Fire Emission Heights in the Climate System – Part 2

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 X class copernicus.cls. E T A , and J. W. Kaiser 3 1 , N. A. J. Schutgens 1 , S. Kloster 1,2

Max Planck Institute for Meteorology, Hamburg,International Germany Max Planck Research SchoolDepartment on of Earth Physics, System University Modelling, ofMax Hamburg, Oxford, Planck Germany Oxford, Institute UK for Chemistry, Mainz, Germany A. Veira concentrations and radiation – Part 2: Impact on transport, black carbon Fire emission heights in the climate system Correspondence to: A. Veira ([email protected]) 2 3 4 Date: 3 June 2015 1

with version 2014/09/16 7.15 Copernicus papers of the L Manuscript prepared for Atmos. Chem. Phys. Discuss. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | of the wild- in the major % % into the planetary boundary % caused by average changes in emis- . Overall, we conclude that simple plume 4 % 2 . − 4 2 ± 9 . 7 . The application of a plume height parametrization 06 W m 2 . − 0 ± 24 . 0 06 W m . − 0 . The standard ECHAM6-HAM2 model in which 25 ± . Regionally, changes in BC burden exceed 30–40 2 − 16 km . 0 − 07 W m . 0 ±

20 .

0 −

height parametrizations. ter emission inventories and aerosol process modeling rather than on improved emission mate modeling. Significant improvements in aerosol wildfire modeling likely depend on bet- sion heights of 1.5–3.5 height parametrizations provide sufficient representations of emission heights for global cli- RF of global atmospheric BC burden of up to tive Forcing (RF) of which agrees reasonably well with observations introduces a slightly stronger negative TOA upper constraints on the emission height climate impact. We find relative changes in mean release at the surface, wildfire emissions entail a total sky Top Of Atmosphere (TOA) Radia- Extreme scenarios of near-surface or free-tropospheric only injections provide lower and global scale these improvements in model performance are small. For prescribed emission plume height parametrization or prescribed standard emission heights in ECHAM6-HAM2. height parametrization slightly reduces the ECHAM6-HAM2 biases regionally, but on the We simulate the wildfire aerosol release using either various versions of a semi-empirical MODIS, AERONET and CALIOP observations indicates that the implementation of a plume spheric long-range transport, Black Carbon (BC) concentrations and atmospheric radiation. biomass burning regions. The model evaluation of Aerosol Optical Thickness (AOT) against fire emissions are injected intolayer, the leads free to troposphere a and TOA 75 RF of the aerosol module HAM2 to investigate the impact of wildfire emission heights on atmo- is still uncertain. In this study we use the general circulation model ECHAM6 extended by ation, but the sensitivity to emission heights has been examined with only a few models and that the height of the aerosol–radiation interaction may crucially affect atmospheric radi- spheric chemistry and cloud micro-physical properties. Previous case studies indicated Wildfires represent a major source for aerosols impacting atmospheric radiation, atmo- Abstract

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10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . km plume emission height plume top heights , of the BC radiative forc- % of the global total BC emissions in- injection heights of the BC mass is found above 5 , % % 3 , although only 24 ﬁre emission heights km have been equally used to describe top heights of wildﬁre emission , which is equivalent to roughly 30 1 − to describe the maximum altitude above surface at which emissions are released

Tg BC yr plume heights

In former studies the terms

height (e.g. prescribed, or calculated by a plume height parametrization). The term and injections and the subsequent vertical distributions. In this study, we propose the term identified as a key parameter for the overall radiative impact of wildfire emissions. Thus, the vertical emission distribution at the time of the wildfire emission release can be ing to altitudes larger than 5 found that most of the AEROCOM models attribute more than 40 Aerosol Comparisons between Observations and Models (AEROCOM) project. The authors published a comprehensive comparison of 12 global aerosol models in the framework of the ing aerosols in turn depend to some extent on the emission heights. Samset et al. (2013) 2013; Schwarz et al., 2013). Long range-transport and removal processes of biomass burn- emission inventories, improper transport mechanisms and removal processes (Bond et al., Important sources of biases could be identified to arise from large uncertainties in the fire concentrations is very limited (Dentener et al., 2006; Kinne et al., 2006; Koch et al., 2009). models to reproduce observations and climate-related changes in carbonaceous aerosol ing of the direct, semi-direct and indirect aerosol effects, the ability of recent global climate Regardless of the considerable progress that has been made concerning our understand- cluding fossil fuels (e.g. Andreae and Merlet, 2001; Reid et al., 2005; Bond et al., 2013). to 3.0 Black Carbon (BC) emissions from vegetation fires are estimated to range between 1.7 and Boucher, 2000; Lohmann and Feichter, 2005; Bowman et al., 2009). The global direct and indirect radiative effects and aerosol–cloud precipitation interaction (Haywood vestigated and quantified the importance of biomass burning aerosols for direct, semi- Within the last two decades, comprehensive observational and modeling studies have in- 1 Introduction

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | % were km emissions injected into PBL and 60 % 4 concentrations, respectively. Chen et al. (2009) x NO (Wang et al., 2006; Matichuk et al., 2007). While Jian and Fu (2014) for BC, CO and km km

are small for convective atmospheric conditions. Chen et al. (2009) used

Although advanced emission height models are available for implementation in global

compared modeling results to MOPITT satellite data, but it turned out that the particular size to simulate global CO concentrations in GEOS-Chem for the year 2006. The authors the 1-D plume model by Freitas et al. (2007), MODIS Fire Radiative Power (FRP) and fire necessary to reproduce observations. Gonzi et al. (2014) applied a modified version of PBL injections performed best, whereas in another case plume heights up to 3 emissions injected into the free troposphere. For a study by Stein et al. (2009) in one case overall model performance for a scenario of 40 between 2–6 simulate the smoke transport from North American forest fires. The authors found the best demonstrated that the differences between a surface-near emission release andthe a GEOS-CHEM release model with Global Fire Emission Data Base 2 (GFED2) emissions to found a large sensitivity of BC concentrations on the emission heights, Colarco (2004) a fixed height of 1.2 release between the surface and the Planetary Boundary Layer (PBL) height, respectively studies provided good agreement of model simulations with observations for an emission which questions the general transferability of the results from trace gas studies to BC. Other showed that emission heights are substantially more important for BC than for trace gases 0–2, 0.5–3 and 3–5 matches of modeled aerosol transport to observations for emission distributions between ousse et al. (1996); Wotawa and Trainer (2000) and Spichtinger et al. (2001) found best studies. By application of inverse Lagrangian modeling techniques, the early studies of Li- tions for specific global modeling applications is largely based on short-term or regional sets of global coverage, our knowledge regarding appropriate emission height parametriza- is required for global Climate Modeling. Due to a lack of observational plume height data is an ongoing discussion which degree of complexity in emission height parametrization circulation models (e.g. Luderer et al., 2006; Freitas et al., 2007; Rio et al., 2010), there plume. implies the complete vertical emission distribution from the surface to the top of the smoke

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | of all 0 % . 1 ± 2 . lower than pre- 5 km ). Only 702 m ± 1382 5 , observed mean global plume heights 646 m

How important is the vertical distribution of the released emissions? To what extent dotions wildﬁre and emission aerosol–radiation interaction? heights impact atmospheric aerosol concentra- ±

– – In summary, our knowledge about both, an appropriate implementation of emission

1411 Sofiev et al. (2012) plume height parametrization (modeled global mean plume heights best agreement of model results to observations was found for a modified version of the data set, we evaluated the performance of different plume height implementations. The parison of simulated plume heights to observations from the MISR Plume Height Project ET-AL-PART-I”, we presented global simulated plume height patterns. Through a com- Global Plume Height Patterns Simulated by ECHAM6-HAM2”, henceforth named “VEIRA- the first part of this two paper series “Fire Emission Heights in the Climate System Part I: long-range transport, atmospheric radiation and other climate variables, is very limited. In heights simulated by the semi-empirical parametrization are 1.1–2.0 heights in global Climate Models as well as the impact of the emission heights on aerosol daytime plumes were injecting emissions into the free troposphere. On average, plume moderate performance of all these models on the global scale. Val Martin et al. (2012) and Goodrick et al. (2012) presented results that showed a poor to specific plume height parametrizations in particular cases studies, other authors including et al. (2007); Rio et al. (2010) and others demonstrated a reasonable performance for their indicate that wildfire plume heights are highly variable on the global scale. While Freitas as observational studies (e.g. Diner et al., 2008; Val Martin et al., 2010; Ichoku et al., 2012) emission height impact on the overall bias was not quantifiable. Overall, modeling as well research questions: BC deposition rates and atmospheric radiation for all simulations to address the following pospheric injections) presented in this paper, we analyze atmospheric BC concentrations, in and VEIRA-ET-AL-PART-I the additional extreme scenarios (pure surface and free tro- scribed standard plume heights in ECHAM6-HAM2. Based on the simulations introduced

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25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 6

What degree of complexity inemission plume height height impact parametrization on is aerosol requiredglobal long-range to climate transport capture models? and the atmospheric radiation in How might a futureand increase radiation? in fire intensity and emissions influence plume heights Does the diurnal cyclechange the of sign of fire the intensity averaged climate and response? emission release enhance, dampen or

– – – The next section introduces our model set-up, the different plume height implementations

et al., 2013). ECHAM6-HAM2 predicts the evolution of microphysically interacting aerosol mospheric component of the Max Planck Institute Earth System Model (MPI-ESM) (Stevens The aerosol–climate modeling system ECHAM6-HAM2 is an extension of ECHAM6, the at- 2.1 ECHAM6-HAM2 2 Methodology: simulations set-up

Models based on our ﬁndings. for future implementations of plume height parametrizations in Climate and Earth System performance. The conclusions section summarizes our results and provides suggestions analyzed. Furthermore we present regional time series and statistical analysis on the model of the wildﬁre emission heights on BC concentrations, deposition rates and radiation is and the observational data sets used for model evaluation. In the results section the impact parametrizations for climate modeling applications. tion (CALIOP) gives us an independent constraint on an adequate choice of plume height ing Spectroradiometer (MODIS) and the Cloud–Aerosol Lidar with Orthogonal Polariza- work (MAN), the AErosol RObotic NETwork (AERONET), the Moderate Resolution Imag- as Aerosol Optical Depth, AOD) to observational data sets from the Marine Aerosol Net- A comprehensive comparison of modeled Aerosol Optical Thickness (AOT, also referred to

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . The 47 ver- s . To allow for appropriate comparisons of ). For details on the implementation of sedi- 2 7 hPa (T63) and a temporal resolution of 600 ◦ 875 . 1 × ◦ 875 . 1

Wildﬁre emissions are represented by three species in the ECHAM6-HAM2 model: BC,

tener et al., 2006) are implemented. These emissions are based on monthly GFEDv2 data In the standard release of ECHAM6-HAM2, AEROCOM phase 2 wildﬁre emissions (Den- 2.2 Emission data sets and therefore excluded from our analysis. be described in the next two sections. The year 2004 is used for the spin-up of the model tions with different emission height implementations for eight years (2004–2011) which will microphysics (Lohmann et al., 2007). Overall, we carry out nine ECHAM6-HAM2 simula- represented by a two-moment cloud microphysics scheme that is coupled to the aerosol tion scheme (Giorgetta et al., 2013; Stevens et al., 2013). Aerosol–cloud interactions are These parameters in turn serve as input for radiation calculations by the ECHAM6 radia- bands and provide single scattering albedo, extinction cross section and asymetry factors. Calculations of aerosol optical properties are based on Mie theory for 24 solar spectral tion of aerosol mass and number concentrations is described in Schutgens and Stier (2014). tion, see Stier et al. (2005). A detailed assessment of the processes which drive the evolu- mentation, wet and dry deposition, thermodynamics and aerosol microphysics parametriza-

Organic Carbon (OC) and Sulfur Dioxide (SO and wildfires. simulates the emission and transport of carbonaceous matter from anthropogenic sources dust, sea salt and sulfur emissions from natural and anthropogenic sources, the model also tributions which describe the nucleation, Aitken, accumulation and coarse modes. Besides et al., 2011). The aerosol module HAM2 employs a superposition of seven log-normal dis- the wildfire Radiative Forcing (RF),six the hours model by is relaxation nudged of against the observational data prognostic every variables to ERA-Interim reanalysis fields (Dee tical layers range from the surface to 0.01

grid of approximately For all our simulations, we use model version ECHAM6.1.0-HAM2.2. We apply a spatial populations, their size distribution and composition (Stier et al., 2005; Zhang et al., 2012).

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | for % for BC and 4.4 % 8

We apply the GFASv1.1 emission data set for eight out of nine simulations, but we also Kaiser et al. (2012) found that GFAS emissions implemented in the global circulation

of GFAS and AEROCOM show mean differences of less than 9.2 which is comparable to other ECHAM6-HAM2 studies. As the global annual emission fluxes run one simulation with the standard AEROCOM wildfire emissions to provide a reference in more detail in a future study. rates). It would be highly desirable to investigate the reasons for the required factor of 3.4 in the representation of aerosol micro-physics in the model (impacting aging and removal tion of wildfire emission factors or burned area, respectively FRP) as well as shortcomings various reasons including an underestimation of emission fluxes (e.g. due to underestima- Basically, the underestimation of AOT in GFAS and other bottom-up inventories could have and Climate) atmospheric composition forecasting system, respectively ECHAM5-HAM1. von Hardenberg et al. (2012) using the global MACC (Monitoring Atmospheric Composition tion also provided reasonable global modeling results in studies by Huijnen et al. (2012) and GFAS wildfire emissions are multiplied by a global factor of 3.4. This zero-order approxima- model ECMWF are only able to reproduce AOT observations in a reasonable way, if global input data set for our simulations. sistent aerosol emission and fire intensity information, GFASv1.1 provides an appropriate calculate combustion rates and fills observational gaps by use of a Kalman filter. With con- sions from the year 2000 to present. GFAS applies land-cover specific emission factors to OC, we decided to apply the 3.4 factor not only to GFAS but also to AEROCOM wildfire et al., 2012) uses FRP retrieved from MODIS satellite observations to estimate fire emission heights. In contrast to GFED, the Global Fire Assimilation System GFASv1.1 (Kaiser ECHAM6-HAM2 model does not represent an appropriate framework to study wildfire emis- for plume height parametrizations, the AEROCOM emission data set within the standard any information on wildfire intensity. As fire intensity is a key input parameter required ations. GFEDv2 emissions are derived from burnt area observations and do not provide (Werf et al., 2006) representative for the year 2000, not accounting for inter-annual vari-

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | to predict ﬁre emission PBL H . as well as atmospheric stability (Brunt- 2 f P 9 ) and PBL height N above the surface). Thus, this simulation provides the is set to PBL height. We use this implementation as p m H , km model layers (1) + 2

PBL height

= In the standard version of ECHAM6-HAM2.2, plume heights for all wildﬁres are pre- In addition, we run one “NO-WILDFIRES” scenario for which wildﬁre emissions are com-

p SP takes into account the total FRP of a fire Parametrization” (SP). In contrast to the standard emission heights in ECHAM6-HAM2, the parametrization introduced by Sofiev et al. (2012), henceforth also referred to as “Sofiev emission heights in ECHAM6-HAM2, we implemented the semi-empirical plume height a reference simulation “HAM2.2-GFAS”. For a more appropriate representation of wildfire Väisälä frequency of the atmosphere If the PBL height exceeds 4 H scribed as the Planetary Boundary Layer (PBL) height plus two model layers: unrealistic upper constraint of the emission height climate impact. the other hand a simulation of purely free tropospheric emission release (“FT”) serves as cific model layer resulted in model instabilities, presumably due to radiative imbalance. On test runs prior to this study, very intense wildfire emission release concentrated at one spe- into the two lowest model layers instead of the surface layer only, because in preliminary model layer (approximately 30–150 to the surface. Wildfire emissions in simulation SURFACE were chosen to be distributed iment “SURFACE” simulates a wildfire emission release into the lowestlower and limit second of lowest the emission height radiative impact due to fast removal of the aerosols close most extreme and unrealistic scenarios for our sensitivity study: on the one hand the exper- on the large range of emission height implementations in the literature, we apply first the Table 1 provides a summary of all plume height parametrizations used in this study. Based 2.3 Emission height parametrizations pletely switched off to calculate the overall wildfire emission impact on radiation. resented by the emission species BC, OC and SO emissions. For both, AEROCOM as well as GFAS simulations, wildfire emissions are rep-

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 (2) SO / as well OC emission f0 / 2 P SO / and 0 OC N / 10 and all modiﬁcations we applied (e.g. introduction 2 0 δ /N 2 and δN γ , − β , α exp above surface. HAM2.2-AEROCOM and HAM2.2-GFAS use the γ m f f0 P P β + PBL

αH

of changes in emission fluxes and emission heights. We assume the BC FRP and emissions, “SOFIEV-2X-EMISSIONS-FRP”, enables a comparison of the impact observations, see A VEIRA-ET-AL-PART-I. hypothetical future scenario with a doubling in plume height parametrization which provides the best agreement to global plume height vertical emission distribution prescribed in the standard HAM2.2 model with a fraction of ing of high plumes “SOFIEV-MODIFIED”. Simulation SOFIEV-MODIFIED represents the “SOFIEV-DCYCLE” and one simulation which applies a diurnal cycle as well as a tun- simulation with additional application of a diurnal cycle in fire emissions and FRP called simple 1-step model as described in Sofiev et al. (2012) called “SOFIEV-ORIGINAL”, one of a diurnal cyclecarry in out FRP), five see simulations and VEIRA-ET-AL-PART-I with Sofiev different et implementations al. of (2012). the Overall SP: we The original and most

approximately 30–150 the simulation SURFACE, all wildfire emissions are injected into the first two model layers a constant mass mixing ratio from the PBL height to the first level below the tropopause. In surface to the top of the plume with a constant mass mixing ratio. Simulation FT also applies surface to the plume height. For all SOFIEV simulations, we distribute emissions from the surface, one has to make assumptions on the vertical distribution of the emissions from the Besides the plume heights which describe the maximum level of emission injection above 2.4 Vertical distribution of wildfire emissions the influence of the vertical emission distribution. ratios provided by GFASv1.1. The last simulation “SOFIEV-TOP-INJECTION” is run to test For more details on theas SP, the a tuning description parameters of the normalizing constants ratios for simulation SOFIEV-2X-EMISSIONS-FRP to be constant and apply BC

H heights:

5 10 25

20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | nm 0.015 ≈ 11 of the emissions are distributed from the surface to the PBL uniformly (constant mass mixing ratio) into the model layers % % of the total emissions into the uppermost layer of the plume and we % . h , every 6 h

of the emissions to be injected into the free tropopshere (in the two levels just above

% The Maritime Aerosol Network (MAN) is an integrated component of AERONET and in- Vertical emission distributions with constant mass mixing ratios have been used in most

of direct solar radiation. These AOT measurements are estimated to have errors of tometers that provides long-term continuous AOT measurements based on the attenuation held Microtops II sunphotometers with ﬁve spectral channels ranging from 320–1020 CALIOP. The AERONET program (Holben et al., 1998) is a ground network of sun pho- cludes data from ship cruises since end of 2004 (Smirnov et al., 2011). It is based on hand grated AOT values are compared to observations from AERONET, MODIS, MAN and For evaluation of the ECHAM6-HAM2 model performance, vertically resolved and inte- over 6 2.5 Observational data sets for model evaluation aerosol observations we have. In this study, we use AERONET AOT which was averaged below (SOFIEV-TOP-INJECTION). (Eck et al., 1999; Schmid et al., 1999) and are considered some of the most accurate in which we emit 50 sions into the upper part of the plumes. To account for this, we perform one sensitivity study, studies that rare, but extraordinarily high injections might emit a large fraction of the emis- et al. (2006) and Fromm et al. (2010) showed in modeling, respectively observational case distribute the remaining 50 tions is even more limited than our knowledge about the plume heights. However, Luderer former global aerosol modeling studiesFreitas even et in al. case (2007). of Our more knowledge advanced about plume the global models, variability e.g. of vertical emission distribu- 25 the PBL). The remaining 75 height with constant mass mixing ratio. days of measurements between November 2006 and March 2010). age due to the limited number of ship cruises which collected data (about 1700 individual providing data for spectral AOT. The MAN data set has limited spatial and temporal cover-

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10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) km are not km (below 8.2 S (Winker et al., m ◦ . MODIS observations h N and 82 ◦ ). Vertical aerosol extinction proﬁles are calcu- 12 km aggregates, available every 6 ◦ (Winker et al., 2013). CALIOP vertical AOT proﬁles have a good nm (between 8.2 and 20.2 m (Kacenelenbogen et al., 2011; Redemann et al., 2012). Therefore, in this km

Vertically resolved information of AOT is provided by the Cloud–Aerosol Lidar with Or- In addition to AERONET data, spectral radiance measurements from the two MODIS

as the Terra NRL L3 data, are 1 by 1 through additional quality checks and empirical correction formulae. Both, the Aqua as well L3 (Hyer et al., 2011; Shi et al., 2011) data that are derived from MODIS L2 observations Adames et al., 2011; Smirnov et al., 2011; Schutgens et al., 2013). Here we use the NRL based AERONET (e.g. Remer et al., 2005; Bréon et al., 2011) and MAN observations (e.g. estimations of MODIS AOT retrievals have been investigated by comparison with ground- June 2006 to acquire global aerosol profile data between 82 bles for different particles which depend on scattering geometries (Tanre et al., 1997). Error thogonal Polarization (CALIOP) on board the CALIPSO satellite which was launched in coverage. MODIS AOT values are calculated by retrieval algorithms based on look-up ta- sensors aboard the Aqua and Terra satellites are used to monitor AOT with a wide spatial flow to the tropical Atlantic (Fig. 1). America, Siberia, the Amazon area and neighboring regions, Congo and the African Out- siderations to six major biomass burning regions: boreal North America, temperate North larger. For the quantitative analysis of the plume height parametrization, we restrict our con- do provide a far wider spatial coverage than AERONET, but uncertainties are significantly a tendency of CALIOP to underestimate low AOT values andlowest the 1.4 lowest 1.4 to uncertainties in lidar ratios (e.g. Campbell et al., 2013;accurate Winker AOT et values al., than 2013). level 2 There data is because of an improved retrieval algorithmanalysis to for the relative vertical AOT profiles. For our analysis, we use only complete CALIOP 2010). CALIOP provides backscatter profilesrespectively at 60 a vertical resolution of 30 global coverage, but the uncertainties in individual AOT profiles are known to be largereasonably due captured. The gridded CALIOP level 3 data has been shown tostudy, we provide apply more multi-annual monthly means of level 3 data for 2006–2011 and restrict our lated at 1064 and 532

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25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (lat- ◦ 5 × ◦ emissions from wildﬁres contribute 2 13 emissions. Table 2 also reports the range of plume heights 2 layers for our comparison to ECHHAM6-HAM2 model simulations. km are not discussed in this paper as SO 2 to the overall SO %

longitude) horizontal resolution. Therefore, the introduced errors in the CALIOP

and deposition rates In contrast to the spatially and temporally collocated MODIS, AERONET and MAN data

only about 5 Changes in SO deposition rates are very similar to the changes in BC, we limit our detailed analysis to BC. periments are presented in Table 2. As the patterns of changes in OC concentrations and by more detailed and region-specific discussions within the next three sections. Note that Global mean values of the atmospheric BC and OC aerosol burdens for all individual ex- tail in All VEIRA-ET-AL-PART-1. global mean values provided in Table 2 are complemented 3.1 BC burdens simulated in the individual experiments. Global plume height patterns are discussed in de- (2005–2011) for the nine emission height scenarios provided in Table 1. concentration profiles and deposition rates. We analyze seven years of model simulations global aerosol transport, we assess regional and global changes in BC burdens, vertical the vertical and horizontal transport of the wildfire emissions. To quantify these changes in simulations, CALIOP level 3 data are only available in monthly temporal and 2 Differences in emission height parametrizations can be expected to cause differences in of 6 hourly resolution, which we use for the comparison of total AOT to ECHAM6-HAM2 3 The impact of changes in fire emission heights on BC burdens, concentrations vertically resolved AOT profiles of global coverage. itude AERONET and MAN data. On the other hand CALIOP is the only data set which provides model-observations comparison are a priori larger for the CALIOP data then for MODIS, aged to 0.5 or 1.0 vertical profiles without missing individual layers. Absolute AOT values are vertically aver-

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . % of the emissions into the highest emission % 14 two model layers, see The VEIRA-ET-AL-PART-1. comparison of +

Figure 2 illustrates the global relative changes in BC burden for the individual experiments

nounced in the Southern Hemisphere and range from 10 to 25 transport is generally reduced. The negative relative changes in BC burden are more pro- and therefore also BC burdens are increased in the vicinity of the sources, while long-range the global BC burden (Fig. 2e). For the near-surface emission injections, residence times nario of pure near-surface emission injections, provides an estimate of the lower limit of deep plumes are rather small on the global scale. Simulation SURFACE, the extreme sce- changes in BC burden introduced by the application of a diurnal cycle and more realistic simulations SOFIEV-ORIGINAL, SOFIEV-DCYCLE and SOFIEV-MODIFIED shows that the heights of PBL height simulated by the Sofiev parametrizations exceeds the HAM2.2-GFAS maximum emission ing below the HAM2.2-GFAS emission heights, a small fraction of strong emission events posphere. Although the majority of emission injections in the SOFIEV simulations is inject- boreal forest fire events which emit significant fractions of the emissions into the free tro- changes in boreal regions. The higher burdens can be attributed to the importance of strong layer, the sign of the relative changes in BC burden compared to simulation HAM2.2-GFAS TOP-INJECTION scenario, which injects 50 sion injections (SOFIEV-MODIFIED), introduce only marginal changes in BC burden. In the increases the height of daytime plumes, and a more realistic representation of deep emis- aerosol long-range transport. The application of a diurnal cycle (SOFIEV-DCYCLE) which the source regions. The simulated changes in BC burden can be attributed to a decreased tation of the plume height parametrization introduces a reduction in BC burden far from While an increase in BC burden is observable close to the source regions, the implemen- global patterns of changes in BC burden are very similar in the tropics and subtropics. SOFIEV-DCYCLE, SOFIEV-MODIFIED, SOFIEV-TOP-INJECTION, see Fig. 2a–d), the various implementations of the Sofiev plume height parametrization (SOFIEV-ORIGINAL, compared to the standard ECHAM6-HAM2 set-up (simulation HAM2.2-GFAS). For the Deviation (SD) of monthly global means for 2005–2011. all uncertainty estimates in Table 2 (except for the plume heights) represent one Standard

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | for OC. % respectively 5.5 % − 7.9 − 2.7 to − for the SURFACE simula- km for BC and % 4.8 − 15 , the global BC burden is non-linearly enhanced 2.6 to % − for the HAM2.2-GFAS prescribed plume heights (see VEIRA-ET-AL-PART-1). compared to simulation SOFIEV-DCYCLE. This dampening of the increase in km %

. These changes are remarkably small due to the fact that median global plume %

Global mean relative changes in BC burden introduced by the replacement of prescribed The extreme and unrealistic scenario of purely free-tropospheric injections shows an in-

8.9 a WRF-CHEM modeling study. Note that the differences in plume heights for a doubling sponse to an increased emission release has been found by Zhang et al. (2014) within source-receptor relationship. A comparable magnitude in damping of the atmospheric re- is impossible to disentangle the contribution of particular processes to the overall non-linear changes in aerosol concentrations and radiation. However, in the framework of this study, it processes and atmospheric feedback via changes in vertical temperature profiles due to height parametrization range between limited to non–linear particle formation, coagulation and deposition, micro-physical cloud emission heights in HAM2.2 by the implementation of various versions of the Sofiev plume tion of multiple aerosol micro–physical and atmospheric effects. These include but are not increase in total BC emissions by 56.7 atmospheric BC concentrations for increased emissions can be attributed to the interac- FRP, which assumes a doubling in FRP and wildfire emissionsby corresponding 38.7 to an overall other simulations except the unrealistic FT scenario. In scenario SOFIEV-2X-EMISSIONS- entails an increase in atmospheric BC burden which largely exceeds the changes of the tive to plume height changes. The scenario of a doubling in FRP and fire emissions (Fig. 2g) Southern Hemisphere, the relative changes in BC burden in these regions are more sensi- of the proportionately higher fraction of wildfire emission to the overall BC burden in the lation FT are dominated by long-range transport rather than the emission sources. Because − crease in BC burden over Antarctica by more than 20 times, see Fig. 2f. BC burdens in simu- For the SURFACE scenario, global BC and OC burdens are reduced by tion to 2.7 heights between these simulations range from about 0.15 efficiently in the lower troposphere. Consequently, these results indicate that the vertical mixing in ECHAM6-HAM2 acts very

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | % days ) and 01 Tg . . Figure 2h with a 33 0 % ± higher than AE- days 36 . for the prescribed of all plumes, see 0 % % 2 days . 0 ± . An increase in plume heights 5 . h 8 N), which dominate the global burden, ◦ on average for 95 S) GFAS is about 18.8 m ◦ S to 20 16 ◦ . Due to the GFAS emission ﬂux factor of 3.4 ap- h N and 20–60 ◦ ) are very similar as a result of similar mean global emission 02 Tg . 0 ± 34 . 0 , the regional differences are considerably larger. For boreal regions (60– for simulation HAM2.2-GFAS compared to simulation SURFACE introduces for the SOFIEV-ORIGINAL simulation and % km 1 days .

N), GFAS BC emission fluxes are roughly two times the AEROCOM emission fluxes. ± ◦ The global mean BC lifetime of realistic plume height implementations ranges between Although the global mean differences in emission fluxes between AEROCOM and GFAS

1 . ROCOM. In the tropical source regions (20

8 AEROCOM BC emission fluxes exceed the GFAS emission fluxes by 17.9 fluxes. 80 In the temperate regions (20–60 Nevertheless, the mean total global BC burdens of HAM2.2-AEROCOM ( are only 9.2 differences in BC burdens largely reflect the spatial differences in the emission inventories. HAM2.2-AEROCOM simulations applying the same plume height parametrization. These VEIRA-ET-AL-PART-1. shows large regional differences in atmospheric burden between the HAM2.2-GFAS and the HAM2.2-GFAS ( in FRP and emissions do not exceed 100–500 EMISSIONS-FRP) enhances the mean BC lifetime by 22.3 heights, a doubling in wildfire emissions (simulation SOFIEV-DCYCLE vs.an SOFIEV-2X- increase in BC lifetime by about 16.3 within the range of the AEROCOM models for which mean lifetimes of 7.1 standard emission heights in HAM2.2-GFAS, see Table 2. For similarby daytime 1.7–3.7 emission plied in this study, these lifetimesin are substantially ECHAM6-HAM2 larger shown than by mean BC Zhang lifetimes et of al. 5.9 (2012). However, the lifetimes in our study are means for 2005–2011. Compared to the HAM2.2-GFAS simulation with a prescribed emis- Figure 3 presents vertical cross sections of relative changes in BC concentrations as zonal 3.2 Vertical BC concentration profiles SD were found (Textor et al., 2006).

15 10

20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . % in the % (Fig. 3a– % for all SOFIEV hPa ). The other extreme % 5 < 17 all over the globe in the HAM2.2-AEROCOM simulation. On the % into the free troposphere, the on average much lower Soﬁev emis- %

For simulation SURFACE, a decrease in mean BC concentrations up to 25 Overall Fig. 3 demonstrates that upper tropospheric and lower stratospheric BC concen-

are applied. trations are not very sensitive to the emission heights if realistic emission height scenarios

are increased by 5–20 centrations to emission heights is very limited (relative changes As a result of the tropical convective transport, BC concentrations in the lower stratosphere Southern Hemispheric free troposphere is found, but the sensitivity of stratospheric BC con- tropical wildfire emission fluxes in the AEROCOM emission data set comparedother to hand, GFAS. the extra-tropical tropospheric BC concentrations are decreased by 5–25 lower stratosphere. Simulation HAM2.2-AEROCOM (see Fig. 3h) reflects the enhanced in BC concentrations is observable in the Southern Hemispheric upper troposphere and (see Sect. 5). For the SOFIEV-2X-EMISSIONS-FRP scenario, the largest relative increase BC concentrations by a factor of 10–100 which substantially impacts the radiative transfer scenario (simulation FT) shows an upper tropospheric and lower stratospheric increase in

BC concentrations, not the emission heights. assume that deep convection is the major process which determines the free tropospheric the regions of increased burden coincide with the strongest tropical convective zones, we show a substantial increase in BC burden in equatorial Africa for the Soﬁev simulations. As ulations compared to the HAM2.2-GFAS standard emission heights. Moreover, Fig. 2a–d a slight increase in BC concentrations is observable betweendirectly 500–300 attributed to differences in emission heights which are smaller in all SOFIEV sim- d). However, for theare SOFIEV-TOP-INJECTION substantially scenario, lower the than near-surface for concentrations the other SOFIEVsimulations. simulations, This see enhancement Fig. in 3d. tropical In free the tropospheric tropics, BC concentrations cannot be versions of the Soﬁev plume height parametrization are largely smaller than 5 centrations in the free troposphere. Differences in BC concentrations between the various sion heights lead to increased BC concentrations near-surface and decreased BC con- sion injection of 25

5 15 25

20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . % for the SURFACE emis- % 18

(Fig. 4g). In the Northern Hemisphere this increase is signiﬁcantly smaller, %

HAM2.2-AEROCOM and the HAM2.2-GFAS simulations, both applying HAM2.2 emissions restricted to four simulations: a zero wildﬁre emissions scenario (NO-WILDFIRES), the Our temporal analysis for six biomass burning regions (see Fig. 1) for 2006–2008 is 4 Comparison of model results to observations

not by wildfires. of 60–140 because the BC release in mid-latitudes is largely dominated by anthropogenic emissions, and fire intensity results in a Southern Hemispheric increase in regional deposition rates Bey (2011); von Hardenberg et al. (2012), may still persist. A global doubling of emissions range transport to the Arctic, which have been found for ECHAM5-HAM1 by Bourgeois and Over Antarctica, total deposition rates are decreased by 20–25 However, although these changes are substantial, the known model biases in aerosol long- (SOFIEV-MODIFIED) only marginally influence the deposition rates on the global scale. cle in fire emissions (SOFIEV-DCYCLE) and a more accurate representation of high plumes atmospheric long-range transport. Changes introduced by a consideration of the diurnal cy- heights. In contrast, the reduced remote deposition rates can be attributed to a decreased of Greenland and the northern polar ice sheet, the reduction ranges between 10–20 crease in deposition rates in the vicinity of the major source regions due to lower emission sion release compared to the HAM2.2 standard implementation. Over the glaciated areas for our various plume height implementations. Simulation SOFIEV-ORIGINAL reflects an in- depend on emission heights. Figure 4 presents the relative changes in total deposition rates In this context, the question arises how strongly deposition rates in the Arctic and Antarctic snow and ice which substantially reduces the surface albedo (e.g. Dumont et al., 2014). Table 2. A potential climate impact of BC emissions is related to the deposition of BC on Wet deposition rates, dry deposition rates and sedimentation rates for BC are provided in 3.3 Total deposition rates

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) % ) and cloudy (RH = 100 % 19

) for clear–sky conditions. The model calculates

AOT values can be observed for HAM2.2-GFAS, especially during the local burning season. and SOFIEV-MODIFIED and subsequent changes in aerosol lifetime (see Table 2), larger emission patterns. Due to the distinct differences in plume heights between HAM2.2-GFAS be seen as a crude approximation which only represents the basic seasonal and regional servations for specific time periods. Therefore, the HAM2.2-AEROCOM simulation should are representative for the year 2000 only and are as such not expected to match the ob- form better than the HAM2.2-AEROCOM simulation. Note here that AEROCOM emissions those simulations based on GFAS emissions (HAM2.2-GFAS and SOFIEV-MODIFIED) per- model bias in these periods has primarily to be attributed to non-wildfire sources. Generally real North America 2006, days 50–100 and Siberia 2008, days 250–300) indicate that the NO-WILDFIRES values show little differences to all other simulations (e.g. Figure 6, Bo- MODIS Aqua observations for the years 2006, 2007 and 2008. Time periods for which the ECHAM6-HAM2 computes AOT at 550 tions. Figures 5 and 6 provide a comparison of simulated and regionally averaged AOT to 4.1 AERONET, MAN and MODIS uated by comparison to observational AOT values which always refer to clear sky condi- tribute to the overall model bias. (see Stier et al. 2005, Sect. 2.6). The modelled AOT has global coverage and can be eval- scenario is used to identify regions and time periods in which wildfires significantly con- a separate Relative Humidityparts (RH) of for a the grid-box clear based (RH on < the 100 grid-box mean specific humidity and the cloud fraction Thus, these simulations can not be expected to match observations. The NO-WILDFIRES realistic states of present-day emission heights, respectively emission inventories (Table 1). SURFACE and FT are excluded from this analysis as these simulations do not represent concentrations, are not included. Likewise, the scenarios SOFIEV-2X-EMISSIONS-FRP, which have been shown to only marginally influence parameters such as BC burden and the global spectrum of plume heights. For the sake of clarity, the other SOFIEV simulations, heights and the SOFIEV-MODIFIED simulation which is most appropriately representing

5 25

20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 20 on average, while SOFIEV-MODIFIED offers the best plume height km

Figure 7 provides Taylor plots which illustrate the model performance with regard to cor-

plume heights by 1–2 MODIFIED). In it VEIRA-ET-AL-PART-I has been shown that HAM2.2-GFAS overestimates (HAM2.2-GFAS and SURFACE) versus parametrized wildfire emission heights (SOFIEV- vertical AOT profiles and focus on the differences in AOT vertical profiles for prescribed regions specified in Fig. 1. For reasons of clarity, we limit our investigations to relative ECHAM6-HAM2 simulations vs. CALIOP observations for the six major biomass burning Figure 8 presents multi-year monthly AOT profiles (relative vertical AOT distribution) of 4.2 CALIOP observable for temperate North America and the Congo region. the biases of SD and correlation slightly increase, whereas there is no significant changes tion in boreal North America, Siberia and the Amazon. In the central African outflow region emission heights in HAM2.2-GFAS provides a moderate, but significant increase in correla- The application of the Sofiev parametrization (SOFIEV-MODIFIED) instead of prescribed reach reasonable correlations of 0.4–0.85 depending on region and observational data set. compared to the NO-WILDFIRES scenario. Model runs with the GFAS emission inventory ROCOM representative for the year 2000 is hardly able to improve the model performance relations and SD. The results show that the application of the fixed emission inventory AE- variations. the model is generally less capable of capturing the magnitude and seasonality of AOT son. In the Western Atlantic outflow region of the central African biomass burning plumes, the Amazon region during 2007 can be slightly reduced for the major biomass burning sea- tion (SOFIEV-MODIFIED), the overestimation in AOT observable for HAM2.2-GFAS over between the two simulations. By implementation of the modified plume height parametriza- in boreal North America 2006 and Siberia 2007 with negligible differences in performance SOFIEV-MODIFIED simulations, large biases are observable for the weak burning periods year. While massive burning events in 2008 are captured very well by HAM2.2-GFAS and In Siberia and boreal North America, the model performance is highly variable from year to

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) did not signiﬁcantly m 21 of all CALIOP proﬁles are known to include par- km of all cases, but the differences in correlation range only between 0.001 and %

As biomass burning is seasonally varying, we further separate the analysis seasonally. There is a tendency of the model to simulate higher AOT values in the extra-tropical free

that differences in emissioncontribute heights to differences (PBL in vs. the prescribed model performance. 50–3000 These findings are basically in line with sion heights in the ECHAM5-HAM2 and the HadGEM3-UKCA model. The authors found Kipling et al. (2013) investigated the sensitivity of BC burdens and vertical profiles to emis- 4.3 Comparison to former studies 0.038. mance in 65 in simulation SOFIEV-MODIFIED compared to HAM2.2-GFAS increase the model perfor- largely similar seasonal patterns in correlations (not shown). More realistic plume heights to wildfire emissions as such. Simulation HAM2.2-GFAS and simulation SURFACE show major shortcomings of the model in simulating vertical AOT profiles are not primarily related correlations during the major wildfire season in these regions (June to August). Thus, the and Siberia, a clear seasonal cycle in the model performance is observable with highest profiles for simulation SOFIEV-MODIFIED compared to CALIOP. For boreal North America Figure 9 provides correlation coefficients of spatially and temporally averaged relative AOT and CALIOP observations. significantly smaller than the inter-annual variability and the differences between the model emission height implementation (HAM2.2-GFAS vs. SOFIEV-MODIFIED or SURFACE) is mation of AOT for low AOT values in the CALIOP data set. Remarkably, the impact of the sarily related to shortcomings in the model, but could also be related to known underesti- troposphere than CALIOP (boreal North America, Siberia), but this feature is not neces- America and Central Africa (Congo). HAM2.2-GFAS and SURFACE show general agreement for boreal and temperate North performance. Note that the lowest 1.5 that the vertical AOT patterns of CALIOP and the model simulations SOFIEV-MODIFIED, ticularly high uncertainties which also impact the higher layers. Nevertheless Fig. 8 shows

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | entail dif- km RF. Aerosol-induced changes in atmospheric 22 total sky ), while anthropogenic emissions are kept constant. The radia- 2 in global BC burdens at least for scenarios which do neither inject %

RF; the RF which also includes indirect and semi-direct effects due to aerosol-

towards reanalysis data every six hours, they are partly suppressed. Figure 10a and b visu- temperature profiles are implicitly included in both RF parameters, but due to our nudging cloud interaction is referred to as clear sky tive perturbation which is attributed to direct aerosol-radiation interference is referred to as emissions (BC, OC and SO Here, the RF represents exclusively the radiative perturbation that is introduced by wildfire alyzed to quantify the climate impact caused by different emission height representations. The Radiative Forcing (RF) at the Top of the Atmosphere (TOA) of wildfire emissions is an- 5 Radiative forcing inventories has also been found by Gonzi et al. (2014) for CO emissions. eral importance of emission heights compared to the large uncertainties in the emission 2011; Ma et al., 2013) exceed by far the uncertainties in emission heights. A minor gen- our study. The general CALIOP uncertainties in AOT profiles (e.g. Kacenelenbogen et al., study is considerably larger than the impact of different emission height parametrizations in simulations including ECHAM5-HAM1. The spread of the model ensemble presented in that (2012) provided a comprehensive comparison of CALIOP AOT profiles to different model application of a simple, empirical plume height parametrization (Briggs, 1969). Koffi et al. ferences of less than 10 also discovered a moderate improvement in model performance for the HYSPLIT model by formance in AOT in the vicinity of the major biomass burning regions. Stein et al. (2009) tion of a semi-empirical plume heightas parametrization well which as takes into ambient account meteorological fire intensity conditions, marginally improves the overall model per- our evaluation of different plume height parametrizations also indicates that the applica- emissions very close to the surface nor only into the free troposphere. On the other hand, our results which show that substantial differences in emission heights of 1–3

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 ± ± ± − 24 62 . . 211 1 . 0 0 − − − 34 W m . (not shown 0 . A doubling 2 2 ± and − . − 2 which is compa- 28 − . 2 3 056 W m − . − 0 W m 16 W m . ± 0.24 1 ± ± 06 W m 196 . . 0 66 0 37 . . − ± 7 which is nearly a doubling in the +0 − 2 and is thus slightly less negative 25 . − 2 0 − − 11 W m . 0 07 W m . ± 0 23 ± 36 . 0 . The same magnitude of changes in surface RF . Although the HAM2.2-GFAS simulation prescribes 20 2 . 2 − − 0 − − 05 W m . 06 W m . 0 0 ± ± 16 . 0 − ). The most realistic implementation of emission heights (simulation SOFIEV- and ranges between the SURFACE and the HAM2.2-GFAS simulation, see 2 ). The total sky TOA RF introduced by the extreme scenario of a SURFACE 2 2 − − −

The FT scenario entails a positive total sky TOA RF of The differences in TOA RF introduced by the differences between the SOFIEV simu- Figure 10c and d shows the total respectively clear sky surface RF for different plume

17 W m 060 W m 05 W m . . . in wildﬁre emissions and FRP would entail a total sky surface RF of

trast, the FT scenario causes a total sky surface RF of also applies for changes in the vertical distribution (SOFIEV-TOP-INJECTION). In con- surface RF by more then nor a more realistic representation of deep plumes (SOFIEV-MODIFIED) alter the global Fig. 10c. Neither the implementation of a diurnal cycle in fire emissions (SOFIEV-DCYCLE) height implementations. The surface RF of the simulation SOFIEV-ORIGINAL is that compensate on the global scale. rable to the HAM2.2-GFAS simulation. Regionally, however, we find significant differences, 0 used (HAM2.2-AEROCOM), the total sky TOA RF is negative RF compared to SOFIEV-ORIGINAL. When the AEROCOM wildfire emissions are EMISSIONS-FRP entails a TOA RF of the surface-near atmospheric levels. A doubling of FRP and emission fluxes in SOFIEV-2X- larger BC concentrations in the upper troposphere and lower stratosphere compared to in Fig. 10). The change in the sign of the RF in the FT simulation can be attributed to the

total sky TOA RF compared to the SURFACE simulation is only 0.08 a certain emission injection into the free troposphere for nearly all plumes, the difference in

emission release is 0 MODIFIED) leads to athan TOA the RF of standard0 model HAM2.2-GFAS (total sky TOA RF of HAM2.2-GFAS:

lations are negligible (total sky TOA RF ranges between mean values for the total sky RF are also provided in Table 2. alizes the total sky and clear sky TOA RF for different plume height implementations. Global

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ± 63 . 1 − . In con- 2 − 5 W m . 0 ± are limited to tropical Africa 2 − W m are found over central South America, 2 − 24 ) is small. The ratio of TOA to surface RF ranges 2 +5 W m − 09 W m . 0 ± 82 . 1 . Between scenarios SURFACE, SOFIEV-ORIGINAL, SOFIEV-DCYCLE, 2 − in large parts of topical Africa, South America and also boreal North America. ) applying the same emission height parametrization (not shown). Similarly to W m 2 2 − − 10

−

W m

Tosca et al. (2013) compared a simulation based on GFEDv3 wildﬁre emissions to a zero- Although the global RF introduced by wildﬁre emissions is negative for all realistic sim-

15 W m . Community Earth System Model (CESM). The authors only considered a prescribed wildfire wildfire-emission control run to estimate the net change in surface shortwave fluxes in the 2–5 (not shown). In the extra-tropics, changestrast, in the surface switch RF from rarely exceed GFAS to AEROCOM emissions introduces a regional surface RF of

duced by changes in emission heights in the order of 1–3 SOFIEV-MODIFIED and SOFIEV-TOP-INJECTION, maximum changes in surface RF intro- ues of the vicinity of the African source regions, where the negative surface RF exceeds mean val- imum TOA RF positive values of up to in Africa. In contrast, the largest regional radiative effects at the surface are detectable in represents the most realistic emission height scenario, are shown in Fig. 11a and b. Max- in Amazon is clearly positive while regions of positive and negative TOA RF are found surface RF introduced by wildﬁre emissions for the SOFIEV-MODIFIED simulation, which sion heights have a similar range in the Amazon region and central Africa, the TOA RF the global values by up to one order of magnitude. Global maps of the total skywhile TOA and a negative TOA RF is observable over most parts of the oceans. Although emis- ulations, regionally, positive and negative TOA RF values are observable which exceed the absorption of solar radiation by BC particles in the stratosphere is particularly important. contrast, for the extreme FT scenario this largely linear response does not apply, because a largely linear response to moderate changes in plume heights of up to a few kilometers. In from 0.11 to 0.14 forface all RF simulations ratios except indicate for that the the FT aerosol–radiation scenario. interaction These within similar the TOA atmosphere to shows sur- which represents roughly a doubling in surface RF compared to SOFIEV-DCYCLE (

(HAM2.2-AEROCOM: the TOA RF, the impact of the choice of the emission inventory on the global surface RF 0

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 to − 06 . 0 ± 07 W m . ). 0 16 2 . . However, in − 0 ± − 29 . 01 K . 0 0 − 10 W m ± . 0 . However, Jones et al. 2 13 . ± − 0 18 − . +0 07 W m . +0 in the GISS atmospheric composition-climate 25 2 − W m 0.25 leading to a surface cooling of − 2 − 2 W m . ) is considerably smaller than the spread between the models. Pre- 0 2 − ± 3 . 1 − 05 W m . 0

24 . A strong surface RF over tropical Africa is observable in both studies, but extra-tropical

0 Simulated by ECHAM6-HAM2”. By comparison of our simulations to AOT observations series “Fire Emission Heights in the Climate System Part I: Global Plume Height Patterns uation of the plume height parametrization is provided by the first part of this two-paper of the emission height implementation of global emission height patterns and a global eval- of the relative importance of changes in total emissions and emission heights. The impact a climate-change induced doubling in emissions and fire intensity provided a first estimate tions with prescribed HAM2 standard plume heights. In addition, a hypothetical scenario of of these references, but the spread between all our realistic simulations ( and compared the introduced changes in aerosol concentrations and radiation to simula- (2007) and Ungerin et the al. HadGEM1 (2010) modelmodel. found and TOA negative RF values TOA simulated RF by values our ECHAM6-HAM2 of simulations lie within the range sion release, we implemented different versions of a simple plume height parametrization In addition to extreme scenarios of pure free-tropospheric and pure near-surface emis- RF patterns showet larger al. differences. (2009) showed For also the a positive Oslo-CTM2 TOA RF model, value simulations of by Myhre deposition rates and radiation using the aerosol–climate modeling system ECHAM6-HAM2. We investigated the impact of wildfire emission heights on atmospheric BC concentrations, − 6 Summary and conclusions heights. port mechanisms, removal processes and absorptivity of BC and OC, not to the emission sumably the differences in RF between the models are attributed to differences in trans-

found to be emission release at the surface. The difference in net short-wave ﬂuxes at the surface was contrast to our study, the sign of the TOA RF was positive (

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | for the unrealistic % alter the mean annual km =0.4–0.85 between simulated and observed 2 26 R

Our comparison of modelingindicates results that to the ECHAM6-HAM2 AERONET,the MAN model AOT and is variability MODIS capable asburning observations of regions. well Mean capturing as correlations the of the magnitude seasonality of in the vicinity of the majorThe biomass comparison of simulated, verticallythat resolved AOT to close CALIOP to observations shows empirical the plume major height parametrization biomass marginally burning increases the regions, model the performance. implementation of the semi- BC deposition rates overscenario in of prescribed the emission Arctic releasesnow at and and the subsequent Antarctica surface. changes Thus, by in BCwildfire surface 5–25 albedo deposition emission show heights. rates only on a moderate sensitivity on instantaneous AOT values cansmall be improvements achieved introduced for by the major plume biomass height burning parametrization. regions with Considerable changes in mean plume heights of 1.1–2.5 Future changes in emission fluxesemission were heights. found to be more important than changes in Upper tropospheric and lower stratospheric BCby concentrations emission are fluxes, mainly tropical determined convection andemission the heights location are of of the limited emission importance. release, while The atmospheric BCmore burden, sensitive to total emission inventories deposition thanmentation. to rates The the application and details of of atmospheric a the emission diurnalheight radiation height cycle parametrization imple- and do are a not model-specific significantly tuning change of these the results. plume

– – – – – –

parametrization. Based on the analysis of our results, we present the following ﬁndings: climate modeling that can be expected from the implementation of a simple plume height from AERONET, MODIS and CALIOP we quantiﬁed the magnitude of improvements for

5 20 25

15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and thus shows little differ- 2 − W m 0.07 ± 20 . for standard prescribed emission heights in 0 27 2 ). − 2 − − W m 2 W m . 0.05 of the emissions injected into the free troposphere) and +0 ± . We also thank Angelika Heil and Samuel Remy (ECMWF, Reading, to for pure surface emission release. The application of a modi- 24 % . 3 0 . 2 0 − − − We are grateful to the DFG for funding the Emmy-Noether junior research W m 0.06 ± 16 . 0 Fire in the Earth System The Top Ofranges Atmosphere between (TOA) RadiativeECHAM6-HAM2 Forcing (25 (RF) of the wildfire emissions Nevertheless, the CALIOP measurement uncertaintiesAOT profiles by caused far by exceed changes the in changes wildfire emission in heights. − fied version of the Sofiev plumeobservations, height provides parametrization, a which TOA offers the RF best of match to ence to the other plumecompared height implementations. to These the changes in spreadclimate TOA RF of models are ( the small overall wildfire emission RF in other state-of-the-art

–

EU H2020 project MACC-III (contract no. 633080). comments during the internal review process by Stephan Bakan. J. W. Kaiser was funded by the comments on the model evaluation section. Moreover, we acknowledge the helpful and critical UK) for providing the GFASv1.1 data set and Stefan Kinne (MPI-M, Hamburg, Germany) for helpful group Acknowledgements. though emission heights are more appropriately represented. improper representation of height-dependent aerosol–cloud interactions will persist even der source of uncertainty. The known biases of global aerosol–climate models such as the deposition rates demonstrates that wildfire emission heights constitute only a second or- assessment of the wildfire emission height impact on global BC concentrations, burden and out to be of limited importance compared to emission fluxes and removal processes. The rection factor of 3.4 to the GFAS wildfire emission inventory, fire emission heights turned more appropriate tools for short term regional studies of high resolution. Applying a cor- aerosol climate interactions. More complex and advanced plume height models might be System Models, simple plume height parametrization are sufficient means to study global Based on these findings, we suggest that for current state of the art Climate and Earth

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 28

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20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 36

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Zhang, K., O’Donnell, D., Kazil, J., Stier, P., Kinne, S., Lohmann, U., Ferrachat, S., Croft, B., Zhang, F., Wang, J., Ichoku, C., Hyer, E. J., Yang, Z., Ge, C., Su, S., Zhang, X., Kondragunta, S., 5 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | % into PBL into PBL % % into the lowest and into FT, const. mass into top layer, 50 into FT, 75 into FT, 75 % % % % % top–bottom top–bottom top–bottom top–bottom mixing ratio second lowest layer top–bottom NO GFAS 100 37 above surface) m Parametrization of FRP Inventory 150 SOFIEV (Original, 2xFRP) NO GFAS const. mass mixing ratio Set-up of ECHAM6-HAM2 simulations for 2005–2011 based on various plume height SOFIEV-ORIGINALSOFIEV-DCYCLE SOFIEV (Original)SOFIEV-MODIFIED SOFIEV (Original)SOFIEV-TOP-INJECTION SOFIEV (Modified) SOFIEV (Original)SURFACE NO YESSOFIEV-2X-EMISSIONS- GFAS YES YESFRP 2 lowest const. GFAS model mass layers mixing (30– ratio GFAS GFAS const. mass mixing const. ratio 50 mass mixing ratio Simulation NameHAM2.2-GFAS Plume Height PBL Height+ 2 model layers Diurnal Cycle NO Emission Emission Distribution GFAS 25 FTHAM2.2-AEROCOM PBL Height+ 2 model layers PBL Height to Tropopause NO NO AEROCOM-II 25 GFAS 100 inventories. sions. See text for a more detailed description of the emission height implementations and emission the listed simulations, a NO-WILDFIRES scenario represents a simulation without any wildfire emis- Table 1. parametrizations. All simulations are nudged towards observations every six hours. In addition to Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ∗ 0.8–1.9 1.0–2.2 1.0–2.3 1.8–3.9 1.0–2.2 0.1–0.2 0.9–2.2 1.8–3.9 10.9–15.8 ] Heights 6 2 . 05 06 07 24 05 06 11 06 ...... 0 . − 0 0 0 0 0 0 0 0 ± ± ± ± ± ± ± ± ± W m 21 . 20 20 20 66 24 16 36 25 . . . . . 0 . . . 0 0 0 0 0 0 0 − − − − − − − − ][ 2 1634 +0 09 17 17 16 16 18 15 ...... − 0 0 0 0 1 0 0 0 0 ± ± ± ± ± ± ± ± ± W m 37 28 82 62 63 63 67 75 51 ...... 1 1 1 1 1 1 7 3 1 − − − − − − − − − 19 19 20 21 23 17 34 29 02 ...... 0 0 0 0 0 0 0 0 2 ± ± ± ± ± ± ± ± ± 08 15 17 28 50 82 58 08 50 ...... ] [km] 1 − 002002 8 8 002 8 002 8 006 9 002 8 002001 7 31 001 8 ...... yr ) in the last column indicates that plume 0 0 0 0 0 0 0 0 0 ∗ ± ± ± ± ± ± ± ± ± 033 032 058 038 035 035 033 033 032 ...... ] [Tg 1 0404 0 0 04 0 05 0 07 0 0702 0 0 04 0 02 0 ...... − 0 0 0 0 0 0 0 0 0 yr 38 ± ± ± ± ± ± ± ± ± 11 11 07 13 46 35 89 06 07 ...... ] [Tg 1 38 1 02 1 6969 1 69 1 1 68 1 6655 1 0 69 1 ...... − 0 0 1 0 0 0 0 0 0 yr ± ± ± ± ± ± ± ± ± [Tg 46 44 56 56 59 55 33 60 60 ...... 19 13 41 21 1615 13 13 08 14 1818 13 13 18 13 20 13 ...... 0 0 0 0 0 0 1 0 0 ± ± ± ± ± ± ± ± ± 95 40 76 01 17 89 90 87 03 ...... 0202 2 2 04 5 0211 2 13 01 3 02 2 02 2 02 3 ...... [Tg] [Tg] Deposition Deposition Sedimentation [days] [ 0 0 0 0 0 0 0 0 0 ± ± ± ± ± ± ± ± ± 33 33 33 57 32 26 36 33 34 ...... 0 0 0 0 0 0 1 0 0 Global mean values (2005–2011) describing aerosol atmospheric transport and radiation HAM2.2-AEROCOM FRP SOFIEV-DCYCLE SOFIEV-MODIFIED SOFIEV-TOP_INJ SOFIEV-ORIGINAL SOFIEV-2X-EMISSIONS- SURFACE FT Simulation NameHAM2.2-GFAS BC Burden OC Burden BC Wet BC Dry BC BC Lifetime Surface RF TOA RF Plume 2011. set-up of all scenarios, see Table 1. Uncertainties represent one SD of monthly means for 2005– height values represent 10th to 90th percentiles. For a detailed plume height and emission inventory simulations we use GFASv1.1 emissions. The Asterisk ( in all simulations. Simulation HAM2.2-AEROCOM is based on AEROCOM emissions, for all other serves as reference for calculation of total sky Top Of Atmosphere (TOA) Radiative Forcing (RF) Table 2. for various parametrizations of plume heights in ECHAM6-HAM2.2. The NO-WILDFIRES simulation Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 39 Region specification of major biomass burning areas for comparison of modeled AOT 2005–2011. Yellow colors indicate low FRP values, dark red colors indicate high FRP values. to observations. Color shading represents mean annual assimilated FRP values of GFASv1.1 for Figure 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

(e) (d)

Relative changes in BC burden BC in changes Relative Relative changes in BC burden BC in changes Relative Relative changes in BC burden BC in changes Relative burden BC in changes Relative 14 12 10 8 6 4 2 0 0.1 0.2 0.3 0.4 0.5 0.04 0.08 0.12 0.16 0.20 0.04 0.08 0.12 0.16 0.20 0.5 0.4 0.3 0.2 0.1 0.0 0.20 0.16 0.12 0.08 0.04 0.00 0.20 0.16 0.12 0.08 0.04 0.00 180°180° 180°180° 180°180° 180°180° 120°E 120°E 120°E 120°E 60°E 60°E 60°E 60°E and SOFIEV-TOP-INJECTION (f) (b) (d) (h) 0° 0° 0° 0° (c) Simulation FT 60°W 60°W 60°W 60°W Simulation SOFIEV-DCYCLE Simulation HAM2.2-AEROCOM Simulation SOFIEV-TOP-INJECTION is based on purely free-tropospheric emis- 120°W 120°W 120°W 120°W (f) 180°180° 180°180° 180°180° 180°180° 0° 0° 0° 0°

30°S 30°S 30°S 30°S 60°S 60°S 60°S 60°S 90°S 90°S 90°S 90°S 30°N 30°N 30°N 30°N 60°N 60°N 60°N 60°N 90°N 90°N 90°N 90°N

Relative changes in BC burden BC in changes Relative Relative changes in BC burden BC in changes Relative burden BC in changes Relative burden BC in changes Relative 40 0.4 0.8 0.16 0.20 0.04 0.08 0.12 0.16 0.20 0.04 0.08 0.12 0.16 0.20 0.04 0.08 0.12 2.0 1.6 1.2 0.8 0.4 0.0 0.20 0.16 0.12 0.08 0.04 0.00 0.20 0.16 0.12 0.08 0.04 0.00 0.20 0.16 0.12 0.08 0.04 0.00 180°180° 180°180° 180°180° 180°180° assumes a doubling in emissions and FRP. HAM2.2- 120°E 120°E 120°E 120°E (g) , SOFIEV-MODIFIED 60°E 60°E 60°E 60°E (b) (c) (e) (g) (a) 0° 0° 0° 0° , see Table 1. Simulation SURFACE 60°W 60°W 60°W 60°W (h) Simulation SOFIEV-ORIGINAL Simulation SOFIEV-MODIFIED Simulation SOFIEV-2X-EMISSIONS-FRP to 120°W 120°W 120°W 120°W (a) 180°180° 180°180° 180°180° 180°180° 0° 0° 0° 0° 30°S 30°S 30°S 30°S 60°S 60°S 60°S 60°S 90°S 90°S 90°S 90°S 30°N 30°N 30°N 30°N 60°N 60°N 60°N 60°N 90°N 90°N 90°N 90°N illustrates the influence of changes in the emission inventory. For a description of , SOFIEV-DCYCLE (h) (a) Mean relative changes in BC burden introduced by various implementations of fire sions. SOFIEV-2X-EMISSIONS-FRP AEROCOM settings for simulations show different versionsrepresents of near surface the emissions, Sofiev simulation plume FT height parametrization. Simulation SURFACE ORIGINAL sion heights combined with GFASv1.1 emissions (simulation HAM2.2-GFAS). Simulations SOFIEV- Figure 2. emission heights. All relative changes refer to the standard implementation of prescribed emis- Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | N N N N 1.00 0.300 100.0 0.300 80 80 80 80 70 70 70 70 50.0 0.50 0.200 0.200 60 60 60 60 50 50 50 50 10.0 0.25 0.100 0.100 40 40 40 40 5.0 0.10 0.050 0.050 30 30 30 30 20 20 20 20 1.0 0.05 0.025 0.025 10 10 10 10 0 0 0 0 0.0 0.00 0.000 0.000 10 10 10 10 (f) (b) (d) (h) Simulation FT Latitude [degrees] Latitude [degrees] Latitude [degrees] Latitude [degrees] 0.1 0.05 0.025 0.025 20 20 20 20 30 30 30 30 Simulation SOFIEV-DCYCLE Simulation HAM2.2-AEROCOM Relative change in BC concentration Relative change in BC concentration Relative change in BC concentration 0.2 Relative change in BC concentration 0.10 0.050 0.050 Simulation SOFIEV-TOP-INJECTION 40 40 40 40 50 50 50 50 0.5 0.25 0.100 0.100 60 60 60 60 0.8 0.50 0.200 70 70 70 0.200 70 80 80 80 80 1.0 1.00 0.300 0.300 S S S S 50 50 50 50 100 100 100 500 500 500 200 200 200

100 500 200

1000 1000 1000 1000 Pressure [hPa] Pressure [hPa] Pressure Pressure [hPa] Pressure Pressure [hPa] Pressure N N N N 41 4.00 0.300 0.300 0.300 80 80 80 80 70 70 70 70 2.00 0.200 0.200 0.200 60 60 60 60 50 50 50 50 1.00 0.100 0.100 0.100 40 40 40 40 0.50 0.050 0.050 0.050 30 30 30 30 20 20 20 20 0.25 0.025 0.025 0.025 10 10 10 10 0 0 0 0 0.00 0.000 0.000 0.000 10 10 10 10 (e) (c) (g) (a) Latitude [degrees] Latitude [degrees] Latitude [degrees] Latitude [degrees] 0.10 0.025 0.025 0.025 20 20 20 20 Simulation SURFACE 30 30 30 30 Simulation SOFIEV-ORIGINAL Simulation SOFIEV-MODIFIED Relative change in BC concentration Relative change in BC concentration Relative change in BC concentration Relative change in BC concentration 0.25 0.050 0.050 0.050 40 40 40 40 Simulation SOFIEV-2X-EMISSIONS-FRP 50 50 50 50 0.50 0.100 0.100 0.100 60 60 60 60 0.75 0.200 0.200 0.200 70 70 70 70 80 80 80 80 1.00 0.300 0.300 0.300 S S S S 50 50 50 50

100 100 100 100 500 500 500 500 200 200 200 200

1000 1000 1000 1000 Pressure [hPa] Pressure Pressure [hPa] Pressure Pressure [hPa] Pressure Pressure [hPa] Pressure Mean relative changes in zonal mean BC concentrations for 2005–2011. All rela- ups is provided in Table 1. Figure 3. GFASv1.1 emissions (simulation HAM2.2-GFAS). A more detailed description of the simulation set- tive changes refer to the standard implementation of prescribed emission heights combined with

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Relative change in BC removal rates removal BC in change Relative rates removal BC in change Relative rates removal BC in change Relative rates removal BC in change Relative 0.4 0.8 0.1 0.2 0.3 0.4 0.5 0.1 0.2 0.3 0.4 0.5 0.1 0.2 0.3 0.4 0.5 0.8 0.4 0.0 0.2 0.1 0.0 0.2 0.1 0.0 0.2 0.1 0.0 2.0 1.6 1.2 0.5 0.4 0.3 0.5 0.4 0.3 0.5 0.4 0.3 180°180° 180°180° 180°180° 180°180° 120°E 120°E 120°E 120°E 60°E 60°E 60°E 60°E (f) (b) (d) (h) 0° 0° 0° 0° Simulation FT 60°W 60°W 60°W 60°W Simulation SOFIEV-DCYCLE Simulation Simulation HAM2.2-AEROCOM Simulation SOFIEV-TOP-INJECTION 120°W 120°W 120°W 120°W 180°180° 180°180° 180°180° 180°180° 0° 0° 0° 0°

30°S 30°S 30°S 30°S 60°S 60°S 60°S 60°S 90°S 90°S 90°S 90°S 30°N 30°N 30°N 30°N 60°N 60°N 60°N 60°N 90°N 90°N 90°N 90°N

Relative change in BC removal rates removal BC in change Relative rates removal BC in change Relative Relative change in BC removal rates removal BC in change Relative rates removal BC in change Relative 42 0.1 0.2 0.3 0.4 0.5 0.4 0.8 0.1 0.2 0.3 0.4 0.5 0.1 0.2 0.3 0.4 0.5 0.2 0.1 0.0 0.8 0.4 0.0 0.2 0.1 0.0 0.2 0.1 0.0 0.5 0.4 0.3 2.0 1.6 1.2 0.5 0.4 0.3 0.5 0.4 0.3 180°180° 180°180° 180°180° 180°180° 120°E 120°E 120°E 120°E 60°E 60°E 60°E 60°E (e) (c) (g) (a) 0° 0° 0° 0° Simulation SURFACE 60°W 60°W 60°W 60°W Simulation SOFIEV-ORIGINAL Simulation Simulation SOFIEV-MODIFIED Simulation SOFIEV-2X-EMISSIONS-FRP 120°W 120°W 120°W 120°W 180°180° 180°180° 180°180° 180°180° 0° 0° 0° 0° 30°S 30°S 30°S 30°S 60°S 60°S 60°S 60°S 90°S 90°S 90°S 90°S 30°N 30°N 30°N 30°N 60°N 60°N 60°N 60°N 90°N 90°N 90°N 90°N Simulated mean total deposition rates from 2005 to 2011. All relative changes refer to Table 1. Figure 4. (simulation HAM2.2-GFAS). A more detailed description of the simulation set-ups is provided in the standard implementation of prescribed emission heights combined with GFASv1.1 emissions Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 300300 300300 300300 Observations Observations HAM2.2−GFAS HAM2.2−GFAS NO−WILDFIRES NO−WILDFIRES SOFIEV−MODIFIED SOFIEV−MODIFIED HAM2.2−AEROCOM HAM2.2−AEROCOM 200200 200200 200200 (i) (f) (c) Days since 1−jan−2008 Days since 1−jan−2008 Days since 1−jan−2008 100100 100100 100100 Congo: NRL aqua (2008) Congo: NRL Outflow: NRL aqua (2008) Amazon: NRL aqua (2008) NO−WILDFIRES HAM2.2−AEROCOM HAM2.2−GFAS SOFIEV−MODIFIED Observations 00 00 00

0.80.8 0.60.6 0.40.4 0.20.2 0.00.0 1.01.0 0.80.8 0.60.6 0.40.4 0.20.2 0.00.0 0.60.6 0.50.5 0.40.4 0.30.3 0.20.2 0.10.1 0.00.0

AOT AOT AOT 300300 300300 300300 Observations Observations HAM2.2−GFAS HAM2.2−GFAS NO−WILDFIRES NO−WILDFIRES SOFIEV−MODIFIED SOFIEV−MODIFIED HAM2.2−AEROCOM HAM2.2−AEROCOM 200200 200200 200200 (e) (b) (h) 43 Days since 1−jan−2007 Days since 1−jan−2007 Days since 1−jan−2007 100100 100100 100100 Congo: NRL aqua (2007) Congo: NRL Outflow: NRL aqua (2007) Amazon: NRL aqua (2007) NO−WILDFIRES HAM2.2−AEROCOM HAM2.2−GFAS SOFIEV−MODIFIED Observations 00 00 00

0.60.6 0.50.5 0.40.4 0.30.3 0.20.2 0.10.1 0.00.0 0.80.8 0.60.6 0.40.4 0.20.2 0.00.0 1.01.0 0.80.8 0.60.6 0.40.4 0.20.2 0.00.0

AOT AOT AOT 300300 300300 300300 Observations Observations HAM2.2−GFAS HAM2.2−GFAS NO−WILDFIRES NO−WILDFIRES SOFIEV−MODIFIED SOFIEV−MODIFIED HAM2.2−AEROCOM HAM2.2−AEROCOM 200200 200200 200200 (a) (g) (d) Days since 1−jan−2006 Days since 1−jan−2006 Days since 1−jan−2006 100100 100100 100100 Congo: NRL aqua (2006) Congo: NRL Outflow: NRL aqua (2006) Amazon: NRL aqua (2006) NO−WILDFIRES HAM2.2−AEROCOM HAM2.2−GFAS SOFIEV−MODIFIED Observations 00 00 00

0.60.6 0.50.5 0.40.4 0.30.3 0.20.2 0.10.1 0.00.0 0.20.2 0.00.0 0.80.8 0.60.6 0.40.4 0.20.2 0.00.0 1.01.0 0.80.8 0.60.6 0.40.4 Temporal evolution of regional AOT for standard HAM2.2 plume heights based on GFAS

AOT AOT AOT AOT. All model data were collocated with the observations prior to averaging. guish wildfire-related biases from others. Observations are MODIS Aqua satellite measurements of and fire intensity (simulation SOFIEV-MODIFIED). The NO-WILDFIRES scenario is shown to distin- Figure 5. fied plume height parametrization of Sofiev et al. (2012) including a diurnal cycle of fire emissions emissions (HAM2.2-GFAS) respectively AEROCOM emissions (HAM2.2-AEROCOM) and a modi- Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 300300 300300 300300 Observations Observations Observations HAM2.2−GFAS HAM2.2−GFAS HAM2.2−GFAS NO−WILDFIRES NO−WILDFIRES NO−WILDFIRES SOFIEV−MODIFIED SOFIEV−MODIFIED HAM2.2−AEROCOM HAM2.2−AEROCOM SOFIEV−MODIFIED HAM2.2−AEROCOM 200200 200200 200200 (i) (f) (c) Days since 1−jan−2008 Days since 1−jan−2008 Days since 1−jan−2008 100100 100100 100100 Siberia: NRL aqua (2008) Boreal North America: NRL aqua (2008) America: NRL aqua Boreal North Temperate North America: NRL aqua (2008) 00 00 00

0.60.6 0.50.5 0.40.4 0.30.3 0.20.2 0.10.1 0.00.0 0.350.35 0.300.30 0.250.25 0.200.20 0.150.15 0.100.10 0.050.05 0.000.00 0.250.25 0.200.20 0.150.15 0.100.10 0.050.05 0.000.00

AOT AOT AOT 300300 300300 300300 Observations Observations HAM2.2−GFAS HAM2.2−GFAS NO−WILDFIRES NO−WILDFIRES SOFIEV−MODIFIED HAM2.2−AEROCOM SOFIEV−MODIFIED HAM2.2−AEROCOM 200200 200200 200200 (e) (b) (h) 44 Days since 1−jan−2007 Days since 1−jan−2007 Days since 1−jan−2007 100100 100100 100100 Siberia: NRL aqua (2007) NO−WILDFIRES HAM2.2−AEROCOM HAM2.2−GFAS SOFIEV−MODIFIED Observations Boreal North America: NRL aqua (2007) America: NRL aqua Boreal North Temperate North America: NRL aqua (2007) 00 00 00

0.60.6 0.50.5 0.40.4 0.30.3 0.20.2 0.10.1 0.00.0 0.350.35 0.300.30 0.250.25 0.200.20 0.150.15 0.100.10 0.050.05 0.000.00 0.250.25 0.200.20 0.150.15 0.100.10 0.050.05 0.000.00

AOT AOT AOT 300300 300300 300300 Observations Observations HAM2.2−GFAS HAM2.2−GFAS NO−WILDFIRES NO−WILDFIRES SOFIEV−MODIFIED HAM2.2−AEROCOM SOFIEV−MODIFIED HAM2.2−AEROCOM 200200 200200 200200 (a) (g) (d) Days since 1−jan−2006 Days since 1−jan−2006 Days since 1−jan−2006 100100 100100 100100 Siberia: NRL aqua (2006) NO−WILDFIRES HAM2.2−AEROCOM HAM2.2−GFAS SOFIEV−MODIFIED Observations Boreal North America: NRL aqua (2006) America: NRL aqua Boreal North Temperate North America: NRL aqua (2006) 00 00 00

0.60.6 0.50.5 0.40.4 0.30.3 0.20.2 0.10.1 0.00.0 0.150.15 0.100.10 0.050.05 0.000.00 0.350.35 0.300.30 0.250.25 0.200.20 0.250.25 0.200.20 0.150.15 0.100.10 0.050.05 0.000.00 Temporal evolution of regional AOT for standard HAM2.2 plume heights based on GFAS

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

5 5 5 1.0 1.0 1.0

9 9 9

. . .

2.0 2.0 2.0 2.0 2.0 2.0

0 0 0

9 9 9

. . .

0 0 0 − − −

NRL aqua NRL terra NRL AERONET_DS MAN aqua NRL terra NRL AERONET_DS MAN aqua NRL terra NRL AERONET_DS MAN

8 8 8

. . .

0 0

0 MODIFIED SOFIEV n MODIFIED SOFIEV n MODIFIED SOFIEV n

tio tio toi la la al

re re re

7 7

r 7 r r . .

. 1.5 1.5

o 1.5 1.5 o 1.5 1.5 o

0 0 0

C C C

−

6 6 6

. .

. − − −

0 0 0

5 5 5

. . . − A22GFAS HAM2.2 GFAS HAM2.2 GFAS HAM2.2

− 0 0 0

1.0 1.0 1.0 1.0 1.0 1.0

(f) (b) (d)

4 4 4

. . .

0 0 0

Standard Deviation Standard Deviation Standard Deviation Standard

3 3 3

. . . 0 0 0

Outflow (2006 2008) Outflow − − − Amazon (2006 2008) Amazon

0.5 0.5 0.5 0.5 0.5 0.5 2 2 2 . . . 0 0 0

A22AEROCOM HAM2.2 AEROCOM HAM2.2 A22AEROCOM HAM2.2

1 1 1 . . . 0 0 0

Temperate North America (2006 2008) America North Temperate

0 . 0 0 . 0 0 . 0 0.0 0.0 0.0 0.0 0.0 0.0

1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 1.0 1.0 0.5 0.5 0.0 0.0 2.0 2.0 2.0 2.0 1.5 1.5

n o i t a i v e D d r a d n a t S n o i t a i v e D d r a d n a t S n o i t a i v e D d r a d n a t S − − − NO WILDFIRES NO WILDFIRES NO WILDFIRES

5 5 5 1.0 1.0 1.0

9 9 9

. . .

2.0 2.0 2.0 2.0 2.0 2.0

0 0 0

9 9 9

. . .

0 0 0 − − −

NRL aqua NRL terra NRL AERONET_DS MAN NRL aqua NRL terra NRL AERONET_DS MAN aqua NRL terra NRL AERONET_DS MAN

8 8 8

. . .

0 0

0 MODIFIED SOFIEV

n MODIFIED SOFIEV n MODIFIED SOFIEV n

o o o ti ti ti la la al

re re re

7 7

r 7 r r . .

. 1.5 1.5 1.5 1.5

o 1.5 1.5 o o

0 0 0

C C C

6 6 6

. .

. − − − −

0 0 0

5 5 5

. .

. GFAS HAM2.2 GFAS HAM2.2 A22GFAS HAM2.2

− 0 0 0 −

1.0 1.0 1.0 1.0 1.0 1.0

(e) (c) (a)

4 4 4

. . .

0 0 0

Standard Deviation Standard Deviation Standard Standard Deviation Standard

3 3 3

. . . 0 0 0

Congo (2006 2008) Congo Siberia (2006 2008) Siberia − − −

0.5 0.5 0.5 0.5 0.5 0.5 2 2 2 . . . 0 0 0

A22AEROCOM HAM2.2 A22AEROCOM HAM2.2 A22AEROCOM HAM2.2

1 1 1 . . . 0 0 0

Boreal North America (2006 2008) America North Boreal

0 . 0 0 . 0 0 . 0 0.0 0.0 0.0 0.0 0.0 0.0

2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 1.0 1.0 0.5 0.5 0.0 0.0 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 2.0 2.0 1.5 1.5

D d r a d n a t S n o i t a i v e D d r a d n a t S n o i t a i v e D d r a d n a t n o i t a i v e S − − − NO WILDFIRES NO WILDFIRES NO WILDFIRES Taylor diagrams for comparison of simulations HAM2.2-AEROCOM, HAM2.2-GFAS and deviations exceed the scale range. Note that for region Congo (e) simulation HAM2.2-AEROCOM is not shown, because the standard set-ups and observational data sets. The NO-WILDFIRES scenario excludes all wildﬁre emissions. based observations (AERONET_DS, MAN), see text for more detailed description of simulation SOFIEV-MODIFIED to satellite observations (MODIS NRL Aqua, MODIS NRL Terra) and ground- Figure 7. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (model simulations) of nm 0.25 0.25 0.25 0.20 0.20 0.20 ). km 0.15 0.15 0.15 Outflow Amazon (f) (d) (b) 0.10 0.10 0.10 Relative Fraction of AOT Relative Fraction of AOT Relative Fraction of AOT Temperate_North_America 0.05 0.05 0.05 0 0 0 0.00 0.00 0.00

2000 2000 2000 6000 6000 6000 8000 8000 8000 4000 4000 4000

10000 10000 10000

Height [m] Height [m] Height Height [m] Height 46 0.25 0.25 0.25 intervals for 5–10 0.20 0.20 0.20 km CALIOP SURFACE HAM2.2-GFAS SOFIEV-MODIFIED (CALIOP) respectively at 550 , 1 0.15 0.15 0.15 nm Congo km Siberia (c) (e) (a) 0.10 0.10 0.10 Relative Fraction of AOT Relative Fraction of AOT Relative Fraction of AOT Boreal_North_America 0.05 0.05 0.05 0 0 0 0.00 0.00 0.00

2000 2000 2000 6000 6000 6000 8000 8000 8000 4000 4000 4000

10000 10000 10000

Height [m] Height [m] Height Height [m] Height intervals for 0–5 m Regional AOT profiles averaged for 2006–2011 for CALIOP observations (solid blue line), resent relative AOT fractions at 532 indicate minimum and maximum monthly means for CALIOP observations. All vertical lines rep- MODIFIED. Dark blue shading indicates one SD of CALIOP monthly means; light blue shadings monthly means; red shading represents one SD of monthly variations for model simulation HAM2.2- red line) and simulation SURFACE (solid green line). Thin red lines indicate individual multi-year height layers (500 Figure 8. simulation HAM2.2-GFAS (bold solid red line), simulation SOFIEV-MODIFIED (bold dashed dark monthly averages for 2006–2011. Relative AOT fractions describe the integrated AOT of individual Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 12 0.96 11 confidence interval, % 0.88 10 9 0.80 8 0.72 7 6 0.64 month of the year 47 5 Pearson Correlation Coefficient Simulation SOFIEV-MODIFIED Simulation 0.56 4 3 0.48 2 0.40 1 Congo Boreal Siberia Outflow Amazon Temperate Pearson correlation coefficients of multi-year monthly means for CALIOP vs. SOFIEV- North America North America Figure 9. while correlations smaller than 0.48 are not significant. MODIFIED. Correlation coefficients greater than 0.48 are significant on a 95

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

SOFIEV- SOFIEV-

TOP-INJECTION TOP-INJECTION

MODIFIED

SOFIEV- SOFIEV- SOFIEV- SOFIEV-

DCYCLE DCYCLE

SOFIEV- SOFIEV- SOFIEV- SOFIEV- net forcing net solar radiative forcing net thermal radiative forcing . The RF of all simulations

net forcing radiative forcing net solar radiative forcing net thermal

ORIGINAL

ORIGINAL SOFIEV- SOFIEV- SOFIEV- SOFIEV- (d)

(b) (d)

SURFACE SURFACE

AEROCOM AEROCOM

HAM2.2- HAM2.2- HAM2.2- HAM2.2-

HAM2.2-GFAS HAM2.2-GFAS

1.2 2.2 1.0 1.8 2.0 1.6 2.6 0.2 1.4 2.4 0.5 0.7 0.3 0.9 0.8 0.6 0.4

0.05 0.10 0.05 0.10 0.00

Surface Radiative Forcing Clear Sky [W/m2] Sky Clear Forcing Radiative Surface TOA Radiative Forcing Clear Sky [W/m2] Sky Clear Forcing Radiative TOA 48 and surface clear sky

(c)

SOFIEV- SOFIEV-

TOP-INJECTION TOP-INJECTION

MODIFIED MODIFIED

SOFIEV- SOFIEV- SOFIEV- SOFIEV-

DCYCLE DCYCLE

SOFIEV- SOFIEV- SOFIEV- SOFIEV- net thermal radiative forcing net forcing net solar radiative forcing

net solar radiative forcing net solar radiative forcing net thermal net forcing

ORIGINAL

ORIGINAL SOFIEV- SOFIEV- SOFIEV- SOFIEV- (c) (a)

, surface total sky

SURFACE SURFACE

(b)

AEROCOM

HAM2.2- HAM2.2- AEROCOM

HAM2.2- HAM2.2-

HAM2.2-GFAS HAM2.2-GFAS

Simulated global mean net Radiative Forcing (RF) for Top Of Atmosphere (TOA) total 2.2 1.2 1.0 1.8 2.0 1.6 2.6 0.2 1.4 2.4 0.5 0.7 0.3 0.9 0.8 0.6 0.4

0.2 0.1 0.3 0.2 0.4 0.1 0.0

Surface Radiative Forcing Total Sky [W/m2] Sky Total Forcing Radiative Surface TOA Forcing Total Sky [W/m2] Sky Total Forcing TOA , TOA clear sky (a) 2005–2011. For a detailed description of the simulation set-ups, see Table 1. refers to the NO-WILDFIRES scenario. Error bars indicate one SD of monthly mean RF values for Figure 10. sky

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

TOA total RF (Clear Sky) [W/m2] Sky) (Clear RF total TOA Surface total RF (Clear Sky)[W/m2] (Clear RF total Surface 1 2 3 4 5 2 4 6 8 10 1 0 5 4 3 2 2 0 10 8 6 4 180°180° 180°180° 120°E 120°E 60°E 60°E (a) (b) 49 0° 0° 60°W 60°W TOA RF Simulation SOFIEV-MODIFIED Simulation TOA RF Surface RF Simulation SOFIEV-MODIFIED 120°W 120°W 180°180° 180°180° 0° 0° 30°S 30°S 60°S 60°S 90°S 90°S 30°N 30°N 60°N 60°N 90°N 90°N Clear sky Top of Atmosphere radiative forcing (top plot) and clear sky surface radia- tions) and the NO-WILDFIRES scenario for which all wildfire emissions were turned off. SOFIEV-MODIFIED (applying the plume height parametrization which matches best to observa- Figure 11. tive forcing introduced by wildfire emissions (lower plot). Both figures show absolute differences of