Public Comment - Exhibit G
From: laura gibbons To: Commission-Public-Records Subject: [EXTERNAL] Written comment for the Feb 9, 2020 port meeting. Date: Saturday, February 6, 2021 9:09:31 AM WARNING: External email. Links or attachments may be unsafe. (This is written only - I won't be able to join the meeting. Thanks!) Commissioners, I would like to comment on item 11a, 2021 Committee Workplans. You need to add to the Aviation Committee Workplan the task of figuring out how you are going to meet your 2030 target of a 50% reduction in Scope 3 greenhouse gas emissions from 2007 levels. This is only 9 years out; you need to get on it! Please remember that AIRPLANE EMISSIONS HAVE 3 TIMES GREATER WARMING IMPACT ON CLIMATE THAN ON-THE-GROUND EMISSIONS, making reducing aviation even more critical than its carbon emissions might indicate. Here is the latest scientific study to quantify the factor of three: (https://www.sciencedirect.com/science/article/pii/S1352231020305689?via%3Dihub) Sincerely, Laura Gibbons Seattle Atmospheric Environment 244 (2021) 117834 Contents lists available at ScienceDirect Atmospheric Environment journal homepage: http://www.elsevier.com/locate/atmosenv The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018 D.S. Lee a,*, D.W. Fahey b, A. Skowron a, M.R. Allen c,n, U. Burkhardt d, Q. Chen e, S.J. Doherty f, S. Freeman a, P.M. Forster g, J. Fuglestvedt h, A. Gettelman i, R.R. De Leon a, L.L. Lim a, M. T. Lund h, R.J. Millar c,o, B. Owen a, J.E. Penner j, G. Pitari l, M.J. Prather k, R. Sausen d, L. J. Wilcox m a Faculty of Science and Engineering, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester, M1 5GD, United Kingdom b NOAA Chemical Sciences Laboratory (CSL), Boulder, CO, USA c School of Geography and the Environment, University of Oxford, Oxford, UK d Deutsches Zentrum fr Luft- und Raumfahrt (DLR), Institut fr Physik der Atmosphare, Oberpfaffenhofen, Germany e State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China f Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA g School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, United Kingdom h CICEROCenter for International Climate ResearchOslo, PO Box 1129, Blindern, 0318, Oslo, Norway i National Center for Atmospheric Research, Boulder, CO, USA j Department of Climate and Space Sciences and Engineering, University of Michigan, 2455 Hayward St., Ann Arbor, MI, 48109-2143, USA k Department of Earth System Science, University of California, Irvine, 3329 Croul Hall, CA, 92697-3100, USA l Department of Physical and Chemical Sciences, Universit`a dell'Aquila, Via Vetoio, 67100, L'Aquila, Italy m National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Earley Gate, Reading, RG6 6BB, UK n Department of Physics, University of Oxford, Oxford, UK o Committee on Climate Change, 151 Buckingham Palace Road, London, SW1W 9SZ, UK HIGHLIGHTS GRAPHICAL ABSTRACT Global aviation warms Earth's surface through both CO2 and net non-CO2 contributions. Global aviation contributes a few percent to anthropogenic radiative forcing. Non-CO2 impacts comprise about 2/3 of the net radiative forcing. Comprehensive and quantitative calcu- lations of aviation effects are presented. Data are made available to analyze past, present and future aviation climate forcing. ARTICLE INFO ABSTRACT Global aviation operations contribute to anthropogenic climate change via a complex set of processes that lead to Dedication: This paper is dedicated to the a net surface warming. Of importance are aviation emissions of carbon dioxide (CO2), nitrogen oxides (NOx), memory of Professor Ivar S. A. Isaksen of the water vapor, soot and sulfate aerosols, and increased cloudiness due to contrail formation. Aviation grew * Corresponding author. E-mail address: d.s.lee@mmu.ac.uk (D.S. Lee). https://doi.org/10.1016/j.atmosenv.2020.117834 Received 9 February 2020; Received in revised form 2 July 2020; Accepted 30 July 2020 Available online 3 September 2020 1352-2310/ 2020 Elsevier Ltd. All rights reserved. D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 University of Oslo, whose scientific excellence, strongly over the past decades (19602018) in terms of activity, with revenue passenger kilometers increasing friendship, and mentorship is sorely missed. from 109 to 8269 billion km yr 1, and in terms of climate change impacts, with CO2 emissions increasing by a factor of 6.8 to 1034 Tg CO2 yr 1. Over the period 20132018, the growth rates in both terms show a marked Keywords: increase. Here, we present a new comprehensive and quantitative approach for evaluating aviation climate Aviation forcing terms. Both radiative forcing (RF) and effective radiative forcing (ERF) terms and their sums are Contrail cirrus calculated for the years 20002018. Contrail cirrus, consisting of linear contrails and the cirrus cloudiness arising Climate from them, yields the largest positive net (warming) ERF term followed by CO2 and NOx emissions. The for- Radiative forcing CO2 mation and emission of sulfate aerosol yields a negative (cooling) term. The mean contrail cirrus ERF/RF ratio of NOx 0.42 indicates that contrail cirrus is less effective in surface warming than other terms. For 2018 the net aviation ERF is +100.9 milliwatts (mW) m 2 (595% likelihood range of (55, 145)) with major contributions from contrail cirrus (57.4 mW m 2), CO2 (34.3 mW m 2), and NOx (17.5 mW m 2). Non-CO2 terms sum to yield a net positive (warming) ERF that accounts for more than half (66%) of the aviation net ERF in 2018. Using normalization to aviation fuel use, the contribution of global aviation in 2011 was calculated to be 3.5 (4.0, 3.4) % of the net anthropogenic ERF of 2290 (1130, 3330) mW m 2. Uncertainty distributions (5%, 95%) show that non-CO2 forcing terms contribute about 8 times more than CO2 to the uncertainty in the aviation net ERF in 2018. The best estimates of the ERFs from aviation aerosol-cloud interactions for soot and sulfate remain undetermined. CO2-warming-equivalent emissions based on global warming potentials (GWP* method) indicate that aviation emissions are currently warming the climate at approximately three times the rate of that associated with aviation CO2 emissions alone. CO2 and NOx aviation emissions and cloud effects remain a continued focus of anthropogenic climate change research and policy discussions. 1. Introduction time and combined to provide a net ERF for global aviation. Quantifying the terms required new analyses of CO2 and NOx ERFs and recalibration Aviation is one of the most important global economic activities in of other individual ERFs accounting for factors not previously applied in the modern world. Aviation emissions of CO2 and non-CO2 aviation ef- a common framework. fects result in changes to the climate system (Fig. 1). Both aviation CO2 In Lee et al. (2009), the net RF was calculated with and without the and the sum of quantified non-CO2 contributions lead to surface full contrail cirrus term but including an estimate for linear contrails. warming. The largest contribution to anthropogenic climate change The exclusion was based on the lack of a best estimate derived from across all economic sectors comes from the increase in CO2 concentra- existing studies. At that time radiative forcing estimates were limited to tion, which is the primary cause of observed global warming in recent linear or line-shaped contrails since the modelling approaches required decades (IPCC, 2013, 2018). Aviation contributions involve a range of scaling contrail formation frequency to observed coverage and only atmospheric physical processes, including plume dynamics, chemical satellite observations of linear contrails existed (Burkhardt et al., 2010). transformations, microphysics, radiation, and transport. Aggregating The contrail cirrus term requires the simulation of the whole contrail these processes to calculate changes in a greenhouse gas component or a cirrus life cycle, starting from persistent linear contrails which spread cloud radiative effect is a complex challenge for contemporary atmo- and often become later indistinguishable from natural cirrus. Persistent spheric modeling systems. Given the dependence of aviation on burning contrail formation requires ice-supersaturated conditions along a flight fossil fuel, its significant CO2 and non-CO2 effects, and the projected track, which are variable in space and time in the troposphere and fleet growth, it is vital to understand the scale of aviation's impact on tropopause region (Irvine et al., 2013). Estimating the RF from contrail present-day climate forcing. cirrus requires knowledge of complex microphysical processes, radiative Historically, estimating aviation non-CO2 effects has been particu- transfer, and the interaction with background cloudiness (Burkhardt larly challenging. The primary (quantified) non-CO2 effects result from et al., 2010). Contrail cirrus forcing dominates that of persistent linear the emissions of NOx, along with water vapor and soot that can result in contrails with the latter on the order of 10% of the combined forcing contrail formation. Aviation aerosols are small particles composed of (Burkhardt and Karcher, 2011). In the present study, we present a best soot (black and organic carbon (BC/OC)) and sulfur (S) and nitrogen (N) estimate and uncertainty based on the results from global climate compounds. The largest positive (warming) climate forcings adding to models employing process-based contrail cirrus parameterizations. that of CO2 are those from contrail cirrus and from NOx-driven changes Emissions of NOx from aviation lead to photochemical changes that in the chemical composition of the atmosphere (Lee et al., 2009). Lee increase global ozone (O3) formation while decreasing the lifetime and et al. (2009) estimated that in 2005, aviation CO2 radiative forcing (RF abundance of methane (CH4). The changes result in positive and nega- (Wm 2)) was 1.59% of total anthropogenic CO2 RF and that the sum of tive (cooling) RF contributions, respectively. Since Lee et al. (2009), aviation CO2 and non-CO2 effects contributed about 5% of the overall improved understanding and modeling capabilities have emerged, as net anthropogenic forcing. well as additional RF terms in response to NOx emissions, namely a Understanding of aviation's impacts on the climate system has longer-term decrease in background O3 and a reduction in H2O in the improved over the decade since the last comprehensive evaluation (Lee stratosphere in response to decreased CH4. Here, model results are used et al., 2009), but remains incomplete. Published studies of aviation to calculate the additional RF terms, and to incorporate the updated CH4 contributions to climate change generally focus on one or a few ERF forcing as assessed by Etminan et al. (2016) and the terms. For example, about 20 studies are cited here that quantify the equilibrium-to-transient corrections for the CH4 term (see Appendix D). contribution from global NOx emissions. In contrast, only a few studies Finally, aviation-specific efficacies (Appendix C) of the individual NOx have addressed the net RF from global aviation (IPCC, 1999; Sausen components are used to estimate a net NOx ERF for the first time. et al., 2005; Lee et al., 2009). A more recent study updated some avia- Lee et al. (2009) includes best estimates for the RFs resulting from tion terms without providing a net RF (Brasseur et al., 2016). Here, a the aerosol-radiation interactions (previously called direct effects) of comprehensive analysis of individual aviation ERFs is undertaken in soot and sulfate aerosols from aviation. However, no best estimates of order to provide an overall ERF for global aviation, along with the RFs from aerosol-cloud interactions (previously called indirect effects) associated uncertainties, which is an analysis unavailable elsewhere. were available in 2009. Subsequent studies discussed here have yet to This step updates and improves the analysis of Lee et al. (2009). Best provide a basis for best estimates of ERFs from aviation aerosol-cloud estimates of individual aviation ERF terms are derived here for the first interactions that may be significant. 2 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 The primary motivations for the present study are to provide an scenario projections of aviation climate impacts to be assessed in context updated, comprehensive evaluation of aviation climate forcings in terms with other sectors, such as maritime shipping, ground transportation of RF and ERF based on new calculations and the normalization of values and energy generation. This updated understanding is especially from published modeling studies, and to combine the resulting best es- important given the potential role of international aviation in meeting timates via a Monte-Carlo analysis to yield a best estimate for the net the goals of the Paris Agreement (Section 2) on limiting future tem- ERF for global aviation for the years 20002018. The three years 2018, perature increases. 2011, and 2005 are notable because the year 2018 is the latest year for The remaining sections address global aviation growth statistics which air traffic and fuel use datasets are available, 2011 is the most (Section 2); a brief summary of methods used in the analysis (Section 3); recent year evaluated for net anthropogenic climate forcing by the IPCC results for the ERF estimates of CO2, NOx, water vapor, contrail cirrus, (IPCC, 2013), and 2005 is the year evaluated in the latest comprehensive and aerosol-radiation and aerosol-cloud interactions with soot and sulaviation and climate evaluation (Lee et al., 2009). By normalizing the fate (Section 4); results for the net ERF of global aviation (Section 5); calculations across these years, more specific and self-consistent com- emission metrics (Section 6); and aviation CO2 vs non-CO2 forcings parisons can be made of the changes in aviation contributions over time. (Section 7). The appendices contain additional detailed information on The normalization step requires addressing in each study, for example, trends in aviation emissions (App. A); aviation CO2 radiative forcing the choice of air traffic inventory, the integration of emissions along calculations (App. B); radiative forcing, efficacy and ERF definitions flight tracks, and the assumed jet-engine emission indices. The new best (App. C); aviation NOx RF calculations (App. D); contrail cirrus RF estimates of aviation ERF, for example, show that the 2018 value is scaling factors and uncertainty (App. E); and emission equivalency about 48% larger than the updated 2005 value. metric calculations (App. F). A Supplemental Data (SD) file is provided In general, previous global aviation climate assessments have made containing the interactive spreadsheet used to calculate RFs and ERFs different assumptions concerning emissions, cloudiness effects, and for each aviation term. aviation operations (e.g., IPCC, 1999). Here, our self-consistent set of component and net aviation ERFs for 2000 to 2018 allows historical and Fig. 1. Schematic overview of the processes by which aviation emissions and increased cirrus cloudiness affect the climate system. Net positive RF (warming) contributions arise from CO2, water vapor, NOx, and soot emissions, and from contrail cirrus (consisting of linear contrails and the cirrus cloudiness arising from them). Negative RF (cooling) contributions arise from sulfate aerosol production. Net warming from NOx emissions is a sum over warming (short-term ozone increase ) and cooling (decreases in methane and stratospheric water vapor, and a long-term decrease in ozone) terms. Net warming from contrail cirrus is a sum over the day/night cycle. These contributions involve a large number of chemical, microphysical, transport and, radiative processes in the global atmosphere. The quantitative ERF values associated with these processes are shown in Fig. 3 for 2018. 3 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 2. Global aviation growth growth is likely to be largely dependent upon the combustion of kerosene fossil fuel (Jet A-1/A) (OECD, 2012), resulting in emission of CO2. Global aviation fuel use and CO2 emissions have increased in the last Renewable biofuels partially offset fossil fuel emissions but these have four decades with large growth occurring in Asia and other developing yet to be produced in sufficient quantities to offset growth of fossil fuel regions due to the rapid expansion of civil aviation (Fig. 2 and Appendix use. Furthermore, considerable uncertainties remain regarding the A). Looking forward, this pattern of growth is expected to be main- life-cycle emissions of biofuels, which determine the reductions in net tainedfor example, of the 1229 orders of Airbus and 1031 orders of CO2 emissions (e.g., Hari et al., 2015). There are current regulations Boeing in 2017, 20.3% and 37.5%, respectively, are for airlines in the regarding aviation emissions of CO2, NOx, and soot mass and number Asia region (Airbus, 2017; Boeing, 2018). Airbus projects 41% of orders based on decisions by the International Civil Aviation Organization over the next two decades to be from the Asia-Pacific region (Airbus, (ICAO). Under the 2016 Paris climate agreement, nations are commit- 2017). The uncertainty in this expectation has increased due to the ting to limiting future increases in global temperatures with Nationally slowdown in aviation operations in the early months of 2020 due to the Determined Contributions (NDCs) (UNFCCC). Whereas domestic avia- COVID-19 pandemic (Le Quere et al., 2020). Annual aviation emissions tion CO2 emissions are included in the NDCs, CO2 emissions from inin 2020 are now expected to be below recent projections that are based ternational aviation are not mentioned in the agreement. It remains on historical growth. open as to whether emissions from international aviation or global A striking feature of Fig. 2a is the sustained multi-decade growth in emissions beyond greenhouse gases (e.g., short-lived (non-CO2) climate CO2 emissions; the average rate for the period 19602018 is 15 Tg CO2 forcers) will be included in future international agreements. yr 1. The growth rate for 2013 through 2018 is much larger (44 Tg CO2 yr 1). The annually averaged growth rate over the period 1970 to 2012 3. Methods is 2.2% yr 1and for 2013 to 2018 is 5% yr 1(increase of 27%). In 2018, global aviation CO2 emissions exceeded 1000 million tonnes per year for The methodologies used to calculate ERF and RF for individual the first time (see methodology for scaling 2016 IEA data in Appendix aviation terms are described in this section, and results of these calcu- A). The cumulative emissions of global aviation (19402018) are 32.6 lations are given in Section 4. Common to the methodologies is a billion (109) tonnes of CO2, of which approximately 50% were emitted comprehensive multi-page spreadsheet (see SD) that begins with a user's in the last 20 years. Current (2018) CO2 emissions from aviation guide. The spreadsheet pages include those for contrail cirrus, CO2, NOx, represent approximately 2.4% of anthropogenic emissions of CO2 H2O, and sulfate and soot aerosol, along with CO2-equivalent metrics, (including land use change) (Fig. 2c). ERF probability distributions, ERF time series, and estimates of forcings Aviation has grown strongly over time (Fig. 2b) in terms of available from aerosol-cloud effects. The spreadsheet displays the results of seat kilometers (ASK, a measure of capacity) and revenue passenger aviation forcings provided by individual published studies. ERF and RF kilometers (RPK, a measure of transport work). Fuel usage and hence values were calculated for 2018 and other years based on the normalized CO2 emissions have grown at a lesser rate than RPK, reflecting increases values of ERF or RF per unit emission or distance, choice of appropriate in aircraft efficiency derived from changes in technology, larger average emission indices, and times series data on fuel use and distance travaircraft sizes and increased passenger load factor. Aviation transport elled. In the case of the contrail cirrus forcing, the flight-track distance efficiency has improved by approximately eightfold since 1960, to 125 was chosen as the proxy over fuel usage. Annual global emissions are gCO2 (RPK) 1. derived from fuel burn by multiplying by the average emission indices At present and for some considerable time into the future, aviation (Table 1). The combined and normalized results are used to create sets of Fig. 2. Data related to the growth of aviation traffic and CO2 emissions from 1940 to 2018. Panel (a): Global aviation CO2 emissions. Underlying fuel usage data for 1940 to 1970 are derived from Sausen and Schumann (2000) and for 19702016 from Interna- tional Energy Agency (UKDS, 2016) data, which include international bunker fuels. For 2017/18, the values are scaled from information from the International Air Transport Association (see Appendix A). The average annual increase of global emissions from 1960 to 2018 is 15 Tg CO2 yr 1 and the corresponding decadal average growth rates are 8.0, 2.2, 3.0, 2.3 and 1.1% yr 1, yielding an overall average of 3.3% yr 1. Panel (b): Global aviation traffic in RPK and ASK from airlines.org (http://airlines.org/datas et/world-airlines-traffic-and-capacity/ ), and the transport efficiency of global aviation in kg CO2 per RPK. The passenger load factor defined as RPK/ASK increased from about 60% in 1960 to 82% in 2018. Panel (c): Total anthropogenic CO2 emissions and the aviation fractions of this total with and without the inclusion of CO2 emissions from land use change (LUC) from the Global Carbon Budget 2018 (Le Quere, 2018). Panel (d)(f): Additional aviation emissions data by region and year. The yearly sums of OECD and non-OECD values in (d) equal the respective global total values. The regional values in (e) and (f) also sum to equal the yearly global total values. Note different vertical scales (http://www.oecd.org/ about/membersandpartners/) (UKDS, 2016) (Coun- try listings in SD Spreadsheet). 4 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Table 1 approach in Lee et al. (2009) to include ERF values in addition to the Emission indices used in ERF and RF calculations. traditional RF values (Tables 2 and 3 and Fig. 3). The distinction be- Emission Emission index Reference Notes tween ERF and RF is presented in Appendix C. ERF is the preferred metric for comparing the expected impacts of climate forcing terms CO2 3.16 kg/kg fuel ICAO (2018) NOx 15.14 g/kg fuel Fleming and Ziegler 2018, 2011 (Myhre et al., 2013). Its use derives from the stronger correlation be- 14.12 g/kg fuel (2016) 2005 tween ERF and the change in the equilibrium global-mean surface Barrett et al. (2010) temperature for some forcing agents than for the corresponding RF. ERF Water 1.231 kg/kg fuel Barrett et al. (2010) is calculated as the change in net top-of-the-atmosphere (TOA) down- vapor ward radiative flux after allowing for rapid adjustments in atmospheric Soot 0.03 g/kg fuel Barrett et al. (2010) 2 1014 particles/ temperatures, water vapor and clouds with globally-averaged sea sur- kg fuela face and/or land surface temperatures unchanged. ERF is preferred over Sulfur 1.2 g/kg fuel Miller et al. (2010) Assumed S content of RF estimates because the imposed forcing and rapid responses to the (SO2) 600 ppm forcing cannot always be separately evaluated, especially for aerosols. In a Assumes mean particle size in the range of 1179 nm diameter. general, the largest differences between ERF and RF are expected for aerosol-cloud interactions and contrail cirrus (Myhre et al., 2013; RF and ERF aviation terms for the years 20002018. In addition to Boucher et al., 2013). In calculating ERF values for 20002018, the facilitating the present study, the spreadsheet also provides a quantita- ERF/RF ratio is assumed to be constant with time. tive framework for follow-on analyses. Most of the results for the non-CO2 terms have associated statistics Calculations of radiative forcing are expanded here beyond the from which the median was chosen as the best estimate, including the net aviation ERF and RF, and the net non-CO2 ERF and RF. For CO2 and Table 2 Best estimates and high/low limits of the 90% likelihood ranges for aviation ERF components derived in this study. ERF (mW m 2) 2018a 2011a 2005a Sensitivity to emissions ERF/RF Contrail cirrus 57.4 (17, 98) 44.1 (13, 75) 34.8 (10, 59) 9.36 10 10 mW m 2 km 1 0.42 CO2 34.3 (28, 40) 29.0 (24, 34) 25.0 (21, 29) 1.0 Short-term O3 increase 49.3 (32, 76) 37.3 (24, 58) 33.0 (21, 51) 34.4 9.9 mW m 2 (Tg (N) yr 1) 1 1.37 Long-term O3 decrease 10.6 ( 20, 7.4) 7.9 ( 15, 5.5) 6.7 ( 13, 4.7) 9.3 3.4 mW m 2 (Tg (N) yr 1) 1 1.18 CH4 decrease 21.2 ( 40, 15) 15.8 ( 30, 11) 13.4 ( 25, 9.4) 18.7 6.9 mW m 2 (Tg (N) yr 1) 1 1.18 Stratospheric water vapor decrease 3.2 ( 6.0, 2.2) 2.4 ( 4.4, 1.7) 2.0 ( 3.8, 1.4) 2.8 1.0 mW m 2 (Tg (N) yr 1) 1 1.18 Net NOx 17.5 (0.6, 29) 13.6 (0.9, 22) 12.9 (1.9, 20) 5.5 8.1 mW m 2 (Tg (N) yr 1) 1 Stratospheric H2O increase 2.0 (0.8, 3.2) 1.5 (0.6, 2.4) 1.4 (0.6, 2.3) 0.0052 0.0026 mW m 2 (Tg (H2O) yr 1) 1 Soot (aerosol-radiation) 0.94 (0.1, 4.0) 0.71 (0.1, 3.0) 0.67 (0.1, 2.8) 100.7 165.5 mW m 2 (Tg (BC) yr 1) 1 Sulfate (aerosol-radiation) 7.4 ( 19, 2.6) 5.6 ( 14, 1.9) 5.3 ( 13, 1.8) 19.9 16.0 mW m 2 (Tg (SO2) yr 1) 1 Sulfate and soot (aerosol-cloud) Net ERF (only non-CO2 terms) 66.6 (21, 111) 51.4 (16, 85) 41.9 (14, 69) Net aviation ERF 100.9 (55, 145) 80.4 (45, 114) 66.9 (38, 95) Net anthropogenic ERF in 2011 2290 (1130, 3330)b a The uncertainty distributions for all forcing terms are lognormal except for CO 2 and contrail cirrus (normal) and Net NOx (discrete pdf). b Boucher et al., 2013. IPCC also separately estimated the contrail cirrus term for 2011 as 50 (20, 150) mW m 2. Table 3 Best estimates and low/high limits of the 95% likelihood ranges for aviation RF components derived in this studya. RF (mW m 2) 2018b 2011b 2005b Lee et al. (2009) 2005 values Sensitivity to emissions (this work) Contrail cirrus 111.4 (33, 189) 85.6 (25, 146) 67.5 (20, 115) (11.8c) 1.82 10 9 mW m 2 km 1 CO2 34.3 (31, 38) 29.0 (26, 32) 25.0 (23, 27) 28.0 Short-term O3 increase 36.0 (23, 56) 27.3 (17, 42) 24.0 (15, 37) 26.3 25.1 7.3 mW m 2 (Tg (N) yr 1) 1 Long-term O3 decrease 9.0 ( 17, 6.3) 6.7 ( 13, 4.7) 5.7 ( 11, 4.0) 7.9 2.9 mW m 2 (Tg (N) yr 1) 1 CH4 decrease 17.9 ( 34, 13) 13.4 ( 25, 9.3) 11.4 ( 21, 7.9) 12.5 15.8 5.9 mW m 2 (Tg (N) yr 1) 1 Stratospheric water vapor decrease 2.7 ( 5.01.9) 2.0 ( 3.8, 1.4) 1.7 ( 3.2, 1.2) 2.4 0.9 mW m 2 (Tg (N) yr 1) 1 Net NOx 8.2 ( 4.8, 16) 6.5 ( 3.3, 12) 6.6 (1.9, 12) 13.8d 1.0 6.6 mW m 2 (Tg (N) yr 1) 1 Stratospheric H2O increase 2.0 (0.8, 3.2) 1.5 (0.6, 2.4) 1.4 (0.6, 2.3) 2.8 0.0052 0.0026 mW m 2 (Tg (H2O) yr 1) 1 Soot (aerosol-radiation) 0.94 (0.1, 4.0) 0.71 (0.1, 3.0) 0.67 (0.1, 2.8) 3.4 100.7 165.5 mW m 2 (Tg (BC) yr 1) 1 Sulfate (aerosol-radiation) 7.4 ( 19, 2.6) 5.6 ( 14, 1.9) 5.3 ( 13, 1.8) 4.8 19.9 16.0 mW m 2 (Tg (SO2) yr 1) 1 Sulfate and soot (aerosol-cloud) Net RF (only non-CO2 terms) 114.8 (35, 194) 88.4 (27, 149) 70.3 (22, 119) Net aviation RF 149.1 (70, 229) 117.4 (56, 179) 95.2 (47, 144) 78.0 a ERF values are shown in Table 2. b The uncertainty distributions for all forcing terms are lognormal except for CO 2 and contrail cirrus (normal) and Net NOx (discrete pdf). c Linear contrails only; excludes the increase in cirrus cloudiness due to aged spreading contrails. d Excludes updated CH 4 RF evaluation of Etminan et al. (2016) and equilibrium-to-transient correction. 5 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Fig. 3. Best-estimates for climate forcing terms from global aviation from 1940 to 2018. The bars and whiskers show ERF best estimates and the 595% confidence intervals, respectively. Red bars indicate warming terms and blue bars indicate cooling terms. Numerical ERF and RF values are given in the columns with 595% confidence intervals along with ERF/RF ratios and confidence levels. ERF and RF values are shown for other years in Tables 2 and 3, Fig. 6 and the SD spreadsheet. RF values are multiplied by the respective ERF/RF ratio to yield ERF values. ERF/RF values designated as [1] indicate that no estimate is available yet. The basis for confidence levels is presented in Table 4. contrail cirrus, for which the sample sizes are small (3, in both cases), the performance of LinClim and CICERO-SCM with respect to aviation mean was used as the best estimate. The best estimates of the non-CO2 emissions is documented in the multi-model comparison of Khodayari terms except contrail cirrus have associated uncertainties expressed as et al. (2013). The CO2 concentrations attributable to aviation in 2018 5% and 95% confidence intervals calculated from 5, 95% percentile based on LinClim, CICERO-SCM and FaIR are 2.9, 2.4 and 2.4 ppm, statistics. The uncertainty distributions for all forcing terms other than respectively, with concentrations nearly doubling in the last 20 years CO2 and contrail cirrus are lognormal and that for net NOx has a discrete (see SD spreadsheet). The ERF/RF ratio for CO2 is assumed to be unity. probability distribution function (PDF). The uncertainties for the ERF The resulting CO2 ERFs, as derived from global concentrations using and RF of CO2 were taken from IPCC (2013) and fitted with a Monte standard IPCC expressions (IPCC, 2001), are 38.6, 32.0 and 32.4 mW Carlo analysis with a normal distribution (see Section 5). The un- m 2, respectively. With only three model estimates, the average of 34.3 certainties for contrail cirrus were estimated partly from expert judge- mWm 2 (5 and 95% percentiles of 29 and 40 mW m 2), is chosen be the ment of the underlying processes, as described in Appendix E, again CO2 RF best estimate. fitted with a Monte Carlo analysis with a normal distribution. 4.2. NOx 4. Calculations of ERFs for aviation terms The photochemical effects of aviation NOx emissions on the atmo- 4.1. CO2 spheric abundances of O3, CH4, carbon monoxide (CO) and reactive hydrogen (HOx) are well established (Fuglestvedt et al., 1999). Earlier The time series of aviation CO2 emissions is shown in Fig. 2 as studies assessed the short-term increase of O3 and the longer-term derived from combined kerosene and avgas usage (UKDS, 2016). reduction in CH4 lifetime and abundance, which yield positive and Calculating CO2 concentrations from emissions requires use of a global negative RFs, respectively (IPCC, 1999; Sausen et al., 2005). Lee et al. carbon-cycle model, which has a range of complexity from a compre- (2009) introduced the concept of the 'net NOx' effect by combining the hensive Earth system model (ESM) to a simple climate model (SCM), two components, extending and updating the study of Sausen et al. with the latter being based on a box model or impulse response function (2005). Later studies expanded the analysis of NOx effects to include the (IRF) model. Three SCMs were used here: LinClim, an IRF model based long-term decreases in both O3 and stratospheric water vapor (SWV) on Sausen and Schumann (2000) (Appendix B); the Finite-amplitude resulting from the CH4 reduction. Both effects yield negative RFs Impulse Response (FaIR) model (Millar et al., 2017); and the (Holmes et al., 2011; Myhre et al., 2011). In the present study, an CICERO-SCM (Fuglestvedt and Berntsen, 1999; Skeie et al., 2017). The ensemble of 20 NOx studies is assessed to provide NOx forcing best 6 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 estimates based on a wide range of global atmospheric chemistry /climate models and a broad range of present-day aviation emission inventories (details in Appendix D and SD spreadsheet). Results from 6 of the studies were adopted from Holmes et al. (2011). The study ensemble represents various model methodologies in calculating and treating both the short-term and the long-term NOx components. In order to avoid gaps and additional uncertainties, standardized ERFs were developed that estimated disparate elements (e.g., CH4 mediated decreases in SWV and long-term O3). Moreover, most of the studies were based upon a parameterization of the CH4 response that assumed a full equilibrium response. In order to calculate the transient response for a specific year more accurately, a correction factor is needed (Myhre et al., 2011). Here, the CH4 responses for individual years were calculated (see Appendix D) using the difference between two simulations with differing aviation NOx emissions. A number of transient and equilibrium simulations were conducted with a 2D chemical-transport model to find that the requirement for a correction factor is well supported and that the 2018 value is 0.79 (see Transient vs. equilibrium in Appendix D and Appendix Table D.2). In addition, a Fig. 4. Results from an ensemble of 18 models from 20 studies for aviation NOx scaling factor (1.23) is applied to derived CH4 ERF numbers to account impacts: short-term O3 increases; CH4 reductions, CH4-induced long-term re- for the effect of shortwave CH4 forcing, following Etminan et al. (2016) ductions of O3, CH4-induced reductions of stratospheric water vapor (SWV) and (see Appendix D). The existence and nature of correlations between the Net NOx. Each data point represents a value of RF per unit emission (mW m 2 NOx RF components were also explored (see Correlations in Appendix D (Tg N yr 1) 1) as normalized from a published study (see SD). CH4-induced O3 and Appendix Fig. D.1) since the degree of correlation between and SWV are calculated using standardized methodology (see text for details). short-term O3 and CH4 terms was a source of uncertainty in the calcu- Note that the displayed values do not include correction factors to account for the non-steady-state CH4 responses to NOx emissions and the new CH4 RF lation of the net-NOx forcing in Lee et al. (2009). The work of Holmes parameterization. These adjustments are applied in forming the best estimates et al. (2011) supports the prior assumption of correlation, which is as discussed in Appendix D. greatly expanded here. Regardless of inter-model differences, significant correlations are observed; for example, a significant negative correlation 4.3. Water vapor emissions (p = 0.7) exists between the short-term and the long-term NOx RF components. A large fraction of annual aircraft emissions from the global fleet The normalized sensitivity results for net NOx in units of mW m 2 (Tg occurs in the stratosphere, primarily in the northern hemisphere (Forster (N) yr 1) 1 for the individual modeling studies are shown in Fig. 4 along et al., 2003). The accumulation of water vapor emissions perturbs the with statistical parameters (see Ensemble values in Appendix D). Given low background humidity in the lower stratosphere and changes the the diversity of studies conducted over nearly two decades, the standard water vapor radiative balance. Calculating the water vapor RF is deviations of the distributions are reasonably small. In contrast, the sign complicated by the sensitivity to the vertical and horizontal distribution of the net-NOx RF obtained from summing over the 4 component values of emissions, seasonal changes in tropopause heights, and short stratovaries from positive to negative. The spread in NOx RF values is caused spheric residence times. Some earlier studies do not include the water by various factors (e.g., emissions inventories, experimental design or vapor effect. inter-model differences) and is particularly sensitive to the NOx distri- The water vapor effects were explored in detail (see SD) using results bution in the model background troposphere (Holmes et al., 2011). The from nine studies: IPCC (1999), Marquart et al., (2001), Gauss et al. NOx efficacies are 1.37 for the short-term ozone increases and 1.18 for (2003), Ponater et al. (2006), Fromming et al. (2012), Wilcox et al. methane decreases (Ponater et al., 2006). The efficacies do not equal the (2012), Lim et al. (2015), Pitari et al. (2015) and Brasseur et al. (2016). ERF/RF ratios, in general (Ponater et al., 2020; Appendix C); nonethe- The reported RFs from these studies vary from 0.4 mW m 2 (Wilcox less, in the present study, we assume the efficacies and the ERF/RF ratios et al., 2012) through 1.5 mW m 2 (Fromming et al., 2012; Lim et al., are equal, in the absence of better information. The factor of 1.18 was 2015) to 3.0 mW m 2 (IPCC, 1999). The differences are attributed to the similarly adopted for the CH4-mediated decreases in long-term ozone different transport models used, with some contribution from the and SWV. It is noted that these ratios are from one study and that, in different meteorologies in different studies. Normalizing to the same general, the ratio of ERF to RF for CH4 and tropospheric O3 are currently emissions and averaging these reported estimates yields a water vapor the subject of some debate (Smith et al., 2018; Xie et al., 2016; sensitivity of 0.0052 0.0026 mW m 2 (Tg (H2O) yr 1) 1. Scaling this Richardson et al., 2019). Given the strength of the net effect of the ERF value linearly to emissions of 382 Tg H2O yields an ERF best estimate of adjustment on the net NOx forcing (more than doubling over its 2.0 (0.8, 3.2) mW m 2 for 2018, which is well within the uncertainty stratosphere-adjusted RF), these ratios warrant further study. range of the 2005 Lee et al. (2009) value of 2.8 (0.39, 20.3) mW m 2. The net-NOx ERF sensitivity of 5.5 8.1 mW m 2 (Tg (N) yr 1) 1 The ERF/RF ratio for stratospheric water increases is assumed to be yields a 2018 best estimate of 17.5 (0.6, 28.5) mW m 2. This best estiunity. We have greater confidence in the new estimate and its smaller mate includes the correction factor for non-steady state conditions as uncertainty since it is based on detailed physical studies, rather than a well as the revised formulation of CH4 RF (Appendix D). scaling of the earlier IPCC (1999) estimate. The new best estimate is also Other potential short-term effects from NOx emissions involve the in good agreement with the earlier results of Gauss et al. (2003) and direct formation of nitrate aerosol and indirect enhancement of sulfate Ponater et al. (2006), after scaling their results to account for emissions aerosol. These effects, addressed in a few modelling studies, are assodifferences. ciated with large uncertainties (Righi et al., 2013; Pitari et al., 2017; Unger, 2011). The effects of NOx on aerosol abundances are not further considered here owing to the limited number of studies and the large 4.4. Contrail cirrus associated uncertainties. The aviation fleet increases global cloudiness through the formation of persistent contrails when the ambient atmosphere is supersaturated 7 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 with respect to ice (IPCC, 1999). Contrail cirrus, consisting of linear groups: those connected with the upper tropospheric water budget and contrails and the cirrus cloudiness arising from them, have cooling the contrail cirrus scheme itself, and those associated with the change in (short-wave) and warming (long-wave) effects, with the effect at night radiative transfer due to the presence of contrail cirrus. We considered being exclusively warming. In past assessments (e.g., IPCC, 1999; Lee uncertainty in upper tropospheric ice-supersaturation frequencies and et al., 2009), a best estimate was only available for the RF of linear their simulation in global models and the uncertainty of ice-crystal persistent contrails, in part because of the difficulty of quantifying the numbers due to uncertainty in soot-number emissions, ice nucleation cloudiness contribution of aging and spreading contrails (Minnis et al., within the plume, and loss processes in the contrail's vortex phase. 2013). The ERF of contrail cirrus was estimated for 2011 as 50 (20, 150) Finally, an important uncertainty comes from the adjustment of natural mW m 2 by Boucher et al. (2013). Results of a recent assessment of clouds (Burkhardt and Karcher, 2011). There is also a small uncertainty contrail cirrus and other aviation effects are included here, although the associated with the contrail cirrus life cycle, which affects the difference study did not propose new best estimates (Brasseur et al., 2016). in nighttime and daytime contrail cirrus cover (Stuber et al., 2006) A persistent contrail requires ice-supersaturated conditions along the based on work analyzing the diurnal cycle (Chen and Gettelman, 2013; flight track. Contrail cirrus life cycles are dependent on the temporal and Newinger and Burkhardt, 2012). spatial scales of the ice supersaturated areas, which are highly variable Uncertainty connected with the radiative response to contrail cirrus in the troposphere and tropopause region (e.g., Lamquin et al., 2012; is largely due to the differences in the radiation schemes across climate Irvine et al., 2013; Bier et al., 2017). Estimating the impact of contrail models and the approximations made therein (Myhre et al., 2009; cirrus on upper tropospheric cloudiness requires the simulation of Gounou and Hogan, 2007); the background cloud field and its vertical complex microphysical processes, contrail spreading, overlap with nat- overlap with contrail cirrus; and assumptions about the homogeneity of ural clouds, radiative transfer, and the interaction with background the contrail cirrus field. Furthermore, the presence of very small ice cloudiness (Burkhardt et al., 2010). We present new best estimates based crystals (<5 m) (Bock and Burkhardt, 2016) and unknown ice-crystal on the results of global climate models employing process-based contrail habits (Markowicz and Witek, 2011) add to the uncertainty. cirrus parameterizations (Appendix E). Due to the small number of in- Our best estimate of the contrail cirrus uncertainty does not include dependent estimates the uncertainty must be estimated from the sensi- the impact of contrails forming within natural clouds, which was tivities of the respective processes and the uncertainty in the underlying recently shown to be observable from space (Tesche et al., 2016), or the parameters and fields. change in radiative transfer due to soot cores in contrail cirrus ice Here, we consider RF and ERF estimates from global climate models crystals (Liou et al., 2013), which decreases the albedo at solar wave- (Burkhardt and Karcher, 2011; Bock and Burkhardt, 2016; Chen and lengths and increases the top of atmosphere net RF. Both effects are very Gettelman, 2013; Schumann et al., 2015; Bickel et al., 2020) to ulti- likely to lead on average to an increase in contrail cirrus RF, causing our mately produce an ERF best estimate. For the present study, the Chen best estimate to be conservative. The estimated uncertainty relates to the and Gettelman study was repeated with lower prescribed initial average contrail cirrus RF. In specific synoptic situations, uncertainties ice-crystal diameters, thereby bringing assumptions in line with mea- may be much larger and correlated with each other. surements e.g., Schumann et al. (2017). Since the RF estimates differ In contrast to other aviation forcing terms, the average ERF/RF ratio regarding the air traffic inventory, the measure of air traffic distance (i. for contrail cirrus is estimated to be 0.42, much less than unity. The e., taking only surface-projected or overall flight distances into account) associated uncertainty is thought to be very large and dependent on and the temporal resolution of the air traffic data, the estimates were prevailing aviation traffic and its geographic distribution. The low ERF/ homogenized using known sensitivities (Bock and Burkhardt, 2016) (see RF value is largely due to the reduction in natural cloudiness caused by Appendix E). Furthermore, the estimates were corrected to account for increased contrail cirrus similar to the reduction in natural cirrus the underestimation of the contrail cirrus RF, as calculated by climate cloudiness as reported by Burkhardt and Karcher (2011). The ERF/RF models that use frequency bands, relative to more detailed line-by-line value is the average of three global climate model studies: two that radiative transfer calculations (Myhre et al., 2009). The Chen and Get- estimated climate efficacies of 31% and 59% (Ponater et al., 2005; Rap telman (2013) study is closer to a calculation of an ERF, since it accounts et al., 2010) and a third that gave a direct estimate of the ERF of contrail for fast feedbacks on natural clouds, which Bickel et al. (2020) show in cirrus that is 35% of the corresponding RF (Bickel et al., 2020). These their model explains most of the differences between an ERF and an RF studies conclude that efficacies equal to that of CO2 overstate the role of calculation. Bickel et al. (2020) presents an explicit calculation of the cirrus changes due to aviation on global mean surface temperatures. The contrail cirrus ERF and uses the same basic model formulation of Bock average ERF/RF ratio was applied to the homogenized estimates of RF, and Burkhardt, so the ERF calculation was not used here directly but while the RF of Chen and Gettelman (2013) was interpreted as an ERF rather the estimation of the ERF/RF ratio was used. (see above). Weighting each study equally, the resulting ERF for contrail The RF best estimate for 2011 was calculated here for comparison to cirrus is 57 (17, 98) mW m 2 for 2018. It is important to note that the the most recent IPCC estimate (Boucher et al., 2013). With each study uncertainty does not include any contribution coming from the ERF/RF weighted equally, the resulting 2011 RF best estimate for contrail cirrus estimate. Despite the large ERF/RF adjustment, this ERF term is the (excluding any adjustments) is approximately 86 (25, 146) mW m 2 (see largest for global aviation in 2018 and is comparable in magnitude to the Table 3). The IPCC best estimate of 50 (20, 150) mW m 2 (including the CO2 term in the normalized results for 2000 to 2018 (Fig. 6). While natural cloud feedback) was derived from scaling and averaging two comparable in magnitude, these ERFs have different implications for studies. IPCC assigned a large uncertainty and low confidence to reflect future climate change (Section 6). important aspects with incomplete knowledge (e.g., spreading rate, optical depth, and radiative transfer). The RF best estimate derived here for 2018 is 111 (33, 189) mW m 2. The uncertainties in the present 4.5. Aerosol-radiation interaction study are reduced due to the development of process-based approaches simulating contrail cirrus in recent years. The uncertainty in the new RF Aircraft engines directly emit soot, defined as mixture of BC and OC, estimate, excluding the uncertainty in the ERF/RF scaling of individual and precursors for sulfate (SO2 4 ) and nitrate (NO 3 ) aerosol along flight RF values, is 70%, a value substantially lower than the factor of three tracks. Soot aerosol is formed from the condensation of unburnt arostated in IPCC. matic compounds in the combustor (e.g. Ebbinghaus and Wiesen, 2001) The 70% uncertainty was derived differently than for the NOx and sulfate aerosol from the oxidation of sulfur in the fuel (Dstan 91-91, forcing due to the smaller number of available studies. Instead, the 2015). Most of the sulfur is emitted as SO2, whilst a small fraction (~3%) uncertainty was derived from the combined uncertainties associated is emitted as oxidized H2SO4 (Petzold et al., 2005). Most of the sulfate with the processes involved (see Appendix E). The processes fall into two aerosol is produced after emission from sulfur precursor compounds by 8 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Fig. 5. Summary of RF estimates for aerosol-cloud interactions for aviation aerosol as calculated in the SD spreadsheet for a variety of published results normalized to 2018 air traffic and 600 ppm fuel sulfur. The results are shown for soot; total particulate organic matter (POM), sulfate and ammonia (NH3); and sulfate aerosol from the indicated studies. The color shading gradient in the symbols indicates increasing positive or negative magnitudes. No best estimate was derived in the present study for any aerosol-cloud effect due to the large uncertainties. In previous studies, the estimates for the soot aerosolcloud effect are associated with particularly large uncertainty in magnitude and uncertainty in the sign of the effect (Penner et al., 2009, 2018; Zhou and Penner, 2014). As part of the present study, an author (JEP) re-evaluated these earlier studies and concluded that the Penner et al. (2018) results supersede the earlier Penner et al. (2009) and Zhou and Penner (2014) results because of assumptions regarding up- draft velocities during cloud formation. In addition, a bounding sensitivity case in which all aviation soot acts as an IN in Penner et al. (2018) is not included here. oxidation in the ambient atmosphere. Both aerosol types create RFs from nucleate on aerosol particles. Thus, aerosol-cloud interactions involving aerosol-radiation interactions: soot absorbs short-wave radiation lead- aviation aerosol potentially result in an ERF. Aviation soot and sulfate ing to net warming and sulfate aerosol scatters incoming short-wave particles are the predominant primary and secondary aerosol from radiation leading to net cooling (IPCC, 1999). As figures of merit, year aircraft. The uncertainties in evaluating the aerosol-cloud interactions of 2000 global aviation emissions increase aerosol mass for both soot and aviation soot and sulfate preclude best estimates of ERF contributions. sulfate by a few percent and aerosol number by 1030% near air traffic Given the potential importance of these ERF terms, placeholders are flight corridors in the northern extratropics (Righi et al., 2013). included in Fig. 3. Furthermore, to promote progress towards future best Past calculations of aerosol-radiation RF values using a variety of estimates, the results of relevant modeling studies were compiled and global aerosol models have yielded values of a few mW m 2 and with normalized to global aviation fuel usages in 2005, 2011, 2018, to a soot large uncertainties (e.g., Righi et al., 2013; Gettelman and Chen, 2013; emission index, and to a fuel S content of 600 pm (except in the cases of Lee et al., 2009). In the present study, 10 estimates across 8 models were low fuel-S content tests) (see Fig. 5 and spreadsheet). As noted in the used to evaluate soot and sulfate aerosol normalized RFs (IPCC, 1999; caption of Fig. 5, some earlier wide-ranging values for the soot aerosol- Sausen et al., 2005; Fuglestvedt et al., 2008; Balkanski et al., 2010; cloud interaction have been superseded by a more recent study (Penner Gettelmann and Chen, 2013; Unger et al., 2013; Pitari et al., 2015; et al., 2018). Brasseur et al., 2016) (see SD spreadsheet). Averaging the normalized values yields a 2018 best estimate of the soot aerosol-radiation RF of 0.9 4.6.1. Sulfate aerosol (0.1, 4.0) mW m 2 for 0.0093 Tg soot emitted. The corresponding best Aviation sulfate aerosol primarily affects liquid clouds in the backestimate for sulfate aerosol is 7.4 ( 19, 3) mW m 2 for 0.37 Tg SO2 ground atmosphere. Sulfate aerosol is very efficient as a cloud condenemitted. The uncertainties are derived from the standard deviation of sation nuclei (CCN) for liquid clouds, and for promoting homogeneous the model values. The ERF/RF ratios for soot and sulfate are assumed to freezing of solution particles at cold temperatures, thus nucleating ice be unity in the absence of any estimates of this ratio. clouds. Two integrated model simulations (Kapadia et al., 2016; Get- telman and Chen, 2013) found large impacts on liquid clouds from 4.6. Aerosol-cloud interaction aviation sulfate aerosol that is transported to liquid clouds at lower altitudes over oceans, which have low albedo. The reported RF values in Aerosol-cloud interactions are those processes by which aerosols these studies, when scaled appropriately, are 37 to 76 mW m 2 in influence cloud formation. For example, cloud droplets and ice crystals 2018, excluding a low fuel-sulfur case. Note that the study of Righi et al. 9 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Fig. 6. Timeseries of calculated ERF values and con- fidence intervals for annual aviation forcing terms from 2000 to 2018. The top panel shows all ERF terms and the bottom panel shows only the NOx terms and net NOx ERF. All values are available in the SD spreadsheet, in Tables 2 and 3, and in Fig. 3 for 2018 values. The net values are not arithmetic sums of the annual values because the net ERF, as shown in Fig. 3 for 2018, requires a Monte Carlo analysis that properly includes uncertainty distributions and correlations (see text). (2013) that yields an RF of 213 mW m2 in 2018 includes sulfate calculated in some studies for moderate ice-nucleating efficiencies aerosol-cloud interactions but cannot be directly compared with Kapa- (Pitari et al., 2015; Gettelman and Chen, 2013). dia et al. (2016) and Gettelman and Chen (2013), since the former treats In sensitivity tests, if soot processed within contrails is assumed to be the combined effects of sulfate, nitrate and particulate organic matter an efficient IN particle, then the RF may be negative by up to 330 mW (POM) rather than isolating the effects of sulfate as done in the latter m 2 due to reductions in ice crystal number in regions dominated by studies. While these RF estimates do not support a best estimate at homogeneous freezing (Penner et al., 2018; see Fig. 5). The RF could be present, they do suggest that the sign of the sulfate aerosol-cloud effect significantly smaller (less negative) if additional ice-forming particles, on low-level clouds is likely to be negative (i.e., a cooling), similar to the such as secondary organic aerosol (SOA), are already present in the ERF for the aerosol-cloud interactions of other anthropogenic sources of background atmosphere (Penner et al., 2018; Gettelman and Chen, sulfate aerosol (IPCC, 2013). 2013). In addition, increases in ice crystal numbers occur when the Sulfate aerosol-cloud interaction forcing estimates are highly background atmosphere has much lower sulfate or haze-forming aerosol dependent on the sensitivity (or susceptibility) of the cloud radiative number concentrations and is dominated by heterogeneous freezing, field to aerosol perturbations, which is dependent on uncertain model causing forcings near zero or even positive (Zhou and Penner, 2014). processes and the model background aerosol state. Clouds that form Other studies predict decreases in cirrus number for smaller numbers of with small CCN number concentrations in the background atmosphere larger soot particles (Hendricks et al., 2011), resulting in a slight are more sensitive to CCN perturbations. Forcing by these cloud effects warming (Gettelman and Chen, 2013). are largely concentrated near flight corridors over oceans because the A dominant uncertainty for the aerosol-cloud effect from soot is the high albedo contrast between the ocean surface and clouds increases IN properties of aviation soot aerosol. Some laboratory studies indicate forcing sensitivity to CCN perturbations. soot particles are not efficient ice nuclei (DeMott et al., 1999), while A large uncertainty was also reported for the magnitude of the other studies indicate higher efficiencies (Mohler et al., 2005; Hoose and aerosol-cloud ERF from all anthropogenic activities, estimated for 2011 Mohler, 2012). The possibility that contrail-processed soot particles to be 450 ( 1200, 0.0) mW m 2 (Myhre et al., 2013). A more recent would show enhanced IN activity after sublimation in the background estimate of the aerosol-cloud RF from all anthropogenic activities has a atmosphere was addressed in the laboratory (Mahrt et al., 2020). The 68% confidence interval of 650 to 1600 mW m 2 (Bellouin et al., effect was limited to large soot particles, suggesting that the impact of 2019). In general, aerosol-cloud interactions contribute the largest un- aviation soot on cloudiness may be overestimated in previous studies certainty in calculations of anthropogenic ERF (IPCC, 2013). that assume soot processed through contrails and not covered by a sulfate coating is an efficient IN (Penner et al., 2018). 4.6.2. Soot Another source of uncertainty is soot number concentrations. For The magnitude and the sign of the global RF from aviation soot ef- individual engines, the soot number can vary by two orders of magnifects on background cloudiness remain highly uncertain. The un- tude (Agarwal et al., 2019). Soot number concentrations from aviation certainties center on the difficulties in accurately simulating vary with the assumed size of the particles emitted as well as the mass homogeneous and heterogeneous ice nucleation in the background at- emissions. Soot emissions from aircraft are set as a regulatory parameter mosphere, variations in the treatment of updraft velocities during cirrus for the landing/take-off (LTO) cycle by ICAO and are measured in terms formation, and the lack of knowledge of the ice nucleating (IN) ability of of mass. Robust conversion factors from mass to number have recently aviation soot particles during their atmospheric lifetime (Zhou and been developed for the ICAO-LTO cycle (Agarwal et al., 2019) but have Penner, 2014; Penner et al., 2018). not yet been made for cruise, although other methodologies exist (Teoh Two studies find moderate effects of soot aerosol on ice clouds, et al., 2019). depending on the ice nucleating efficiency and the size distribution. RF values of about 1113 mW m 2 (normalized to 2018 emissions) are 10 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 5. Calculated net aviation ERF and RF values contributions of aviation to the net ERF in 2011 are 3.5% (4.0, 3.4%) and 1.59% (1.65, 1.56%) for the sum of all terms and the CO2 term ERF and RF values for the terms associated with global aviation alone, respectively. The 2005 and 2018 percentages are likely the same emissions and cloudiness are given in Tables 2 and 3, respectively, for because the fraction of aviation CO2 emissions of total anthropogenic the years 2018, 2011, and 2005, along with uncertainties, sensitivities to CO2 emissions has averaged 2.1% (0.15) for the last two decades (see emissions and the ERF/RF ratio for selected terms. ERF values are shown Fig. 2). Normalized relative probabilities of CO2 and non-CO2 ERFs for for all years in Fig. 6. All ERF and RF values are available in the analysis 2018 as derived from the Monte Carlo simulations show that non-CO2 spreadsheet (SD). Through normalization and scaling, all 2000 to 2018 uncertainties are the predominant contribution to the uncertainty in the values are self-consistent. The sensitivity of each term to emission aviation net ERF (Fig. 7). IPCC also separately estimated the contrail magnitudes or flight track distances is derived in the normalization cirrus term for 2011 as 50 (20, 150) mW m 2 as discussed above, which process. ERF best estimates and uncertainties (95% confidence limits) compares well with the updated value of 44.1 (13, 75) mW m 2. are highlighted for year 2018 in Fig. 3 along with their assessed confi - The determination of net aviation ERFs and their uncertainties dence levels. No best estimates are included for sulfate and soot aerosol- shown in Fig. 3 and accompanying tables required a Monte Carlo cloud interactions because of the substantial uncertainties noted above. approach to summing over terms with discrete probability distributions. However, placeholder spaces are included in both Tables 2 and 3 and A similar method was employed in Lee et al. (2009). PDFs of each term Fig. 3 to indicate the potential importance of these terms and to flag the were constructed from the respective individual studies as normal, associated knowledge gaps for consideration in future research and lognormal or discrete distributions (see SD spreadsheet). Monte Carlo assessment activities. The confidence levels and their justifications samplings (one million random points) of the individual forcing PDFs shown in Fig. 3 are obtained by employing the methodology of Mas- were then used to combine terms to yield net ERFs and the uncertainties trandrea et al. (2011), which is based on evidence and agreement in (95% likelihood range) for the sum of all terms and for only non-CO2 accordance with IPCC guidance (Table 4). terms (Fig. 7). The forcing terms are generally assumed to be indepen- In Fig. 3, contrail cirrus formation yields the largest positive dent (uncorrelated) with the notable exception of the NOx component (warming) ERF term, followed by CO2 and NOx emissions. For the 1940 terms which have strong paired correlations as shown in Appendix to 2018 period, the net aviation ERF is +100.9 mW m 2 (595% like- Fig. D.1. Only the short-term O3 and CH4 terms were included in Lee lihood range of (55, 145)) with major contributions from contrail cirrus et al. (2009) and a 100% correlation was assumed, in part, because the (57.4 mW m 2), CO2 (34.3 mW m 2), and NOx (17.5 mW m 2). The assumption of uncorrelated effects was deemed less acceptable. A subaerosol and water vapor terms represent minor contributions. The for- sequent study showed that these terms are indeed strongly correlated mation and emission of sulfate aerosol yields the only significant (R2 = 0.79) (Holmes et al., 2011), similar to the present results in Apnegative (cooling) term. Non-CO2 terms sum to yield a positive pendix Fig. D.1. The Holmes et al. (2011) study further concluded that (warming) ERF that accounts for 66% of the aviation net ERF in 2018 the assumption of 100% correlation in this case would lead to an un- (66.6 (21, 111) mW m 2). The application of ERF/RF ratios more than derestimate of uncertainty in the NOx RF. Another correlation of forcing halves the RF value of contrail cirrus while approximately doubling the terms not considered here may be the dependence of the soot direct NOx value. ERF/RF ratios were not included in the Lee et al. (2009) effect and contrail properties on the soot number index since ice analysis. Uncertainty distributions (5%, 95%) show that non-CO2 forc- nucleation at the time of contrail formation depends on the soot number ing terms contribute about 8 times more than CO2 to the uncertainty in index (e.g., Karcher, 2018). the aviation net ERF in 2018. The best estimates of the ERFs from aviation aerosol-cloud interactions remain undetermined. 6. Emission equivalency metrics The time series of ERF values for individual terms is shown in Fig. 6 for the 20002018 period. Through normalization and scaling the terms Using the best estimate ERFs, we calculate updated aviation-specific are self-consistent over this period. The increase in all of the terms with Global Warming Potential (GWP) and Global Temperature change Potime is consistent with the growth of aviation fuel burn and CO2 emis- tential (GTP) values, presented for 20-, 50-, and 100-year time horizons sions over the same period (Fig. 2). Note that net ERF values shown for in Table 5. These metrics assign so-called 'CO2-emission equivalences' each year are not linear sums over the component terms due to the for non-CO2 emissions via ratios of time-integrated ERF and changes in separate probability distributions associated with each component term future temperatures, respectively. The choice of metric depends upon in the sum, and instead are calculated with a Monte Carlo sampling the particular underlying application (Fuglestvedt et al., 2010) such that method described below. there is no uniquely 'correct' metric or time horizon, and alternative A comparison of updated RF estimates with Lee et al. (2009) values metrics are available. GWP and GTP are the most commonly applied for 2005 is given in Table 3. The large increase in the contrail cirrus RF metrics and the values calculated here allow a comparison with previous between 2005 and 2018 results in part because the 2005 value only estimations (e.g., Lee et al., 2010; Lund et al., 2017). In calculating the includes linear contrails. In Lee et al. (2009), only an estimate of 2005 GWPs and GTPs, the CO2 IRF from Joos et al. (2013) is used and the contrail cirrus was provided rather than a best estimate. The present climate response IRF from Boucher and Reddy (2008) for the GTPs (see study now includes a process-based model estimate of the contrail cirrus Appendix F for futher details about the metrics calculations). term (Section 4.4). The NOx treatment in Lee et al. (2009) did not GWPs and GTPs for contrail cirrus and for water vapor reported here include the negative forcing contributions of the long-term O3 decrease are similar to, albeit slightly smaller than, corresponding results previor the SWV decrease, the updated treatment of CH4 of Etminan et al. ously reported, while soot and sulfate numbers are larger in magnitude (2016), nor an equilibrium-to-transient correction. As a result, the (positive and negative) than previous estimates (Fuglestvedt et al., 2010; updated RF values for NOx are approximately a factor of 2 smaller. Lund et al., 2017). The Fuglestvedt et al. (2010) estimates for soot are Incorporating all the updated information in the RF calculations of the based on RF due to soot emissions from all sources, not just aviation, NOx and contrail cirrus terms yields an approximately 30% increase in which yields a lower radiative efficiency (i.e., forcing per unit emission) the net aviation RF for 2005, from 78.0 to 95.2 mW m 2. In the ERF than in the present study. Also given in Table 5 are CO2-equivalent evaluation for 2005 the net aviation forcing is reduced from 95.2 to 66.9 aviation emissions, along with ratios of total CO2-equivalent emissions mW m 2 because the ERF/RF ratios for NOx and contrail cirrus are to CO2 emissions. Such ratios are sometimes used as 'multipliers' to different than unity. illustrate the additional climate impact from aviation non-CO2 terms In seeking comparison of net aviation ERF with net anthropogenic over those from CO2 emissions alone. Here, estimated multipliers for ERF, we note that IPCC (Myhre et al., 2013) provides a value for 2018 range from 1.0 to 4.0 depending on the choice of time horizon and 17502011 of 2290 (1130, 3330) mW m 2. The percentage emission metric. This is broadly consistent with what has been reported 11 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Table 4a Confidence levels for the ERF estimates in Fig. 3. *This term has the additional uncertainty of the derivation of an effective radiative forcing from a radiative forcing. **This term differs from 'Very High' level in IPCC (2013) because additional uncertainties are introduced by the assessment of marginal aviation CO2 emissions and their resultant concentrations in the atmosphere from simplified carbon cycle models. 12 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Table 4b that when the C-cycle feedback is consistently accounted for, the Basis for confidence levels in Table 4a.a non-CO2 emission metrics increase, but less so than initially suggested by Myhre et al. (2013). They also find that removing the C-cycle feedback from both numerator and denominator give similar metric values as including it in both places. Using the CO2 IRF without the C-cycle feedback provided by Gasser et al., 2017, we calculate a second set of aviation emission metrics (Table F.1a and Table F.1b), showing that the changes to the GWP100 and GTP100 values from those given in Table 5 are rather small. In response to the challenges related to comparing short-lived and long-lived forcing components, a number of new 'flow-based' methods have been introduced representing both short-lived and long-lived climate forcers explicitly as 'warming-equivalent' emissions that have approximately the same impact on the global average surface temperature over multi-decade to century timescales (Lauder et al., 2012; Allen a The basis for the confidence level is given as a combination of evidence et al., 2016, 2018; Cain et al., 2019; Collins et al., 2019). A simple (limited, medium, robust) and agreement (low, medium and high) based on version of these methods, known as GWP*, defines the average annual guidance given by Mastrandrea et al. (2011). rate of CO2-warming-equivalent emissions (E*CO2e) over a period of t years arising from a particular component of RF or ERF by (Cain et al., 2019): / E*CO2e = [(1 )H / AGWPH] F t + [ / AGWPH] F, (1) where F is the ERF change and F the average ERF arising from that component over that period, AGWPH is the Absolute GWP of CO2 (Wm 2 kg 1 year) over time-horizon H and is a small coefficient depending on the previous history of that RF component. Eqn (1) gives the rate of CO2 emission that would, alone, create the same rate of global temperature increase as the combined effect of aviation climate forcings. For historically small and/or rapidly changing RF components, may be neglected, and hence to a good approximation, total CO2-warmingequivalent emissions over this period (tE*CO2e) are approximated by an increase in forcing, F, multiplied by H / AGWPH (see Appendix F ), which is about 1000 GtCO2 per W/m2 for H in the range 20100 years (Myhre et al., 2013; IPCC, 2018, Figure SPM.1, caption). This result follows from the definition of AGWP: since all GWP calculations assume a linearization, the AGWPH is equivalent to the forcing change resulting from the emission of H tonnes of CO2 spread over H years (Shine et al., 2005), so AGWPH/H is the forcing change per tonne of CO2. Under the historical profile of increasing global annual aviation-related emissions and associated ERFs, CO2-warming-equivalent emissions based on GWP* indicate that aviation emissions are currently warming the climate around three times faster than that associated with aviation CO2 Fig. 7. Probability distribution functions (PDFs) for aviation ERFs in 2018 emissions alone (Table 5). based on the results in Fig. 3 and Table 2. PDFs are shown for separately for It is important to note that, unlike the conventional GWP and GTP CO2, the sum of non-CO2 terms, and the net aviation ERF. Since the area of each metrics given in Table 5, the ratio between total CO2-warming-equiva- distribution is normalized to the same value, relative probabilities can be lent emissions from all forcing agents and those from CO2 alone will intercompared. Uncertainties are expressed by a distribution about the best- change substantially if future aviation emissions deviate from their estimate value that is normal for CO2 and contrail cirrus, and lognormal for current growth trajectory (calculated here over the period 20002018). all other components. A one-million-point Monte Carlo simulation run was used If annual global aviation emissions were to stabilize, this ratio declines to calculate all PDFs. towards unity, as F/t would decline to zero. This does not indicate, however, that the non-CO2 effects do not have a warming affect. This and used previously (Lee et al., 2010). The broad range emphasizes the human-induced warming still represents a mitigation potential. challenges associated with developing comparisons of emission equiv- Warming-equivalent emissions capture the fact that constant emission of alences for short- and long-lived climate forcers within a common short-lived climate forcers maintain an approximately constant level of framework and how such considerations strongly depend on the chosen warming, whilst constant emissions of long-lived climate forcers, such as perspective. CO2, continue to accumulate in the atmosphere resulting in a constantly One of the significant uncertainties in calculating GWPs and GTPs is increasing level of associated warming. Hence warming-equivalent the treatment of climate-carbon (C-cycle) feedbacks in the modeling emissions show that the widely-used assumption of a constant 'multiframework. The efficiency of carbon sinks reduces with increasing plier', assuming that net warming due to aviation is a constant ratio of warming (Ciais et al., 2013) and this climate feedback is implicitly warming due to aviation CO2 emissions alone, only applies in a situation included in the Absolute GWP of CO2 through the IRF used (Joos et al., in which aviation emissions are rising exponentially such that the rate of 2013). However, Myhre et al. (2013) highlighted that this introduces an change of non-CO2 RF is approximately proportional to the rate of CO2 inconsistency since the numerators for the GWP and GTP do not include emissions (assuming non-CO2 RF is proportional to CO2 emissions, and such a climate carbon feedback. One of the studies that have proposed noting that the rate of change any quantity is proportional to that ways of addressing this inconsistency is Gasser et al. (2017). They show 13 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Table 5 Emission metrics and corresponding CO2-equivalent emissions for the ERF components of 2018 aviation emissions and cloudiness. Metrics ERF term GWP20 GWP50 GWP100 GTP20 GTP50 GTP100 CO2 1 1 1 1 1 1 Contrail cirrus (Tg CO2 basis) 2.32 1.09 0.63 0.67 0.11 0.09 Contrail cirrus (km basis) 39 18 11 11 1.8 1.5 Net NOx 619 205 114 222 69 13 Aerosol-radiation Soot emissions 4288 2018 1166 1245 195 161 SO2 emissions 832 392 226 241 38 31 Water vapor emissions 0.22 0.10 0.06 0.07 0.01 0.008 CO2-eq emissions (Tg CO2 yr 1) for 2018 ERF term GWP20 GWP50 GWP100 GTP20 GTP50 GTP100 GWP*100 (E*CO2e) CO2 1034 1034 1034 1034 1034 1034 1034 Contrail cirrus (Tg CO2 basis) 2399 1129 652 695 109 90 1834 Contrail cirrus (km basis) 2395 1127 651 694 109 90 1834 Net NOx 887 293 163 318 99 19 339 Aerosol-radiation Soot emissions 40 19 11 12 2 2 20 SO2 emissions 310 146 84 90 14 12 158 Water vapor emissions 83 39 23 27 4 3 42 Total CO2-eq (using km basis) 4128 2366 1797 1358 1035 1135 3111 Total CO2-eq/CO2 4.0 2.3 1.7 1.3 1.0 1.1 3.0 quantity only when both are growing exponentially). In contrast, under halt anthropogenic global warming on multi-decadal time scales." (IPCC, a future hypothetical trajectory of decreasing aviation emissions, this 2018, bullet A2.2, SPM). Crucially, both conditions would need to be GWP* based multiplier could fall below unity, as a steadily falling rate of met to halt global warming. Hence, to halt aviation's contribution to emission of (positive) short-lived climate forcers has the same effect on global warming, the aviation sector would need to achieve net-zero CO2 global temperature as active removal of CO2 from the atmosphere. The emissions and declining non-CO2 radiative forcing (unless balanced by GWP* based 'multiplier' calculated here (which depends on the ratio of net negative emissions from another sector): neither condition is suffi - the increase in net aviation warming to the increase in warming due to cient alone. Some combination of reductions in CO2 emissions and aviation CO2 emissions alone over the recent past), should not be non-CO2 forcings might halt further warming temporarily, but only for a applied to future scenarios that deviate substantially from the current few years: it would not be possible to offset continued warming from trend of increasing aviation-related emissions. The broad range of values CO2 by varying non-CO2 radiative forcing, or vice versa, over for a 'multiplier' presented here is an illustration of the limitations of multi-decade timescales. using a constant multiplier in the assessment of climate impacts of That aviation's non-CO2 forcings are not included in global climate aviation, and a reminder that the choice of metric for such a multipler policy has resulted in studies as to whether they could be incorporated involves subjective choices. into existing policies, such as the European Emissions Trading Scheme, using an appropriate overall emissions 'multiplier'; however, scientific 7. Aviation CO2 vs non-CO2 forcings uncertainty has so far precluded this (Faber et al., 2008). In addition, as noted above, the multiplier is highly dependent on the future emissions Since IPCC (1999), the comparison of aviation CO2 RF with the scenario (Section 6). Alternatively, proposals have been made to reduce non-CO2 RFs has been a major scientific topic, as well as a discussion aviation's non-CO2 forcings by, for example, avoiding contrail formation point amongst policy makers and civil society (ICAO, 2019). Aviation as by re-routing aircraft (Matthes et al., 2017), or optimizing flight times to a sector is not unique in having significant non-CO2 forcings; the same is avoid the more positive (warming) fractional forcings (e.g., by avoiding true of agriculture with significant CH4 and N2O emissions, or maritime night flights, Stuber et al., 2006). There is a developing body of literashipping with net-negative current-day RF despite CO2 emissions of a ture on this topic (e.g., Newinger and Burkhardt, 2012; Yin et al., 2018). similar magnitude to those from aviation (Fuglesvedt et al., 2009). Similarly, studies have assessed whether changes in cruise altitudes However, unlike direct emissions of the greenhouse gases N2O and CH4 could mitigate NOx impacts (e.g. Fromming et al., 2012). The potential from the agricultural sector, aviation non-CO2 forcings are not covered impacts of changes in technology have also been examined to reduce the by the former Kyoto Protocol. It is unclear whether future developments non-CO2 forcings such as lowering the emission index for NOx (Freeman of the Paris Agreement or ICAO negotiations to mitigate climate change, et al., 2018) or soot particle number emissions (Moore et al., 2017) to in general, will include short-lived indirect greenhouse gases like NOx reduce net NOx and contrail cirrus forcings, respectively (Burkhardt and CO, aerosol-cloud effects, or other aviation non-CO2 effects. Avia- et al., 2018). tion is not mentioned explicitly in the text of the Paris Agreement, but Avoidance of contrail formation through re-routing can incur a fuel according to its Article 4, total global greenhouse-gas emissions need to penalty and therefore additional CO2 emissions during a flight, and be reduced rapidly to achieve a balance between anthropogenic emis- changes in combustor technology to minimize NOx generally increases sions by sources and removals by sinks of greenhouse gases in the second marginal fuel burn and CO2 emission. Both methods invoke the usage of half of this century. climate metrics such as those calculated and presented in Section 6 to The IPCC concludes: "Reaching and sustaining net-zero global anthro- evaluate whether there is a net climate benefit or disbenefit over a pogenic CO2 emissions and declining net non-CO2 radiative forcing would defined period. In examining such mitigation scenarios involving 14 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 tradeoffs (e.g. Teoh et al., 2020), the perceived success or otherwise of Skowron: Investigation, Methodology, Writing - review & editing, Data the outcome will be a function of the user's choice of metric and time curation, Formal analysis, Software. M.R. Allen: Writing - review & horizon. A limitation noted for the GWP is that it has an 'artificial editing, Investigation, Methodology, Writing - original draft, Data memory' over longer time horizons, since the integrated-RF nature of curation, Formal analysis. U. Burkhardt: Writing - review & editing, the metric accumulates 'signal' over time that the climate system has Investigation, Methodology, Writing - original draft, Data curation, 'forgotten' (Fuglestvedt et al., 2010). The GTP, being an 'end point' Formal analysis. Q. Chen: Writing - review & editing, Investigation, metric that captures the temperature response, overcomes this limita- Methodology, Writing - original draft, Data curation, Formal analysis. S. tion of the GWP but is not yet in usage within current climate policy. J. Doherty: Writing - review & editing, Investigation, Methodology, Changes to aviation operations or technology that result in a Writing - original draft, Data curation, Formal analysis. S. Freeman: reduction of a non-CO2 forcing with the added consequence of increased Writing - review & editing, Investigation, Methodology, Writing - orig- CO2 emissions can result in net reductions of forcing on short timescales inal draft, Data curation, Formal analysis. P.M. Forster: Writing - rewhile increasing the net forcing on longer timescales (e.g., Freeman view & editing, Investigation, Methodology, Writing - original draft, et al., 2018). In a case study of contrail avoidance through routing Data curation, Formal analysis. J. Fuglestvedt: Writing - review & changes, Teoh et al. (2019) found that the resultant small increase in editing, Investigation, Methodology, Writing - original draft, Data CO2 emissions still reduces the net forcing over a timescale of 100 years. curation, Formal analysis. A. Gettelman: Writing - review & editing, In such 'tradeoff cases' the balance between non-CO2 and CO2 forcings Investigation, Methodology, Writing - original draft, Data curation, have to be weighted carefully, since CO2 accumulates in the atmosphere Formal analysis. R.R. De Leon: Writing - review & editing, Investigaand a fraction has millennial timescales (Archer and Brovkin, 2008; tion, Methodology, Writing - original draft, Data curation, Formal IPCC, 2007). Prior to the COVID-19 pandemic, global aviation traffic analysis. L.L. Lim: Writing - review & editing, Investigation, Methodand emissions were projected to grow to 2050 (Fleming and de Lepinay, ology, Writing - original draft, Data curation, Formal analysis. M.T. 2019). As the COVID-19 pandemic diminishes, aviation traffic is likely Lund: Writing - review & editing, Investigation, Methodology, Writing - to recover to meet projected rates on varying timescales (IATA, 2020), original draft, Data curation, Formal analysis. R.J. Millar: Writing - with continued growth further increasing CO2 emissions. Thus, reducing review & editing, Investigation, Methodology, Writing - original draft, CO2 aviation emissions will remain a continued focus in reducing future Data curation, Formal analysis. B. Owen: Writing - review & editing, anthropogenic climate change, along with aviation non-CO2 forcings. Investigation, Methodology, Writing - original draft, Data curation, The latter increase the current-day impact on global average tempera- Formal analysis. J.E. Penner: Writing - review & editing, Investigation, tures by a factor of around 3 (using GWP*) above that due to CO2 alone. Methodology, Writing - original draft, Data curation, Formal analysis. G. Pitari: Writing - review & editing, Investigation, Methodology, Writing - Funding original draft, Data curation, Formal analysis. M.J. Prather: Writing - review & editing, Investigation, Methodology, Writing - original draft, DSL, AS, RRdL, LL, BO acknowledge support from the UK Depart- Data curation, Formal analysis. R. Sausen: Writing - review & editing, ment for Transport. PMF acknowledges support of the European Union's Investigation, Methodology, Writing - original draft, Data curation, Horizon 2020 Research and Innovation Programme under grant agree- Formal analysis. L.J. Wilcox: Writing - review & editing, Investigation, ment number 820829 (CONSTRAIN) by the UK National Environment Methodology, Writing - original draft, Data curation, Formal analysis. Research Council (NERC) SMURPHS project (NE/N006038/1). MRA acknowledges support from the EU H2020 grant agreement number 821205 (FORCeS) and the Oxford Martin Programme on Climate Pol- Declaration of competing interest lutants. MTL and JSF acknowledges support from the Norwegian Research Council (RCN) grant number 300718 (AVIATE), for which DSL The authors declare that they have no known competing financial and RS have a collaboration agreement. JEP acknowledges support from interests or personal relationships that could have appeared to influence the National Science Foundation (NSF 1540954). the work reported in this paper. CRediT authorship contribution statement Acknowledgements D.S. Lee: Investigation, Methodology, Writing - review & editing, We gratefully acknowledge discussions with many colleagues during Data curation, Formal analysis, Project administration, Supervision. D. the preparation of this paper, in particular Andreas Bier and Bernd W. Fahey: Investigation, Methodology, Writing - review & editing, Data Karcher. We acknowledge help with graphical displays from Beth Tully curation, Formal analysis, Project administration, Supervision. A. (Fig. 1) and Chelsea R. Thompson (Figs. 57). Supplementary data Supplementary data to this article is a spreadsheet that can be found online at: https://doi.org/10.1016/j.atmosenv.2020.117834. Appendices. A. Trends in aviation CO2 emissions Global aviation CO2 emissions for 19401970 were taken from Sausen and Schumann (2000) and for the years 19712016 were calculated from International Energy Agency (IEA) data on usage of JET-A and aviation gasoline, largely from annual 'Oil Information' digests (e.g., https://webstore. iea.org/oil-information-2019). The regional data are from the same source but accessed online from the IEA Oil Information (19602017) held at the UK Data Service (IEA, 2019). Note that these data are proprietary and must be purchased from IEA. Data were unavailable for 2017 and 2018, so incremental annual percentage increases in global aviation fuel usage and, therefore CO2 emissions, for those years were taken from reports of the International Air Transport Association (IATA, 2019). Some uncertainties exist from the annual fuel estimations and to a much smaller extent, the emission factors. The IEA does not give uncertainties for annual kerosene fuel sales or usage. Sausen and Schumann (2000), from which the 1940 to 15 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 1970 data are based here, estimated that the uncertainty in cumulative fuel consumption from 1940 to 1995 (their dataset) is 20%. There is a known discrepancy of IEA estimates of aviation fuel usage being greater by about 10% than that derived from bottom-up global civil aviation inventories. Actual fuel usage is likely to be somewhere between the two estimates: aviation emissions inventories are known to be incomplete, with only scheduled traffic being available from some air traffic regions, and fuel usage potentially being underestimated from flight routing and cruise altitudes; IEA data on the other hand includes military aviation fuel (not included in civil aviation inventories) and a small fraction of kerosene not used in aviation, but sold for that purpose (Lee et al., 2009). The CO2 emission factors for aviation fuel on the other hand are well determined, and the uncertainty is likely within 1%. B. Aviation CO2 radiative forcings Calculation of CO2 concentrations from emissionsLinClim SCM The response of CO2 concentrations, C(t), to a CO2 aviation emissions rate, E(t), is modelled using the method described in Hasselmann et al. (1997) and is expressed as: t C(t)= GC(t t )E(t )dt (B.1) t0 where in Eqn (B.1) 5 GC(t)= je t/j (B.2) j=0 and j in Eqn( B.2) is the e-folding time of mode j and the equilibrium response of mode j to a unit emissions of jj. The mode parameters used in this study are presented in Sausen and Schumann (2000) and approximate the carbon-cycle model in Maier-Reimer and Hasselmann (1987). The applicability of these parameters in the context of aviation response was tested in a model intercomparison exercise (Khodayari et al., 2013). For the time horizon of 5060 years into the future, these were found to compare well with other more sophisticated carbon-cycle models such as MAGICC 6.0, which is widely used in the IPCC Fourth Assessment Report (IPCC, 2007). Beyond this horizon, aviation CO2 concentrations begin to have an impact on the ocean and biosphere uptake of CO2 and the non-linearities of the system must be accounted for. Calculation of CO2 concentrations from emissionsCICERO-2 SCM The CICERO-2 SCM (Fuglestvedt and Berntsen, 1999; Skeie et al., 2017) uses interconnected process-specific IRFs with explicit treatment of air-sea and air-biosphere exchange of CO2 (Joos et al., 1996; Alfsen and Berntsen, 1999) that forms a nonlinear carbon cycle. The ocean and biosphere IRFs in CICERO-2 express how the CO2 impulse decays within each reservoir. The CO2 partial pressure in each reservoir is calculated as a function of the carbon in that reservoir, and the CO2 partial pressure in each reservoir is related to the CO2 partial pressure in the atmosphere by explicitly solving for the atmosphere/ocean/biosphere CO2 mass transfer. Therefore, the CICERO-2 carbon cycle takes into account the nonlinearity in ocean chemistry and biosphere uptake at high CO2 partial pressures since it represents the atmospheric change in CO2 as a function of total background. Calculation of CO2 concentrations from emissionsFaIR SCM The FaIR SCM is described by Millar et al. (2017) and summarized as follows. FaIR is a modified version of the IPCC AR5 four time-constant impulse response function (IRF) model, which represents the evolution of atmospheric CO2 by partitioning emissions of anthropogenic CO2 between four reservoirs of an atmospheric CO2 concentrations change, following a pulse emission (see Myhre et al., 2013 for more details). In more comprehensive models, ocean uptake efficiency declines with accumulated CO2 in ocean sinks (Revelle and Suess, 1957) and uptake of carbon into both terrestrial and marine sinks are reduced by warming (Friedlingstein et al., 2006). FAIR captures some of these dynamics within the simple IRF structure, mimicking the behaviour of Earth System Models/Earth System Models of Intermediate Complexity in response to finite-amplitude CO2 injections; this is achieved by introducing a state-dependent carbon uptake with a single scaling factor, , to all four of the time constants in the carbon cycle of the IPCC AR5 impulse response model used for the calculation of CO2-equivalence metrics. This approach is described in more detail by Millar et al. (2017). C. Radiative forcing, efficacy and effective radiative forcing (ERF) Radiative forcing (RF) has been introduced as a predictor for the expected equilibrium global mean of the (near) surface temperature change Ts that results from the introduction of climate forcers, such as additional atmospheric CO2 or a change in the solar irradiation (e.g., IPCC, 2007): Ts = RF (C.1) where is the climate sensitivity parameter (K (W m 2) 1). Several definitions of RF exist. According to the simplest one, the instantaneous RF is the change in the total irradiation (incoming short-wave solar radiation minus the outgoing long-wave terrestrial radiation) at the top of the atmosphere over the industrial era. However, for most of the climate forcers a better definition (with respect to the linearity of Eq. (C.1)) is the stratosphereadjusted RF at the tropopause. Here, after the introduction of the new climate forcer, the temperature of the stratosphere is allowed to reach a new radiative equilibrium, while all other atmospheric state variables are kept constant. The stratosphere-adjusted RF at the tropopause was used in many of the earlier IPCC reports (IPCC, 1999) and in earlier assessments of aviation climate impacts (Sausen et al., 2005; Lee et al., 2009). 16 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 While Eq. (C.1) is a fairly good approximation for many nearly spatially homogeneously distributed climate forcers, such as global increases of CO2 or CH4, Eq. (C.1) fails to some extent for many forcers that are heterogeneously distributed either horizontally or vertically; such is the case for aviation-induced ozone perturbations and contrail cirrus (e.g., Hansen et al., 1997, 2005; Forster and Shine, 1997; Stuber et al., 2005). To overcome this problem Hansen and Nazarenko (2004) introduced the efficacy, ri, into Eqn (C.1): Ts = ri CO2 RF = i RF with i = ri CO2 (C.2) Here CO2 is the climate sensitivity parameter for a CO2 perturbation. While in (C.1) is considered a universal constant, which can only be determined by climate models and hence is model dependent, i depends on the type of forcing, as does ri. (While rCO2 is 1 by definition, rlinear contrails is < 1 (Ponater et al., 2005; Rap et al., 2010)). Eqn (C.2) can also be expressed differently: Ts = CO2 RFi* with RFi* = ri RF (C.3) In Eqn (C.3) RFi* is the forcing modified by the efficacy, which yields a better approximation for the surface temperature change than RF. However, the calculation of the RFi* is computationally much more expensive than the calculation of RF, as it requires the determination of the equilibrium temperature change, Ts, with a comprehensive climate model. As an alternative, the effective radiative forcing (ERF) has been introduced as a more practical indicator of the eventual global mean temperature response (IPCC, 2013). While RFi* assumes equilibrium climate change, ERF only includes all 'fast' atmospheric responses to a given climate forcer. For example, rapid adjustments in cloud cover, such as from aerosols, or in properties that respond to changes in water vapor, can either increase or decrease the initial RF. In contrast, the instantaneous, stratosphere-adjusted, and effective RFs for well-mixed greenhouse gases are nearly equal. In practice, ERF is determined with a comprehensive climate model, which calculates a new equilibrium radiative imbalance, while the sea surface temperature and/or the global surface temperature is kept constant. As a consequence, an ERF value is expected to be somewhere between RF and RFi* values and closer to RFi* values. D. Aviation NOx radiative forcings Impacts of NOx emissions on ozone, methane and stratospheric water vapor Model studies. In this ensemble analysis of the climate forcing from aviation NOx emissions, the results of 20 studies published since the IPCC (1999) aviation report were considered: IPCC (1999), Sausen et al. (2005), Stordal et al. (2006), Kohler et al. (2008), Hoor et al. (2009), Myhre et al. (2011), Fromming et al. (2012), Olivie et al. (2012), Gottschaldt et al. (2013), Kohler et al. (2013), Olsen et al. (2013), Skowron et al. (2013), Khodayari et al. (2014a, 2014b), Khodayari et al., 2014, Svde et al. (2014), Skowron et al. (2015), Pitari et al. (2015), Kapadia et al. (2016), Pitari et al. (2017), Lund et al. (2017). Three studies that reported results from a 100-year integration of a pulse NOx emission (Wild et al., 2001; Derwent et al., 2001; Stevenson et al., 2004) were not included in this analysis, nor has as Unger et al. (2010) which uses a different methodology to the aforementioned. This model ensemble represents various methodologies in calculating and treating the long-term effects; in order to avoid gaps and additional uncertainties, standardized RFs for reductions in CH4-induced O3 and SWV were adopted, except for one study that calculates the 'real' long-term effects from their 50-yr integrations (Pitari et al., 2017): All analyzed short-term O3 RFs account for a stratospheric adjustment: Assuming that it reduces the instantaneous RF by ~20% (Myhre et al., 2013; Stevenson et al., 1998), a factor of 0.8 was applied to any O3 RF that is an instantaneous RF (e.g., in the cases of Khodayari et al. (2014a, 2014b) and Olsen et al. (2013)). Reductions in CH4-induced O3 and SWV are defined as 50% (Myhre et al., 2013) and 15% (Myhre et al., 2007) of reported CH4 RFs, respectively. This is applicable for studies that either originally did not provide CH4-induced O3 and SWV estimates (e.g., IPCC, 1999; Sausen et al., 2005; Olsen et al., 2013) or derived these RFs using another assumptions (e.g., Stordal et al., 2006; Kohler et al., 2008; Hoor et al., 2009; Gottschaldt et al., 2013; Kohler et al., 2013; Skowron et al., 2013; Khodayari et al., 2014a). Further assumptions regarding data treatment are: Fromming et al. (2012), Olivie et al. (2012), Khodayari et al. (2014b) and Kapadia et al. (2016) provide the short-term O3 RFs only and p-TOMCAT in Stordal et al. (2006) calculates just the long-term effects; thus, these numbers are included in the respective NOx variable analysis but do not contribute to the net NOx estimate. Whenever the same estimate appears repetitively in subsequent studies, it is treated as a single entry: this is the case for CAM4 short-term O3 RF that appears in Khodayari et al. (2014a, 2014b) and Olsen et al. (2013), CAM5 short-term O3 RF that can be found in Khodayari et al. (2014a, 2014b) and NASA ModelE2 short-term O3 and CH4 RFs presented by Unger et al. (2013) and Olsen et al. (2013). In addition, the ERF estimates for the CH4 term include shortwave RF (Etminan et al., 2016). The inclusion of shortwave forcing in the simplified expression increases CH4 RF from aviation NOx emissions by 23% (based on MOZART-3 CTM runs driven for all the aircraft emission inventories represented in the model ensemble) (Table D.1). Ensemble values. This ensemble analysis covers a period of almost two decades; however, none of the RF per unit of emitted N estimates show any trends over time of publication and the spread in RF per unit of emitted N values has not changed. The short-term O3 RF varies from 6.2 to 45.1 mW m 2 (Tg (N) yr 1) 1, where these values come from the NASA ModelE2 (Olsen et al., 2013) and p-TOMCAT (Hoor et al., 2009) models, respectively. The long-term CH4 RF varies from 27.9 to 8.1 mW m 2 (Tg (N) yr 1) 1, from the p-TOMCAT (Kohler et al., 2008) and MOZART3 (Skowron et al., 17 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 2015) models, respectively. The spread of other CH4-induced long-term effects follows that of CH4. The net-NOx RF varies from 17.5 to 11.9 mW m 2 (Tg (N) yr 1) 1 from ECHAM/MESSy (Gottschaldt et al., 2013) and CAM4 (Khodayari et al., 2014a), respectively. The Results from the mid-1990s CTMs are within the envelope of RFs generated more recently (Fig. 3). The numbers from IPCC (1999) and related studies, Sausen et al. (2005) and Lee et al. (2009), where the non-CO2 effects were originally calibrated to the results from IPCC (1999), do not alter the best NOx RF values and their uncertainties (Table D.2). Correlations. The correlations between the NOx RF components are shown in Fig. D.1. In addition to the significant negative correlations between the short-term and the long-term aviation RF components, correlations between the net-NOx effect and its components are also apparent, especially for the short-term O3 and net-NOx components; however, their strength is around half. The high correlations (p = 1, R2 = 1) across the long-term effects is expected since CH4-induced O3 and SWV are all derived based on CH4 RFs. In units of mW m 2 (Tg(N yr 1) 1, 49% of this ensemble short-term O3 RF is concentrated between 20 and 35, 43% of CH4 RFs is found between 14 and 10, 41% of CH4-induced O3 RFs is between 7 and 5 and 45% of SWV RFs vary from 2.5 to 1.5. Of the normalized net-NOx RFs resulting from this ensemble, 44% are observed between 5 and 10 mW m 2 (Tg(N) yr 1) 1. Transient vs. equilibrium. In calculating the CH4 RF response to aviation NOx emissions, the lack of steady-state conditions is an important consideration. Since methane (CH4) has a lifetime of the order 812 years (largely model-dependent) any NOx perturbation takes on the order ~40 years to come within 2% of the steady state solution. Moreover, the timescale of removal of CH4 from the atmosphere is made longer through a positive chemical feedback (Prather, 1994). In order to overcome the necessity to run a global chemical transport model (CTM) with full chemistry for such long integrations, a parameterization to account for this perturbation was originally developed by Fuglestvedt et al. (1999) and has been widely adopted since then. However, with the significant annual increases in aviation NOx emissions over the last several decades (Fig. D.2a) the CH4 response does not reach its steady-state value in any given year of emissions, so the steady-state solution generally overstates the CH4 response in a particular year from historical time-evolving emissions. Similar considerations apply to other sectors with substantial NOx emissions such as shipping (Myhre et al., 2011). If steady-state conditions are utilized, there is a conceptual and quantitative mismatch when comparing the NOx RF from aviation with other RF terms, since RF represents a particular condition at a point in time, not the steady-state conditions. To remedy this mismatch, Myhre et al. (2011) suggested that a factor accounting for the non-steady-state condition of CH4 be introduced, thereby modifying the CH4 impact for a given year of interest, and further suggested that for the aviation RF in the year 2000 the CH4 term be reduced by approximately 35% for aircraft emissions using a simplified estimation derived from Grewe and Stenke (2008). Here, we present an updated methodology to calculate the non-steady-state aviation-NOx-induced CH4 perturbation for the specific year of 2018. The method relies on transient and steady-state runs of the TROPOS 2D CTM. The results of the steady-state runs using constant emissions for a given year are compared with those of transient runs using background historical surface emissions from anthropogenic activities and the corresponding aviation NOx emissions. The latter requires full implementation of time-varying CH4 emissions into the model simulation, a requirement that is not a standard set-up for many of the CTM/GCMs currently in use where CH4 conditions are defined from observations as fixed concentrations with relaxation terms introduced to accommodate perturbations to these concentrations. The use of CTM runs explicitly accounts for changing background atmospheric conditions over the integration period as well as the change in emission rate dependence of the O3 and CH4 responses. Method. In order to compare these two methods, two types of experiments were performed: Transient experiment: a long-term simulation with anthropogenic (surface and aviation) emissions evolving over time covering the period 19502050, using historical data up to 2000 and the RCP-4.5 scenario after 2000 (Fig. D.2a), Steady-state experiment: a 100-year simulation with constant anthropogenic (surface and aviation) emissions representing the year 2000, 2018 or 2050 (Fig. D.2a); the steady-state CH4 response starts to be observed 6070 years into the run. Each of these experiments was run twice, with and without aviation emissions, and the difference between these two Results defined as the aircraft response (e.g., Fig. D.2d-f). The initial concentrations of CH4 were set using the observations from NOAA surface stations (Montzka et al., 2000) for 1950 and 2000; for the year 2050 the CH4 concentrations are taken from projections of the MAGICC model (Meinshausen et al., 2011). The background anthropogenic emissions of CO, CH4, NOx, N2O, and non-methane volatile organic carbon (NMVOC) compounds, as well as aircraft NOx emissions, evolve during the period 19502050 (Lamarque et al., 2010; Clarke et al., 2007) (Fig. D.2a). The natural emissions from soils and oceans were kept constant and represent the year 2000 (Prather et al., 2001). The TROPOS CTM is a latitudinally-averaged, two-dimensional Eulerian global tropospheric chemistry model extensively evaluated by Hough (1989, 1991). The model's domain extends from pole-to-pole (24 latitudinal grid cells) and from the surface to an altitude of 24 km (12 vertical layers). TROPOS is driven by chemistry, emissions, transport, removal processes and upper boundary conditions. There are 56 chemical species in the chemical mechanism of the model, which consists of 91 thermal reactions, 27 photolytic reactions and 7 more reactions, which include night-time NO3 chemistry. The reaction rates and cross sections were updated to the evaluation of Sander et al. (2006) (see Skowron et al., 2009). There are no fixed concentrations within the model domain other than the upper boundary conditions, which are specified for long-lived species and for gases that have stratospheric sources. This 2D CTM has the disadvantage of zonal symmetry but has the advantage of an adequate chemical scheme and computational efficiency, such that long-term integrations can be reasonably performed. Owing to the aforementioned reasons, the O3 response in TROPOS is overestimated by a factor of ~2 by comparison with a range of up-to-date 3D models. As a consequence, the CH4 results in Fig. D.2d-f were reduced accordingly. This modification of the original TROPOS responses does not affect the core result of this study, which is the relative difference of CH4 responses between transient and equilibrium methods. Results. Fig. D.2b shows the evolution of the global CH4 burden over the period 19502050 in the transient TROPOS simulation. There is a steady growth in the atmospheric CH4 burden, with a small decline over the period 19972007 in response to the decrease in CH4 emissions over the period 19902000. The steady-state simulations for the year 2000 and 2050 agree well (within 1%) with transient CH4 responses for the respective years. A similar agreement is observed for modelled transient and steady-state CH4 lifetimes in Fig. D.2c. Most of the CH4 loss in the atmosphere is driven by OH and the oxidative capacity of the atmosphere changes over time (thus CH4 lifetime as well), influenced by emissions of CO, NOx, NMVOC or CH4. Fig. D.2c shows the evolution of global CH4 lifetime (LT) over the period 19502050: there is a decrease in the CH4 lifetime between 1950 and 2000 18 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 (until around 2007), whilst under the RCP-4.5 scenario the opposite is observed, with the CH4 lifetime increasing by 3.5% by the end of 2050 compared with 2000. The TROPOS CH4 lifetimes agree relatively well with other studies (e.g., Holmes et al., 2013; Voulgarakis et al., 2013; Dalsren et al., 2016) not only in terms of absolute numbers but also the rate of changes; a detailed comparison is presented in Table D.3. The perturbation lifetime of CH4 in TROPOS is 37% longer than its global lifetime and the sensitivity coefficient s = ln(LT)/ln(CH4) is 0.27, placing these estimates in the middle of model ranges (e.g., Prather et al., 2001; Holmes et al., 2011). These terms were calculated using a 5% increase of CH4 global levels for the year 2000. There is no need to apply the feedback factor (1.37) to the TROPOS CH4 estimates as it is already included in the observed responses; TROPOS does not have a fixed boundary conditions, so CH4 and OH can freely interact. Aircraft NOx emissions, via the chemical coupling to OH and HO2, enhance OH, which reduces the global CH4 lifetime. Fig. D.2d shows the evolution of the CH4 lifetime reduction in the transient 19502050 simulation and in steady-state runs for conditions representing the years 2000 and 2050. In the transient run, there is a steady decrease of global CH4 lifetime as a consequence of a constant increase of aviation NOx emissions during the period 19502050. The agreement in 2000 and 2050 between the transient and steady-state CH4 lifetime reductions is within 6% (on a global scale) (see Table D.3). These relatively small differences in CH4 lifetime lead to much more pronounced differences in the associated global CH4 burdens as shown in Fig. D.2e. In contrast to the lifetime results, the CH4 burden response in the transient run lags behind the steady-state CH4 response with differences of 27% in the year 2000 and 20% in the year 2050. Similarly, the calculations for 2018 emissions yield a multiplicative correction factor of 0.79 (Fig. D.2f), which has been incorporated into the ERF values of CH4, long-term O3 and SWV shown in Fig. 5. The CH4 results contrast with O3 changes from aircraft NOx emissions, which agree within 3% between transient and steady-state experiments with aircraft O3 burdens of 10.3 and 10.6 Tg (O3), respectively, in the year 2000. These TROPOS O3 magnitudes are at the upper limit of model ranges, as present-day aircraft O3 perturbations found in the literature vary from 3 to 11 Tg (O3) (e.g., Hoor et al., 2009; Holmes et al., 2011; Khodayari et al., 2014a). The aircraft O3 burden increases by 41% in 2050, reaching 17.2 and 18.0 Tg(O3) for transient and steady-state experiments, respectively. This agrees with other studies (e.g., Olsen et al., 2013) that report a multi-model average increase of 44% in O3 burden from future aircraft NOx emissions under the RCP-4.5 scenario. The present approach is in general agreement with that presented by Grewe and Stenke (2008), which accounts for CH4 concentrations not being in steady-state with OH changes in the year of simulation. The present CTM Results further demonstrate the importance of explicitly calculating CH4 changes in response to time-dependent aviation NOx emissions rather than assuming constant emissions. The difference between transient and steady-state CH4 for the year 2000 found with TROPOS is smaller than that resulting from the Grewe and Stenke (2008) approach (Myhre et al., 2011) (27% and 35%, respectively). Table D.4presents a further comparison of CH4 correction factors derived in this study. The systematic differences are likely due to the Grewe and Stenke (2008) values being based on a simplified chemistry/climate model (AirClim) and the present TROPOS simulations having a different experimental setup (all our emissions (surface + aircraft) are time-varying) and a full chemical reaction scheme with explicit calculations performed on time-varying emissions. Indeed, if TROPOS is run with constant background emissions representing the year 2000 in a similar manner using Grewe and Stenke (2008) methodology, the difference between transient and steady-state CH4 for the year 2000 increases from 27% to 31%. This change shows that background emissions modify the CH4 correction factor and further emphasizes the need to have surface and aircraft emissions that simultaneously follow historical pathways. In other studies using the Grewe and Stenke (2008) methodology, CH4 correction factors vary from 0.74 to 1.15 depending on the investigated year (2025 or 2050) and aircraft emission scenario (SRES A1B, B1 and B1 ACARE) (the factor can be larger than 1 if the aircraft emissions are assumed to decrease in the preceding years) (Hodnebrog et al., 2011, 2012). Uncertainties in the CH4 correction factor are associated mainly with inter-model differences and the applied emission scenarios; the correction factor is sensitive, within ~10%, to inter-model differences (based on two models, TROPOS and AirClim) and it can vary by another 10% depending on emission scenario (based on a range of RCP projections up to 2050). Given that the uncertainties of the CH4 correction factor on the net-NOx RF are rather small, especially when compared with overall uncertainties, we do not include in the estimated uncertainty of the net-NOx RF value a separate uncertainty due to the correction factor. E. Contrail cirrus The global contrail cirrus RF is calculated by homogenizing existing estimates through the use of specific scaling factors. The factors relate to the choice of air traffic inventory and its basis year; the use of the full 3D flight distance; the use of hourly air traffic data; the feedback of natural clouds; and correcting for weaknesses in the radiative transfer calculations. The corrections and scaling actions are: The estimate of Chen and Gettelman (2013) was corrected by redoing the CAM simulation using a lower ice crystal radius of 7 m and a larger contrail cross-sectional area of 0.09 km2 for the initialization of contrails at an age of about 1520 min, in agreement with observations (Schumann et al., 2017). The resulting change in cirrus cloudiness including the adjustment in cloudiness due to the presence of contrail cirrus leads to a radiative forcing of 57 mW m 2. A scaling S1 of 1.4 is applied for estimates based on the AERO2k inventory for the year 2002 instead of the AEDT inventory for the year 2006 (Bock and Burkhardt, 2016); A scaling S2 of 1.14 is applied to estimates that are based on track distance instead of slant distance (Bock and Burkhardt, 2016). The 'slant' air traffic distance is the full flight distance and not the ground projected 'track' distance. A scaling S3 of 0.87 is applied to estimates that used monthly instead of hourly resolved air traffic data. This scaling is based on an estimate for the impact of the temporal resolution of the air traffic data of 25% to 30% within CAM (Chen et al., 2012) and one of no significant change in ECHAM4-CCMod. A scaling S4 of 1.15 is applied to account for the underestimation of RF in radiative transfer calculations that use frequency bands instead of line by line calculations (Myhre et al., 2009). 19 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 The study details and scaling results are shown in Table E.1. Weighting each estimate equally, the best estimate of global contrail cirrus RF is approximately 66 mW m 2. As noted in the main text, the Chen and Gettelman (2013) calculation is interpreted as being closer to an ERF than an RF, so was excluded from this averaging. This mean RF estimate does not include the RF due to contrails forming within natural cirrus. Uncertainty due to scalings S3S4 is included in the uncertainty discussion below, whereas uncertainty in scalings S1S2, namely updating the ECHAM4-CCMod estimates using sensitivities from ECHAM5-CCMod, is neglected. The statistical uncertainty of global contrail cirrus RF cannot be estimated from the small number of available studies. Uncertainties affecting our contrail cirrus estimates are, on the one hand, due to (A) uncertainties in the radiative response to the presence of contrail cirrus and, on the other hand, (B) uncertainties in the upper tropospheric water budget and the contrail cirrus scheme. In most cases, we can only infer very rough estimates for the uncertainties related to specific processes. (A) Uncertainties associated with the radiative response to contrail cirrus are: A1 Uncertainty related to the model's radiative transfer scheme of approximately 35% (Myhre et al., 2009). A2 Uncertainty in the inhomogeneity of ice clouds within a grid box of a climate model (Carlin et al., 2002; Pomroy and Illingworth, 2000), the vertical cloud overlap, and the use of plane parallel geometry as compared to full 3D radiative transfer (Gounou and Hogan, 2007), which together amount to approximately 35%. A3 Uncertainty estimating radiative transfer in a global climate model in the presence of very small ice crystals within young contrails, which may amount to about 10% (Bock and Burkhardt, 2016). The uncertainty is dependent on the contrail cirrus ice water content. A4 Uncertainty due to the ice crystal habit is approximately 20% according to Markowicz and Witek (2011). A5 Uncertainty in the radiative transfer due to soot cores within the contrail cirrus ice crystals is thought to be large, as the change in the shortwave (SW) albedo is large (Liou et al., 2013). The soot impact on contrail cirrus RF has not yet been quantified. Overall, uncertainty in the radiative response to contrail cirrus (excluding A3) is estimated to be about 55%, assuming independence of different uncertainties and excluding the impact of ice crystal soot cores. The uncertainty A3 is included in the uncertainty estimate under (B) because A3 and B2 are dependent uncertainties. (B) Uncertainty in contrail cirrus RF associated with the upper-tropospheric water budget and the contrail cirrus scheme are: B1 Uncertainty in contrail cirrus RF associated with the uncertainty in upper-tropospheric ice supersaturation. This results from a lack of knowledge in ambient conditions due to the low vertical resolution of satellite instruments (Lamquin et al., 2012) and to the ability of models to reproduce the observed statistics of ice supersaturation. This contributes about 20% to uncertainty. B2 There is uncertainty related to ice crystal number densities within young contrails. Ice nucleation within the plume can vary drastically depending on the water supersaturation reached within the plume and on the soot emissions (Karcher et al., 2015, 2018). This dependency on the atmospheric state leads to a reduction in the number of nucleated ice crystals in particular in the tropics and at lower flight levels (Bier and Burkhardt, 2019) leading to a large uncertainty in the impact of tropical and subtropical air traffic. Depending on the atmospheric state and ice crystal numbers, a varying fraction of ice crystals can be lost in the contrail vortex phase (Unterstrasser, 2014). We assume an uncertainty in average contrail ice crystal numbers after the vortex phase of about 50% leading to an uncertainty in contrail cirrus RF of about 20%. This estimate of the sensitivity of contrail cirrus RF to ice crystal numbers in newly formed contrails is based on simulations with ECHAM5-CCMod (Burkhardt et al., 2018). B3 The uncertainty in the lifetime of contrail cirrus, affecting the day-/night-time contrail cover, has only a small impact on the estimated contrail cirrus RF (Chen and Gettelman, 2013; Newinger and Burkhardt, 2012). We estimate the associated uncertainty to be 510%. B4 From the sensitivity of the contrail cirrus RF to the temporal resolution in the air traffic dataset in ECHAM5 and CAM, we deduce an uncertainty of about 10%. B5 The estimate of the feedback of natural clouds, due to contrail cirrus changing the water and heat budget of the upper troposphere, is very uncertain and has not been properly quantified yet (Burkhardt and Karcher, 2011; Schumann et al., 2015). We assume here the uncertainty related to this estimate to be only slightly smaller than the estimate itself, or about 15%. B6 Uncertainty in the RF estimate of Chen and Gettelman (2013) to assumptions in the initial ice-crystal radii and contrail cross-sectional areas is about 33%. We assume independence of the uncertainties except for the dependence of A3 and B3 on the uncertainty in B2. The overall uncertainty due to the water budget and the contrail cirrus scheme (including uncertainty A3) is about 40% and more than 50% in the case of the Chen and Gettelman (2013). From the two different sources of uncertainty (list A, radiative, and list B, contrail cirrus properties, above) we calculate an overall contrail cirrus RF uncertainty of about 70%, assuming independence of the overall uncertainties described in A and B. Note that we do not attempt to infer an estimate for the uncertainty of the factor ERF/RF. When calculating the contrail cirrus ERF, the error range given refers to the error range of contrail cirrus RF and not ERF. F. Emission metrics calculations We calculate the AGWP and AGTP, and corresponding GWPs and GTPs, for aviation CO2, NOx (which encompasses the ERF of short-term O3, CH4, CH4-induced O3 and SWV), soot, SO2, and contrail cirrus. The methodology and analytical expressions for the emissions metrics are described in detail in previous literature (e.g., Fuglestvedt et al., 2010; Myhre et al., 2013). The impulse response function (IRF) that describes the atmospheric decay of CO2 upon emission is taken from Joos et al. (2013). For the other species, the atmospheric decay is given by a constant e-folding time taken as the 20 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 'perturbation lifetime'. The lifetimes used here are broadly consistent with Fuglestvedt et al. (2010). The radiative efficiency (RE) for CO2 is calculated using year 2018 background concentrations of 407 ppm (annual mean, from monthly mean observed concentrations from NOAA GMD - ftp://aftp.cmd l.noaa.gov/products/trends/co2/co2_mm_gl.txt). This yields a RE of 1.68 10 15 W m 2 kg 1), 4% lower than used in the IPCC Fifth Assessment report (AR5) (Myhre et al., 2013). The climate response IRF is taken from Boucher and Reddy (2008). The latter has an inherent equilibrium climate sensitivity (ECS) of 1.06K (W m 2) 1, equivalent to a 3.9K equilibrium response to a doubling of CO2. For the calculation of the average rate of CO2-warming-equivalent emissions for aviation non-CO2 forcings (ECO2e*) under the GWP* metric in Table 5, we use the relationship between recent changes in effective RF and CO2-equivalent emissions from Allen et al. (2018) (or Equation (1) with = 0), ECO2e* = [F / t] [H / AGWPH(CO2)] (F.1) where F in Eqn (F.1) is the change in ERF over the recent period, t, and AGWPH(CO2) is the absolute global warming potential of CO2 at time horizon H. We use updated AGWPH(CO2) values incorporating the updated radiative efficiency of CO2 as described in the previous paragraph. Allen et al. (2018) used a backward-looking period of 20 years as t, whereas here we use a backward-looking 18-yr period as our time series of ERF components only extends back to 2000. G. List of Acronyms and abbreviations used in tables and figures of the Appendices ACARE Advisory Council for Aeronautical Research in Europe ACCMIP Atmospheric Chemistry and Climate Model Intercomparison Project AEDT Aviation Environmental Design Tool AEM Advanced Emission Model AERO2K Global aircraft emissions data project for climate impacts evaluation AGAGE Advanced Global Atmospheric Gases Experiment CAM Community Atmosphere Model CCMod Contrail Cirrus Module CH3CCl3 Methyl chloroform COCIP Contrail Cirrus Prediction Tool CTM Chemical Transport Model ECHAM European Centre/Hamburg Model IPCC Intergovernmental Panel on Climate Change MAGICC Model for the Assessment of Greenhouse Gas Induced Climate Change MOZART Model for OZone And Related chemical Tracers NOAA National Oceanic and Atmospheric Administration QUANTIFY Quantifying the Climate Impact of Global and European Transport System REACT4C Reducing Emissions from Aviation by Changing Trajectories for the benefit of Climate RCP Representative Concentration Pathway SRES Special Report on Emission Scenarios TAR Third Assessment Report TRADEOFF Aircraft emissions: contribution of different climate components to changes in radiative forcingtradeoff to reduce atmospheric impact TROPOS 2D global TROPOSpheric model WDCGG World Data Centre for Greenhouse Gases Table D.1 The CH4 RFs derived for all the aircraft emission inventories that are present in the model ensemble.a Inventories CH4 RF, mW m 2 Old New AEDT 6.67 8.22 AEM 6.82 8.41 AERO2K 7.09 8.74 REACT4C 6.97 8.59 QUANTIFY 6.96 8.58 TRADEOFF 7.11 8.76 a Values are those represented in the model ensemble based on MOZART-3 CTM simulations (Old) and recalculated values using a revised simplified expression for the CH4 RF (New) as presented by Etminan et al. (2016). The NOx emissions of each inventory are normalized so that all RFs are scaled to the same global total emissions (0.71 Tg(N) yr 1) as in the REACT4C model. 21 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Table D.2 The best NOx RFs per unit emission derived for datasets that include and exclude late 1990s numbers and related estimates, see text for details. Components Value Uncertainty* Value Uncertainty* (mW m 2 (Tg (N) yr 1) 1 with IPCC (1999) without IPCC (1999) Short-term O3 25.6 7.3 25.1 7.2 CH4 13.8 4.7 13.4 4.5 CH4-induced O3 6.9 2.3 6.7 2.3 SWV 2.1 0.7 2.0 0.7 Net NOx 3.9 5.7 4.0 5.8 *Stated uncertainties are one standard deviation (68% confidence interval). Table D.3 Methane response in TROPOS and other studies Variable Year 2D CTM, TROPOS Literature Transient Steady-statea Study Ref Model/Years Variable estimate/change CH4 burden, Tg 2000 4770.8 4785.1 IPCC TAR 1998 4850 Tg Voulgarakis et al. (2013) ACCMIP 4750d Tg Dalsren et al. (2016) Oslo CTM3 4560d Tg Dalsren et al. (2016) 19702012 +15% This studyc +13% 2050 5051.6 5081.4 Voulgarakis et al. (2013) ACCMIP 5000d Tg Voulgarakis et al. (2013) +5.3d % This studyc 20002050 +5.9% CH4 abundance, ppb 2000 1784.2 1787.5 Observations NOAA 1773 ppb AGAGE 1774 ppb WDCGG 1783 ppb 2050 1886.2 1897.6 Meinshausen et al. (2011) MAGICC 1833 ppb CH4 lifetime (CH4+OH)b, yr 2000 10.6 10.5 Prather et al. (2012) CH3CCl3-based 11.2 1.3 yr Voulgarakis et al. (2013) ACCMIP 9.8 1.6 yr Holmes et al. (2013) 1980/852000/05 2.2 1.8% This studyc 2.06% Voulgarakis et al. (2013) 19802000 4% This studyc 2% 2050 11.0 11.0 Voulgarakis et al. (2013) 20002050 +1.0d % This studyc +3.5% aircraft CH4 lifetime (CH4+OH), yr 2000 0.137 0.145 Hoor et al. (2009) AERO2K 1.55% Tg(N) 1 Myhre et al. (2011) QUANTIFY 1.46% Tg(N) 1 Holmes et al. (2011) Model ensemble 1.77% Tg(N) 1 Svde et al. (2014) REACT4C dENOx = QUANTIFY 1.36% Tg(N) 1 This studyc 1.48% Tg(N) 1 2050 0.293 0.311 Hodnebrog et al. (2011) SRES B1 1.61% Tg(N) 1 B1 ACARE 1.48% Tg(N) 1 Hodnebrog et al. (2012) SRES A1B 1.22% Tg(N) 1 Khodayari et al. (2014a) AEDT Scenario1 1.88% Tg(N) 1 AEDT Baseline 1.59% Tg(N) 1 This studyc RCP45 1.36% Tg(N) 1 a this is an average of the last 10 years of simulations b the chemcial ( CH4+OH) lifetime is around 7% greater than the total CH4 lifetime, as modelled by TROPOS c numbers are based on transient simulation d numbers might not be very accurate as they are read directly from the graphs found in the respecitve papers 22 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Table D.4 Calculated CH4 correction factors Aviation emissions year CH4 correction factors This study Grewe and Stenke (2008) methodology 2000 0.73 0.65 2005 0.75 0.73 2011 0.78 0.81 2018 0.79 0.86 Table E.1 Scaling of contrail cirrus RF and ERF Results a Model Inventory Representation of flight distance RF (mW/m2) Scalings Scaled RF (mW/m2)b Reference ECHAM4-CCMod AERO2K 2002 track 38 S1, S2, S4 70 Burkhardt and Karcher (2011) ECHAM5-CCMod AEDT 2006 slant 56 S3, S4 56 Bock and Burkhardt (2016) COCIP AEDT 2006 flight vectors 63 S4 72 Schumann et al. (2015) CAM5 AEDT 2006 slant 13 [57]c S3, S4 57 Chen and Gettelman (2013) Best estimate 66d a Adapted from Table 1 of Bock and Burkhardt (2016). b RF that would be expected in 2006 when using slant distance from the AEDT inventory with hourly resolution. c An updated simulation (see text) yielded 57 mW m 2. d The best estimate is of RFs, and excludes the Chen and Gettelman (2013) results since this is closer to an ERF (see main text). Table F.1a Emission metrics and corresponding CO2-equivalent emissions for the ERF components of 2018 aviation emissions and cloudiness using CO2 IRF without C-cycle feedbacks from Gasser et al., 2017, and climate IRF from Boucher and Reddy (2008). Metrics ERF term GWP20 GWP50 GWP100 GTP20 GTP50 GTP100 CO2 1 1 1 1 1 1 Contrail cirrus (Tg CO2 basis) 2.39 1.15 0.68 0.70 0.11 0.10 Contrail cirrus (km basis) 40 19 11 12 1.9 1.6 Net NOx 637 216 122 231 75 14 Aerosol-radiation Soot emissions 4409 2125 1252 1295 210 177 SO2 emissions 856 412 243 251 41 34 Water vapor emissions 0.22 0.11 0.06 0.07 0.01 0.009 Table F.1b Emission metrics and corresponding CO2-equivalent emissions for the ERF components of 2018 aviation emissions and cloudiness using CO2 IRF without C-cycle feedbacks, and climate IRF from Gasser et al. (2017). Metrics ERF term GWP20 GWP50 GWP100 GTP20 GTP50 GTP100 CO2 1 1 1 1 1 1 Contrail cirrus (Tg CO2 basis) 2.39 1.15 0.68 0.3 0.19 0.15 Contrail cirrus (km basis) 40 19 11 4 3.3 2.6 Net NOx 637 216 122 420 18 22 Aerosol-radiation Soot emissions 4409 2125 1252 466 360 284 SO2 emissions 856 412 243 90 70 55 Water vapor emissions 0.22 0.11 0.06 0.03 0.018 0.014 23 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Fig. D.1. Matrix of pair-wise scatter plots of RF values from NOx terms: short-term O3, CH4, CH4-induced O3, SWV and net NOx (i.e., the sum of all 4 components), all represented as normalized RFs (mW m 2 (Tg(N)yr 1) 1) from the ensemble studies (see details in text). The red line is the linear fit, the ellipse shows the 95% confidence level and histograms present frequencies. 24 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 Fig. D.2. (a) Past and future anthropogenic emissions of CO, CH4, NOx, NMVOC, N2O and aircraft NOx (IIASA RCP Database: http://www.iiasa.ac.at/web-apps/tnt/ RcpDb/). Dots represent conditions for 'constant 2000' and 'constant 2050' simulations. (b) Evolution of the global CH4 burden in TROPOS for transient aircraft NOx emissions combining historical emissions (19502000) and RCP-4.5 emissions (20002050); and constant emissions for the years 2000 and 2050. (c) Global CH4 lifetime due to aircraft NOx emissions in TROPOS for transient emissions combining historical emissions (19502000) and RCP-4.5 emissions (20002050); and constant emissions for the years 2000 and 2050. (d) Global CH4 lifetime reduction due to aircraft NOx emissions in TROPOS for transient emissions combining historical emissions (19502000) and RCP-4.5 emissions (20002050); and constant emissions for the years 2000 and 2050. The dashed lines represent 2000 and 2050 equilibrium values (light and dark blue) and 2000 and 2050 transient values (red). (e) Global CH4 burden reduction due to aircraft NOx emissions in TROPOS 25 D.S. Lee et al. Atmospheric Environment 244 (2021) 117834 for transient emissions combining historical emissions (19502000) and RCP-4.5 emissions (20002050); and constant emissions for the years 2000 and 2050. The dashed lines represent 2000 and 2050 equilibrium values (light and dark blue) and 2000 and 2050 transient values (red). 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