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Quantifying the Sensitivity of Aerosol Optical Properties to the Parameterizations of Physico-Chemical Processes During the 2010 Russian Wildfires and Heatwave

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Quantifying the Sensitivity of Aerosol Optical Properties to the Parameterizations of Physico-Chemical Processes During the 2010 Russian Wildfires and Heatwave Atmos. Chem. Phys., 20, 9679–9700, 2020 https://doi.org/10.5194/acp-20-9679-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Quantifying the sensitivity of aerosol optical properties to the parameterizations of physico-chemical processes during the 2010 Russian wildfires and heatwave Laura Palacios-Peña1, Philip Stier2, Raquel Lorente-Plazas3, and Pedro Jiménez-Guerrero1,4 1Physics of the Earth, Regional Campus of International Excellence “Campus Mare Nostrum”, University of Murcia, Murcia, Spain 2Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK 3Dept. of Meteorology, Meteored, Almendricos, Spain 4Biomedical Research Institute of Murcia (IMIB-Arrixaca), Murcia, Spain Correspondence: Pedro Jiménez-Guerrero ([email protected]) Received: 16 January 2020 – Discussion started: 11 March 2020 Revised: 30 June 2020 – Accepted: 13 July 2020 – Published: 18 August 2020 Abstract. The impact of aerosol–radiation and aerosol– when reducing the dry deposition velocity; in particular, cloud interactions on the radiative forcing is subject to large for the accumulation mode where the concentration of sev- uncertainties. This is caused by the limited understanding eral species increases (while a decrease might be expected). of aerosol optical properties and the role of aerosols as These nonlinear responses are highly dependent on the equi- cloud condensation/ice nuclei (CCN/IN). On the other hand, librium of the thermodynamics system sulfate–nitrate–SOA aerosol optical properties and vertical distribution are highly (secondary organic aerosol). In this sense, small changes in related, and their uncertainties come from different pro- the concentration of one species can strongly affect others, cesses. This work attempts to quantify the sensitivity of finally affecting aerosol optical properties. Changes in this aerosol optical properties (i.e. aerosol optical depth; AOD) equilibrium could come from modifications in relative hu- and their vertical distribution (using the extinction coef- midity, dry deposition, or vertical convective transport. By ficient, backscatter coefficient, and concentrations’ species itself, dry deposition also presents a high uncertainty influ- profiles) to key processes. In order to achieve this objective, encing the AOD representation. sensitivity tests have been carried out, using the WRF-Chem regional fully coupled model by modifying the dry deposi- tion, sub-grid convective transport, relative humidity, and wet scavenging. The 2010 Russian heatwave–wildfires episode 1 Introduction has been selected as case study. Results indicate that AOD is sensitive to these key pro- Since the First Assessment Report of the Intergovernmen- cesses in the following order of importance: (1) modification tal Panel on Climate Change (IPCC), a wide scientific con- of relative humidity, causing AOD differences of up to 0.6; sensus identifies atmospheric aerosols and clouds as one of (2) modification of vertical convection transport with AOD the forcing agents with the largest uncertainty in the climate differences around − 0:4; and (3) the dry deposition with system (Charlson et al., 1992; Schimel et al., 1996; Pen- AOD absolute differences of up to −0:35 and 0.3. Moreover, ner et al., 2001; Randall et al., 2007; Forster et al., 2007; these AOD changes exhibit a nonlinear response. Both an in- Boucher, 2015). Atmospheric aerosols modify the Earth’s ra- crease and a decrease in the RH result in higher AOD values. diative budget through aerosol–radiation interactions (ARIs) On the other hand, both the increase and offset of the sub-grid and aerosol–cloud interactions (ACIs). ARIs lead to a re- convective transport lead to a reduction in the AOD over the distribution of radiative energy in the atmosphere through fire area. In addition, a similar nonlinear response is found scattering and absorption. In addition, ACIs modify cloud Published by Copernicus Publications on behalf of the European Geosciences Union. 9680 L. Palacios-Peña et al.: Aerosol optical properties sensitivity microphysical properties and precipitation regimes as well properties, such as AOD. An increase in downward solar ra- as cloud effects on radiation (Randall et al., 2007; Boucher diation was found by Forkel et al.(2015) and Romakkaniemi et al., 2013). et al.(2012) when ACIs were taken into account. This lat- ARIs and ACIs are strongly dependent on aerosol optical ter contribution found a relationship between a reduction in properties and the ability of aerosols to act as cloud conden- the AOD and CCN, because the inclusion of ACI in numer- sation nuclei (CCN) or ice nuclei (IN), which are controlled ical models leads to a reduction in CCN by the condensa- by the spatio-temporal aerosol distribution, the aerosol size, tion kinetics of water during cloud droplet formation. This composition and mixing state (Stier et al., 2005). Thus, to de- induces a reduction of the cloud droplet number, the cloud termine and constrain the uncertainty in aerosol optical prop- liquid water, and, finally, an increase in downward solar radi- erties is a key issue for a better assessment of the uncertainty ation. In addition to AOD, CCN conditioned the uncertainty in aerosol effects. in ACI, as well as cloud occurrence and cloud-related pro- Numerical models are useful tools for understanding the cesses (updraught speeds, precipitation processes, etc.). Be- different parameters influencing the atmospheric system, cause of that, the high uncertainty existing when modelling such as aerosol optical properties. The complexity of how CCN was evaluated by Lee et al.(2013), finding that dry aerosols are treated in models varies widely, since these mod- deposition was the most important process for this uncer- els take into account processes as emission, transport, de- tainty over more than 28 model parameters selected by ex- position, microphysics, and chemistry (Kipling et al., 2016). pert elicitation, including nucleation, aerosol ageing, pH of Differences in complexity primarily arise from representa- cloud drops, nucleation scavenging, dry deposition, modal tions of aerosol size distribution and mixing states. The most with mode separation diameter, emissions, and production complex and realistic models are those considering the in- of secondary organic aerosols (SOA). These results, which clusion of ARIs and ACIs, since they allow a fully coupled are partly because wet deposition was not fully varied, were interaction of aerosols, meteorology, radiation, and chem- found in one model framework (with its own structural un- istry. One example of these numerical models is WRF-Chem certainties). (Grell et al., 2005), used in this work. Notwithstanding the Another source of uncertainty is the aerosol variability at complexity of aerosol treatment in these models, there are scales smaller than the model’s grid box, which can hamper still high uncertainties in processes representing the aerosol the representation of aerosol optical properties. This fact was optical properties. brought to light in Weigum et al.(2016), where the aerosol As stated by previous works (e.g. Palacios-Peña et al., water uptake through aerosol–gas equilibrium reactions was 2017, 2018, 2019a), uncertainties in aerosol optical proper- established as one of the most affected processes by this vari- ties may be influenced by a number of factors, namely emis- ability. The inherent nonlinearities in these processes result sions, aerosol mass concentration, particle size representa- in large changes in aerosol properties that are exaggerated by tion (Balzarini et al., 2015), vertical distribution and loca- convective transport. The uncertainties in RH also contribute tion with respect to other forcing agents as clouds (Kipling to those of aerosol optical properties due to their dependence et al., 2016), dry deposition and CCN (Romakkaniemi et al., in hygroscopic growth (Yoon and Kim, 2006; Zhang et al., 2012; Lee et al., 2013; Forkel et al., 2015), relative humidity 2012; Altaratz et al., 2013; Palacios-Peña et al., 2019a). (RH; Yoon and Kim, 2006; Zhang et al., 2012; Altaratz et al., Bearing in mind the uncertainties described above, the 2013; Weigum et al., 2016), and aerosol internal mixing rules aim of this work is to shed some light on the uncertain- (Curci et al., 2019; Zhang et al., 2012). ties when representing aerosol optical properties. In order Precisely, aerosol vertical distribution is highly influenced to achieve this aim, this contribution quantifies the sensi- by aerosol optical properties (Palacios-Peña et al., 2018, tivity of aerosol optical properties and their vertical distri- 2019a). Hence, Kipling et al.(2016) investigated the uncer- bution (which may condition aerosol radiative forcing) to tainty in the vertical layering of aerosol particles for dif- several aerosol processes and parameters. This quantification ferent parameters: convective transport, emissions injection, has been estimated by sensitivity tests carried out using the and size; vertical advection, boundary-layer mixing, entrain- WRF-Chem regional fully coupled model. Modified aerosol ment into convective plumes, condensation, coagulation, nu- processes and parameters are dry deposition, sub-grid con- cleation, aqueous chemistry, ageing of insoluble particles, vective transport, relative humidity, and wet scavenging. Aitken transition to accumulation mode, dry deposition, in- cloud and below-cloud scavenging, and re-evaporation. The convective transport
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