generates a negative RF under clear sky conditions, but positive if overlying cloud due to reduced cloud reflectivity through absorption (Forster et al. 2007). Overall, BB may exert a net positive RF, including clouded sky conditions. e vertical profile of BB particle distribution is then an important determinant of its effect. For example, Hsu et al. (2003) found that biomass burning aerosol in South East Asia — present as smoke — darkened existing stratiform, low-altitude cloud during boreal spring, causing a reduction in cloud reflectance as large as –100 W m−2. ermal emission from the same particles generated positive forcing as strong as +20 W m−2. e sign of BB aerosol forcing is therefore not certain, and depends on particular atmospheric conditions and emission composition.

2.4.2.6 Mineral dust

Anthropogenic emissions of mineral dust derive mostly from agricultural practises (harvesting, ploughing, overgrazing), surface water drainage, and industrial practises such as cement production and transport (Forster et al. 2007). Mineral dust aerosol’s climatic effects are difficult to measure — the sign and magnitude of RF depends on SSA optical characteristics for shortwave radiation, but depends on the vertical dispersion profile for longwave. AR4 noted that newer in situ measurements suggest mineral dust is ‘considerably less’ radiatively absorbing than previously thought, with a central estimate for SSA of 0.96 at wavelength 0.67 µm (Forster et al. 2007). Such an SSA suggests that a positive RF from mineral dust in the solar spectrum is unlikely; however, absorption by dust from diverse mineralogical sources is not represented in models. e estimated anthropogenic fraction of mineral dust emissions is a maximum of 20 %.

45 Chapter 2 Aerosol eﬀects on climate

2.4.2.7 Secondary organic aerosol

Secondary organic aerosol (SOA) forms from atmospheric reactions of precursor gaseous volatile organic compounds (VOCs) such as terpene; SOAs are regarded as a type of POM, along with OC (section 2.4.2.2)(CCSP 2009; Dentener et al. 2006). SOAs are thought to form through photochemical oxidation of these precursors by one of three electrophilic atmospheric trace gases: the hydroxyl radical (OH), ozone (O3), and the nitrate radical (NO3)(Jacobson et al. 2000). Aircra measurements indicate that POM is highly correlated with secondary anthropogenic gas-phase species, suggesting that POM is being formed from these secondary sources on time scales of a day or more (CCSP 2009). In general SOAs are poorly understood, particularly their emission sources, and in fact, along with nitrates are not yet simulated eﬀectively by current generation climate models (CCSP 2009).

Note that AR4 does not examine SOA in its discussion of anthropogenic aerosols’ climatic impact. In their high-resolution aerosol model driven by observational data, Myhre et al. (2009) calculated an SOA RF close to –0.1 W m−2.

2.5 Climatic eﬀects and influence

e range of aerosol effects and their influence on climate are detailed below, illustrated in overview by Figure 2.4. Note that these effects are not necessarily discrete and distinct — specific aerosol species (and combinations thereo) behave in different ways, potentially even changing from one effect to another under differing temporal, spatial, and local atmospheric conditions.

46 2.5 Climatic eﬀects and inﬂuence

Figure 2.4: Illustrations of aerosol inﬂuence on climate (Fig. 2.10, Forster et al. 2007). Copyright the Intergovernmental Panel on Climate Change, 2007.

2.5.1 Direct radiative eﬀect

e most well-known and relatively well-quantified aerosol climatic influence is the direct radiative effect (DRE), so named for aerosol particles’ direct interaction with incoming solar shortwave radiation. ose aerosol species characterised by a sufficient single scaering albedo, such as sulphates, scaer a significant proportion of incident shortwave radiation, increasing optical depth. Some fraction of shortwave radiation is hence backscaered to space — ie, reflected — effectively increasing planetary albedo and so reducing shortwave energy flux received at the surface. Reduced surface irradiance is a negative radiative forcing, leading to a relative reduction in global mean surface temperature, TS. is direct radiative forcing, abbreviated as DRF, is broadly the same when calculated for the surface, tropopause, or at TOA; although under some definitions that include reduced surface irradiance due to aerosol absorption (eg, Yu et al. 2006), forcing at the surface can be up to twice or more that at TOA (see section 2.5.4 and 2.5.11). Anthropogenic emissions of sulphate, fossil fuel OC, fossil fuel BC, biomass burning, and mineral dust all contribute significant DRFs (Forster et al. 2007).

47 Chapter 2 Aerosol eﬀects on climate

DRF is determined by a key set of aerosol optical properties including SSA, specific extinction coefficient, and asymmetry parameter (Forster et al. 2007). All of these vary with light wavelength and relative humidity (RH), and are subject to wide ranges in atmospheric loading, geographical distribution, and horizontal and vertical profiles of any particular aerosol species. Remote sensing platforms are able to combine observations of AOD with changes to TOA radiative flux to determine a net TOA DRF (CCSP 2009). Of all aerosol effects, calculating forcing in this way is most applicable to the DRE (see discussion in section 2.3, 2.5.11, and 2.7). In situ observational field campaigns have found that shortwave scaering as a function of RH tends to decrease as the mass fraction of POM increases, likely because of POM’s effects on the relative hygroscopicity of scaering aerosol species (CCSP 2009).

Regional factors affect the DRF. Shindell and Faluvegi (2009) show that sensitivity to the aerosol DRE is relatively reduced at high-latitudes because these overlay already highly reflective surfaces (ie, snow and ice). ough when indirect effects are included (see section 2.5.2 and 2.5.3), net aerosol sensitivity at high-latitudes is actually some 50 % greater than at the tropics.

Continually improving in situ and remote sensing observations serve to beer constrain the total anthropogenic aerosol DRF. ese estimates of aerosol RF are in turn extensively validated by models based on the whole vertical atmospheric column using optical depth measurements. As it encompasses the entire aerosol burden, AOD ‘implicitly provides an alternative procedure to estimating the RF uncertainty’ and may be more robust than propagating the uncertainties identiﬁed for individual aerosol components (Forster et al. 2007, 168). Moreover, AOD necessarily includes nonlinear processes such as mixing dynamics that modify the net radiative properties of the combined aerosol burden. ese nonlinear eﬀects are also beer captured by estimating aerosol RF based on the combined output of multiple models; the result is more robust than per-component or single model estimates.

48 2.5 Climatic eﬀects and inﬂuence

e mean model total DRF in AR4 was –0.20 ±0.20 W m−2 (Forster et al. 2007). However this is a low-end estimate as most model simulations neglected nitrate and mineral dust aerosol; adding these species takes the estimate to –0.40 ±0.40 W m−2 at the 90 % conﬁd- ence interval. By contrast, satellite observations from three studies placed the central estimate at –0.55 W m−2, indicating a systemic model underestimation of 20 % to 40 %. e inﬂuence of absorbing aerosols is a large source of uncertainty in DRF estimates. Black carbon in particular can lead to large uncertainty in TOA RF due to the importance of particulate vertical position with respect to clouds (see section 2.4.2.3 and 2.5.4).

e total combined anthropogenic aerosol direct radiative effect for all species, relative to the preindustrial atmospheric loading, is estimated by AR4 as –0.50 ±0.40 W m−2. e DRF for individual anthropogenic species is listed in Table 2.1, showing the best estimate and 90 % confidence intervals; these values are ploed in Figure 2.5. Note that black carbon and biomass burning aerosol produce a positive RF due to their degree of shortwave absorption; their effects are explained in section 2.5.4.

Table 2.1: DRF (Fa) by anthropogenic species reported by AR4 (data from Forster et al. 2007) Aerosol species Best estimate 90% CI Sulphate –0.40 ±0.20 Organic carbon (fossil fuel) –0.05 ±0.05 Black carbon (fossil fuel) +0.20 ±0.15 † Black carbon (all sources) +0.34 ±0.25 Biomass burning +0.03 ±0.12 ‡ Nitrate –0.10 ±0.10 Mineral dust –0.10 ±0.20

† Forster et al. (2007) Tab. 2.13, note k: mean of model results from fossil fuel, biofuel, and biomass burning sources. ‡ With large uncertainty.

However, satellite observational studies surveyed by CCSP (2009) give an anthropogenic fraction of clear-sky DRF over ocean as –1.1 ±0.37 W m−2, about twice that of models at –0.6 W m−2. Globally averaged anthropogenic DRF ranged from –0.9 W m−2 to –1.9 W m−2, again substantially higher than model estimates of –0.8 W m−2. Con-

49 Chapter 2 Aerosol eﬀects on climate 0.5 0.0 ) 2 − m

W (

DRF -0.5

sulphate OC (ff) BC (ff) BC (all) BB nitrate mineral dust TOTAL

Figure 2.5: AR4 DRF at TOA by anthropogenic species (data as per Table 2.1)

versely, Myhre (2009) found consistency between a global aerosol model and an adjusted observational method — coupled with a signiﬁcant revision of estimated preindustrial aerosol composition (discussed in section 2.7.2) — concluding a global mean RF of –0.3 ±0.2 W m−2, substantially lower than AR4. Such ongoing discrepancies and large uncertainties in estimating even the nominally ‘simpler’ DRF underscore the serious diﬃculty in fully quantifying the anthropogenic forcing of climate from aerosol emissions.

AR4 concluded that scaering aerosol forcing efficacy (section 2.3.1) is very similar to that of a change in solar constant (ie, incoming solar shortwave radiation), though stronger at high latitudes. Efficacy ranges from 0.7 to 1.1 for the DRE, with assessed medium confidence (Forster et al. 2007).

50 2.5 Climatic eﬀects and inﬂuence

2.5.2 Indirect cloud albedo eﬀect

Aerosols produce two principal eﬀects on cloud. Both are termed indirect as they modify cloud optical properties, rather than interact with solar radiation directly. Cloud optical properties, in particular reﬂectivity of solar radiation, are a function of light wavelength and depend on key parameters such as droplet size distribution or ice crystal concentration, and on cloud-type morphology (Forster et al. 2007).

e cloud albedo effect, originally described by Twomey (1977), was the first of the two to be identified, and is hence also known as the first indirect effect. An increased aerosol loading increases the number of particles available to act as cloud condensation nuclei (CCN) — the subset of total particle population with a radius of 0.05 µm and above facilitate condensation of water vapour to form cloud water droplets (Rosenfeld et al. 2008; Forster et al. 2007). For a given and fixed cloud liquid water content (LWC), an increase in available CCN results in a greater number of water droplets, each of smaller re (effective radius; see section 2.2.1) — the same volume of water is now spread across more droplets but of smaller size than would otherwise be the case. is change in cloud droplet number concentration, Nd, and size distribution strengthens cloud optical brightness: the cloud is now more reflective to incoming shortwave radiation and so increases albedo. As is the case for the DRE (section 2.5.1), increasing planetary albedo in this manner induces a negative RF, leading to cooling. e cloud albedo effect is illustrated in Figure 2.6.

e relation of aerosol to cloud droplet number concentration is nonlinear, depending on the hygroscopicity and other characteristics of the aerosol species.

b Nd ≈ (Na) (2.10)

Where Nd is the cloud droplet number density, Na is the aerosol number concentration, and b indicates the aerosol particle hygroscopicity (Forster et al. 2007). e parameter

51 Chapter 2 Aerosol eﬀects on climate

Figure 2.6: Indirect cloud albedo effect (Fig. 7.20 (top panel), Denman et al. 2007). Copyright the Intergovernmental Panel on Climate Change, 2007. b ranges widely, from 0.06 to 0.48, illustrating the sensitivity of cloud droplet activation to aerosol properties such as size distribution in particular, and to updra velocity; it further reflects the use of aerosol extinction as a proxy for actual CCN observations and the difficulty in quantifying the effect via remote sensing (Forster et al. 2007).

Determining the detailed effect of aerosol loading on CCN and droplet size distribution is challenging. Studies surveyed in AR4 indicate that aerosol particle size is the principal determinant in their efficacy as CCN, rather than composition, though local atmospheric conditions are also important (Forster et al. 2007). e observational and aerosol model synthesis study of Spracklen et al. (2011) similarly emphasised the large sensitivity of the effect to particle size. However results varied as to the potential sensitivity of liquid water path (LWP)29 to aerosol loading in the vertical column, changes to which in turn affect cloud optical depth. AR4 concluded that significant difficulty exists in observational studies separating cloud albedo effects from other influences on cloud LWP and hence resultant cloud RF (Forster et al. 2007).

Modelling of aerosol effects is similarly difficult, and most general circulation models (GCMs) use parameterisation schemes to represent the relation between cloud droplet number concentration and aerosol loading (Forster et al. 2007). Considerable differ-

29 e mass of water available for cloud formation by unit area.

52 2.5 Climatic effects and influence ences in these parameterisations and other aspects of aerosol-cloud interaction have also largely prevented model result intercomparison. Large uncertainties in underlying aerosol and precursor gas emission rates and distribution — particularly in the preindustrial era (see section 2.1.5) — are a further limiting factor. Nonetheless, all models reviewed in AR4 estimated negative mean global RF for the cloud albedo effect, though with wide variation ranging from –0.22 W m−2 to –1.85 W m−2, and that this effect tends to be substantially larger over land than over ocean (Forster et al. 2007). Importantly, the review highlights the continuing weaknesses and biases in model simulation of cloud processes, which would affect these results.

Treatment of cloud droplet size distribution and an assumed ‘invariant’ droplet spectral shape not found under atmospheric conditions are further uncertainties: Liu and Daum (2002) estimated that a 15 % increase in the size distribution width reduced the cloud albedo effect between 10 % and as much as 80 %. AR4 termed this the dispersion effect (Denman et al. 2007). A widened droplet size distribution means that there are greater numbers of droplets both of smaller and of larger sizes, as well as an overall increase in number concentration Nd. Because larger size droplets are less reflective, this widening of the distribution in turn offsets some of the cooling effect of the enhanced Nd. AR4 found that subsequent studies confirmed that by not taking changes in droplet spectra into account, models overestimate the cloud albedo effect by some 15 % to 35 % (Forster et al. 2007).

Satellite observations are uncertain, both because of the satellite retrievals themselves (and assumptions in those processes), and because retrieval of aerosol and cloud properties are not coincident, instead assuming that AOD can be linked to aerosol concentration below the cloud layer. Satellites are also unable to unambiguously distinguish anthropogenic from natural aerosols. However, the global coverage of satellite instruments makes these a highly useful observational platform and work continues to improve their resolution and capabilities.

53 Chapter 2 Aerosol eﬀects on climate

e total estimated cloud albedo effect30 reported in AR4 from all aerosols is a best estimate median value of –0.7 W m−2, with a 5 % to 95 % range of –1.8 W m−2 to –0.3 W m−2 (Forster et al. 2007). McComiskey and Feingold (2008, 1) note that this uncertainty range ‘has remained nearly constant through time’ across the second and third IPCC assessment reports, underscoring the difficulty in quantifying and modelling aerosols’ complex influence on cloud microphysics. CCSP (2009) conclude a discussion of cloud indirect effects and radiative forcing by noting that ‘observational estimates … are still in their infancy’ (p. 49).

Returning brieﬂy to the possibility for black carbon to act as CCN mentioned in section 2.4.2.3, Chen et al. (2010) state that two steps are required for this to occur. First, BC must be internally mixed with other hydrophilic aerosol compounds, such as sulphates or other organics; and second, the resultant particle size must be large enough (via condensation growth) to activate as a CCN, approximately 100 nm in diameter. In their assessment, ‘[u]nder essentially all reasonable atmospheric conditions, the second step is the limiting one for a soot [ie, BC] particle to become a CCN’ (p. 2).

2.5.3 Indirect cloud lifetime eﬀect

In 1989, Albrecht hypothesised a second indirect effect of an increased aerosol loading, the cloud lifetime effect (Albrecht 1989). As highlighted in section 2.3 on page 32, only the cloud albedo indirect effect is regarded by the IPCC in AR4 as ‘a purely radiative forcing’ — other aerosol influences on cloud and precipitation, discussed here and in the following sections, necessarily involve feedback response processes (Denman et al. 2007, 559). ese cloud feedbacks remain one of the largest single sources of uncertainty in estimates of climate sensitivity.

e ‘distinguishing quality’ of the various hypotheses broadly referred to as the cloud

30 RF estimates are for liquid water clouds only, as knowledge of ice-phase clouds was too poor.

54 2.5 Climatic effects and influence lifetime effect is that aerosol loading affects cloud macrostructure by modifying the efficiency of precipitation development (Stevens and Feingold 2009, 607). As in the first indirect effect, an increased aerosol loading in warm clouds tends to raise cloud droplet number concentration, Nd, while simultaneously decreasing individual droplet size, re, for a given LWC. e second indirect effect posits that cloud precipitation efficiency is a monotonically decreasing function of Nd, and hence increasing Nd will suppress precipitation relative to the unadulterated state. Reduced precipitation thereby affects cloud macrostructure, extending its lifetime and so increasing net albedo over time (CCSP 2009; Denman et al. 2007; Stevens and Feingold 2009). e extension of albedo changes over time is what distinguishes the cloud lifetime effect from the cloud albedo effect: albedo is postulated to be a decreasing function of precipitation efficiency, though both indirect effects stem from aerosol-induced change in Nd (Stevens and Feingold 2009). Aver- aged globally over the long term, precipitation fundamentally must balance evaporation (Rosenfeld et al. 2008; Trenberth et al. 2009), which Rosenfeld et al. (2008) argue must in turn mean that suppressed precipitation in shallow cloud is compensated by enhanced precipitation in deeper cloud (including possible changes in cloud height).

Decades of research have struggled to determine the extent of any cloud lifetime effect; it remains controversial, in no small part due to a complex range of challenges relating to observations of aerosol optical depth and cloud amount, and distinguishing between the two (Stevens and Feingold 2009). Deriving observational methods to separate the cloud albedo effect from changes to cloud lifetime is therefore extremely difficult, and studies have commonly evaluated the effects in combination (Denman et al. 2007). Indeed, the studies surveyed in AR4 point to, at that time at least, a distinct lack of agreement between indications from satellite observations and model results in the scope and role of aerosol indirect effects on cloud. One such study of warm convective cloud (Jiang et al. 2006) found that although precipitation was suppressed with enhanced aerosol concentration, cloud lifetime was not extended — this was likely due to the competing

55 Chapter 2 Aerosol effects on climate effect of enhanced evaporation of smaller, more numerous cloud droplets. Jiang et al. (2008) used near-simultaneous satellite observation of ice cloud and an aerosol proxy over South America to conclude that polluted ice cloud did cause a decline in ice particle radii and subsequent precipitation efficiency suppression, though weakly during the wet season. CCSP (2009) cite the Hansen et al. (2007b) model study finding that either aerosol indirect effect will produce a similar reduction in tropical precipitation, as does the direct effect. From this, CCSP (2009) infers that the reduced precipitation is caused by decreased shortwave irradiance at the surface (see section 2.5.11) and the feedback relative suppression of evapotranspiration that results; that is, not strictly due to indirect changes in cloud precipitation efficiency. Such results highlight the complex dependencies on cloud type, aerosol species, and atmospheric conditions for any resulting cloud feedback effects.

Indeed, the CCSP (2009) review, citing a major 2007 WMO report examining aerosol impact on precipitation, stated that ‘to date there is no statistically robust proof of surface rain suppression’ (p. 72), calling into doubt the reality of any substantial cloud lifetime effect. However, Rosenfeld et al. (2008) proposed a detailed conceptual model of cloud-aerosol precipitation effects, claiming to reconcile the WMO report’s principal finding with previous work that does indicate precipitation suppression.31 ey intro- duce the concept of aerosol thermodynamic forcing (TF), representing the ‘aerosol induced change in the atmospheric energy budget that is not radiative in nature’, which is the change in latent heat flux in the atmospheric column (Rosenfeld et al. 2008, 1312). e details of this TF-based model are beyond the scope of this thesis, but using it Rosen- feld et al. (2008) demonstrate reconciliation of the apparently dichotomous evidence for low CCN tropical cloud raining out too quickly to become long-lived, versus strongly polluted cloud evaporating most of their water content before precipitation can occur,

31 Rosenfeld et al. (2008) was not cited by CCSP (2009), but this likely simply reﬂects the inherent lag between publication date and the underlying research.

56 2.5 Climatic effects and influence if the cloud can form at all (see the semi-direct effect below also). eir model predicts an optimal aerosol loading regime — AOD around 0.25 — that in turn optimises cloud development, which has been borne out by observation in at least one case.

Stevens and Feingold (2009) reviewed the available literature and evidence for a cloud lifetime effect, including that of Rosenfeld et al. (2008), to arrive at more cautious conclusions. ey discuss the evidence for multiple likely cloud regimes (and regime-dependent aerosol effects), and the existence of buffering systems (internal feedbacks) at micro and macrophysical scales that serve to dampen the aggregate cloud response to aerosol perturbation. Stevens and Feingold ultimately argue that knowledge of aerosol effects on cloud precipitation regimes is progressing, but more research is necessary.

AR4 concluded that forcing efficacy for the cloud albedo indirect effect is very likely similar to that of the direct effect. Including the cloud lifetime indirect effect indicates efficacy is greater than unity, but with high uncertainty. Overall AR4 assessed forcing efficacy of indirect aerosol effects as >1.0 (Forster et al. 2007).

2.5.4 Semi-direct eﬀect

e semi-direct effect refers to a complex array of processes relating to absorbing aerosols, the most prominent species being black carbon (section 2.4.2.3). Predominantly these processes involve aerosols suspended alo and a consequent localised heating of the atmospheric column, but some occur at ground level. Absorption of incoming shortwave radiation reduces energy received at the surface, exerting a negative surface forcing (explained in section 2.5.11); a TOA forcing, either negative or positive, may be generated depending on the relative reflectivity of the surface or cloud below the aerosol layer. Absorption of outgoing reflected shortwave radiation, conversely, necessarily reduces planetary albedo and generates a positive forcing at TOA (Ramanathan and Carmichael 2008). e localised atmospheric heating from absorption (at altitude) pro-

57 Chapter 2 Aerosol effects on climate duces a substantial positive atmospheric forcing within the troposphere, increasing air stability beneath relative to the surface and modifying the atmospheric temperature gradient and hence the lapse rate32 (Denman et al. 2007). To further complicate maers, Forster et al. (2007) state that the semi-direct effect should not be considered an actual forcing due to atmospheric heating’s impact on the tropospheric hydrological cycle and other feedback mechanisms (discussed below); yet other authors treat at least some aspects as forcings proper. Absorbing aerosol atmospheric forcing is illustrated in Figure 2.7, and a schematic of semi-direct effects is shown in Figure 2.8.

Figure 2.7: Illustrative atmospheric forcing at altitude due to aerosol SW absorption (in W m−2 ±30 %), for the period 2001 to 2003 (Fig. 1 panel (b), Ramanathan and Car- michael 2008). Reprinted by permission from Macmillan Publishers Ltd: Nature Geoscience, copyright 2008.

If absorbing aerosols are located in a cloud-free sky (lemost in Figure 2.8), they likely produce cooling by inducing negative surface forcing (Forster et al. 2007). Further, that surface forcing may not produce a corresponding TOA forcing: radiative transfer at the top of the atmosphere (and indeed the tropopause) is unaffected because aerosol absorption of shortwave radiation within the (clear-sky) tropospheric air column modifies neither outgoing shortwave nor longwave energy flux. at is, only at and below the

32 Rate of decrease in temperature with increase in height within the troposphere.

58 2.5 Climatic eﬀects and inﬂuence

Figure 2.8: Semi-direct eﬀect, cloud-free and within cloud (Fig. 7.20 (mid panel), Denman et al. 2007). Copyright the Intergovernmental Panel on Climate Change, 2007. point of absorption in the vertical proﬁle is incoming shortwave radiation extinguished — the surface would have absorbed that radiation if airborne aerosols had not.

However if shortwave absorption occurs above highly reflective surfaces, the result may be either warming or cooling. Extinction of incoming shortwave radiation by absorption in the atmosphere may be less (warming) or more (cooling) than the surface reflection that would otherwise have occurred (Forster et al. 2007). At the same time, absorption of shortwave radiation that is reflected by the surface-atmosphere-cloud system — outgoing shortwave radiation originating beneath the aerosol layer — reduces planetary albedo, because that radiation cannot now exit to space (Ramanathan and Carmichael 2008). In both of these contexts, a TOA RF as well as surface forcing would occur; ie, outgoing energy fluxes are now modified.

Absorbing aerosols — black carbon in particular — also aﬀect cloud fraction33 and cloud formation directly, though understanding of these processes remains poor (eg, Koch and Del Genio 2010).

33 at portion of the sky covered by cloud.

59 Chapter 2 Aerosol eﬀects on climate

e effect of absorbing aerosols alo above cloud depends in part on cloud type (Koch and Del Genio 2010). Stratocumulus clouds34 may be enhanced through the strengthening of atmospheric stability in the air above them, by reducing downwelling shortwave radiation, and by other effects on atmospheric processes. Conversely, studies indicate suppression of cumulus cloud cover through the same stabilising effects, and also from a reduction in moisture availability due to decreased evaporation rates caused by the reduced surface shortwave irradiance (somewhat counteracting the enhancing effect for stratocumulus). ese perturbations to evaporation also have important consequences for the hydrological cycle (see Box 2.2).

Box 2.2: Aerosol eﬀects on the hydrological cycle

ough this thesis predominantly addresses aerosol effects on climate through perturbations to the energy budget and albedo, their influence on the hydrological cycle is of no less a concern. By affecting cloud type and extent, surface energy balance and, consequently, evaporation rates, aerosols can in turn affect precipitation amount and location (eg, CCSP 2009). ose impacts are important, as significant modifications to the hydrological cycle will have serious implications for local ecosystems and species, food production, water supplies, and the frequency and intensity of extreme weather events. Indeed AR4 notes that as anthropogenic aerosols may have a suppressing effect on precipitation, their removal would then have benefit, despite any consideration of radiative influence (Denman et al. 2007). In a worrying example, Koch and Del Genio (2010) note that several BC studies indicate the potential for large scale effects on Asian monsoons (though uncertainty remains as to the detail). Ramanathan and Carmichael (2008) highlight that the negative precipitation trend observed in the last 50 years over many tropical regions (Africa, South Asia, and northern China in particular) cannot be explained by GHG warming alone, and that along with natural variability anthropogenic aerosols are ‘emerging as major players in the observed trends’ (p. 224). Aerosols further affect water vapour transport from the Indian Ocean to South Asia land areas (Ramanathan and Carmichael 2008). e ability of aerosols to affect climate in ways that — by definition — are not reflected in global mean changes in surface temperature or radiative forcing underscores that anthropogenic climate change fundamentally encompasses far more than ‘global warming’.

Where BC is present within clouds (rightmost in Figure 2.8), its absorption of shortwave radiation may be suﬃcient to raise the temperature of droplets containing BC CCN such

34 Which are not yet simulated well by models.

60 2.5 Climatic effects and influence that they evaporate, ‘burning o’ the cloud and decreasing LWP (Conant et al. 2002; Denman et al. 2007). is evaporation decreases reflection of incoming shortwave radiation by reducing cloud fraction, exerting a positive forcing at the surface and at TOA. Ramanathan and Carmichael (2008) point out this cloud feedback increases the already significant BC forcing, though non-absorbing aerosol-induced increases in cloud cover dominated overall (except in heavily polluted areas).

When suspended below the cloud layer, absorbing aerosols have been found to enhance or even induce cloud fraction, and hence shortwave reﬂectivity over time, under some circumstances (Koch and Del Genio 2010). Enhancement of convective vertical air move- ments below the cloud from localised heating is the likely cause, as well as an increase in LWP. Changes to surface energy ﬂuxes are an important factor in these processes, making physical surface characteristics a critical determinant.

When black carbon becomes mixed within or deposited on snow and ice it modifies the optical properties of these surfaces, reducing their reflectivity and increasing melt rates, hence perturbing albedo causing a warming feedback effect (Flanner et al. 2007; Ramanathan and Carmichael 2008). In fact, as ‘a straight-forward consequence of positive albedo feedbacks and atmospheric stability at high latitudes’, the forcing of BC on snow and ice is nearly twice as effective as that of CO2 (Hansen and Nazarenko 2004); indeed, BC deposition is implicated in Arctic sea ice retreat (Ramanathan and Carmichael 2008).

Incorporation of BC soot within snowﬂakes, in combination with atmospheric heating, is implicated as the cause for a substantial fraction of the accelerating melt rates of Tibetan Plateau glaciers in recent decades (Xu et al. 2009). Advection of BC-heated air from other parts of Asia over the Himalayas increases lower and mid tropospheric temperatures by about 0.6 ℃ averaged annually (Ramanathan and Carmichael 2008). And in yet another complex feedback mechanism, more severe forest ﬁres produced by global warming- induced drought can increase levels of BC deposition on sea ice, further enhancing its

61 Chapter 2 Aerosol eﬀects on climate decline (Ramanathan and Carmichael 2008). AR4 adopted a best estimate RF for BC on snow of +0.10 ±0.10 W m−2 (Forster et al. 2007).

AR4 did not determine a best estimate forcing efficacy (refer to section 2.3.1) for absorbing aerosols as a whole, though Arctic albedo sensitivity to black carbon snow deposition enhances its efficacy to 1.7 (Forster et al. 2007; Hansen et al. 2005b). For aerosols within a certain SSA range, a linear forcing-response relationship breaks down: despite a negative RF the net result is a rise in mean global temperature increase, hence E is actually negative. Studies of increased BC atmospheric burden found large E (>1) when resident within the boundary layer, but much smaller E (<1) when above it. is is at least partly due to semi-direct effects modifying the tropospheric temperature gradient and cloud fraction. AR4 concluded that available studies are of insufficient agreement to determine a consensus BC efficacy and, again, that this ‘may represent problems with the stratospherically adjusted definition of RF’ (Forster et al. 2007, 199).

In summary, the semi-direct effect encompasses a complex set of atmospheric processes induced by absorbing aerosols, with potentially conflicting radiative and surface forcing implications. In all cases where absorbing aerosols are alo, a localised heating and resultant positive atmospheric forcing occurs. Nil, positive, or negative TOA RF, and positive or negative surface forcings — with varying combinations of RF and surface forcing sign — can all occur, highly contingent on aerosol vertical profile, surface geography and reflectivity, and the presence and type of cloud.

2.5.5 Thermodynamic eﬀect

e indirect eﬀects on cloud from an increased aerosol burden lead to smaller cloud droplet size (section 2.5.2). Where this occurs within super-cooled, deep-convective water clouds, the thermodynamic eﬀect means that the freezing of these smaller droplets is delayed (Denman et al. 2007; Lohmann and Feichter 2005). e resulting

62 2.5 Climatic effects and influence suppression of precipitation causes the cloud top to extend to higher, colder altitudes; increases in lightning and the risk of severe weather may result (CCSP 2009). e thermodynamic effect (and glaciation effect, described below) is illustrated in Figure 2.9.

Figure 2.9: Aerosol eﬀects on mixed-phase clouds (Fig. 7.20 (boom panel), Denman et al. 2007). Copyright the Intergovernmental Panel on Climate Change, 2007.

e forcing due to the thermodynamic effect is unclear. Either a positive or negative forcing may be produced, depending on cloud optical properties and the specific geography of land areas now covered by cloud. In their 2005 review of aerosol indirect effects, Lohmann and Feichter found that no forcing estimate could be given; the level of understanding does not appear to have changed substantially since.

2.5.6 Glaciation eﬀect

Increased loading of hydrophilic aerosol in the presence of mixed-phase clouds may produce a glaciation eﬀect in temperatures low enough for those particles to act as ice nuclei (IN), analogous to that of CCN in water clouds (section 2.5.2). By acting as IN, certain aerosol particles such as BC and dust may induce condensation of water in super-cooled stratiform clouds,35 forming new ice crystals within the cloud (Denman et al. 2007).

35 Due to the diﬀerence in water vapour pressures over ice and water (CCSP 2009).

63 Chapter 2 Aerosol eﬀects on climate

In distinct contrast to water clouds, however, increased ice crystal concentration within ice-phase clouds actually enhances precipitation efficiency: faster growing ice crystals — glaciation — aain precipitation-size sooner, thereby reducing mean cloud cover. is glaciation effect essentially competes against the cloud lifetime effect in warm clouds. e larger of the two will depend on the chemical characteristics of the particles involved, and the behaviour of continental versus maritime clouds are distinctly different (Denman et al. 2007). e glaciation effect is illustrated along with the thermodynamic effect in Figure 2.9.

AR4 concludes that the glaciation effect consequently reduces net cloud albedo, generating a positive forcing at both TOA and the surface (Denman et al. 2007). However, CCSP (2009) state in their discussion of IN effects that while some observations of aerosol influences exist, a clear link between aerosol loading, IN concentration, and ice crystal growth inducing precipitation ‘has not been established’ (p. 46).

2.5.7 Biomass fertilisation eﬀect

Perhaps the most surprising aerosol effect is that of biomass fertilisation. High SSA aerosols scaer sunlight in all directions, not only as backscaer to space (reflection), thereby increasing the relative diffusion of sunlight reaching the surface. at is, a larger AOD due to an increased scaering aerosol burden in otherwise clear skies results in an increased fraction of diffuse radiation at the surface (diffusion is further discussed in section 2.5.11). In general, plant photosynthesis increases with available shortwave irradiance, and its efficiency is enhanced under conditions of diffuse sunlight (Mercado et al. 2009). In recent years it has been realised that the role of aerosols would then be to increase net plant growth via shortwave scaering. For example, Niyogi et al. (2004) conducted field experiments comparing biomass carbon flux and the relative diffusion of solar irradiance that support this. Enhanced plant growth means that biosequestra-

64 2.5 Climatic eﬀects and inﬂuence tion of atmospheric carbon dioxide is increased, strengthening the biomass carbon sink (Denman et al. 2007). e net result from an increased scaering-aerosol burden is a reduction in atmospheric CO2 concentrations via biomass fertilisation. As a negative feedback mechanism,36 biomass fertilisation removes the positive radiative forcing that the drawn-down CO2 would otherwise have exerted.

Modelling by Mercado et al. (2009) found that aerosol diﬀusion of sunlight caused, inter alia, by anthropogenic sulphate aerosol, has increased the biomass carbon sink by up to 25 % over the period 1960 to 1999. e authors note that removal of the sulphate aerosol burden would see this fertilisation eﬀect decline rapidly, reaching near zero by the end of this century. Biomass fertilisation in this way is extremely poorly understood, and could potentially become an important factor as mitigation progresses.

2.5.8 Thin Arctic cloud

Optically thin, low-level liquid-phase Arctic cloud has been shown to exert a warming influence over the region via absorption and re-emiance of outgoing longwave radiation (Lubin and Vogelmann 2006; inn et al. 2008). Arctic aerosol concentrations37 are seasonal, increasing markedly in late winter and early spring; the dry troposphere at that time also results in lile wet deposition (inn et al. 2008). In the case of low-level Arctic cloud the aerosol first indirect effect (section 2.5.2) can actually lead to enhanced efficiency of longwave absorption, and hence an increase in downwelling longwave radiative flux. Indeed, the indirect effect of aerosols on cloud droplet effective radii is ‘particularly high’ in the Arctic region (Garre et al. 2004). inn et al. (2008) states that the shortwave indirect cooling effect will occur at high sun angles over dark ocean surfaces, but that longwave indirect warming effect dominates at low sun angles over bright Arctic surfaces, inducing a significant positive forcing. e effect of short-lived

36 Biomass fertilisation is not a forcing and hence cannot be expressed in W m−2. 37 Consisting of sulphate, nitrate, OC, and BC aerosol.

65 Chapter 2 Aerosol eﬀects on climate pollutants, including tropospheric aerosols,38 has been posited as a possible cause for the accelerated rate of Arctic warming relative to global averages in recent decades (inn et al. 2008).

2.5.9 Longwave absorption eﬀect

Certain aerosol species with particle size greater than 2 µm in diameter will absorb outgoing longwave radiation and consequently cause a warming effect in essentially the same way that GHGs do (Penner 2000). For example, a hybrid observational (downwelling) and modelling (upwelling) study of longwave radiative flux in an urban Indian location by Panicker et al. (2008) found enhanced tropical surface forcing on the order of 6 W m−2 to 8 W m−2, depending on varying temperature and humidity profiles. High altitude clouds (such as cirrus) can also trap OLR, causing additional warming (CCSP 2009).

2.5.10 Further eﬀects on climatic processes

Beyond their radiative and beer-known indirect feedback eﬀects on cloud, aerosols may modify still other climatic processes.

By cooling the Earth’s surface (reflection) and warming the atmosphere (absorption), aerosols act to decrease the atmospheric lapse rate — the surface is now cooler than normal, relative to the upper troposphere. e modified lapse rate in turn suppresses the water vapour feedback mechanism (Denman et al. 2007). ese factors then affect local atmospheric stability,39 changes to which are strongly related to the altitude of black carbon absorption.

38 Note that this includes warming through semi-direct and ice albedo effects, as well as the indirect longwave effect on cloud described here. 39 Atmospheric stability is a function of the ‘environmental temperature profile’ or lapse rate and the adiabatic lapse rates (Sturman and Tapper 2005).

66 2.5 Climatic eﬀects and inﬂuence

Aerosols have also been found to aﬀect large-scale atmospheric circulation paerns. Transient model simulations surveyed in AR4 found that a substantial Northern Hemi- sphere aerosol cooling caused a southward movement of the Inter-Tropical Convergence Zone and associated tropical rainfall belt; other studies suggest that aerosol eﬀects (including reduced surface irradiance and changes to sea surface temperature (SST) gradi- ents) may explain rainfall reductions experienced in the Sahel in the 1970s and 1980s (Denman et al. 2007; Ramanathan and Carmichael 2008).

is is reinforced by the recent ‘state-of-the-art Earth system climate model’ study of Booth et al. (2012). ey find that documented links between observed multidecadal variability in North Atlantic Ocean sea surface temperature and climate shis relating to hurricane activity, and droughts in the Sahel and Amazon, are significantly affected by aerosol emissions. e authors’ analysis shows that the combination of aerosol emission and volcanic activity accounted for 76 % of SST variation. Importantly, the authors note that ‘it is the inclusion of aerosol indirect effects that allows us to capture the magnitude and the temporal and spatial structure of SST variability’, concluding that their study ‘suggests that we need to reassess the current aribution to natural ocean variability of a number of prominent past climate impacts linked to [North Atlantic SST], such as Sahel drought’ (p. 231).

Suppression of precipitation by the indirect cloud lifetime effect, while potentially reducing rainfall in some areas, may reduce the frequency of extreme precipitation events. e negative forcing induced by aerosols is in effect ‘competing’ with GHG warming in determining changes to evaporation and precipitation paerns (Denman et al. 2007, p. 566). e scope of aerosol-cloud interactions in transient model simulations at the time of AR4 were insufficient to give conclusive findings as to the net effect on cloud.

67 Chapter 2 Aerosol eﬀects on climate

2.5.11 Eﬀects on surface irradiance

Aerosols can modify solar irradiance received at the surface in a range of ways, with complex implications. at modiﬁcation can exert a surface forcing that diﬀers markedly from radiative forcing at TOA (see discussion in section 2.3).

Recall that planetary albedo is by definition the fraction of incoming shortwave radiation reflected back to space, viewed at TOA — a strengthened albedo is a negative TOA RF. And to reiterate, in general an increase in cloud cover or aerosol optical depth (section 2.2.5) increases extinction of incoming shortwave radiation, enhancing albedo and reducing irradiance at the surface.40 e direct effect of scaering aerosol (section 2.5.1) is the classic case: increased aerosol burden enhances atmospheric scaer of shortwave radiation (increased AOD), reflecting some back to space (stronger albedo, negative RF) and thereby decreasing shortwave irradiance at the surface (negative surface forcing). Scaering also reduces the direct–to–diffuse surface irradiance ratio (with a potential biomass feedback relation, discussed in section 2.5.7). is is not necessarily the case for scaering aerosol in clear skies, however: scaering aerosol with an asymmetry parameter approaching +1 — ie, mostly forward scaering (section 2.2.3) — still increases optical depth, as AOD is defined with reference to irradiance received in a direct beam to the surface. Yet the total surface irradiance may be largely undiminished (negligible backscaer), with radiation simply arriving at an array of angles of incidence. Negligible backscaer means lile change to shortwave flux at TOA, hence lile to no change to planetary albedo and RF. ough surface irradiance is more diffuse, the actual quantity of energy received (W m−2) is not substantially decreased, hence producing lile to no surface forcing.

Absorbing aerosols extinguish incoming shortwave radiation entirely — that fraction absorbed — but the extent to which this changes shortwave ﬂux at TOA or surface irra-

40 A reduction in surface irradiance is the sum of that shortwave radiation absorbed and that scaered back to space.

68 2.5 Climatic effects and influence diance depends on the characteristics of underlying cloud and surface features (see section 2.5.4). In general though, an increase in aerosol absorption of incoming shortwave radiation at altitude within the troposphere will decrease irradiance at the surface and exert a negative surface forcing, oen much larger than any radiative forcing at TOA. In fact, under the right conditions TOA RF may be nil. Ramanathan and Carmichael (2008, 223) describe a change to surface irradiance without change to TOA shortwave flux as a ‘redistribution of the direct solar radiation between the surface and the atmosphere’, which globally can weaken ‘the radiative-convective coupling of the atmosphere and decrease global mean evaporation and rainfall’.

Given such complexities, Forster et al. (2007) noted that the surface forcing is ‘arguably a more useful measure of the climate response’ for absorbing aerosols than TOA RF, particularly when assessing their effect on the hydrological cycle (p. 196) (see Box 2.2). Moreover, a surface forcing induces corresponding changes in latent and sensible heat fluxes, separate to those for shortwave and longwave energy, which can be substantially different to RF values calculated at the tropopause or TOA. at is, RF by definition does not convey the energy balance perturbation of the surface-troposphere system (Forster et al. 2007, 196).

us the fundamental distinction between aerosol scaering and absorption of shortwave radiation points to the limitations of radiative forcing as a single measure of per- turbed energy ﬂux in all cases.

e physics outlined above is best illustrated by speciﬁc empirical examples.

A substantial decline in surface irradiance was observed at multiple locations over the second half of the twentieth century, the eﬀect eventually becoming known as global dimming.41 Estimates vary, but surface irradiance reduced on the order of 1 to 3 percent per decade to 1990, reaching around –7 W m−2 (Trenberth et al. 2007). Since 1990 sur-

41 Although further studies concluded this eﬀect was ‘conﬁned to large urban areas’; ie, not truly global (Trenberth et al. 2007).

69 Chapter 2 Aerosol eﬀects on climate face irradiance has recovered at most observation sites, brightening by around 6 W m−2. AR4 states that in general this increase is in agreement with satellite and surface observations of declining cloud cover (Trenberth et al. 2007, 279), only tentatively referring to an ‘open question’ as to a possible aerosol contribution (including induced changes in cloudiness), though noting that in China the direct aerosol eﬀect likely played a key role. A more recent review of global changes in surface irradiance highlights the central role of aerosols in stronger terms: Wild (2009) concludes that the main causes of these variations ‘appear to be changes in cloud and aerosol characteristics and abundance, which may or may not be microphysically linked’, further noting that the relative importance of these factors (including aerosol-cloud microphysical interactions) ‘may not be uniform over the globe but varies from region to region’ (p. 27). e change in surface irradiance from the combined anthropogenic aerosol loading is illustrated in Figure 2.10, a composite of multiple observational sources.

Figure 2.10: Surface dimming due to all anthropogenic aerosols (surface forcing in W m−2 ±30 %) (Fig. 1 panel (c), Ramanathan and Carmichael 2008). Reprinted by permission from Macmillan Publishers Ltd: Nature Geoscience, copyright 2008.

Further, a study of aerosol eﬀects on surface irradiance over Germany concluded that total aerosol extinction must have reduced from 1975 to 1990 in order to explain the simultaneous increase in the direct:diﬀuse shortwave irradiance ratio and weakened aerosol

70 2.5 Climatic effects and influence forcing (Liepert and Tegen, 2002, cited by Denman et al. 2007). at is, decreased aerosol emissions meant both a reduced extinction of shortwave radiation by scaering — hence a relatively weakened negative radiative forcing — and a simultaneously reduced extinction by absorption — hence a relatively higher surface irradiance. e combined result is that more shortwave radiation is reaching the surface directly, indicated by the larger direct:diffuse ratio.

ere are disagreements in the literature on this issue. Ramanathan and Feng (2009) make the following important observation.42

e major source of dimming is [aerosol] absorption of direct solar radiation. is is further enhanced by the reﬂection of solar radiation back to space by [aerosols]. is should be contrasted with the TOA forcing that is solely due to the reﬂection of solar radiation back to space. is distinction has been ignored frequently; as a result, the dimming has been mistakenly linked with surface cooling trends (e.g., Wild et al., 2004; Streets et al., 2006). e problems with this approach are the following: for black carbon, the dimming at the surface is accompanied by positive forcing at the top of the atmosphere (Ramanathan and Carmichael, 2008), thus it is erroneous to assume dimming will result in cooling. Further- more, as we will show later, the surface dimming due to … absorbing aerosols is a factor of 2–5 larger than the aerosol TOA forcing, and for many regions they can be even of opposite sign. (Ramanathan and Feng 2009, 41-2)

Bond et al. (2011, p. 1512) support this assessment, stating that ‘[e]ach gram of emied BC adds about 1 GJ to the Earth-atmosphere system, but that energy is distributed as 2.4 GJ of increased atmospheric absorption, counteracted by 1.5 GJ that does not reach the surface’.

GCM estimates of reduced surface irradiance due to aerosols vary between –1.3 W m−2 to –3.3 W m−2 averaged globally as reported in AR4, larger than TOA ﬂux because of aerosol absorption (Denman et al. 2007). ough in their later review paper, Ramanathan and Carmichael (2008) report annual mean surface forcing (for 2001–2003) of –4.4 W m−2. Further, these decreases are markedly larger over land, illustrating the spatial depend-

42 Citations in original.

71 Chapter 2 Aerosol eﬀects on climate ency of aerosol emissions.

On the other hand, absorbing aerosols have a ‘well known’ capability to reduce TOA forcing, and even change the sign from one of cooling under clear skies to a ‘large net heating’ in the presence of low altitude clouds43 (Ramanathan et al. 2001, 28386). e principal mechanism for this is aerosol absorption of cloud-reflected, outgoing shortwave radiation, exerting only minor change to surface irradiance but significantly suppressing cloud albedo and hence decreasing negative RF (see section 2.5.4). Ramanathan et al. (2001) observed a reduction of natural cloud RF at TOA by 5 W m−2 under these conditions: the semi-direct effect of absorbing aerosol in clear skies produced a TOA RF of –7.0 ±1 W m−2, and a factor of ten greater surface forcing of –23 ±2 W m−2; in cloudy skies TOA RF fell to –2.0 ±2 W m−2, but surface forcing was still –20 ±3 W m−2, reducing the difference to a factor of 3. Podgorny et al. (2000) and Satheesh and Ramanathan (2000) reported similar findings. Haywood and Shine warned in their 1997 study that, as a result of such complexities, even the sign of absorbing aerosol RF44 cannot be inferred from surface irradiance changes alone.

Both transient and equilibrium GCM studies point to decreased surface irradiance from an increased AOD and aerosol indirect effects as more important for changes to the surface energy budget than GHG-induced forcing of surface temperatures (Denman et al. 2007). All components of the surface energy budget — radiant energy, sensible and latent heat fluxes — decrease in response to reduced irradiance. As mean global evaporation must equal mean global precipitation, a reduction in latent heat flux (ie, evaporation) ought to cause a matching reduction in precipitation, again underscoring the complex effects of aerosols on the hydrological cycle as well as radiative flux and global mean temperature. However, observations over the twentieth century do not appear to demonstrate this result, indicating that aerosol-induced declines in precipitation have

43 Below around 3 km. 44 e authors refer to this as the ‘direct forcing’ (p. 1924), but this is the semi-direct eﬀect in the cat- egorisations herein.

72 2.5 Climatic effects and influence been overwhelmed by increased evaporation and specific humidity from GHG-warming.

e principal point here is that aerosol radiative effects may vary markedly from those of GHGs, exhibiting large variation in forcing at the surface, and within and at the top of the atmosphere. ese and other important effects on climate are not captured by assessing changes to TOA radiative flux alone, expressed as radiative forcing.

2.5.12 Summary of eﬀects

Table 2.2 presents a summary of the aerosol eﬀects on climate examined in the preceding sections. A synthesis of these eﬀects follows, discussing current understanding of the overall role of aerosols within anthropogenic climate change.

73 Chapter 2 Aerosol eﬀects on climate

Table 2.2: Summary of aerosol eﬀects on climate (aer Tab. 7.10 from Denman et al. 2007, with updates and revisions based on other sources cited in this chapter)

Effect Climatic influence Meanism Direct effect Negative TOA RF, negative Direct scaering incoming SW surface forcing radiation Indirect cloud Negative TOA RF, negative Feedback enhancement of cloud albedo effect surface forcing albedo Indirect cloud Negative TOA RF, negative Feedback increase of cloud fraction lifetime effect surface forcing Semi-direct effect Nil, positive or negative TOA RF, Absorption of incoming or outgoing in clear skies positive or negative surface SW radiation forcing Semi-direct effect Positive or negative TOA RF, Feedback change to cloud fraction above cloud positive or negative surface forcing Semi-direct effect Positive TOA RF, positive surface Feedback reduction of cloud fraction within cloud forcing Semi-direct effect Likely negative TOA RF, Feedback increase of cloud fraction below cloud negative surface forcing BC on snow & ice Positive TOA RF Feedback reduction of surface albedo ermodynamic Positive or negative TOA RF, Feedback suppression of precipitation effect positive or negative surface forcing Glaciation effect Likely positive TOA RF, likely Feedback reduction of ice cloud positive surface forcing fraction

Biomass Reduction in CO2 TOA RF Feedback enhancement of biomass fertilisation effect CO2 uptake Effect on low Positive TOA RF, positive surface Feedback enhancement of cloud OLR Arctic cloud forcing absorption Longwave Positive TOA RF, positive surface Direct absorption and re-emiance of absorption effect forcing OLR

74 2.6 Synthesis and implications

2.6 Synthesis and implications

AR4 does not provide a clear and unambiguous statement of the aggregate influence of aerosols on climate. In fact, the two relevant chapters — Chapter 2 (Forster et al. 2007) and Chapter 7 (Denman et al. 2007) — offer final estimates of the net aerosol effect that, while numerically similar, are distinctly different in character.

AR4 Chapter 7 provides what could be interpreted as the most complete estimate, as it incorporates the direct (section 2.5.1), indirect cloud albedo (section 2.5.2), indirect warm-cloud lifetime (section 2.5.3), and semi-direct (section 2.5.4) effects: Denman et al. (2007) find total net TOA forcing from all anthropogenic aerosol species relative to the preindustrial is an average of –1.2 W m−2 , with a range of –2.3 W m−2 to –0.2 W m−2. Chapter 2 considers only the direct and indirect cloud albedo effects because, as noted previously, the other aerosol effects are not regarded as radiative forcings: Forster et al. (2007) conclude that the combined total net TOA forcing from these two effects is a median value of –1.3 W m−2 with a 90 % CI of –2.2 W m−2 to –0.5 W m−2.

AR4 assessed the total positive anthropogenic forcing of LLGHGs and tropospheric ozone as +2.9 ±0.3 W m−2 (Forster et al. 2007, 200). e Chapter 2 mean anthropogenic aerosol forcing (–1.3 W m−2) therefore gives an oﬀset of ≈45 %, with a total possible range of ≈15 % to ≈85 %; using the Chapter 7 value (–1.2 W m−2), this oﬀset is ≈41 % with a total possible range of ≈6 % to ≈88 %. In their review, CCSP (2009) cited the Chapter 2 range as constituting the IPCC AR4 value (p. 56).

Confusingly however, the AR4 Summary For Policymakers (SPM) (IPCC 2007b) does not report any combined value, instead restating only the individual direct and indirect cloud albedo eﬀect RFs from Chapter 2.45 Figure 2.11 below, taken from the SPM,46 shows the aerosol component of radiative forcing in context (blue coloured blocks), from the

45 And the LLGHG plus tropospheric ozone forcing sums to –2.99 W m−2, not –2.9 W m−2. 46 e similar Fig. 2.20 in Chapter 2 does not actually show the RF values but estimates of climate eﬃcacy instead (see section 2.3.1). Fig. SPM.2 appears in that form only in the SPM.

75 Chapter 2 Aerosol eﬀects on climate preindustrial period to 2005. ese best estimates sum to –1.2 W m−2, the same numerical value reported by Chapter 7. But that numerical value refers to diﬀering physical processes in Chapters 2 and 7, and Chapter 2’s stated net forcing is, again, –1.3 W m−2 not –1.2 W m−2. Indeed, adding the positive forcing of BC on snow (+0.10 W m−2) gives a net aerosol forcing of –1.1 W m−2.

Figure 2.11: AR4 climate forcings by major component, between 1750 and 2005 (Fig. SPM.2, WG I SPM, IPCC 2007b). Copyright the Intergovernmental Panel on Climate Change, 2007.

is lack of clarity in expressing the climatic influence of aerosols suggests two conclusions. First, the inherent limitations of using RF as a comparison measure for various climatic agents are underscored — if important aerosol effects cannot readily be quantified by RF methodologies, then other approaches may be needed. Second, and apart from the limitations of RF itself, the uncertainty ranges for aerosol effects are an order

76 2.6 Synthesis and implications of magnitude greater than those for LLGHGs (as noted by Ramanathan and Feng 2008). Such large uncertainty is the principal reason for the ﬂaened and widened net anthropogenic forcing probability distribution function (PDF), shown in Figure 2.12. Moreover, such complexity may act as a disincentive — explicitly acknowledged or otherwise — for policy makers to grapple with anthropogenic aerosols in devising mitigation responses.

Figure 2.12: Aerosol and GHG forcing probability distribution functions, between 1750 and 2005 (Fig. 2.20 panel (b), Forster et al. 2007). Copyright the Intergovernmental Panel on Climate Change, 2007.

Importantly, unlike GHGs the aerosol eﬀect PDF is non-Gaussian in character, meaning that the uncertainty ranges are asymmetric about the median (Forster et al. 2007): a signiﬁcant proportion of the area under the aerosol PDF is for negative forcing values substantially greater than the best estimate of –1.3 W m−2. at is, while AR4 considered –1.3 W m−2 most likely, there is a non-trivial probability that the true value is greater, and the likelihood of it being greater than –1.3 W m−2 is stronger than the likelihood of it being less. Consequently, the total net anthropogenic forcing is similarly asymmetrical, with a best estimate of +1.6 W m−2 and a 90 % CI of +0.6 W m−2 to +2.4 W m−2, where the PDF is biased toward smaller net positive values due to the aerosol uncertainty (Forster et al. 2007). Hansen has repeatedly pointed out that, should aerosol forcing be close to –

77 Chapter 2 Aerosol eﬀects on climate

1 W m−2 for net forcing close to +2 W m−2, a major reduction in aerosol emissions — say, half — would remove –0.5 W m−2, increasing net forcing by one quarter to +2.5 W m−2; yet if aerosol forcing is close to –2 W m−2 for net forcing close to +1 W m−2, that same reduction by half removes –1 W m−2, nearly doubling the actual net forcing (eg, Hansen 2009; Hansen et al. 2011).47

Other estimates derived from model studies since AR4 produce similar but not equivalent results. Shindell and Faluvegi (2009) derive a global mean net aerosol forcing for the period 1890 to 2007 as the diﬀerence between observations and modelled changes due to GHGs, natural forcing, and ozone. e authors obtain net aerosol forcing of – 1.31 ±0.52 W m−2, which they note is ‘consistent’ with the –1.2 W m−2 of Forster et al. (2007).

In a review of the literature, Ramanathan and Carmichael (2008) determined total aerosol cooling (BC and non-BC) is –1.4 W m−2, masking as much as 50 ±25 % of GHG warming. Referring to this study, Ramanathan and Feng (2008) state that aerosols mask ≈47 % of anthropogenic warming, with a 90 % CI of 20 % to 80 %, contrasting this with a claimed IPCC value of ≈40 %.48 is increase in the assessed aerosol mask is more signiﬁcant than it appears, because the assessed BC warming of +0.9 W m−2 is markedly larger than AR4 (see section 2.4.2.3), as is the net non-BC-aerosol cooling of –2.3 W m−2. Bond et al. (2011) suggest such high values for modelled BC forcing might indicate greater than expected emissions, not necessarily a stronger BC forcing per mass.

Conversely, the substantially smaller DRF estimate of –0.3 ±0.2 W m−2 from Myhre (2009) (see section 2.5.1) necessarily reduces that mask. In fact, because that downward revision partly stems from an adjustment to the assessed preindustrial aerosol burden, the relative forcing of aerosol eﬀects beyond the direct eﬀect may logically be reduced also.

47 e presence of these asymmetric uncertainties and potentially large negative consequences suggest extreme caution in the mitigation policy response. See Box 4.2. 48 Which presumably refers to aerosol cooling relative to LLGHGs plus tropospheric ozone but excluding BC on snow.

78 2.7 Observation and modelling

Accordingly, only a crude assessment of a revised total aerosol cooling is possible.49 I estimate a value between –1.1 W m−2 and –1.0 W m−2, proportionally between ≈38 % and ≈34 %. While Myhre (2009, 190) notes that the smaller DRF value suggests it oﬀ- sets ‘only a modest 10% of the radiative forcing that is due to increases in well-mixed greenhouse-gas concentrations at their current concentrations’, this does not account for other aerosol eﬀects, a substantial caveat.

aas et al. (2009) used ten separate GCMs to diagnose global mean total aerosol shortwave forcing via two methods. Combining models and satellite observations, they found a value of –1.5 ±0.5 W m−2, the highest of any discussed here. In an alternate method involving scaling Nd by satellite estimates of AOD, the authors found a total all-sky forcing of –1.2 ±0.4 W m−2, again broadly in line with AR4 estimates.

Alternative approaches for deriving total net aerosol forcing via planetary energy balance studies are detailed in section 3.2.

2.7 Observation and modelling

e sections to follow elaborate on observational and model based assessment of aerosol climatic eﬀects, particularly the limitations and persisting uncertainties.

2.7.1 Observation and measurement

AR4 highlighted that differentiating aerosols of anthropogenic versus natural origin in satellite retrievals is ‘very challenging’ (Forster et al. 2007, p. 155). is view is reiterated in the detailed CCSP (2009) survey of aerosol knowledge, and observational capabilities in particular. Part of the reason for these difficulties is the lack of appropriate observational systems, briefly examined in Box 2.3.

49 Simply by decreasing the AR4 Chapter 2 and 7 DRF values only.

79 Chapter 2 Aerosol eﬀects on climate

Box 2.3: Missing satellite observations

Hansen (2009) explains that effective observation of aerosols requires satellite platforms designed for the purpose — the two key measurements needed are polarimetry of reflected sunlight, and interferometry of planetary thermal emission.a He then lays out the history of specific proposals to achieve the needed measurements in the early 1990s, requiring four instruments of moderate size and cost each (pp. 65–9). Unfortunately, Hansen believes the political dynamics of the US space program (desire for large, expensive projects) were a significant factor in these proposals failing to be adopted. NASA’s Glory satellite was set to improve aerosol measurement dramatically — a purpose-built platform to observe total solar irradiance, and an innovative new ‘aerosol polarimetry sensor’ (Mishchenko et al. 2007).b Tragically, Glory was lost when its launch vehicle failed in early March, 2011 (the same vehicle that failed in 2009, destroying the Orbiting Carbon Observatory spacecra).

a e physics of this was known in 1970 (p. 65). b NASA’s Glory web site is still available at http://glory.gsfc.nasa.gov/.

Discrepancies among different satellite products remain and uncertainties persist — indeed systemic biases may exist. Retrieval of aerosol data over land is necessarily more complex, simply because of the natural variations in geography, topography, surface reflectance and so on; model-satellite integrations must commonly be relied on instead (CCSP 2009). Observations of aerosol loading and properties are generally limited to clear-sky conditions. As current satellite platforms do not measure chemical composition, they have limited ability to distinguish the anthropogenic aerosol fraction; although retrieval of aerosol fine-mode fraction may be an avenue for deriving the anthropogenic component of AOD. Satellite remote sensing is also not sensitive to particles less than 0.1 µm in diameter — which are a ‘significant fraction’ of CCN — with clear consequences for accurate measurement of indirect effects on cloud (CCSP 2009, 25).

In satellite-based estimates of aerosol direct radiative forcing, uncertainties in the anthropogenic fraction are greater than for all species combined, especially over land (CCSP 2009). Nearly 80 % of overall uncertainty in anthropogenic DRF stems from ﬁve parameters, each accounting for 13 % to 20 %: the ﬁne-mode fraction and anthropogenic portion of it over both land and ocean, and AOD over ocean.

80 2.7 Observation and modelling

Ground-based in situ programs (such as those operated by the WMO) and remote sensing platforms (such as NASA’s global robotic AERONET system50) also have a vital role to play, particularly since these can observe aerosol behaviour below the cloud layer, which is normally opaque to satellites. In situ observations can provide much more detailed, localised measurement and at higher temporal resolution than remote sensing. CCSP (2009) notes that accurate observations of aerosol ‘extinction proﬁles’ (discussed in section 2.5.11) are ‘pivotal’ to improved calculation of radiative forcing and atmospheric responses. New lidar systems in particular are increasingly being applied to this need. In fact lidar systems can even observe local aerosol entering cloud formations, rather than relying on the column-integrated AOD view of satellites (CCSP 2009). Surface network measurements of aerosol optical depth have obtained an accuracy of 0.01 ~ 0.02, which is considered adequate for assessing clear-sky TOA DRF to within 1 W m−2 (CCSP 2009).

In situ studies at multiple global locations found that while increased aerosol burden does lead to increased Nd, the process is highly variable and always sub-linear, highlighting that cloud droplet number concentration is a function of numerous parameters and not only aerosol concentrations (CCSP 2009). One reason for the variability is the role of updra velocity as an important factor in the activation of aerosol particles to water droplets (such as that found in ‘vigorous’ cumulus clouds). Further, not all aerosols will increase Nd — certain ‘giant’ CCN around a few micrometres in diameter can lead to

‘signiﬁcant suppression in cloud supersaturation’ and reduced Nd (CCSP 2009, 49).

Studies based on surface radiometers, reported in CCSP (2009), actually found that variance in cloud LWP accounted for most of the variance in cloud optical depth. is means that detection of aerosol indirect eﬀects becomes even more diﬃcult, swamped as those changes are by the larger shis in water content. A number of post-AR4 studies examining the complex interactions of aerosol particles with cloud, and discrepancies in

50 e Aerosol Robotic Network of ground-based remote sensing sun photometers and radiometers; see http://aeronet.gsfc.nasa.gov.

81 Chapter 2 Aerosol effects on climate various observational methods, ‘reinforced the importance of LWP and vertical velocity as controlling parameters’ for particle radius and cloud opacity, and have begun to reconcile the underlying reasons for those discrepancies in observation (CCSP 2009, 47). McComiskey and Feingold (2008) note that improving the measurement accuracy of cloud droplet effective radius response to aerosol burden will strengthen estimates of RF and narrow their uncertainty range. Satellite observations showing a weak response were used as inputs to the GCMs that produced the lowest RF estimates for the indirect cloud albedo effect in AR4 — McComiskey and Feingold (2008) point out that in situ observations find a stronger response, more in line with theory, and would consequently produce a stronger RF.

antifying aerosol effects on cloud precipitation efficiency is a complex task for satellite platforms: their instantaneous view does not readily allow causal relationships to be determined (CCSP 2009). is difficulty stems from the temporal aspect of the cloud lifetime effect, in that aerosol induced changes to precipitation rates and cloud lifetime are self-evidently not observable by instantaneous snapshots. However, CloudSat, launched in 2006 and riding as part of the A-Train satellite group,51 provides substantial oppor- tunities for inferring aerosol precipitation effects.

Reconciliation and synergy of observational data with model simulations is another important area of continued eﬀort. Closure studies employ coordinated use of multiple instruments sampling regional aerosol properties combined with intensive in situ ﬁeld experiments. One such study reviewed in CCSP (2009) determined uncertainties for clear-sky DRF estimates of around 25 % for sulphates and carbonaceous species and as much as 60 % for aerosol containing dust. Along with detailed in situ measurement of aerosol properties, closure studies can in part be used to constrain regional chemical transport model (CTM) simulations of aerosol DRF (CCSP 2009). CTM results can then be provided as inputs to climate models in place of assumed values for aerosol proper-

51 Glory was intended to join the A-Train (L’Ecuyer and Jiang 2010).

82 2.7 Observation and modelling ties; though CTMs are themselves subject to a range of uncertainties in emissions and complex aerosol factors. Satellite observations of global parameters such as AOD permit evaluation of large scale climate model simulation results, constraining aerosol contributions. Integrating observational and model data aids the goal of reducing discrepancies between them and narrowing uncertainties in both.

2.7.2 Aerosol modelling

Modelling of atmospheric aerosols has developed rapidly since the start of the twenty- ﬁrst century (CCSP 2009). e eﬀort and complexity involved is a serious challenge, as models must account for atmospheric chemical transformation, transport, and removal processes, as well as the highly heterogeneous distribution of primary aerosols and secondary precursor emissions. Complex additional atmospheric processes such as hygroscopic growth of aerosol particles via condensation of ambient water vapour are commonly included as parameterisations (in this case as a function of relative humidity) (CCSP 2009). AOD (section 2.2.5) is the parameter in best agreement across models: CCSP (2009, pp. 59-60) reports that the median value for global annual mean AOD across 16 AeroCom models is 0.127, with a standard deviation of 18 %. is value also agrees with global AOD means from recent satellite observations.

However, in another section comparing a different set of five models with the MODIS and MISR satellite platforms, CCSP (2009, p. 38) reports that global annual mean AOD from the models is smaller than the results obtained from both satellites, and that these differences are substantially larger on regional scales. Integrated satellite-model products tend to fall somewhere in between model AOD projections at the low end and satellite observations at the high end, though they are in beer agreement with the ground-based AERONET. Agreement for total AOD among the AeroCom models also masks significant diversity in individual aerosol species’ AOD, mass loading, and extinction efficiencies.

83 Chapter 2 Aerosol eﬀects on climate

On balance, these issues suggest that AOD alone is not suﬃcient to fully assess aerosol forcing, and that it may mask cancelling or compensating eﬀects across species. CCSP (2009) highlights the parallel importance of the asymmetry factor (section 2.2.3), SSA (section 2.2.4), and other optical properties. Further, regional and seasonal diversities are much larger than global means, again underscoring aerosols’ temporal and spatial heterogeneity.

Successful reproduction of observed twentieth century climate is an important valida- tion of climate models. But to date models have included very uncertain aerosol forcing (CCSP 2009). A survey of GCMs used in reproduction of twentieth century climate found a strong inverse correlation between climate sensitivity and total anthropogenic forcing: models with high climate sensitivity generated low total forcing and vice versa, so that all arrived at broadly the same reproduction of climate52 (Kiehl 2007). Uncer- tainty ranges for climate sensitivity and for total forcing are of the same magnitude, and the primary reason for the intermodel variation in total forcing is an almost threefold spread in aerosol negative forcing contribution. is spread among nine of the models used by AR4 meant that though individual model climate sensitivities also varied threefold53 — between 1.5 ℃ and 4.5 ℃ — all reproduced the observed trends (CCSP 2009). For this reason, CCSP (2009) cautions that successful reproduction of past climate does not necessarily mean projections of change for the twenty-ﬁrst century will be accurate. ough Kiehl (2007) argues that these uncertainties do not invalidate the use of (current) models for future projections, in part because future emission scenarios include large GHG increases, which would come to dominate the total anthropogenic forcing.

Writing in 2005, Andreae et al. made the implications of these relationships stark: if climate sensitivity is low then aerosol forcing is necessarily weak (and total forcing relatively high); but if sensitivity is actually high then aerosol forcing is strong (and total

52 Recall ∆TS = λ RF , as described in section 2.3. 53 e variation in climate sensitivity is largely due to uncertainties in cloud feedback processes (Kiehl 2007).

84 2.7 Observation and modelling forcing relatively low) and we have ‘a worrying future that may bring a much faster temperature rise than is generally anticipated’ (p. 1188).

e climate modelling community employs two broad approaches relevant to aerosols: forward modelling aempts to explicitly calculate the forcing from aerosol particulate and precursor emission inputs through models of aerosol physics and chemistry; the inverse approach assumes observed climate change is the result of known forcings, so with assumed climate sensitivity (or a range thereo), aerosol forcing can be inferred from the residual forcing of GHGs and other agents54 (CCSP 2009; Hegerl et al. 2007). As discussed in section 2.6, AR4 concluded from forward modelling studies that tropospheric aerosols oﬀset ≈45 % of the positive forcing of GHGs and other agents, but with a large uncertainty range (15 % to 85 %). Studies of the inverse type examined by AR4 Chapter 9 (Hegerl et al. 2007) found an aerosol forcing range of –1.7 W m−2 to –0.6 W m−2, which brackets the possible extent of aerosol forcing without requiring detailed knowledge of complex aerosol processes (CCSP 2009). is inferred aerosol forcing range is both narrower and contained within the ranges reported in AR4 chapters 2 and 7.

Aempts to estimate aerosol emissions are also part of the overall modelling eﬀort. Nat- ural sources are actually less well quantiﬁed than anthropogenic, and can have dramatic spatial and temporal variation. A 2004 study, for example, concluded that available data allowed only an order of magnitude estimate of sea salt aerosol production rates (CCSP

2009). In contrast, secondary sulphate and nitrate aerosols formed from SO2 and NOx precursors have relatively well understood emission sources (CCSP 2009). In comparisons of observations with chemical transport model simulations, sub-micrometre sulphate and black carbon were found to be the best represented (CCSP 2009). Large discrepancies between model projections and observations were found for other species such as POM, and especially those of super-micrometre size such as mineral dust (in total,

54 e Shindell and Faluvegi (2009) study discussed in section 2.6 is an example of this.

85 Chapter 2 Aerosol eﬀects on climate underestimated in models by a factor of three). Model projections of POM are a ‘severe problem’, in large part because of serious diﬃculty in representing the formation of secondary organic aerosols (CCSP 2009, 35). Models also struggle to represent organic aerosols’ vertical distribution, underpredicting their presence in the free troposphere (CCSP 2009). In fact, organic aerosols are one of the greatest challenges facing climate modellers, and even the distribution of well-characterised sulphates are not necessarily estimated correctly (CCSP 2009).

Surveying a range of historical emission inventory studies, CCSP (2009) concluded that the growth in primary aerosol emissions was ‘not nearly as rapid’ as the growth in CO2: heavy use of biofuels and a lack of technology and controls on coal burning led to relatively high late nineteenth and early twentieth century emissions of BC and POM; over the twentieth century much stronger particulate controls were introduced, and biofuel use declined. In the modern period, while small-diameter sulphate, BC, and POM constitute about 10 % of total aerosol mass, these species are primarily anthropogenic and so located in the most populated regions, hence their climate and air quality eﬀects are of major concern (CCSP 2009).

Related to these inventories is the determination of a reasonable preindustrial aerosol baseline. is is no simple task given the paucity of available data and spatial coverage — CCSP (2009) notes that large uncertainties necessarily exist in estimates of the preindustrial aerosol burden. e relative abundance of the various aerosol species is also difficult to assess. It had been assumed that the preindustrial aerosol composition was broadly the same as that of the modern period, simply at lower quantities. But a recent revision by Myhre (2009) concludes that black carbon has in fact grown at twice the rate of other species, which has significant consequences for net aerosol effects, reducing total SSA (see section 2.2.4) from a preindustrial value of 0.986 to 0.970 today — about twice as absorbing.

Model calculations of DRF (section 2.5.1) are deﬁned by three key aerosol optical para-

86 2.7 Observation and modelling meters, as a function of given wavelength: AOD, SSA, and the asymmetry parameter (g) (CCSP 2009). All parameters are influenced by relative humidity, and varying particle size will also change SSA (see definition in section 2.2.4). Model estimates of DRF tend to be smaller than observational estimates (by around 55 % to 80 %), and even more so on regional scales (CCSP 2009). In part this is because models usually use the preindustrial atmospheric aerosol burden as their comparative baseline, whereas observational estimates are commonly calculated by comparison to the total absence of anthropogenic aerosols.55 Other factors also play a role, such as differences in AOD and SSA values, unperturbed surface albedo, model parameterisations, and radiative transfer schemes. At the same time, two of the major satellite platforms (MODIS and MISR) also diverge substantially in estimates of AOD, by 10 % to 15 % (CCSP 2009).

Model calculations of aerosol indirect effects are markedly more difficult — ‘a great deal of complexity’ remains unresolved within GCMs, particularly in their representation of aerosol-cloud interactions (and clouds in general), the principal context of indirect effects (CCSP 2009, 66). In fact, in their survey of the field, CCSP (2009) noted that most models had not incorporated the indirect cloud lifetime effect (see section 2.5.3).56 e presence of cloud-aerosol feedbacks make projection of global mean temperature response based on linear addition of RF values problematic once GCMs include aerosol- cloud interactions beyond the cloud albedo effect, though linearity can be restored if these feedbacks are treated as additional forcing terms (Forster et al. 2007, 197). CCSP (2009) contrasts the conclusions of Hansen et al. (2005b) regarding such feedbacks with the discussion in AR4 (Forster et al. 2007; Denman et al. 2007): Hansen et al. argue that the cloud lifetime effect (represented by time-averaged cloud cover) is actually more

55 Recall that Myhre (2009) found a substantial reduction in estimated DRF, in part due to the revision of preindustrial aerosol burden. 56 is no doubt reflects the enormous difficulty of doing so: ‘Deficiencies in the data record, and a poor understanding of what processes regulate the behaviour of cloud regimes, frustrate aempts to aribute real correlations among the aerosol, clouds and precipitation to lifetime effects’ (Stevens and Feingold 2009, 608).

87 Chapter 2 Aerosol effects on climate likely to dominate the cloud albedo effect (section 2.5.2), and that this dominance is supported by studies of both cloud-resolving models and satellite observations.57 An important issue in resolving aerosol indirect effect uncertainties is that satellite observational platforms to date are not able to provide aerosol and cloud field retrievals with temporal resolution greater than once per day, and that higher level cloud obscures those at lower levels. CCSP (2009) summarises the situation by stating that aerosol indirect effects, particularly cloud lifetime, remain ‘to a large extent unconstrained by satellite observations’ (p. 69).

Because current generation GCMs and available computational resources cannot yet resolve cloud microphysics nor cloud-aerosol interactions, modellers must represent aerosol effects through parameterisation schemes — and doing so is itself extremely difficult (CCSP 2009).58 For the indirect cloud albedo effect, the main unresolved question is the degree to which water droplet concentrations increase with enhanced aerosol burden — b recall relation eq. 2.10 from section 2.5.2, Nd ≈ (Na) — and the relative importance of particle size distribution, aerosol composition, and updra velocity in affecting that concentration. Aerosol number concentration and particle size distribution are believed to be the most significant; updra velocity becomes important when aerosol number concentrations are high, but the inherent small scale makes this perhaps the hardest aspect to model (CCSP 2009). ough composition is generally regarded as the least influen- tial of these factors (represented by fairly simple parameterisations of the soluble and insoluble fractions), it cannot be ignored, as any substantial shi away from fossil fuels toward biofuels — triggering a decline in sulphate — could change aerosol composition in possibly significant ways.

e indirect cloud lifetime eﬀect is represented in GCMs by a change to precipitation production and is commonly parameterised by cloud fraction (CCSP 2009; Hansen et al.

57 e two studies referred to were published in 2004 and 2005, respectively. 58 Given the pace of growth in computational power, however, this assessment may now be outdated as regards a resource constraint.

88 2.7 Observation and modelling

2005b). CCSP (2009) identified a range of complexities in aerosol effects on precipitation and cloud lifetime, which reinforce the overall conclusion that understanding of these processes remains an open challenge. e large eddy simulation study of Ackerman et al. (2004, cited in CCSP 2009) found that cloud water content may increase or decrease with additional aerosol loading in stratocumulus clouds, depending on the overlaying air’s relative humidity. Wang et al. (2003, cited in CCSP 2009) showed that increased aerosol concentration in stratocumulus cloud tends to reduce water content because smaller droplets more readily evaporate, inducing a feedback process that dilutes the cloud. In their recent large eddy simulation study, Xue et al. (2008) identify evidence of two distinct regimes for aerosol effects on cloud lifetime: the first, ‘precipitating regime’, exists for increases in Na up to some threshold level, where cloud lifetime is extended by precipitation suppression (increased ‘cloud condensate’ residence time); but in the ‘non- or weakly precipitating regime’, further increases in aerosol loading beyond a certain

Na level reduces the cloud condensate residence time through enhanced evaporation of the smaller droplets. Given these persistent complexities and sometimes conﬂicting results, CCSP (2009) observes tellingly that ‘[i]t could be argued that the representation of these complex feedbacks in GCMs is not warranted until a beer understanding of the processes is at hand’, and that ‘[m]oreover, until GCMs are able to represent cloud scales, it is questionable what can be obtained by adding microphysical complexity to poorly resolved clouds’ (p. 72).

In general, only a fraction of current generation models consider aerosol species and climatic effects other than sulphate DRF (CCSP 2009). e CCSP (2009) review goes so far as to state explicitly that model simulations used in AR4 did ‘not adequately account for’ aerosol forcing (p. 72); their summary table of AR4 models clearly shows the relative lack of accounting for species such as BC, OC, or mineral dust, and even the indirect effects of sulphate. A further concern among models is that global average values that agree with other models and with observations mask serious differences in the spatial

89 Chapter 2 Aerosol effects on climate character and relative species contribution of aerosol loading. GCMs also tend to assume that multi-species aerosol loadings are externally mixed (see section 2.1.2), while observations indicate that internal mixing is actually more common, which can have non-trivial implications for the resultant radiative forcing (CCSP 2009). Some modelling groups are developing ‘sophisticated aerosol mixtures’ using detailed calculations of microphysics, and these could well produce ‘very different’ forcing values for both direct and indirect effects (p. 78). Finally, CCSP (2009) notes that almost all model simulations are for all-sky conditions — because obtaining cloud-free scenes from GCM output is difficult — whereas satellite and remote sensing observations are all currently for clear sky conditions, as these platforms are not able to measure aerosol optical depth through cloud. Because of the effect of relative humidity on AOD, the actual value for AOD under cloudy conditions is expected to be higher.

e short residence time and geographically-based nature of aerosols mean there is an important time dependency in calculating the net forcing and consequent changes to global mean surface temperature (CCSP 2009). at is, the type and quantity of aerosol species has varied over time (especially the twentieth century), and this has a marked influence on net forcing and ∆TS. Figure 2.13 shows an illustrative example of this time dependence from 1850 to 2000. e arrested growth in sulphate optical depth as a result of Northern Hemisphere air pollution regulations is evident — though not an actual net decline because of offseing increases in Asia; see Box 2.1 — as is the resultant flaening of effective forcing.

90 2.7 Observation and modelling

Figure 2.13: Illustrative time dependence of aerosol optical thickness and eﬀective forcing from the GISS GCM (Fig. 3 panel (c), Hansen et al. 2007b). Reproduced with kind permission from Springer Science & Business Media B.V.

Figure 2.14: Illustrative model-derived temporal evolution of the surface forcing and instantaneous radiative forcing (panels from Fig. 2.23, Forster et al. 2007). Copyright the Intergovernmental Panel on Climate Change, 2007.

91 Chapter 2 Aerosol eﬀects on climate

Figure 2.14 shows an illustrative model-derived temporal evolution of the surface and instantaneous radiative forcing59 for all main agents over the same period — though less discernible in this graphic, the aerosol direct and cloud albedo radiative forcing are similarly ﬂaened from the 1990s onward, while the LLGHG forcing continues to grow apace. Newer quantitative estimates of individual forcing agents and net forcing discussed in Chapter 3 have modiﬁed this picture somewhat, but the principal aspects of forcing over time remain.

CCSP (2009) concludes their review of aerosol modelling by returning to the persistent large differences in model calculations of aerosol forcing distributions, the large uncertainties in modelling atmospheric processes and emission profiles, and the fact that agreement among models on total AOD oen obfuscates substantial variation in component species and their consequent contribution to (direct) forcing. Similarly reinforced is the warning that a degree of agreement on global averages among models, and between models and observations, masks much larger discrepancies at the regional level. Because the spatial and temporal heterogeneity of aerosols means their effects are far more significant regionally, ‘it is insufficient or even misleading to just get the global average right’ (CCSP 2009, 81).

Crucially, modelling of indirect effects remains a great challenge. Efforts to reduce uncertainties are confounded by substantial intermodel discrepancies in the complex input parameters described above (particle size, composition, mixing state, microphysical and atmospheric processes), and confronted by a lack of necessary global-scale observational data for comparison. Again, the majority of models available for IPCC AR4 did not even include aerosol indirect effects. e final CCSP statement is compellingly blunt: ‘Im- provements must be made to at least the degree that the aerosol indirect forcing can no longer be used to mask the deficiencies in estimating the climate response to green-

59 is is the ‘true’ forcing of a radiative perturbation applied instantaneously, with no temperature or atmospheric state adjustments permied; Fi in Figure 2.2.

92 2.7 Observation and modelling house gas and aerosol direct RF’ (2009, p. 81). Such strong caution is echoed by Stevens and Feingold (2009), who warn of the great diﬃculties remaining in modelling of cloud microphysics and the continued use of parameterisations; global model estimates of the cloud lifetime eﬀect in particular may well underestimate the true uncertainty.

Chapter 3 next examines alternative approaches to estimate net aerosol forcing that are able to avoid many of these persistent modelling diﬃculties.

3 Climate response and associated risk

e range of aerosol species, their properties, and array of eﬀects on climate are laid out in Chapter 2. is chapter now expands from that foundation to site aerosols within the full context of anthropogenic climate change. Recent studies of planetary energy balance uncover important new insights into climate response, and provide alternative, empirically-driven means to infer net aerosol forcing beyond the primarily model- derived estimates discussed in section 2.6. e potential ultimate consequences of a reduced aerosol loading under abatement scenarios are then investigated, particularly in relation to risks of dangerous climate change. Finally, available quantitative metrics are reviewed for their capacity to provide meaningful, policy-relevant assessment of both LLGHG and aerosol contributions to anthropogenic climate change.

3.1 Climate response to forcing

e actual response of the climate system to any forcing is the proper concern for policy makers. antifying the forcing exerted by particular agents is policy-relevant only to the extent that they can meaningfully indicate this response over time. e principal measure of ultimate climate response is the change in global mean surface temperature,

95 Chapter 3 Climate response and associated risk

TS, at radiative equilibrium. Per eq. 2.7 in section 2.3, this is global mean radiative forcing multiplied by climate sensitivity. However, RF alone does not readily indicate how much of that perturbation is yet to be responded to, or, conversely, how great a fraction of the perturbation has already changed climate. Policy makers need information on the transient response, not only the ﬁnal result. In that light, it is worth now re-examining RF in more detail.

3.1.1 Radiative forcing revisited

Some of the difficulties surrounding radiative forcing and the range of methodologies available for calculating it were discussed in section 2.3. Further issues also exist, particularly relating to comparing forcing agents using their RF values. RFs are usually calculated as averages for the Earth as a whole, which obscures potentially significant variation in regional forcing (Forster et al. 2007). Net RF may be zero if substantial regional asymmetries cancel out, even though global mean temperature is still affected. At the same time, similar paerns of climatic response can be caused by RFs exhibiting very different spatial paerns and hence appearing quite dissimilar. e spatial and temporal heterogeneity of aerosol emissions and their effects are not adequately represented if global-scale RF alone is the metric used for assessing climate change (CCSP 2009). Comparison of particular forcing agents can also be misleading if dissimilar RF methodologies are employed: forcing values can vary substantially between methods, and express qualitatively distinct physical processes. Similarly, summation of calculated per-agent RF values is subject to a range of uncertainties and methodological difficulties. In all, while RF is clearly a useful and important indicator of perturbations to the climate system, Forster et al. (2007, 133) warn that it ‘provides a limited measure of climate change as it does not aempt to represent the overall climate response’.

Even within a single methodology, not all radiative forcings are ‘interchangeable’. Con-

96 3.1 Climate response to forcing sider RFs derived for greenhouse gases and for aerosols. GHGs perturb climate by absorbing and then re-radiating OLR, so that an increase in GHG concentrations causes more energy to be absorbed by the planetary surface — GHGs produce a longwave radiative forcing. Aerosols affect climate via qualitatively different and much more heterogeneous mechanisms. e most significant radiative effects of anthropogenic aerosols are in modifying incoming shortwave radiation; they both change planetary albedo through reflection, and further modify surface irradiance through atmospheric absorption (section 2.5.11). e distinction with regard to GHGs is highly salient: absorbed shortwave radiation is the originating energy input to the Earth-atmosphere system, and temperature responds rapidly to significant changes in insolation. Furthermore, unlike GHGs, the forcing exerted by aerosols at TOA can differ substantially from that at the surface. Aerosol absorption in particular may produce small or even nil observable change in TOA RF while producing a large negative surface forcing. As explained in section 2.5.4 and 2.5.11, this is because aerosol absorption extinguishes surface shortwave irradiance without necessarily inducing a corresponding change in TOA shortwave flux.

Radiative forcings for particular agents acting on longwave versus shortwave radiation are then most meaningfully compared with reference to final planetary radiative equilibrium. Direct comparison of RF values during ongoing transient perturbations may obscure large differences in temperature response and other important climate variables (eg, Forster et al. 2007, 195-9). e radiative forcing concept is hence important but alone is insufficient to capture the range of climate responses generated by forcing agents of substantially differing character. Forcing efficacy (section 2.3.1) addresses some of these problems, but cannot resolve them all. As we will see in the sections that follow, the transient response is likely to become increasingly critical for mitigation policy, making these difficulties of intercomparison via global-average RF an emerging policy issue.

97 Chapter 3 Climate response and associated risk

3.1.2 Equilibrium climate sensitivity

As explained in section 2.3, equilibrium climate sensitivity deﬁnes the ultimate response to a forcing of the Earth-atmosphere system induced by a change in boundary conditions, commonly given as the equilibrium ∆TS induced by a doubling of atmospheric CO2 concentrations. A brief further explanation of this factor is warranted, in part because of the interdependence of derived climate sensitivity and inferred net aerosol cooling inherent in inverse modelling studies (as noted in section 2.7.2; see also Tanaka and Raddatz (2011)). Climate sensitivity derived from model studies is therefore subject to structural uncertainties, and as noted in section 2.3, these have proved challenging to reduce.

Hansen and Sato (2011) and Hansen et al. (2008) instead derive equilibrium climate sensitivity from the paleoclimatic record. eir central estimate remains 3 ℃ for doubled CO2 as in many previous studies, but the uncertainty is narrowed slightly to give 3 ±0.5 ℃ rather than the ±1 ℃ commonly found previously. Stated more technically, their central −2 estimate is 0.75 ±0.125 K/W m for 2×CO2.

In both studies, the authors emphasise the need to clearly diﬀerentiate between the fast- feedback or ‘Charney’ sensitivity,1 and the full slow-feedback sensitivity of ﬁnal equilibrium. Fast-feedbacks refer to near-immediate changes in response to a forcing by water vapour, clouds, natural aerosols, snow cover and sea ice; slow-feedbacks involve changes to surface albedo, including vegetation and ice sheets, and LLGHGs, which lag by decades to millennia (depending on the forcing itsel). Most discussions of climate sensitivity actually describe fast-feedbacks only — and this is the value given here — but slow-feedback sensitivity, or ‘Earth system sensitivity’ may be as much as double this, particularly due to CO2. e full Earth system sensitivity should therefore be an important consideration for policy decisions around ‘acceptable’ climate change and cor-

1 Named in reference to the lead author of the ﬁrst report examining climate sensitivity in 1979.

98 3.2 Planetary energy imbalance and climate response responding atmospheric LLGHG concentrations. However, this thesis is concerned with the extent to which anthropogenic aerosols represent a threat to GHG mitigation eﬀorts themselves, and hence climate response in the short term is the relevant factor. Fast- feedback sensitivity is therefore the applicable form and should be assumed henceforth.

3.2 Planetary energy imbalance and climate response

e planetary energy budget is ‘a fundamental characterization of the state of the climate’ (Hansen et al. 2011, 13441); it provides a powerful tool for evaluating the climate response to forcing and the extent of perturbation to radiative equilibrium (Murphy et al. 2009; Hansen et al. 2005a; Church et al. 2011; Trenberth et al. 2009; Huber and Knui 2012). Imbalance over several years between the energy absorbed by the Earth (absorbed insolation) and that emied back to space (OLR) is the ‘primary symptom’ of planetary thermal inertia, thereby providing ‘an invaluable measure of the net climate forcing’ (Hansen et al. 2005a, 1431). A positive planetary energy imbalance means that more energy is being absorbed than emied — the climate system has not yet fully responded to a net positive forcing. at extra energy will hence continue to heat the planet, as over the long term emied energy must match absorbed energy to restore radiative equilibrium: further warming is ‘in the pipeline’. e geophysical processes that constitute the climate response are complex, but the overall picture is well illustrated by Figure 3.1.

Figure 3.1 shows an idealised step change in GHG concentration inducing a positive radiative forcing (top panel). By raising atmospheric longwave opacity, elevated GHG levels cause outgoing longwave radiation to space (OLR) to fall immediately (boom panel), inducing a positive radiative forcing. e planet now has a positive energy imbalance (the shaded area). e climate responds at two time scales (mid panel): a rapid partial surface temperature increase over about a decade, with consequent increase in OLR, reducing much of the initial energy imbalance; followed by the long response of

99 Chapter 3 Climate response and associated risk

Figure 3.1: Illustrative sketch of the response to positive energy imbalance (Fig. 1, Murphy et al. 2009). Reproduced with kind permission from John Wiley & Sons, Inc. the oceans over centuries, slowing as radiative equilibrium is approached. ese scales reﬂect the small heat capacity of the planetary surface and atmosphere relative to the vast heat store of the oceans.

antification of thermal inertia and associated climate response is highly policy-relevant. e delay between imposition of a forcing and realisation of its full effect means that a degree of GHG stabilisation-target overshoot may be manageable: if the net forcing can be reduced in time, the ultimate temperature response will be lessened. e question is, how much time? Accurate assessment of planetary energy imbalance helps to answer that question; it provides a key global diagnostic of anthropogenic interference, defining ‘how much current climate forcings must be altered to stabilize climate’ (Hansen et al. 2011, 13441).

100 3.2 Planetary energy imbalance and climate response

Ocean heat content is the predominant component of the planetary energy budget and the principal source of thermal inertia — around 90 % of additional energy goes to heating the oceans (Murphy et al. 2009; Trenberth and Fasullo 2010). erefore, while the ultimate response to any forcing is the change in global mean surface temperature at equilibrium, TS, a function of climate sensitivity, the pace of that change depends largely on the rate of heat exchange between the surface mixed layer and deep ocean, and, to a much smaller degree, rates of ice sheet disintegration (Hansen et al. 2005b; Wigley and Schlesinger 1985; Hansen et al. 2008, 2011). Calculating the planetary energy budget over time requires combining ocean heat content data with the observational temperature record (land and sea surfaces) and all forcings. ese forcings include the well-constrained LLGHGs, for which atmospheric concentrations are directly measured; other GHG species; anthropogenic aerosols, which are primarily tropospheric; and significant variations in natural boundary conditions, principally the solar radiation cycle and volcanic aerosol emissions, which are reasonably well-quantified as they reside in the stratosphere and hence survive for a few years. e change in ocean heat content (known as ‘uptake’) roughly equals the energy imbalance: while the imbalance continues — surface warming is not yet sufficient to restore OLR to radiative equilibrium — that energy is heating the oceans as the planet’s major heat reservoir.

Accurate knowledge of the full proﬁle of ocean heat content — and its rate of change — both at surface level (to ~700 m) and at depth (to 2000 m), is consequently vital for quantifying that imbalance. e recently-established international Argo ﬂoat network,2 continuously measuring ocean temperature throughout the vertical water column to 2000 m, is now making possible ‘dramatic improvements’ in this knowledge (Hansen et al. 2011).

Planetary energy imbalance can shed light on the actual rate of change in TS during

2 Argo has continually expanded coverage and data quality since its initiation in 2000. Measurements became available from 2003, with full deployment achieved in 2007. See http://www.argo.ucsd.edu/.

101 Chapter 3 Climate response and associated risk the transient response to forcing, where climate sensitivity by itself can tell us lile. is rate of change of the climate response is highly important for mitigation strategy and for determining how much ‘room to move’ exists under conditions of stabilisation target overshoot; reliable estimation of the temporal paern of temperature response is enormously valuable. e climate response function determines that rate of change, deﬁned as the fraction of equilibrium change to TS realised as a function of time, subsequent to imposition of a forcing (Hansen et al. 2011). Climate sensitivity tells us how much, the climate response function tells us how fast.

antifying the planetary energy budget affords one final insight: an empirically constrained derivation of total negative aerosol forcing (Murphy et al. 2009; Church et al. 2011; Hansen et al. 2011). Positive forcings are the GHGs and minor changes to solar output over the ~11 year cycle; this is offset by any stratospheric aerosol cooling. Total energy absorbed at the planetary surface is balanced by increased OLR as a function of rising TS, and the storage of energy as heat in the oceans and melting of ice. e difference between this total energy absorbed and the remaining positive forcing — the energy residual — is the net negative forcing from the sum of aerosol direct and indirect effects.3

e energy budget since 1950 determined by Murphy et al. (2009) is illustrated in Fig- ure 3.2, where panel (a) shows the total cumulative positive forcing and panel (b) shows shows the cumulative negative forcing, including ocean heat storage and the land and atmosphere energy absorption leading to increase in TS. To reiterate, ocean heat content is a proxy for planetary energy imbalance.

3 As well as any unknown mechanism; though none is suggested by the evidence.

102 3.2 Planetary energy imbalance and climate response

Figure 3.2: Cumulative planetary energy budget since 1950 (Fig. 6, Murphy et al. 2009). Repro- duced with kind permission from John Wiley & Sons, Inc.

3.2.1 antitative estimates of energy imbalance and aerosol forcing

Studies providing specific estimates of planetary energy imbalance, both current and historical, are difficult to compare — they may use differing methodologies, assess possibly overlapping but discrete time periods, and do not necessarily give values for a consistent set of indicators. eir estimates of the absolute and relative forcing contribution of aerosols may more profitably be compared, however. ese estimates from relevant available studies are discussed below, with a summary given in Table 3.1.

An early quantitative estimate is that of Hansen et al. (2005a). Imbalance did not exceed a few tenths of a wa per square metre until the 1960s, but increased GHG forcings gives an imbalance in 2003 of 0.86 ±0.15 W m−2, with aerosol forcing estimated at –1.39 W m−2 (approximately 50 % uncertainty). Commied warming with then-current atmospheric composition was a further ~1 ℃ above 2000 mean temperature. ough an important study, this work has eﬀectively been superseded by Hansen et al. (2011), discussed below.

103 Chapter 3 Climate response and associated risk

Murphy et al. (2009) performed an energy balance GCM study strictly constrained by observations of surface temperature, GHG forcing, solar radiation, volcanic emissions, and ocean heat content. Estimated planetary energy imbalance is given graphically but not stated explicitly in the text. ey infer from energy residuals a total anthropogenic aerosol forcing best estimate4 of –1.06 ±0.4 W m−2 for the period 1970–2000 at the 1σ confidence level. e authors note that their derived residual forcing agrees with the direct and cloud albedo effect forcings for 2005 in Forster et al. (2007), and fits the total aerosol forcing estimate of Denman et al. (2007). However, the range of –1.9 W m−2 to –0.3 W m−2 at the 2σ confidence level excludes large negative aerosol forcing beyond –1.9 W m−2, hence serving to narrow the AR4 uncertainty range shown in Figure 2.12. Murphy et al. (2009) question the validity of an apparent jump in energy residuals around 1995, greater than –1.5 W m−2 in 2000, concluding it is likely due to errors in ocean heat uptake data or temporary heat transport to the deep ocean.5 ey ‘largely exclude’ an actual increase in aerosol forcing itself as, in part, no increase in albedo or AOD was evident in satellite observations since that time.

Murphy et al. (2009) underscore the ‘striking result’ that since 1950 only about 10 % of the positive forcing of GHGs and solar radiation have actually gone into heating the Earth: 20 % has been balanced by increased OLR; a further 20 % has been offset by volcanic aerosol emissions;6 and the remaining 50 % has been offset by the combined direct and indirect effects of anthropogenic aerosols.

In a short perspective article, Trenberth and Fasullo (2010) state a post-2000 planetary energy imbalance of 0.9 ±0.5 W m−2. However, ocean heat content data then available to the authors suggested a discrepancy in observed energy absorption: it appeared less than expected, thus preventing closure of the energy budget. Hansen et al. (2011) address

4 Other estimates are given (some larger) depending on speciﬁc ocean heat content and GHG forcing datasets; the one cited here is their best estimate overall. 5 Interestingly, the higher rate of heat transport to the deep ocean noted by Hansen et al. (2011) below would seem to support this. 6 Particularly El Chichón in 1982 and Mt Pinatubo in 1991.

104 3.2 Planetary energy imbalance and climate response this point directly, arguing that their calculations using revised and expanded data show no such discrepancy, a smaller imbalance, and hence a closed budget (pp. 13436–7).

Church et al. (2011) used sea level and land water reservoir data in concert with updated ocean heat content data to examine the energy budget since 1961. Applying the approach of Murphy et al. (2009), they infer aerosol forcing from energy balance residuals, ﬁnding a central estimate of –0.8 ±0.4 W m−2 for the 1980s and early 1990s. is value, though lower for the best estimate, has an uncertainty range consistent with the results of Murphy et al. (2009) and Hansen et al. (2005a). (It is however outside the bounds of Hansen et al. (2011) below.) e authors also discuss a post-1995 jump in energy residuals to about –1.5 W m−2, similar to that noted (but rejected) by Murphy et al. (2009). However, here Church et al. (2011) argue that the updated ocean data do not readily support a conclusion that this is an artefact of measurement error. In fact they conclude that this jump and inferred increase in negative aerosol forcing is ‘robust’ (p. 6), and necessary to close the planetary energy budget, given the slowdown in TS increase over the previous decade. e authors suggest a range of possible reasons for an increased negative aerosol forcing:

• A substantial surge in developing country sulphur and other aerosol species emissions, such as that postulated by Kaufmann et al. (2011) (see section 3.6).

• A small decline in stratospheric water vapour on the order of 0.1 W m−2 to 0.2 W m−2, which is not taken into account in their analysis.

• Moderate volcanic activity (a natural contribution to enhanced cooling).

• Emerging evidence from lidar observations at Mauna Loa Observatory and Boulder, Colorado reported by Hofmann et al. (2009). ese show increased aerosol backscaer in the stratosphere at altitudes of 20 km to 30 km of around 4 % to 7 % per year since 2000, indicating partial transport of tropospheric aerosol to the stratosphere, due in part to a large increase in Chinese coal burning since 2002.

105 Chapter 3 Climate response and associated risk

e comprehensive study of Hansen et al. (2011) calculates energy imbalance of 0.58 ±0.15 W m−2 for 2005–2010, a period coincident with the most prolonged solar minimum of the satellite era (ie, a small negative solar forcing). is period is also marked by a trans- ition to decreased upper ocean heat storage and increased heat uptake at depth. For 1993–2008, upper ocean heat storage dominated, but calculated energy imbalance varies substantially with the particular ocean heat content analysis, ranging from 0.59 W m−2 to 0.80 W m−2. Figure 3.3, taken from this work, shows an indicative history of planetary energy imbalance over recent decades; a useful summary is given below it.

Net aerosol forcing is inferred to be –1.6 ±0.3 W m−2 in 2010, the largest of any to date, implying substantial forcing from indirect effects. Hansen et al. (2011) remark that their findings agree well with the ‘insightful’ work of Murphy et al. (2009) discussed above. ey find aerosol forcing of –1.2 ±0.3 W m−2 for the 1970–2000 period Murphy et al. used, and estimate from Murphy et al.’s Fig. 4c a forcing of about –1.5 W m−2 in 2000, consistent with their own. Hansen et al. (2011) further note that though their findings do not exceed the a priori estimates of AR4 including all indirect forcings (discussed in section 2.6), they do exceed the range used in most model simulations undertaken for TAR and AR4.

Inference of the total aerosol cooling effect from energy imbalance in this way is a robust approach, but it cannot determine the contribution of individual species or precisely quantify the relative role of direct and indirect effects. Hansen et al. (2011) declare that ‘continued failure to quantify the specific origins of this large forcing is untenable, as knowledge of changing aerosol effects is needed to understand future climate change’ (p. 13421) and that ‘until aerosol forcing is measured, its magnitude will continue to be crudely inferred, implicitly or explicitly, via observations of climate change and knowledge of climate sensitivity’ (p. 13422).

Huber and Knui (2012) employed an alternative method for anthropogenic aribution of climate warming based on ensemble modelling of energy balance driven by ‘boom-

106 3.2 Planetary energy imbalance and climate response up’ estimates of historical forcing, constrained by observations of temperature since 1850 and ocean heat uptake since the 1950s. ey found an average net forcing between 1850 and 2010 of 0.54 W m−2, ranging between 0.36 W m−2 and 0.76 W m−2, and a most likely current net forcing of 1.6 W m−2 (no uncertainty stated). Aerosol direct and indirect eﬀects (including volcanic emissions to the stratosphere) have oﬀset ‘about hal’ of the

7 warming eﬀect of GHGs, giving a total change in TS since the 1950s of about 0.55 ℃.

Figure 3.3: Indicative planetary energy imbalance over time (Fig. 15b, Hansen et al. 2011). Imbalance is the top of the red area, which shows energy uptake by ice and land and air warming; ocean heat uptake is in blue. Real-world interannual climatic variability (noise) is removed in this representation. Measured ocean denotes Argo data era.

[A] precipitous decline in the growth rate of GHG forcing about 25 [years] ago caused a decrease in the rate of growth of the total climate forcing and thus a flaening of the planetary energy imbalance over the past two decades. at flaening allows the small forcing due to the solar cycle minimum, a delayed bounceback effect from Pinatubo [volcanic aerosol] cooling, and recent small volcanoes to cause a decrease of the planetary energy imbalance over the past decade. (Hansen et al. 2011, 13439)

7 Somewhat confusingly, the authors also state that ‘About 83% of the accumulated energy from carbon dioxide forcing alone … is oﬀset by the combined negative direct and indirect eﬀect of aerosols’ (p. 1), and later that simulated temperature increase from GHGs of 1.31 ℃ is countered by direct and indirect aerosol cooling of –0.85 ℃, which is approximately 65 %.

107 Chapter 3 Climate response and associated risk , obtained from 2 − content discrepancy based on available OHC data and estimated energy imbalance, as discussed above. Offset is interpreted from data presented in source graphic Fig. 3(a). Offset is derived relative to total LLGHG forcing of slightly more than 3 W m source graphic Fig. 18(b). Aerosol cooling offset ‘about hal’ of LLGHG forcing, though this includes stratospheric aerosol contribution. Notes translation of fossil fuel emissions with effect parameterisation rather than inferred. Offset explicitly stated as discussed above. 46 % 50 % 50 % 50 % Not given Authors posit a possible heat ≈ Not given Aerosol forcing is estimated by ≈ ≈ ≈ Period 2000 2000 ) 2 − (W m –1.6 ±0.3 2010 Inferred aerosol forcing Offset –1.06 ±0..4 1970– –1.5 Period Estimate 2000 2010 ) 2 − Imbalance Estimate (W m 0.86 ±0.15 2003 –1.39 ±0.7 2003 0.9 ±0.5 post 0.58 ±0.15 2005– Method GCM study checked against OHC data Energy balance GCM study, constrained by obs of positive forcing and OHC Unstated. Employs energy budget approach of Murphy et al. 2009, with additional constraint of land and ocean water balance data Energy balance GCM constrained by obs, including updated OHC data Ensemble modelling of energy balance from ’boom up’ historical forcing estimates, constrained by obs of temp and OHC 2009 2005a 2011 2011 2010 Estimates of planetary energy imbalance and inferred aerosol forcing Study Hansen et al. Murphy et al. Trenberth and Fasullo Church et al. Hansen et al. Huber and Knui 2012 Table 3.1:

108 3.3 Commied warming

3.3 Commied warming

e additional temperature response expected without further anthropogenic interference is known as the committed warming, where commitment refers to the consequences of historical emissions. Projection of this commitment is ‘a fundamental metric for both science and policy’ (Armour and Roe 2011). Mahews and Weaver (2010) argue that discussion of commied warming — viewed as ‘unavoidable’ due to climate system inertia — is oen misleading, owing to a basis in ﬁxed atmospheric concentrations, ignoring atmospheric drawdown by carbon sinks. is is actually a ‘constant- composition commitment’,8 such as atmospheric stabilisation targets expressed as various CO2 ppm concentrations, which in fact require ongoing GHG emissions to maintain — the IPCC stabilisation ranges in AR4 are a highly inﬂuential example (Tab. SPM.5, IPCC 2007c).

Mahews and Weaver (2010) argue the correct deﬁnition is for cessation of all emissions, the ‘zero-emissions commitment’, where atmospheric concentrations will begin to decline following emission termination, leading to temperatures remaining roughly constant or beginning to fall. It is hence socioeconomic inertia, not geophysical, that will determine long term future warming. However, it must be stressed that Mahews and Weaver (2010) refer to CO2-commitment only, and so do not take into account the long residence times of some non-CO2 LLGHGs, or the cooling oﬀset of anthropogenic aerosols.

Armour and Roe (2011) point out that once these are taken into account the abrupt termination of aerosol emissions induces immediate signiﬁcant temperature increase (see section 3.4.1). ey argue that this temperature spike should be treated separately. e ultimate commitment at equilibrium is the commied warming due to past GHG emissions, aligning with Mahews and Weaver (2010) and the “‘geophysical” warm-

8 Hare and Meinshausen (2006) describe this as a “‘present forcing” warming commitment’.

109 Chapter 3 Climate response and associated risk ing commitment’ identiﬁed by Hare and Meinshausen (2006). e transient commitment is deﬁned as the peak temperature increase following emission termination.

3.3.1 Derived climate response function

As outlined in Box 3.1, the energy balance study of Hansen et al. (2011) reached two important conclusions: first, inferred current net negative aerosol forcing is large,9 in turn indicating that indirect effects exceed direct, possibly substantially; second, the climate response function is more rapid than represented in models, due to the study’s finding of less-efficient nature of ocean heat mixing. If correct, aerosol masking is hence greater, and when it is removed the climate will respond faster to the then-strengthened positive GHG forcing. is approach to deriving a climate response function appears to be novel in the literature at time of writing. e authors do emphasise however that it is necessarily provisional, partly based on qualitative reasoning given the data available (but incorporating sensitivity analysis to varying key assumptions), and that substantial effort is being expended on active investigation of the flaws indicated in the GISS ocean model.

Hansen et al. (2011) derive a climate response function producing 40 % of the change in

TS within the ﬁrst 5 years, followed by a subsequent long, slow ‘recalcitrant’ response phase, producing 75 % of the change in TS within 100 years. ey note that while the slow response tail remains in this assessment as with previous research, it is exaggerated in many climate models (including that of their own GISS model). e rapid response in TS ‘implies that even moderate ongoing changes of the climate forcings can have a noticeable eﬀect, despite the fact that the climate system is still in a mode of trying to come to equilibrium with forcing changes that occurred over the past century’ (p. 13436).

9 For details and comparison to other energy balance studies, see Table 3.1 above.

110 3.3 Commied warming

Box 3.1: Is climate response faster than previously thought?

In late 2011, James Hansen and colleagues published a detailed assessment of the climate response function (Hansen et al. 2011). eir study determined net climate forcing and the rate of ocean heat mixing, constrained by the detailed observations of ocean heat content now afforded by the Argo network, in concert with observed land and sea surface temperatures. A number of research groups are actively assessing the raw Argo data, and the study carefully examined each group’s output in turn. Hansen et al. conclude that most climate modelsa mix heat into the deep ocean too efficiently. Novel application of ocean mixing data derived from observed downward transport of transient chemical tracers (such as chlorofluorocarbons) further support this finding, as do other empirical indicators such as an unrealistically deep thermocline at lower latitudes. e implications of this likely flaw are enormously significant. slower ocean heat mixing ⇒ surface ocean warms faster ⇒ climate responds more rapidly A relatively less efficient real-world ocean heat mixing means that heat is transported to the depths slower than represented in climate models. Ocean surface waters will therefore be warmer than expected, because they retain more of the heat absorbed due to a persistent positive forcing. Consequently, global mean surface temperature will be higher than for more efficient ocean heat mixing, which means that the climate response to that forcing is in turn faster than models indicate. Yet the major climate models reproduce recent warming with good skill. How? Building on previous work by Knui (2008)b, Hansen et al. (2011) argue that the answer must be that net forcing is actually less than calculated by climate models: an exaggerated net forcing employed by models counters their unrealistically slow climate response function, generating a good match to the observed temperature record. is conclusion is not made arbitrarily; indeed the authors note that a ‘family of solutions’ is consistent with observed warming, involving varying values for the key factors of net forcing, equilibrium climate sensitivity, and the climate response function (which is dependent on ocean heat mixing). Climate sensitivity is known from paleoclimatic evidence, as discussed above. Planetary energy imbalance and observed ocean heat content provides the means to discriminate between the remaining possible solutions. e positive forcing of LLGHG concentrations is well-constrained by observations, so this term is accurately known. In contrast the forcing of anthropogenic aerosols is ‘practically unmeasured’. Ocean heat content constrains the feasible range of the climate response function. e net forcing derived from planetary energy imbalance therefore permits the aerosol forcing term to be inferred, and the result strongly indicates that aerosol negative forcing is underestimated, both their direct effects and indirect influence on clouds. a Possibly because of a common ancestor in the GFDL Bryan-Cox ocean sub-model. b ough here it must be pointed out that Knui (2008) arrives at largely the opposite conclusion, that total aerosol forcing is relatively small.

111 Chapter 3 Climate response and associated risk

As of the completion of this thesis, however, no other study is known to have reached a similar conclusion nor replicated the particular ﬁndings regarding modelled ocean heat mixing eﬃciency — the Hansen et al. (2011) result may hence be regarded as something of an outlier.

A contrasting view is given in Oo et al. (2013), using an intermediate type of energy budget approach. Oo et al. combine data-based estimates of heat uptake from the oceans, land surfaces, ice, and atmosphere, with a current-generation multi-model ensemble estimate of total and aerosol forcing to determine equilibrium climate sensitivity and ‘transient climate response’.10 eir study finds a lower temperature increase for both equilibrium and transient response, based on data from the most recent decade — the implication is that temperature response under conditions of a substantial drop in aerosol loading is not likely to be a serious concern. However, aerosol forcing is here obtained from forward modelling studies and is in fact further reduced to beer accord with recent satellite-constrained estimates (Oo et al. 2013, Supplemental); the value used is substantially lower than those surveyed above and those reviewed in section 2.6. Given the potential systemic underestimates in both models and satellite observations (section 2.7), this input aerosol forcing value may be biased low. e resultant values for transient and equilibrium temperature response may therefore also be underestimates. While the approach of Oo et al. (2013) offers an important contrast to the comparatively strong negative aerosol forcing estimates discussed above, available observational data remains constrained and the limited understanding of complex microphysical aerosol effects on cloud persist. In my view, such model-based studies therefore cannot yet rule out the risk of a large and rapid temperature change consequent to a weakened aerosol mask implied by those larger negative forcing estimates.

10 e laer is deﬁned as the change in TS evident at the point where 2×CO2 is reached under conditions of a 1 %/yr rate of CO2 increase.

112 3.4 Dangerous anthropogenic interference

It is worth highlighting the conclusions of Murphy et al. (2009) here: the relatively small fraction of energy actually retained by the Earth thus far reminds us that in reality energy imbalances can heat the planet quickly, and that ‘it is energetically possible for surface temperature to increase or sea levels to rise much more rapidly than they have in the recent past’ (p. 11).

3.4 Dangerous anthropogenic interference

e central objective of climate change mitigation is to prevent dangerous anthropogenic interference in the climate system, as articulated by Article 2 of the UNFCCC (reproduced on page 8). Dangerous interference is usually understood as the need to keep temperature rise above the preindustrial level to some upper limit, most commonly the 2 ℃ ‘guard rail’, a widely-nominated goal in international mitigation negotiations (Richardson et al. 2009; WBGU 2009; Anderson and Bows 2011).11 e rate of that rise is as important as the magnitude, however, and this facet is oen neglected in policy discussion, despite its inclusion in Article 2 (Ross and Mahews 2009). e rate of change maers most to natural ecosystems: temperature rising too rapidly may overwhelm adaptive capacity, threatening species extinction, ecosystem vulnerability, and loss of biodiversity. Ross and Mahews (2009) noted that no consensus as to a safe rate of change threshold exists in the literature, but rates above 0.2 ℃/decade seem likely to be dangerous for most systems. At localised warming rates above 0.3 ℃/decade, the bulk of ecosystems are unable to adapt (Raes and Seinfeld 2009). Any rapid transient warming triggered by the loss of a signiﬁcant fraction of the aerosol mask therefore risks wide- spread ecosystem damage, even if the magnitude is tolerable.

Further, dangerous anthropogenic interference may manifest across a spectrum of vul- nerable elements in the climate system, as well as natural ecosystems (Lenton et al. 2008;

11 See also discussion in section 1.2.

113 Chapter 3 Climate response and associated risk

Schellnhuber 2009).12 Nonlinearity is a prominent feature of the Earth-atmosphere system, and abrupt changes may occur at differing levels of temperature rise. If tipping points are exceeded for long enough, changes may be triggered that are irreversible on any time frame meaningful to humans. Nonlinear shis in some elements also risk significant positive feedbacks, exacerbating warming further, such as the loss of highly reflective Arctic sea ice; methane release from melting permafrost and undersea marine hydrates; or ice sheet disintegration in Greenland and Antarctica. Recent research indicates substantial Arctic methane release is already occurring (Anthony et al. 2012), and that Greenland disintegration may be triggered by lower temperatures than previously thought (Robinson et al. 2012).

Ramanathan and Feng (2008) argue that the reality of aerosol cooling means that the commied warming relevant for mitigation policy is that of the full GHG forcing. Fig- ure 3.4 shows this commitment as a probability distribution, based on a climate sensitivity probability density function; tipping elements are superimposed, demonstrating the temperature thresholds assessed by Lenton et al. (2008). Aerosol-induced temperature change may therefore play a central role in understanding dangerous anthropogenic interference, so we turn now to quantitative assessments of its possible magnitude and pace.

3.4.1 Aerosol-induced temperature change

Bond et al. (2011, p. 1506) describe the effect of short-lived species as akin to ‘a burst of energy’ applied to the Earth-atmosphere system, the temperature response to which ‘is very different from response to longer-lived species’. e effect of an instant removal of a large fraction of aerosol cooling would be similar to a sudden and sustained spike in solar output: energy imbalance increases by the same amount as the negative aerosol

12 Recall the initial discussion in section 1.2.

114 3.4 Dangerous anthropogenic interference

Figure 3.4: Dangerous anthropogenic interference for a range of tipping elements (Fig. 1, Ramanathan and Feng 2008). Commied warming due to GHG emissions 1750–2005; temperature thresholds as assessed by Lenton et al. (2008), except for Himalayan glaciers. Copyright the National Academy of Sciences, 2008. forcing that has just been lost, as more solar radiation now reaches the surface. antitative estimates of the likely magnitude and rate of change to TS induced by significant shis in anthropogenic aerosol loading over time are therefore key complements to the knowledge of the climate response function in determining effective mitigation policy. e temperature response to such changes in shortwave radiative flux is hinted at by the anomalous diurnal temperature range observed during the grounding of all commercial aircra13 in the United States following the September 2001 terrorist aacks: over this three-day period, the temperature range rose 1.1 ℃ above the mean, the only such period in 30 years where the increase was greater than two standard deviations (Travis et al. 2002).

13 Aircra contrails are known to, in part, aﬀect transfer of incoming solar radiation.

115 Chapter 3 Climate response and associated risk

Model studies have been undertaken to examine various aspects of the temperature response to a significant loss of aerosol cooling, either in scenarios of abatement, or where deliberate geoengineering schemes are aborted or fail. Brasseur and Roeckner (2005) modelled a ‘drastic reduction’ in aerosol load, specifically sulphate. ey found that TS could rise by 0.8 ℃ in under a decade if all anthropogenic sulphate were removed, with regional increases of 1 ℃ on most continents and as much as 4 ℃ in the Arctic. Hare and Meinshausen (2006) examined one illustrative scenario where all emissions cease in- stantly, which induced pronounced TS rise of around 0.3 ℃ to 0.4 ℃ in the years following (depending on the year when emissions cease), remaining elevated for some decades.14 Ramanathan and Feng (2009) state that current LLGHG concentrations give commied warming of 2.4 ℃, with negative aerosol forcing of about –1.4 W m−2 offseing that increase to date, leading to a rise of 1.3 ℃ if aerosols are removed. Model studies of aerosol and GHG interaction also indicate nonlinearity in their combined effect on temperature response (Ming and Ramaswamy 2009).

Mahews and Caldeira (2007) modelled a complex set of circumstances comparing BAU emissions and deliberate geoengineering, with abrupt failure occurring at particular years this century. With BAU emissions oﬀset by geoengineering, a failure in 2075 induced a rate of change as high as 4 ℃/decade in the years immediately following. eir lowest scenario was 2 ℃/decade, still ten times faster than current rates. e authors state that ‘global temperature responded quickly to changes in incoming solar radiation’ in all cases (p. 9951). eir results showed rapid convergence to a non-geoengineered climate forced by remaining LLGHG concentrations when geoengineering was abruptly removed, a clear analogue for a precipitous loss of aerosols.15

Similar work by Ross and Mahews (2009) examined future deliberate geoengineering

14 is study used a simpler GSM than the current state of the art. 15 Mahews outlined preliminary model results for a loss of anthropogenic aerosols in the case of mitigation, with immediate warming of 0.2 ℃ to 0.3 ℃, followed by convergence with the CO2-only result (per. comm. 2011).

116 3.4 Dangerous anthropogenic interference and the consequences of abrupt removal. ey found that very rapid warming can occur in the year immediately following removal, around 0.4 ℃ to 0.5 ℃ per year (dependent on climate sensitivity) and beyond 1.0 ℃/decade under some circumstances. ese conditions are not directly translatable to more common mitigation scenarios, but they indicate a capacity for rapid change, particularly in the years immediately following a signiﬁcant loss of aerosol forcing.

In a speciﬁc form of commied warming study (refer to section 3.3 above), Tanaka and Raddatz (2011) examined two future emissions scenarios: SRES A1B to 2100 and a hypothetical total cessation of all anthropogenic emissions in 2020.16 eir approach is based on a deliberate analysis of the interdependent inverse relation between climate sensitivity and negative aerosol forcing inherent in inverse modelling studies constrained by the temperature record. ey ﬁnd that an initial abrupt warming under the cessation scenario is the ‘hidden commitment’ represented by aerosol emissions, arguing that this has received lile aention in studies of commied warming (noting the exception of Armour and Roe (2011)). For all ranges of climate sensitivity considered, this hidden commitment under cessation led to a marked initial TS increase, with the rate of increase strongly dependent on sensitivity. Importantly, even with low sensitivity the rate of increase exceeds 0.2 ℃ per decade.

Other studies examine implications for temperature change from a loss of aerosols due only to imposition of air pollution controls. Raes and Seinfeld (2009) state that the Kloster et al. (2009) scenario of maximum possible air pollution control to 2030 concur- rent with continued increase in GHGs leads to long term increase of 2.2 ℃,17 with a rate of increase between 2000 and 2030 of 0.4 ℃/decade globally, and as high as 0.8 ℃/decade over large areas of the Northern Hemisphere. Any robust imposition of air pollution controls is thereby likely to increase the current warming rate of around 0.2 ℃/decade.

16 Emissions scenarios are examined in section 3.6.3. 17 e Kloster et al. (2009) paper is somewhat ambiguous as to what base this increase is relative to — presumably it is preindustrial climatology.

117 Chapter 3 Climate response and associated risk

ere is no sure way to know in advance the rate of temperature rise induced by aerosol emission abatement consequent to climate change mitigation, or indeed from strong air pollution controls. e outcome is too heavily dependent on aerosol emission rates which cannot reliably be predicted in advance. Scenario studies examining the rate of change in future emissions then bracket the possible rate of temperature change, but more exact projections are inherently extremely difficult. ese model studies are also subject to the large uncertainties surrounding indirect effects, which may not be fully included, and to the possible errors in ocean heat mixing discussed above. But the primary risk is that under scenarios of wide-scale loss of aerosol cooling, the rate of temperature change is likely to exceed adaptive capacities; even if the global mean is tolerable, the rate of change over continental areas may be dangerous. On the other hand, Schellnhuber (2008) points out that in reality reductions of aerosol emissions could be more haphaz- ard and gradual, possibly avoiding any significant warming spike. Regardless, the fundamental implication must be that the full ramifications of air pollution and climate change mitigation policies on aerosol cooling must be understood.

Raes and Seinfeld (2009) provide a useful illustration of these ramiﬁcations for temperature rise and its rate of change, reproduced in Figure 3.5 — the dashed lines indicate the commonly perceived outcome of mitigation based only on LLGHGs, the solid lines indicate the potential for unintended side eﬀects when aerosols are also considered.

3.5 Toward appropriate metrics

Appropriate measurement of aerosols and their effects is problematic. As discussed previously, metrics such as radiative forcing are not a good fit for these spatially and temporally heterogeneous forcing agents, tending to obscure important differences regionally and between TOA forcing versus that at the surface or in the atmospheric column.

118 3.5 Toward appropriate metrics

Figure 3.5: Illustrative response to air pollution and climate change mitigation policies (Fig. 1, Raes and Seinfeld 2009). Reprinted from Atmospheric Environment, Copyright 2009, with permission from Elsevier.

Aempts to refine RF using forcing efficacy or the related RF index18 do not substantially address these concerns. is problem is even more pronounced at the point of mitigation policy instruments, commonly reliant on global warming potentials and CO2- equivalence, which are far from suitable for short-term forcing agents in general, and especially for those with negative forcing effects. Beer quantification of aerosol emissions and their radiative effects is clearly necessary.

3.5.1 The problem with GWP and CO2-e

e global warming potential (GWP) is an index expressing the climate change impact of a forcing agent relative to CO2. GWP is deﬁned by the IPCC as the ratio of time- integrated global mean RF induced by a 1 kg pulse emission of some compound relative

18 e ratio of total CO2 + non-CO2 RF to that of CO2 alone, developed to assess sectoral emission proﬁles (especially transport and aviation) (Unger 2010). Fuglestvedt et al. (2010) highlight that RF index has been misapplied in some cases, incorrectly framing it as a per-emission metric where it is not, as RF depends on previous emissions.

119 Chapter 3 Climate response and associated risk

to a 1 kg pulse emission of a reference gas, CO2 (Forster et al. 2007). e time horizon is usually either 20, 100, or 500 years, with 100 years the most commonly stated and the metric applied by the Kyoto Protocol.19 GWP is employed within multi-gas abatement frameworks by deriving a carbon dioxide equivalent (CO2-e) emission mass that would produce the same time-integrated RF as the non-CO2 species. In this way, all species can be expressed in ‘equivalent’ CO2 amounts and then treated as interchangeable (and tradeable). ere is no actual requirement in the deﬁnition of GWP that CO2 be the reference gas, but in practice this is the standard, and the IPCC calculates GWPs on this basis (eg, Forster et al. 2007, 210–3). is equivalence therefore implicitly assumes that all agents induce an exclusively positive forcing, as CO2 does.

GWP has always been subject to strong criticism. Shine (2009) reviews the history of the global warming potential, highlighting the strong caveats in the IPCC First Assess- ment Report in 1990 regarding the lack of agreement on a methodology to combine all relevant factors of a forcing agent within a single metric. In fact, GWP was chosen to illustrate the difficulties in doing so. But once adopted, GWP became vital for the multi- gas approach of the Kyoto Protocol where, inter alia, its transparency is an advantage, despite real concerns such as a lack of specificity as to what climate effect was actually being compared. e Kyoto Protocol may not have been possible without it. Shine (2009) canvasses criticism of the IPCC for failing to fully evaluate alternatives and for failing to integrate the perspectives of all Working Groups,20 in effect treating the issue as a question of physical science alone and thereby neglecting the serious mitigation policy implications. e IPCC has begun to address these concerns, however; alternatives are likely to be beer evaluated for the Fih Assessment Report.

19 Shine (2009) notes there is no real scientific reason for that choice over other time periods, and Fuglestvedt et al. (2010) point out that the decision reflects value judgements as to the importance of future impacts. 20 In particular that of WG III (mitigation), where a range of alternatives have been raised, such as economic damage functions or price ratios taking into account how close temperatures are to some target limit; though these too may suffer from a range of contentious assumptions regarding abatement cost, discounting, and so on.

120 3.5 Toward appropriate metrics

e problem for application of GWP and especially CO2-e to cooling aerosols is that such agents do not satisfy the positive forcing assumption, and are subject to marked regionality in effect. To be at all meaningful for these species, GWPs must be negative. Studies have calculated negative GWPs for species such as organic maer and sulphate (Bond et al. 2011; Fuglestvedt et al. 2010), but doing so hardly reflects the metric’s original design. Constructing GWP from time-integrated RFs compounds the serious difficulties in intercomparison of RF between GHGs and aerosols, as the stratospherically-adjusted

TOA methodology (Fa in section 2.3) is likely used for both despite its poor representation of aerosol eﬀects. e global RF is only a ‘crude approximation’ for such regionally and temporally heterogeneous species (Smith and Wigley 2000a, pp. 446,448).

In general, GWP has been strongly criticised for methodological ﬂaws that are directly relevant to policy, and this purely for GHGs. GWP is based on integrated RF arising from pulse emissions rather than the sustained emission over time seen in reality, and this integral takes no account of dynamic responses to that forcing or of changes in the relation of emissions to actual atmospheric concentrations, which are oen nonlinear (Smith and Wigley 2000b).

e large disconnect in atmospheric residence time between CO2 and aerosols weakens the approximation further still. ere is lile logical equivalence of aerosol lifetimes

(days) with that of CO2 (decades to centuries) — not at even 20 years, let alone horizons of 100 or 500. Moreover, its applicability for any short-lived agent is tenuous at best when using the conventional 100 year horizon, even for those having unambiguously positive forcing. How can tropospheric ozone, a non-uniformly mixed species with atmospheric lifetime on the order of months, accurately be quantiﬁed as an approximation of CO2 unit forcing integrated over 100 years, despite the climatic forcing of ozone being undoubtedly positive? Further, 100 year GWP is highly problematic for mitigation policy if the goal is to limit warming to some target maximum: GWP treats all pulse emissions equally, regardless of how close the target actually is; a low GWP value for

121 Chapter 3 Climate response and associated risk aerosol species (or potent warming species such as methane) is then dangerously misleading if the target is near (Shine et al. 2007). Shindell et al. (2009) also demonstrate that relative forcing is not static, as atmospheric chemical interactions between GHGs and other species such as aerosols aﬀect their GWPs. Taken together, GWP is hence ‘questionable for the short-lived non-CO2 eﬀects and robust values do not currently exist’ (Unger 2010, 5332).

For cooling aerosols the implication is unavoidable: they are not equivalent to carbon dioxide in any meaningful sense. Policy instruments reliant on CO2-e that aempt to include cooling aerosols are thus likely to be seriously ﬂawed and unworkably reduc- tionist.

3.5.2 Alternative methods

Some of the problems with GWP and CO2-e might be avoided by careful use of the radiative forcing methodology. But RF is not without its own difficulties, as discussed above and in section 3.1.1, even with the newer definitions, and ambiguity as to which definition is being applied in any given quantitative assessment also remains a concern. RF is also applicable principally at planetary or regional scales, and hence unlikely to be suited to the task of reporting and emission target-seing, in the manner that CO2-e conventionally is. In fact, Ramanathan and Feng (2009, 46) argue that RF is ‘an inad- equate and even an inappropriate metric’ for evaluating the regional effects of aerosol loading. Fuglestvedt et al. (2010) point out that RF is “backward looking”, in that it signifies the forcing exerted by cumulative emissions relative to some baseline state, rather than being concerned with the future impact of emission. Further, mitigation policy really needs metrics that express information on a per-emission (mass) basis, especially if any form of trading is to be involved. Finally, a policy-relevant metric is needed that is capable of properly capturing aerosol effects, most importantly those with negative

122 3.5 Toward appropriate metrics forcing.

No readily deployable alternative metric is apparent today that perfectly meets those broad criteria, but two approaches offer substantial improvements over GWP and CO2- equivalence — each is examined below, and a summary of their characteristics follows in Table 3.5. Global temperature potential is analogous to GWP, but redresses some of its limitations regarding time horizon and lack of clarity as to what effect is being compared. Specific forcing pulse was constructed explicitly for short-lived species, principally aerosols, and is well-suited to quantifying emission-pulse effects on a regional basis. Both metrics are likely to be important for future mitigation policies as they are further developed, but both also have limitations of their own that will continue to pose challenges for aerosol-integrated policy design. Perhaps this is unavoidable to some degree, as any aempt to devise a metric that can assign exchange values between differing forcing agents by ‘[a]dding together the climate impact of species that have different characteristics is a bit like adding apples and oranges’ (Tol et al. 2008, p. 3, via Fuglestvedt et al. 2010) — determining what should be compared or aggregated, and why, is a subjective decision of policy.

Other approaches that incorporate economic perspectives — damage functions, abatement costs, and the like — are not considered here. ese inherently involve subjective assumptions and hence move away from a purely physical concern with the underlying geophysical changes.

3.5.2.1 Global Temperature Potential

e global temperature change potential (GTP) was developed to represent the actual change in global mean surface temperature induced by a forcing agent, rather than the RF (Shine et al. 2005b, 2007). GTP ‘follows the general philosophy’ of GWP, but a major diﬀerence is that GTP indicates the change in TS at time t (an end-point) rather

123 Chapter 3 Climate response and associated risk than the integral of changes up to t, which in effect ‘remembers’ the earlier temperature change (Shine et al. 2005b; Shine 2009).21 As for GWP, GTP is calculated as the ratio of change in TS induced by an emission of 1 kg of a gas relative to that of 1 kg of CO2. Distinction is made between GTP for a pulse emission and one that is sustained over time, wrien GTPP and GTPS respectively. GTPS is then calculated for specified time horizons, such as the 20, 100, and 500 years used for GWP. e value of GTPP at a given time may be strongly dependent on a given emission scenario (due to differing residence times), if a target temperature is taken into account.

A limitation of GTP is the difficulty in applying it to aerosol species, which was only briefly examined in the original paper. Shine et al. (2005b) points to the same problems as calculating GWP for such short-lived species, including inhomogeneous changes to atmospheric concentration resulting from an emission, and the significant uncertainties surrounding radiative effects and interaction with other atmospheric species. ey suggest that GTPS would be the appropriate metric, because GTPP would be very small for time horizons beyond a year. While that statement is of course true, GTPS would suffer the same flaws as any metric that compares such short-lived species to the LLGHGs. Shine et al. (2007) provide a more extensive account of GTP for use with short-lived species. ey calculate GTPP for black carbon, while reiterating the substantial challenges in quantifying radiative effect: GTPP ranges across three orders of magnitude, depending on emission scenario, climate sensitivity, and proximity to a target temperature threshold; where that threshold is near, GTPP exceeds 20,000, but even at its smallest

GTPP is above 100. For comparison, similar GTPP values for methane and nitrous oxide range between 0 to 100 and 200 to 300, respectively; the 100 year GTP for methane is only 4, compared to a GWP of 25 (Shine 2009; Fuglestvedt et al. 2010).

21 Fuglestvedt et al. (2010) highlight the fact that the original calculations of GTP (Shine et al. 2005b, 2007) were performed with a simpliﬁed model, which did not include a deep ocean and hence understates the ‘climate system’s long-term memory to a pulse perturbation in forcing’, leading to errors in estimated GTP for short-lived species at far time horizons. For this reason they prefer the approach of Boucher and Reddy (2008), who employed a GCM, though this adds complexity to the metric.

124 3.5 Toward appropriate metrics

In their detailed study, Fuglestvedt et al. (2010) calculated comparative GWP and GTPP across a range of time horizons for BC, OC, and sulphate aerosol. ey explain that studies examining regional variation in forcing for these species do not in general agree, producing differing results for the highest metric by region. Consequently, Fuglestvedt et al. (2010) conclude it is premature to determine regional values, choosing instead to use only global means; they further include only direct aerosol forcing effects. e results for GWP and GTPP are reproduced in Table 3.2, where GWP is calculated at the usual 20, 100, and 500 year time horizons, while GTP is for 20, 50, and 100 years. Emissions of SO2 are translated to sulphate (SO4), allowing for lifetime and chemical conversion. e difference between GWP and GTP is large in all cases, underscoring the deliberate choice of metric required for policy making.

Table 3.2: Comparative GWP and GTPP at time horizon H years for BC, OC, and SO2 (reproduced from Tab. 5, Fuglestvedt et al. 2010)

BC OC SO2 (as SO4) GWP H=20 1600 –240 –140 H=100 460 –69 –21 H=500 140 –21 –12

GTPP H=20 470 –71 –41 H=50 77 –12 –6.9 H=100 64 –10 –5.7

Shine et al. (2005b, p. 297) suggest the advantage for policy of GTP over GWP is that it represents ‘an actual (if crude) impact’ rather than the more abstract integrated RF — it is closer to the actual impacts that are the cause for concern (Shine et al. 2007).

GTPS may also be preferable because it represents a sustained change in emissions rather than a single pulse (which of course describes reality); Shine et al. (2005a) show that

125 Chapter 3 Climate response and associated risk

GTPS can also readily be modified to incorporate forcing efficacy for a given agent (see section 2.3.1). Conversely, Shine et al. (2007) examine GTP applied to mitigation policy with specific maximum temperature targets, suggesting that GTPP is appropriate and superior to GWP by virtue of its time-dependency and responsiveness to the chosen policy objectives. Fuglestvedt et al. (2010, p. 4657) also regard GTPP as the appropriate choice for ‘target-oriented’ policy making, noting that GTPS is ‘quite closely related’ to GWP and hence not a real improvement.

3.5.2.2 Specific Forcing Pulse

Bond et al. (2011) created a new metric designed to capture the geographically and temporally specific effects of short-lived forcing agents. e specific forcing pulse (SFP) is defined as the energy immediately added to or removed from the Earth-atmosphere system at a receptor geographic region, per emission mass of some chemical species in a source region over that species’ full lifetime. SFP is explicitly restricted to those species with a lifetime less than one year, the meaning of ‘immediate’ — a species with an e-folding lifetime of 4 months should have 95 % of its effect captured by a one year in- −1 tegral. Units are GJ g for a specific species (S) at a stated emission region (Rj) exerting S effect on a receptor location (E), wrien as SFPE(Rj). An SFP with negative sign denotes energy removed from the climate system.

SFP was developed to accurately express the climatic effects of aerosols and other short- lived species. e restriction on lifetime ‘effectively divides species that have impacts only in the very near future and those for which accumulated burdens are important’ (Bond et al. 2011, 1506). SFP captures the energy added to a region — not power, as RF does — because this beer reflects the short atmospheric lifetime of such species. eir perturbation of the climate system is akin to a ‘burst of energy’ (a delta function),22 not

22 ough their lifetimes are non-zero.

126 3.5 Toward appropriate metrics the sustained ﬂow over time appropriate to long-lived forcing agents (an integral). As an expression of energy change, SFP can be used directly in planetary energy-balance approaches, but Bond et al. (2011) warn that forcing is not equivalent to warming and that there is no simple way to translate SFP to a change in temperature, even on a regionally- speciﬁed basis.

Important limitations of SFP are that it considers only the direct effect of forcing agents as measured at TOA, including changes to the cryosphere (snow and ice albedo) — indirect effects on cloud are not quantified, and these ‘could greatly alter the estimate of impact’ (Bond et al. 2011, 1510). e authors point out the ‘dearth’ of model studies that examine cloud response to emissions rather than total impact,23 which is necessary for any measure to include cloud effects. Presumably there is no a priori reason why cloud or other indirect effects could not be added subsequently, as understanding increases.

SFP offers a policy-relevant metric that quantifies the impact of short-lived species per emission mass, but avoids the need for subjective value decisions such as time horizon, as GWP does. However, Bond et al. (2011) demur from employing SFP in mitigation instruments as a trading metric, explaining that ‘it captures impacts that cannot be reflected in globally-averaged, integrated metrics’ (p. 1508).

In developing the SFP, Bond et al. (2011) calculated forcings for black carbon (BC) and organic maer (OM). Table 3.3 lists SFP values as averages for all regions,24 and these are hence temporally and spatially emission-dependent. OM uncertainties are asymmetric.

BC BC SFPatmo is all-region direct atmospheric forcing, SFPatmo+cryo is total including effect on cryosphere. Energy is fossil fuel and biofuel combustion; OM includes organic carbon aerosol species. More than an order of magnitude separates the warming effect of BC per emission mass compared to the cooling of OM, as BC is ‘far more effective at interacting with visible radiation’ (Bond et al. 2011, 1511). BC energy-related SFP varied by around 23 Here they cite the study of Chen et al. (2010) (briefly discussed in section 2.4.2.3 on page 42) that found indirect cloud effects actually outweighed BC positive forcing through absorption. 24 See full Tab. 1 in Bond et al. (2011) for regional variation.

127 Chapter 3 Climate response and associated risk

45 % between regions and by a factor of 4 for OM. Seasonality for BC forcing is marked in the Arctic due to availability of sunlight; open burning SFPBC is greater as burning primarily occurs in the NH summer.

Table 3.3: Speciﬁc forcing pulse for black carbon and organic maer (reproduced from Tab. 1, Bond et al. 2011)

−1 BC BC OM (GJ g ) SFPatmo SFPatmo+cryo SFP Average energy 0.96 ±0.46 1.11 ±0.47 –0.042 (–0.02, –0.08) Average open burning 1.16 ±0.63 1.23 ±0.63 –0.074 (–0.03, –0.15) Global average 1.03 ±0.52 1.15 ±0.53 –0.064 (–0.02, –0.13)

Bond et al. (2011) then used calculated SFP to derive global mean RF and GWP for both species, comparing these to the values reported by AR4. Table 3.4 shows these derived values for both the anthropogenic emissions speciﬁed in AR4, and an updated inventory (it is not clear whether this is anthropogenic only). Updated emissions are 4.8 Tg BC and 15 Tg OM from energy, and 2.6 Tg BC and 30 Tg OM from open burning — estimated forcing is consequently systematically larger. Bond et al. (2011) note that their estimate of total anthropogenic BC direct RF (+0.40 W m−2) is 18 % greater than AR4 for the same emissions (+0.34 W m−2), which is an improved estimate as it includes beer understanding of internal mixing eﬀects causing increased absorption. BC on snow (+0.05 W m−2) is less than the AR4 best estimate of +0.10 W m−2,25 incorporating newer physically-based studies.

At time of writing, no SFP calculated for other cooling aerosol species such as sulphate were evident in the literature, but there is no reason why this cannot be done given the OM|OC precedent.

25 See section 2.5.4.

128 3.5 Toward appropriate metrics

Table 3.4: Comparative RF and 20 & 100 year GWPs for BC and OM, derived from SFP (reproduced from Tab. 2, Bond et al. 2011)

BC TOA direct BC direct+cryo OM TOA direct

Anthropogenic RF, AR4 emissions (W m−2) Energy related 0.25 ±0.12 0.29 ±0.16 –0.03 (–0.01, –0.06) Open burning 0.15 ±0.06 0.16 ±0.13 –0.10 (–0.04, –0.20) Global total 0.40 ±0.18 0.44 ±0.29 –0.13 (–0.05, –0.25) Total RF, updated emissions (W m−2) Energy related 0.28 ±0.19 0.33 ±0.22 –0.04 (–0.02, –0.08) Open burning 0.19 ±0.07 0.20 ±0.08 –0.14 (–0.05, –0.27) Global total 0.47 ±0.26 0.53 ±0.30 –0.17 (–0.07, –0.35) GWP, 20-year Energy related 2400 ±1600 2800 ±1800 –110 (–40, –210) Open burning 2900 ±1100 3100 ±1300 –180 (–70, –360) Global average 2600 ±1300 2900 ±1500 –160 (–60, –320) GWP, 100-year Energy related 690 ±450 790 ±530 –30 (–12, –60) Open burning 830 ±330 880 ±370 –53 (–20, –100) Global average 740 ±370 830 ±440 –46 (–18, –92)

3.5.2.3 Alternative metrics summary

Table 3.5 summarises selected important characteristics for the GTP and SFP metrics based on the preceding discussion. is set of characteristics is intended to be indicative only as such a summary cannot fully convey the metrics’ complexities and nuance. Filled circles indicate that the metric satisfies the characteristic in question; outline circles indicate the characteristic is partial; crossed circles indicate the characteristic is satisfied but is regarded as a disadvantage for accurate quantification of aerosol cooling.

129 Chapter 3 Climate response and associated risk

Table 3.5: Comparison of selected characteristics of alternative metrics

Characteristic GTPP GTPS SFP Refers to particular explicit geophysical eﬀect • • • Refers to a readily understood impact† • • ◦ Designed to be appropriate for short-lived forcing agents ◦‡ • Expresses eﬀect for point time • • Requires selection of time horizon (integral) ⊗

Calculated effect is relative to GHG (CO2) ⊗ ⊗ Evaluation limited to global-scale effect ⊗ ⊗ Captures spatially heterogenous species • Includes aerosol indirect effects – – – Has been calculated for cooling aerosol species • •

† GTP expresses the relative potential to change temperature, whereas SFP expresses energy added or withdrawn from the climate system, an inherently less understandable measure for non-experts.

‡ As noted in section 3.5.2.1, more recent assessments of GTP indicate GTPP can be useful for short-lived forcing agents.

3.6 Aerosol emission rates

ite obviously, recent trends in anthropogenic aerosol emissions and scenario projections for future emissions are central considerations in evaluating the inherent risk of the aerosol mask. As noted in section 2.4, declines in aerosol emissions in North Amer- ica and Europe over recent decades have oen been balanced by signiﬁcant increases in Asia, particularly China and India. Indeed, Kaufmann et al. (2011) argue that surging Chinese coal combustion in the past decade led to rapid increases in sulphur emissions and a consequent suppression of temperature increase: Chinese coal consumption re- portedly more than doubled in only four years from 2003 to 2007, where the previous doubling took 22 years; in that four year period, China accounted for 77 % of the 26 % overall rise in global coal use.

A number of studies (eg, Skeie et al. 2011; Unger et al. 2010; Shindell and Faluvegi 2010) produce time series of aerosol emissions as calculated radiative forcing; Figure 2.13 (p. 91) showed one example. However, these are dependent on accurate modelling of the RF

130 3.6 Aerosol emission rates induced by a unit emission, which as we have seen tends to understate indirect effects, or exclude them entirely. Inferred aerosol forcing derived from planetary energy imbalance studies (section 3.2) further suggests that calculations of RF in this manner underestimate the net effect; questions regarding BC effect on cloud (Chen et al. 2010; Spracklen et al. 2011) also add uncertainty.26 I therefore present available data on emissions by mass rather than derived RF, or even SFP, as these give the clearest picture of the underlying physical activity.

Important emission inventories are maintained by a number of groups, such as Bond et al. (2007) for BC and OC (with updates given in the discussion of SFP above) or Smith et al. (2011) for sulphate. Aggregate data for total anthropogenic emissions of primary aerosol or secondary precursor species are examined ﬁrst, followed by a sectoral proﬁle.

3.6.1 Aggregate emissions

Skeie et al. (2011) provide detailed proﬁles of aerosol emissions from the preindustrial (1750) to 2010, based on a recent comprehensive dataset compilation combined with chemical transport modelling to derive atmospheric burden. Time series evolution of regional aerosol burdens for the period 1900 to 2010, relative to 1850, are reproduced in Figure 3.6 for sulphate (a), fossil fuel and biofuel (FFBF) derived OC (b), biomass burning

27 (BB) OC (c), SOA (d), and ﬁne-mode nitrate (as HNO3) (e). Figure 3.6 panel () plots the evolution of modelled global mean aerosol burden since 1900, relative to 1750, using data provided in Skeie et al. (2011). e additional species FFBF BC and BB BC are included;

ﬁne-mode nitrate is separated as NO3 and NH4.

26 Refer to the discussion in section 2.4.2.3 and 2.5.2. 27 Figure 3.6 (e) shows the original plot from Skeie et al. (2011) where nitrate is wrien ‘NO3’; however, the text explains nitrate’s complex activation pathway from NH3 and NOx precursor emissions (Figure 3.7), including interaction with ambient sulphate and meteorological conditions, describing the plot as showing HNO3 concentration. See section 2.4.2.4 also.

131 Chapter 3 Climate response and associated risk

Figure 3.6: Modelled aerosol burden over time. (a) to (e) are reproduced with permission from right panels of Fig. 9 of Skeie et al. (2011); () plots data provided in Tab. 2 of Skeie et al. (2011). (a) is regional sulphate (SO4) burden since 1900, relative to 1850; (b) to (e) are as (a), for FFBF OC, BB OC, SOA, and ﬁne-mode nitrate, respectively. () is modelled global mean aerosol burden since 1900, relative to 1750, for FFBF BC, FFBF OC, SOA, BB BC, BB OC, and ﬁne-mode nitrate precursors, in Gg (le axis); sulphate is ploed in Tg S (right axis).

132 3.6 Aerosol emission rates

Figure 3.7 plots the underlying emission data provided by Skeie et al. (2011) as total annual anthropogenic and biomass burning primary aerosol or precursors since 1850.28 Figure 3.7 shows that emissions of many aerosol species are at or close to their historical record maxima. Nitrate precursors, BC and OC emissions are substantial, especially from biomass burning for the laer carbonaceous species. Sulphate has declined from its peak in the 1970s and 1980s, but remains elevated; coal combustion in India and China could possibly see a returning upward trend. Importantly, no species shows a signiﬁcant downward trend over recent decades.

25 70

FFBF BC (Tg) BB BC (Tg) 60 FFBF OC (Tg) 20 BB OC (Tg) 50 NOx (Tg N) 15 NH3 (Tg N) SO2+SO4 (Tg S) 40

30 10

Carbonaceous aerosol emissions (Tg) 10

0 0 Non−carbonaceous aerosol emissions (Tg N, Tg S) 1850 1900 1950 2000

Figure 3.7: Total annual emissions of major aerosol species since 1850. Data provided by Tab. 1 of Skeie et al. (2011). Carbonaceous species are in Tg annual emissions (le axis), non-carbonaceous species are in Tg N and Tg S (right axis).

Deriving aerosol emission inventories is a diﬃcult task involving complex and disparate data sources and a range of decisions regarding calibration and the like. e various available inventory products from diﬀerent research groups do not necessarily agree, even within uncertainty bounds (which may be large for some species, where they are

28 e dataset begins at 1750, but as the next time increment is 1850 (ie, a 100 year gap), 1850 has been set as the base year for consistency with the previous plots.

133 Chapter 3 Climate response and associated risk quantiﬁed at all; BC is a factor of 2). Granier et al. (2011) note that agreement is best for SO2 and NOx but remains low for BC; agreement in global aggregate is also not necessarily seen in regional contributions. Research into understanding and reducing the diﬀerences among inventories is an ongoing challenge. For instance, the total emissions listed by sector in the assessment of Unger et al. (2010, Supplemental) diverge markedly from those shown in Figure 3.7: total OC is some 40 % greater and sulphate is around 30 %

29 greater, while BC is within a few percent. e dedicated anthropogenic SO2 emissions study of Smith et al. (2011) is close to agreement with the data from Skeie et al. (2011) ploed in Figure 3.7, giving a 2005 global total of 115 Tg SO2 (119 Tg SO2 including open burning), or ~58 Tg S (~60 Tg S including open burning).

3.6.2 Emissions by source

Global total aerosol emissions probably maer most for the climate (though with strong regional variation), but proportional emissions by source maer most in assessing the effects of mitigation action. However, inventories by sector are somewhat difficult to obtain. e data provided by Unger et al. (2010, Supplemental) provide emission data by source for a number of key species, but the totals do not agree well with global aggregate emissions, as noted above. An integrated inventory of year 2000 historical emission data by sector is available from the Representative Concentration Pathways Database (v2.0), combining a number of existing inventories and considering differences between them.30 ese data do closely match the totals of Skeie et al. (2011); they are presented as percentages of the per-species total in Table 3.6, giving a broad indicator of relative sectoral importance.

29 Using a factor of ~0.5 to convert Unger et al. (2010) Tg SO2 molecular mass to Tg S. 30 e inventory compilation and assessment of diﬀerences among datasets is described by Granier et al. (2011); see van Vuuren et al. (2011c) for a general overview. e dataset used (‘ID - RCPINV’ year 2000 historical data) and future emissions under the RCP scenarios are available online at http://www.iiasa. ac.at/web-apps/tnt/RcpDb (accessed June 2012). Representative Concentration Pathways are discussed in section 3.6.3 below.

134 3.6 Aerosol emission rates

Table 3.6: Proportional anthropogenic emissions of major aerosol species by sector, year 2000 emissions. Major emission sectors are highlighted for each species. Data obtained from the RCP Database (v2.0) ID - RCPINV historical inventory via the online comparison tool.

Sector SO2 BC OC NOx NH3 Surface transportation 4.0% 17.3% 4.0% 28.6% 1.0% International shipping 10.3% 1.7% 0.4% 14.2% 0.0% Aviation 0.0% 0.1% 0.0% 2.2% 0.0% Power plants, energy conversion, extraction 49.2% 0.7% 1.0% 18.6% 0.1% Solvents 0.0% 0.0% 0.0% 0.0% 0.0% Waste (landﬁlls, waste water, incineration) 0.1% 0.5% 0.1% 0.2% 0.0% Industry (combustion and processing) 25.0% 19.3% 6.3% 12.6% 0.3% Residential and Commercial 7.7% 25.1% 21.6% 7.2% 0.7% Agriculture (waste burning on ﬁelds) 0.2% 1.9% 1.9% 0.5% 1.3% Agriculture (animals, rice, soil) 0.0% 0.0% 0.0% 1.7% 73.8% Savannah burning 1.5% 19.1% 30.1% 9.2% 8.5% Land-use change (deforestation) 2.1% 14.5% 34.5% 4.9% 14.3%

Table 3.6 shows that many sectors that produce a signiﬁcant fraction of anthropogenic greenhouse gas emissions also contribute substantially to the aerosol mask. Energy conversion, particularly electricity generation, factors strongly, as does surface transportation, industry, and the residential and commercial sectors. Further, all of those sectors are (or are likely to be) subject to the ﬁrst deployments of GHG mitigation policy instruments.

Agriculture and biomass burning are also important aerosol sources, but these sectors are more diﬃcult to measure and their inclusion in mitigation instruments such as emissions trading is less likely, at least initially; they are more likely to be covered by speciﬁc schemes such as programs that seek to avoid deforestation.

Given the large likely forcing of sulphate, SO2 emissions deserve special aention. Smith et al. (2011) report that the proportion of global SO2 emissions arising from fossil fuel combustion remains about 50 % from coal and 30 % from petroleum. Figure 3.8, reproduced from Smith et al. (2011), shows the evolution of global SO2 emissions since 1850. ese proportions are in general agreement with Table 3.6, which also highlights the

135 Chapter 3 Climate response and associated risk

Figure 3.8: Global SO2 emissions since 1850 by source and by end-use sector (Fig. 5, Smith et al. 2011) large contribution of industry. However, Ramanathan and Feng (2009) state that coal contributes about 78 % of total SO2 emissions, and oil a further 10 %. From a ﬁnal-use perspective, SO2 emissions from energy conversion — mainly electricity generation — have risen steadily over the past century to now account for about half of the global total.

For all the ﬁnger pointing at rising Chinese emissions, it should be noted that in 2000

Chinese coal-ﬁred electricity generation emied some 5.4 Tg SO2 — less than half of the

US at 12 Tg SO2 (Shindell and Faluvegi 2010). ough as the inventory of Smith et al.

(2011) shows, US SO2 emissions have declined signiﬁcantly since their peak around the mid ’70s while China’s have risen dramatically in the past two decades, the US remains a major SO2 emier (Ramanathan and Feng 2009).

3.6.3 Future emissions

e range of possible emissions futures in the IPCC Special Report on Emissions Scenarios (SRES) (Nakićenović et al. 2000) played a dominant role in the decade since they were

136 3.6 Aerosol emission rates

Box 3.2: Australian aerosol emissions

As an Australian, it is only right to make brief mention of our national emissions. e National Pollution Inventorya (NPI) data for 2010/11 show that electricity generation is the largest single source category of SO2 emissions at around 44 % of the 1.3 Tg SO2 total, followed closely by non- ferrous metal manufacturing at 43 %, then a large gap to sub-reporting threshold fuel combustion at 3 %. Oxides of nitrogen include aerosol precursor NOx emissions, but also the GHG N2O, making useful comparison difficult at the level of data published. With that caveat, electricity generation accounts for 30 % of the 1.4 Tg total, followed by diffuse motor vehicle emissions on 24 % and diffuse burning on 9 %; coal mining accounts for 5 %. Diffuse burning accounts for about 85 % of the 147 Mg HNO3 emissions to air (as nitric acid), with fertiliser and pesticide manufacturing another 13 %. e NPI does not currently provide data on BC or OC emissions. However, electricity generation accounts for 40 % of non-specific fine particulate maer up to 2.5 µm diameter, coal mining 17 %, and metal ore mining 15 %. For particulate maer diameter 10 µm or less, coal mining contributes 23 %, metal ore mining 21 %, disperse burning 19 %, and disperse windblown dust 15 %. Overall, LLGHG emission sources also account for the bulk of Australian aerosol-related emissions.

a Available at http://www.npi.gov.au.

published in 2000. A pivotal feature of all such scenarios is that they do not incorporate explicit climate change mitigation policies, by design. ey instead represent a range of possible futures where deliberate action to abate GHG emissions is not undertaken; or at least, not undertaken for climate purposes. Further, Cofala et al. (2007) make the important point that in comparison to this extensive scenario analysis of future GHG emissions, similar assessment of future aerosol and precursor emissions is ‘comparably modest’ (p. 8486).

e lack of mitigation in SRES is clearly highly problematic, given the urgent need for abatement if catastrophic climate changes are to be avoided. New scenarios are needed that do explicitly examine mitigation (such as the 2 ℃ maximum temperature increase target), and that also include recent research in areas such as economics, sociology, and technology, as well as the physical science (Moss et al. 2010). In 2007, the IPCC requested new scenarios be developed that met this need (van Vuuren et al. 2011c).

at eﬀort culminated in the Representative Concentration Pathways (RCPs), scenarios

137 Chapter 3 Climate response and associated risk that will be assessed in the IPCC’s Fih Report (van Vuuren et al. 2011b). Each RCP signiﬁes one possible emission scenario that would lead to a speciﬁc characterised radiative forcing, ‘representative’ of the set of scenarios in the literature that corresponds with that RF. And each should be a ‘plausible and internally consistent description of the future’ (van Vuuren et al. 2011b, 7–8). e scenario is a ‘pathway’, emphasising that the emission trajectory is of interest and not only atmospheric concentrations (Moss et al. 2010); however it is atmospheric LLGHG concentrations not emissions that are the basic input to climate models (van Vuuren et al. 2011b).

Taken together, the RCPs cover the range of year-2100 RFs canvassed by the literature, from 2.6 W m−2 to 8.5 W m−2, and are named according to that RF: RCP8.5, RCP6, RCP4.5, and RCP2.6 (van Vuuren et al. 2011c). RCP2.6 is representative of scenarios that seek to restrain global mean surface temperature rise to 2 ℃ above the preindustrial, requiring a 70 % reduction in cumulative LLGHG emissions between 2010 and 2100 relative to the baseline no-mitigation scenario (van Vuuren et al. 2011a).

Tanaka and Raddatz (2011) note that SO2 emissions decline faster in RCP than under SRES. Indeed, the total anthropogenic aerosol burden is projected to ‘strongly decrease’ in this century under all RCPs, and also in the baseline scenario (van Vuuren et al. 2011c, 3). ese declines are in part due to assumed air pollution control policies becoming more stringent over time, as well as abatement stemming from climate change mitigation (van Vuuren et al. 2011b). SO2 emissions drop substantially under RCP2.6 as coal combustion decreases, for example (van Vuuren et al. 2011a).

Figure 3.9 shows the projected decline of aerosol species under all four RCPs.

138 3.6 Aerosol emission rates

Figure 3.9: Projected anthropogenic aerosol emissions 2000 to 2100 under all RCP scenarios for total BC, OC, nitrate precursors, and sulphate (as SO2). RCP8.5 (brown line), RCP6 (red line), RCP4.5 (blue line), RCP2.6 (green line), and year 2000 emissions ID - RCPINV historical inventory (black marker). Images compiled from the RCP Data- base (v2.0) online comparison tool.

139 Chapter 3 Climate response and associated risk

ere are two problems inherent in an RCP-based assessment of the risk of a significant loss of aerosol cooling under climate change mitigation at this time. First, the GCM runs that assess the likely temperature response to a given RCP are, as noted above and in section 2.7, subject to persistent uncertainties in representation of aerosol indirect effects,31 and may be underestimating the resultant net negative forcing. Added to this is the uncertainty in actual current emissions, and the generally unsatisfactory quantification of aerosol burden, optical depth, and so on. ose issues must be redressed, and consensus on an accurate modelling of the full range of aerosol effects established, if nasty surprises stemming from a loss of aerosol cooling are to be ruled out.

Second, given the current state of international climate change mitigation negotiations and continuing record GHG emissions, there is no guarantee that any such pathway will actually be followed. It is surely a very real possibility that emissions continue un- abated for some years to come, only to see a dramatic and precipitous decline driven by a step-change in political response, as discussed in Chapter 4. Moreover, fossil fuel consumption continues to grow, particularly exploitation of unconventional oil in the form of tar sands and surging production of unconventional gas in the form of shale gas or coal seam methane, especially in the US. Added to that is the increasingly apparent scale of fugitive methane emissions in those extraction and processing operations. And Australian coal exports are undergoing enormous expansion, with much more planned. e point here is that the actual shape of future emissions of both GHG and aerosol species is far from certain and cannot be assumed to follow any RCP. Consequently it cannot be assumed that aerosol emissions will follow a path that precludes realisation of a short-term temperature spike when serious abatement action does ﬁnally begin. Fi-

31 In the first model results of future GHG concentrations and temperature response presented with the RCP Special Issue of Climatic Change (see van Vuuren et al. (2011c) for an overview), Meinshausen et al. (2011, 235) note that ‘… uncertainties arise as well from second-order effects of tropospheric ozone and aerosols, for example. To the extent that our non-GHG modeling assumptions deviate from current generation chemistry modeling results …, indirect effects via global-mean temperature and gas-cycle feedbacks will impact derived GHG concentrations presented here, although this indirect second-order effect is likely limited.’

140 3.7 Policy implications

nally, even RCP2.6 equates to a peak atmospheric concentration of some 490 ppm CO2-e by 2050 — 440 ppm CO2 alone — and remains above 400 ppm CO2 at 2100, not returning below current levels until about 2300 (Meinshausen et al. 2011; van Vuuren et al.

2011b). ese levels are beyond the 350 ppm CO2 long-term maximum suggested by the paleoclimatic evidence as necessary to avoid catastrophic climate change this century (Hansen et al. 2008). erefore, the chapters to follow make no speciﬁc reference to projected emission trajectories, but instead tend to assume something of a worst case scenario for mitigation, characterised by a seriously delayed onset of abatement and the resultant risk of triggering dangerous anthropogenic changes without deliberate use of an aerosol-integrated mitigation framework.

3.7 Policy implications

Several important implications for the climate change mitigation policy response may be distilled from this chapter. e conclusions stemming from the planetary energy imbalance studies examined above are threefold:

• Planetary energy budget is highly relevant to mitigation policy development.

• e climate response function derived from energy balance studies may be more rapid than commonly appreciated or indeed modelled; however investigations into associated complexities are ongoing and no clear consensus currently exists.

• e aerosol mask is substantial and ongoing, oﬀseing as much as half of extant anthropogenic GHG forcing.32

Planetary energy imbalance can therefore provide a robust metric for international climate negotiations, indicating as it does both the global magnitude of remaining forcings and framing the consequent time window for mitigative action. ese aspects are not

32 However, GCM studies tend to ﬁnd an oﬀset around 35 %, as shown in section 2.6.

141 Chapter 3 Climate response and associated risk well communicated by radiative forcing. Planetary energy imbalance also provides further evidence that the aerosol dilemma cannot be ignored if mitigation is to be eﬀective in avoiding ‘dangerous anthropogenic interference’.

Moreover, the difficulties in quantifying aerosol emissions and adequately representing their cooling effects with the standard metrics of GWP and CO2-e strongly indicate that new alternatives are required for any aerosol-integrated policy framework. e specific forcing pulse appears to meet that need admirably.

Finally, emission inventories, though subject to persistent uncertainties and gaps in coverage, show that global anthropogenic aerosol emissions are large and ongoing. How- ever, the relative shis in regional burden — falling in the industrialised countries of Europe and North America, but rising strongly in the developing world — are likely to present signiﬁcant challenges for international negotiation of any aerosol-integrated UNFCCC Protocol.

142 4 Comparative analysis of mitigation policy

is chapter examines the ontology, form, and scope of mitigation policy approaches applied to the domains of climate change and local air pollution. In particular, it analyses the interrelation and overlap between these two domains, their commonality and conﬂict in objective and practical implementation. Most importantly, it assesses the degree to which the climate change implications of aerosols are, or might be, integrated within these frameworks.

4.1 Pollution taxonomy

A consistent taxonomy is necessary to effectively examine the inclusion of aerosol emissions within climate change mitigation policies, particularly for comparison with GHG and air pollution regulation. Key differentiating characteristics for a given pollutant are rates of assimilation by environmental sinks, the degree of atmospheric mixing, and the types of environmental effects consequent to emission. Whether a particular substance is regarded as a pollutant depends on the combination and interaction of these characteristics.

143 Chapter 4 Comparative analysis of mitigation policy

4.1.1 Emission and assimilation

A substance is emied to the atmosphere from some originating physical process or mechanism, termed the source of emission; a particular geographical emission site is commonly termed a point source. Once emied, the substance may1 be aﬀected by some environmental process that absorbs or otherwise aenuates that substance over time, termed a sink. In the troposphere, sink processes are usually chemical reactions, precipitation, or gravitational deposition (Almqvist 1974). rough such biogeochemical sink processes, emissions are assimilated by the environment and, if those processes are not overwhelmed, the substance may be returned to a ‘useable source’ (Daly and Farley 2011, 422).

However, any such environmental sink will have a ﬁnite capacity to perform that function over time, likely including both a maximum rate of absorption and a maximum cumulative limit (which itself will be related to the time frame in question). Available sink capacity, along with the environmental impact of an emission (examined below), is therefore an important characteristic in deﬁning whether a given atmospheric emission is regarded as a pollutant. Further, the atmosphere itself — and ambient levels of a pollutant within it — is the site of ‘contamination’ and not an actual sink in this context.2

Pollutants are usefully categorised with respect to their emissions quantity relative to environmental sink capacity. An assimilative or ﬂow pollutant is deﬁned as one whose rate of emission can readily be absorbed (assimilated) by available sink capacity: a given year’s ambient pollution level is independent of the quantity emied at previous times as those past emissions have already been absorbed, hence the pollutant does not accu- mulate in the environment (Tietenberg 2006; Harris 2006). Whether such a substance is

1 Not all substances have naturally available sink processes, such as many of the halocarbons which exist only as products of human industry. 2 Many gas species are permanently resident in the atmosphere in some background concentration (a steady state). ey become pollutants when some threshold is passed, giving rise to environmental impact.

144 4.1 Pollution taxonomy regarded as a pollutant then depends on its environmental eﬀect at this ambient loading, despite the lack of accumulation; ie, damage from pollution does not require that the pollutant accumulates.

Expressed symbolically, an assimilative pollutant can be wrien as

∑J At = at + ejt (4.1) j=1

at ≥ 0; ejt ≥ 0 j = 1,...,J (4.2)

Where At is the steady-state ambient pollution loading at time t (usually a given year), at is a random variable representing any natural ambient emission level, and ejt is the emission rate of the jth anthropogenic point source at time t, in the set of all such point sources J. Many traditional air pollutants are assimilative, such as volatile organic compounds, and the aerosol species discussed in this thesis.

Conversely, an accumulative or stock pollutant has an emission rate in excess of available sink capacity, or no sinks exist: emissions are greater than can be assimilated and so the pollutant accumulates in the atmosphere. e pollution level at a point in time is the sum of the current emissions rate plus some fraction of all previous emissions, accounting for the decay rate of natural sink removal processes. Symbolically, an accumulative pollutant can be wrien as the change in atmospheric loading L at time t

∑J ∆Lt = αt + ejt − βt (4.3) j=1

and βt = f (L, s) (4.4)

Where ejt is anthropogenic emissions as before, αt is a random variable representing any natural emission rate at time t, and βt is any natural assimilation of that pollutant during time t. βt represents the pollutant-dependent decay rate which is a function of

145 Chapter 4 Comparative analysis of mitigation policy the loading L, and s, a term representing any dynamic and feedback changes in sink capacity, and hence may also vary over time.

If no assimilative capacity exists, then βt is zero and Lt is simply the sum of all emissions. e total cumulative pollutant loading L at time t is therefore, by deﬁnition

Lt = Lt−1 + ∆Lt ∑J Lt = Lt−1 + αt + ejt − βt (4.5) j=1

e enhanced greenhouse eﬀect of carbon dioxide and other greenhouse gases is a central example of accumulation. In the Earth-atmosphere system at current boundary conditions, natural CO2 emissions are large but, in the absence of signiﬁcant anthropogenic emissions, are balanced by the natural removal processes of the carbon cycle. On average αt is therefore cancelled by βt, such that their expected combined value is zero

(E [αt − βt] = 0), and Lt is thereby in a steady-state on time scales of centuries to tens of millennia.

at steady-state will persist until a shi in boundary conditions occurs, suﬃciently modifying natural emissions or sink absorption such that Lt decreases or increases over time (non-anthropogenic perturbations are the Milankovitch orbital forcings, large volcanic events, changes in continental conﬁguration in geologic timescales, and the like).

Further, the carbon cycle responds under conditions of elevated total CO2 emission, increasing net absorption by sinks, meaning that the βt function strengthens (greater decay) to partially oﬀset those elevated emissions.

To date the airborne fraction of anthropogenic CO2 emissions — that fraction of an emission pulse remaining in the atmosphere in the years following — is holding at about 40 % to 55 % (Le éré et al. 2009; Knorr 2009; Solomon et al. 2009; Bes et al. 2011; Hansen

146 4.1 Pollution taxonomy et al. 2011).3 However, this fraction is unlikely to persist indeﬁnitely under future climate changes, as sinks have ﬁnite capacity, are subject to counteracting feedbacks (eg, ocean CO2 outgassing with increasing surface temperature), and hence must eventually weaken.

4.1.2 Atmospheric mixing

Pollutants are further categorised by the extent of mixing within the atmosphere once emied. Mixing is a result of the interaction of factors such as the rate of removal by sink processes (ie, atmospheric residence time for an emission pulse), the physical properties of the pollutant itself, and atmospheric transport through chemical reactions and the physical processes of advection and convection.

A pollutant may be uniformly mixed in the atmosphere within some geophysically meaningful time period, such that the ambient loading is generally equivalent (uniform) at all geographic locations, allowing for transport processes to distribute the initial emission pulse. e ambient pollutant loading is therefore independent of the geographical distribution of the underlying emission sources. As such, a uniformly mixed pollutant can be represented by the previously deﬁned eq. 4.1 and eq. 4.5, for the purposes here. e greenhouse gases are, again, central examples of uniformly mixed pollutants.

A non-uniformly mixed pollutant, in contrast, produces heterogeneous ambient loadings that vary with geographic location and strength of emissions. e interaction of atmospheric transport and removal processes are such that the ambient pollutant loading at any location is dependent on proximity to and emission rate of particular point sources. Ambient loadings are hence highest at point source sites with greatest emission rate, and decline with increasing distance from emission sources. However, transport

3 Of these, Hansen et al. (2011), citing Hansen et al. (2008, Supplemental), is the largest estimate at 55 %; Solomon et al. (2009) gives ~50 %; 40–45 % is the more common.

147 Chapter 4 Comparative analysis of mitigation policy processes and the topography of a particular geographical area may mean that the site of highest ambient loading is some distance from the ultimate emission source. Again, the majority of traditional air pollutants are non-uniformly mixed, such as tropospheric ozone or sulphur dioxide.

Symbolically, a non-uniformly mixed pollutant can be expressed as an extension of the assimilative representation in eq. 4.1 above

∑J Ait = ait + τij ejt (4.6) j=1

ait ≥ 0; 0 ≤ τij ≤ 1 i = 1,...,I (4.7)

Where Ait is the ambient pollution loading occurring at time t at a geographic receptor site i in the set of all considered receptor sites I, ait is a random variable representing any natural ambient pollutant loading incident at that site, ejt is the emission rate of the jth point source as before, and τij is an atmospheric transport coeﬃcient that represents the increase or decrease in pollutant loading at site i due to emissions originating at point source j.

Whether or not any instances of a non-uniformly mixed accumulative pollutant are known, such a pollutant class is not relevant for the purposes here and will not be discussed further.

4.1.3 Environmental impact

A pollutant is cause for concern due to the effect it induces in the environment; the presence of such an effect is a defining characteristic that in fact makes emission of a substance ‘pollution’. Generally speaking, the environmental effect of emission must first be discernible by human societies before it will be regarded as a pollutant and any policy response enacted. However, a failure to identify some substance as a pollutant in

148 4.1 Pollution taxonomy no way precludes the existence of environmental eﬀects, possibly signiﬁcant ones — the history of the discovery of and policy response to anthropogenic climate change over the past 150 years amply demonstrates this.

Importantly, a pollutant’s effect on the environment is characterised as deleterious in nature, at least as traditionally considered. at is to say, pollution causes negative, damaging, undesired effects, either to humans indirectly through damage to the environment itself (such as smog, acidification, eutrophication) or directly to human health or selements (such as respiratory illness, infrastructure deterioration). In economic terms, these damaging effects make pollutants an environmental bad.4 Further, a pollutant may induce more than one single environmental effect, such as CO2 acting both

5 as a greenhouse gas and acidifying the oceans, or SO2 causing acid rain as well as particulate impacts to human respiratory health. ese eﬀects are also commonly known as the environmental impacts of pollution.

e impacts of pollution may not manifest for any and all emission levels: some threshold may first need to be passed beyond which damage begins to occur, or is greater than some socially tolerable level. Consideration of effects that might be apparent but nonetheless regarded as socially acceptable therefore distinguishes impact, as perceived by society, from the more technical distinction between assimilation and accumulation with respect to environmental sinks. is subjective assessment of acceptability forms part of the concept of a ‘socially optimum’ pollution level, where the purported economic costs of mitigation are weighed against the estimated economic cost of environmental damages. A risk management approach would further refine that socially optimum pollution level, taking account of uncertainties in both the costs of damage and of mitigation controls. e negative environmental impacts of pollution are well established in policy

4 An economic bad is the opposite of a normal good, in that the object itself has negative value. 5 Ocean acidiﬁcation demonstrates a higher degree of complexity, in that the ocean is itself an environmental sink for excess (disequilibrium) atmospheric CO2 concentrations while simultaneously being damaged by that very sink function.

149 Chapter 4 Comparative analysis of mitigation policy making and in the regulatory responses that seek to mitigate them.

4.1.4 Applying the taxonomy

Given the codified taxonomy above, we can now compare the properties of aerosols and the long-lived greenhouse gases as pollutants. Table 4.1 lists the characteristics defined above, indicating which is most appropriate for the two aggregated pollutants. at is, LLGHGs and aerosols are shown here as generalised classes of pollutants to demonstrate their dominant common characteristics,6 rather than examining particular species individually. Atmospheric residence time is also restated, to underscore the large divergence in this property between the two classes, as well as its link to the emission– sink function. Finally, impacts are shown at a very high level, simply to differentiate the two broad domains of climate change and local air pollution.

Table 4.1: Comparative dominant pollutant characteristics

Characteristic Aerosols LLGHGs Assimilative • Accumulative • Uniformly mixed • Non-uniformly mixed • Residence time (yrs) 10–2 101 to 104 Impact Climate change damages ◦⋆ • Air pollution damages •

⋆ BC causes positive forcing; aerosols aﬀect the hydrological cycle.

6 Recall from section 2.1.3 that other GHG species such as tropospheric ozone are non-uniformly mixed; moreover, ozone is a signiﬁcant cause of local air pollution damage.

150 4.1 Pollution taxonomy

4.1.5 A new term for emission sources

One further consideration regarding the emission of pollutants across these two domains is some indication of the common sources for both GHGs and aerosols. at is, in many instances anthropogenic emissions of these two classes stem from the same underlying physical process or industrial activity. Identifying common sources is policy-relevant, because abatement action that affects one pollutant class will necessarily also affect the other if that action targets a single activity that emits both. Policy design should account for such corollary effects. It is hence useful to define a term to indicate instances of common emission source, but none is readily apparent in the literature.7

Given that emissions of the two pollutant classes are tightly coupled to the underlying physical process, I suggest a new term: coupled emissions are the simultaneous emission of both GHG and aerosol (primary or precursors) pollutant species from a single physical activity, a biological or chemical process not readily separable to independent parts, where the quantity of each pollutant class emied is climatically-signiﬁcant.

Combustion of fossil fuels across a range of technological applications is the archetypal example; though the specific emission profile for a given activity depends on fuel type and the technology employed. Coal-fired electricity generation produces, inter alia, significant quantities of both LLGHG (CO2) and aerosol (BC, SO2) emissions. Natural gas combustion, in contrast, emits lile to no aerosol species, so coupled emission cannot be read as synonymous with fossil fuels. Indeed, biomass combustion also produces coupled emissions of CO2 along with varying combinations of BC, OC, and other aerosol species (see section 2.4).

7 Caplan and Silva (2005) define a related term, ‘correlated externalities’, but this signifies that a common emission source generates multiple pollutants that have differing externalities. at situation of course describes aerosol and GHG emissions well and is germane to the discussion here, but it refers to the externalities themselves rather than more neutrally pointing specifically to the fact of a common emission source.

151 Chapter 4 Comparative analysis of mitigation policy

4.2 Policy ontology and form

e principal implication of the aerosol cooling dilemma laid out in the preceding chapters is that a specific policy response is required, integrating the full effects of aerosol emissions on climate. An obvious question arising from this implication is what should that response look like; what policy instrument should be applied? Clearly, the first place to look for an appropriate instrument is the existing frameworks employed within the climate change and air pollution domains. What may not be so clear is that the form and the philosophical underpinnings of these pollution control policies have a direct bearing on their ability to handle the problem. To appreciate why this is the case requires an exposition of the character of those policies — the ontology and assumptions, both explicit and unstated, underlying their objectives and the regulatory instruments deployed to fulfil them.

4.2.1 Less is beer

e overarching intent of any policy is defined by its stated objective. Pollution control policy-making has traditionally been approached with, at heart, the clear intent to mitigate the environmental damage caused by some offending pollutant: the objective is to mitigate those damages; the means is a reduction in pollutant emissions. Trade- offs will complicate the situation and are likely to result in an actual emission target being non-zero. A prominent example is the economic costs of mitigation versus the economic benefits stemming from the underlying production processes that ultimately generate those emissions (ie, pollution is an archetypal negative production externality). In conventional economic analysis, these benefits would be weighed against mitigation costs using the equimarginal principle.

However the purpose here is to tease out the underlying ontological framing of pollution control policy. So, seing aside complications such as the presence of a minimum eﬀect

152 4.2 Policy ontology and form threshold and real-world considerations of mitigation costs, philosophically there is no a priori reason for a non-zero target emission level, and hence the basic policy objective is to reduce emissions as much as possible. Stated in the negative: the only reason pollutant emissions would not be reduced is precisely those real-world considerations of the costs of abatement technology (which assumes any is in fact available), a loss of beneﬁts from the underlying productive activity, or a minimum geophysical response threshold. Yet even so, should a new abatement technology become available at lower cost, for example, the target emission level is likely to be reduced in consequence. In simplistic language to make this point clear then, the objective of pollution control policy can be crudely characterised as the less emissions the beer.

Particular regulatory instruments are then applied in the service of that objective, specifying various measures such as control technologies or economic regimes that regulate these production processes so as to achieve the emission target. And, of course, these instruments will take into account the real-world considerations set aside in the previous statement. In the simplest case, a single assimilative pollutant generating a single environmental impact might be regulated by a single mitigation measure, such as particular control technologies prescribed for all liable emiers — SO2 abatement via sulphur scrubbers to mitigate acid rain is a seminal example. Increasing regulatory complexity is required in accordance with increasing complexity of the pollution problem: hand- ling non-uniform or accumulative pollutants; multiple individual pollutants, or multiple geophysical damage eﬀects across a spectrum of impact areas (such as eutrophication, acidiﬁcation); and combinations thereof.

ese more complex forms may be classified as ‘multi-pollutant, multi-effect’ problems (eg, EEA 2004). Taken together, these two dimensions — pollutant types and environmental effects — can be interpreted as constituting a general classification schema for pollution control, albeit somewhat informally. at is, a given pollution problem is classified by those dimensions in a rough spectrum, ranging from single-pollutant, single-

153 Chapter 4 Comparative analysis of mitigation policy eﬀect, to multi-pollutant, multi-eﬀect, indicating increasing complexity. e policy approach applied is hence potentially more complex in turn, though that is not necessarily the case.

A fundamental characteristic of even the most complex multi-pollutant, multi-effect policies is that they hold the overarching objective of mitigating negative environmental impacts. Relating this classification schema to the ontological frame, it is therefore axio- matic of pollution control policies that all targeted effects are deleterious in nature, even if this axiom is only ever implicitly assumed. As such, the axiom of deleterious consequences is a foundational component of pollution control policy: they are conceived as, and designed to achieve, the reduction of pollutant emissions. is is their raison d’être: less is beer.

But what if less is not beer? What if less is in fact worse because a key environmental eﬀect is actually beneﬁcial in some vital aspect, such that reducing pollution may exacerbate, not mitigate against, some other negative environmental impact?

4.2.2 Less is worse

In the aerosol cooling dilemma, less may indeed be worse; that is the crux of the problem.

Can aerosol effects on climate in the contemporary context be properly understood within the ontological frame that less is beer? I contend that they cannot, because aerosol masking violates that frame’s implicit axiom of deleterious consequences. at is, despite the clear and serious damaging effects of aerosol emission to ecosystems, human selements, human health, and indeed, the climate8 — all of which are negative impacts to human welfare and environmental bads — aerosols simultaneously generate beneficial effects in their masking of the enhanced greenhouse effect, dampening

8 Such as changes to precipitation paerns; see Box 2.2.

154 4.2 Policy ontology and form the global warming that would have occurred in their absence — a contingent environmental good and positive welfare impact, relative to that enhanced greenhouse effect. Table 4.2 aempts to illustrate this benefit by extending Table 4.1 above to contrast the actual environmental effects of the two pollutant classes:9 alongside all their undoubted and serious damaging effects, aerosols also generate a large net negative climate forcing.

Table 4.2: Comparative pollutant eﬀects

Eﬀects Aerosols LLGHGs Positive radiative forcing ◦† • Negative radiative forcing • Ocean acidiﬁcation •‡ Air pollution damages •

† Black carbon has positive atmospheric radiative forcing and perturbs snow and ice albedo.

‡ From CO2.

In the contemporary situation confronting humanity, anthropogenic aerosol emissions cannot correctly be characterised as causing only negative impacts, as pollutants histor- ically have been. e benefit of aerosol masking exists in a very real sense; it cannot be dismissed, and the mitigation policy response must be capable of fully incorporating it. But the contingent nature of this benefit must be heavily emphasised: first, the value of the aerosol mask is entirely predicated on the prior existence of significant anthropogenic climate change being well underway; second, the confounding implications of that mask for mitigation actions affecting coupled emission sources are certainly not positive, as we shall see.

For if current atmospheric aerosol loading masks as much as half of the warming eﬀect of current atmospheric GHG concentrations, as the evidence canvassed in Chapter 2 and Chapter 3 strongly indicates, is it prudent to continue the pursuit of air pollution or climate change mitigation action that assumes removing those aerosols will increase

9 e same qualifying caveat of fn. 6 on page 150 also applies here.

155 Chapter 4 Comparative analysis of mitigation policy net human welfare? Should the risk of signiﬁcant temperature increase inherent in sub- stantive declines in aerosol emissions not be carefully considered for mitigation policy design? Fundamental changes in mitigation instruments may well be required in light of that consideration. And so policy making must adopt an ontological frame that at least permits the case where reductions in pollution do not add to net human welfare but rather harm it — less pollution can under certain circumstances in fact be worse.

4.2.3 The policy challenge

A full accounting of aerosols’ role within anthropogenic climate change thus presents a serious ontological challenge for policy making: policy objectives and the instruments designed to achieve them must be constructed from the ground up to allow the case where less is worse; and indeed, where less is both beer and worse simultaneously, reflecting the conflicted multi-dimensional nature of aerosols’ place within the climate and air pollution domains. Policy making must be reconceived to incorporate the implications of the simultaneous existence of both damaging (air pollution) and beneficial (climatic) effects, and their dynamic co-dependencies such as the reality of coupled emission sources. Above all, climate change mitigation policy making must recognise that reducing aerosol emissions is no longer necessarily prudent nor desirable without further deliberate and coordinated compensatory measures; or at the very least, the option of doing so should the geophysical evidence require it.

It is difficult to identify another instance of policy making under such conditions. is challenge is distinct, and markedly harder, from more familiar trade-offs between what appear to be opposing objectives, such as short-term economic profit versus the preven- tion of environmental damage. Here the objectives themselves are essentially congruent, not opposed, yet seem to demand contradictory actions to meet them despite their common purpose in seeking to avoid environmental damage and raise human welfare. is

156 4.2 Policy ontology and form contradiction operates on two levels: first, the conflict between air pollution objectives that call for reductions in aerosol emissions, and climate change objectives that now may have to prevent that from occurring; and second, the same conflict arising from the existence of coupled emissions, as even reducing GHG emissions themselves will in key instances also reduce aerosols. erefore, the mandating of aerosol emission abatement in the service of an objective of mitigating air pollution damages may now confront a parallel need to prevent a totally unmanaged loss of those same pollutants in serving the objective of averting dangerous climate change.

e full array of environmental effects at the nexus of air pollution and climate change — the complex interactions and interrelations that are revealed when the pollutants causing them are considered holistically — have not been comprehensively captured by the policy processes in those two domains. Regulatory measures that have always been entirely appropriate when applied to pollution control in traditional form, including climate change, may now be rendered counter-productive, even dangerous, when these complexities are incorporated. Legras (2011) terms this vexed problem ‘incomplete environmental model specification’, arguing that ‘policy approaches to atmospheric pollution have typically been based on incomplete environmental models, in the sense that they have not captured all the contributors to the issue, or if they have, that the correlations between the contributors have not been properly described’ (p. 528). Similarly, Caplan and Silva (2005) define a new term, ‘correlated externalities’, denoting the same general issue, noting that ‘previous mechanisms designed to control single externalities are generally incomplete…. [and their] adoption would typically create inefficiencies, since correlated effects are neglected’ (p. 69).

Yet even the necessary complete environmental model specification may not be sufficient to overcome these flaws in policy response. In their ontological framing that less is beer, existing policy instruments — and the ways in which pollution control policies are conceptualised as a whole — may be irredeemable in their current form. at is

157 Chapter 4 Comparative analysis of mitigation policy to say, traditional pollution control policies, spanning the complete range of pollution taxes, tradeable property rights, or direct regulatory stipulations concerning production technology and permissible emissions, may at their foundation be unable to handle the confounding case where less is worse.

A further complication for instrument design in particular is the inﬂuence of time horizon. Where progressive reductions in pollutant emissions serve the underlying objective, as in the traditional case, the policy instrument has performed its function once a target emission level is reached. at instrument will have no ongoing regulatory burden aer achieving that target, other than maintaining the restriction on excess emissions. It can also safely be presumed that maintenance of that restriction requires a relatively small degree of regulatory oversight, because existing activities do not need to be amended further.

In the case of aerosol cooling, conversely, the policy instrument may be required to maintain the deliberate and coordinated compensatory measures suggested above. Provision of any such compensatory mechanism is then a regulatory burden that must persist for an uncertain length of time that is unlikely to be well-known in advance, perhaps many decades — it is an active requirement in contrast to the largely passive traditional case of maintaining an already-achieved emission target. In the context of the aerosol cooling dilemma, the need to maintain the compensatory mechanism has important ramiﬁca- tions for the design of the overall policy response, especially regarding ongoing liability and funding; these aspects are examined further in Chapter 5.

158 4.3 A revised ontology

4.3 A revised ontology

A revised ontology for the formation of policy objectives is the necessary starting point for the reconception argued above. e task is, in essence, to expand the multi-pollutant, multi-effect classification schema to formally recognise the existence of contingent beneficial consequences of some pollutants in some circumstances, and to incorporate the factor of time. However, in order to do so, we must first revisit the ambiguous usage of the terms effect and impact.

4.3.1 Clarifying eﬀect and impact

In the policy discourse, it seems fair to say that the terms effect and impact are oen poorly delineated or used largely interchangeably. But are they the same? Most importantly, should they have the same connotation of damage? is ambiguity in definition in the discussion above may now be more visible in hindsight (such as the initial statement of the term in section 4.1.3), aer considering the discussion of ontology in section 4.2. To be specific, the central concern is the question of whether effect and impact should both be assumed to refer to the consequences of pollution and, simultaneously, that those consequences are assumed to be deleterious. Such assumptions confuse and con- flate the nature of the consequences of pollution with their underlying mechanism of action. A robust definition should clearly separate the mechanism(s) of action, which is an objective statement of physics and chemistry, from the more contingent — and arguably, subjective — assessment of the normative consequences of that mechanism. And the term employed to signify the laer should permit consequences to be assessed as beneficial, given carefully constrained prerequisite circumstances.

e words effect and impact could serve those functions, if appropriately specific definitions are given. However, the word ‘impact’ is quite likely to connote damage in and

159 Chapter 4 Comparative analysis of mitigation policy of itself; certainly in the context of pollution control policy that is the sense in which it is used. Redefinition of terms is hence unlikely to remove such connotations in wider discourse, even if it may within a given analysis. It therefore appears necessary to define a new term, both free from any ambiguous conflation with ‘effect’, and without obvious negative connotations. I hence propose refined, specific definitions as follows: effect is the particular objective geophysical mechanism of action in the environment resultant from emission of a given pollutant; any pollutant may produce multiple effects. outcome is the assessed consequences of a given environmental effect with reference to an affected entity (eg, humans, physical infrastructure, other species, the eco- sphere or constituent elements); outcome should include an explicit qualifier that signifies the nature of those consequences as damaging or beneficial (or perhaps, benign).

e term multi-effect is thereby properly constrained to specifically refer only to effect in the sense of mechanism of action. at is, a given pollution case involves multiple geophysical effects (such as smog, acidification of waterways, acid rain deposition) without inherently prefiguring what the consequences to those effects are, or their nature, particularly regarding human welfare. Accordingly, use of ‘impact’ would be discouraged; where it is used, it might be read as roughly synonymous with effect, or with the subset of possible outcomes that contains only deleterious consequences.

For example, emission of SO2 has the effect of inducing acid rain deposition, the outcome of which is acid damage to natural ecosystems and to human physical infrastructure. However, those same emissions also have the effect of direct reflection of incoming solar radiation and indirect effects on cloud, the outcome of which is a benefit to human and natural systems by dampening the positive radiative forcing of elevated atmospheric

GHG concentrations. SO2 therefore produces multiple eﬀects and multiple outcomes,

160 4.3 A revised ontology and some of those outcomes add to human welfare given the prerequisite circumstance of signiﬁcant anthropogenic climate change.

4.3.2 Expanded classification schema

With these revised definitions, we can now return to the task of expanding the classification schema. I propose two additional dimensions for the statement of a pollution case: outcome is as per the definition above, codified with explicit cardinal qualifiers indicating the nature of the consequence as either damaging or beneficial for an affected entity; benign outcomes are not considered. Note that outcome should not consider any benefit stemming from the underlying activity that produces a pollutant, only to the nature of the environmental effects themselves. Outcome is also applied in the aggregate, where a set of particular effects can then be classed as damaging, beneficial, or counter, the last where at least one effect has a damaging outcome and at least one other effect has a beneficial outcome (ie, counteracting or counterposing). e scope of effects considered therefore has a direct bearing on the assessment of aggregate outcome. Clearly the vast majority of pollutants produce effects that are damaging-outcome only. Further, the majority of traditionally considered multi-pollutant, multi-effect problems have scopes that are classed as damaging-outcome in aggregate, such as the consequences of particulate emissions for local air quality, or, indeed, the consequences of greenhouse gas emissions for climate change. time horizon similarly indicates the period over which the majority of an outcome is manifested. An outcome can then be classed as near, meaning occurring immediately, on the order of days to a year; medium, meaning unfolding over a period of some years to a decade; or far, meaning more than a decade is required. At

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the aggregate level, a set of particular outcomes may be classed as near, medium, far, or split, where the last signifies that different outcomes operate across different time horizons. Pollution that damages local air quality is hence near-horizon, while GHG-induced climate change is broadly far-horizon. More importantly, we can now beer classify the full consequences to climate change from the combination of aerosol and GHG emissions: climate change now becomes split-horizon, because the outcome of fluctuating aerosol emissions is near-horizon once GHG- induced climate change is already underway. Note that time horizon is a conceptual tool and should not be interpreted as an accurate description of complex realities, wherein outcomes are rarely so neatly delineated. Emissions of methane, for example, produce substantial climatic effects in their first few years in the atmosphere; indeed, so does carbon dioxide. e point here, to emphasise, is how long is required to produce the majority of the outcome in question.

Employing these additional dimensions in the full schema, we can classify the major pollution cases as follows:

Case 1 Aerosol effects on local air quality, with the scope limited to the traditional air pollution effects, is multi-pollutant, multi-effect, damaging-outcome, near-horizon.

Case 2 Climate change as usually conceived, with the scope limited to GHG effects alone through the enhanced greenhouse effect, is multi-pollutant, single-effect, damaging-outcome,10 far-horizon.

Case 3 ough the enhanced greenhouse effect and its consequences is the major focus, ocean acidification must not be ignored,11 so the classification of climate change is expanded to multi-pollutant, multi-effect, damaging-outcome, far-horizon.

10 Notwithstanding the potential for beneﬁcial impacts in some locations during the initial onset of climatic changes (such as decreased winter deaths or increased agricultural yields). 11 In fact, Archer and Rahmstorf (2010) state that in their view, ‘this direct threat to the health of our oceans would be enough reason to halt the further rise of atmospheric CO2 concentration, even if CO2 did not cause climatic changes’ (p. 148).

162 4.3 A revised ontology

Case 4 Aerosol effects on climate change, with the scope limited specifically to climate forcing effects (diminishing a pre-existing positive planetary energy imbalance), is multi-pollutant, multi-effect, beneficial-outcome, near-horizon.

Case 5 Similar to ocean acidification in Case 3, aerosol effects on the hydrological cycle (damaging-outcome) must also be recognised, so the classification is expanded to multi-pollutant, multi-effect, counter-outcome, near-horizon.

Case 6 Climate change when considering all forcing agents is multi-pollutant, multi- eﬀect, counter-outcome, split-horizon.

Any analytical merit provided by this proposed schema risks being obscured by expressing the four dimensions in word-form only. I therefore offer these concepts in illustrative form below. e diagrams are loosely based on the ‘parallel coordinates’ approach to visualising multi-variate data: each of the four dimensions are represented in a column panel, where the range of possible values is arrayed vertically: pollutant and effect can be one or many, and outcome and time horizon take the cardinal values identified above. A simple marker is then placed in each dimension panel to indicate the classification appropriate to the pollution case in question. Multiple markers within the outcome or time horizon dimensions signify a case of either or both of the counter-outcome, split-horizon classification.

Pollution Case 1 in the list above is illustrated in Figure 4.1. e initial ‘minor’ form classification of climate change as the primary enhanced greenhouse effect, Case 2, is illustrated in Figure 4.2. Figure 4.3 shows the more complete Case 3 form, using a dashed marker to represent the inclusion of damaging-outcome ocean acidification. Figure 4.4 illustrates Case 4, where the orange marker is used simply to underscore the beneficial- outcome of reduced net forcing.

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