Smoke particles can be injected by pyrocumulonimbus (pyroCb) in the upper troposphere and lower stratosphere, but their effects on the radiative budget of the planet remain elusive. Here, by focusing on the record‐setting Pacific Northwest pyroCb event of August 2017, we show with satellite‐based estimates of pyroCb emissions and injection heights in a chemical transport model (GEOS‐Chem) that pyroCb smoke particles can result in radiative forcing of ∼0.02 W/m2 at the top of the atmosphere averaged globally in the 2 months following the event and up to 0.9 K/day heating in the Arctic upper troposphere and lower stratosphere. The modeled aerosol distributions agree with observations from satellites (Earth Polychromatic Imaging Camera [EPIC], Cloud‐Aerosol Transport System [CATS], and Cloud‐Aerosol Lidar with Orthogonal Polarization [CALIOP]), showing the hemispheric transport of pyroCb smoke aerosols with a lifetime of 5 months. Hence, warming by pyroCb aerosols can have similar temporal duration but opposite sign to the well‐documented cooling of volcanic aerosols and be significant for climate prediction.

Developing predictive capability for future atmospheric oxidation capacity requires a detailed analysis of model uncertainties and sensitivity of the modeled oxidation capacity to model input variables. Using oxidant mixing ratios modeled by the GEOS-Chem chemical transport model and measured on the NASA DC-8 aircraft, uncertainty and global sensitivity analyses were performed on the GEOS-Chem chemical transport model for the modeled oxidants hydroxyl (OH), hydroperoxyl (HO 2 ), and ozone (O 3 ). The sensitivity of modeled OH, HO 2 , and ozone to model inputs perturbed simultaneously within their respective uncertainties were found for the flight tracks of NASA's Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) A and B campaigns (2008) in the North American Arctic. For the spring deployment (ARCTAS-A), ozone was most sensitive to the photolysis rate of NO 2 , the NO 2 + OH reaction rate, and various emissions, including methyl bromoform (CHBr 3 ). OH and HO 2 were overwhelmingly sensitive to aerosol particle uptake of HO 2 with this one factor contributing upwards of 75 % of the uncertainty in HO 2 . For the summer deployment (ARCTAS-B), ozone was most sensitive to emission factors, such as soil NO x and isoprene. OH and HO 2 were most sensitive to biomass emissions and aerosol particle uptake of HO 2 . With modeled HO 2 showing a factor of 2 underestimation compared to measurements in the lowest 2 km of the troposphere, lower uptake rates (γ HO 2 < 0.055), regardless of whether or not the product of the uptake is H 2 O or H 2 O 2 , produced better agreement between modeled and measured HO 2 .

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

Concentrations of particulate aerosols and their vertical placement in the atmosphere determine their interaction with the Earth system and their impact on air quality. Space-based lidar, such as the Cloud–Aerosol Transport System (CATS) technology demonstration instrument, is well-suited for determining the vertical structure of these aerosols and their diurnal cycle. Through the implementation of aerosol-typing algorithms, vertical layers of aerosols are assigned a type, such as marine, dust, and smoke, and a corresponding extinction-to-backscatter (lidar) ratio. With updates to the previous aerosol-typing algorithms, we find that CATS, even as a technology demonstration, observed the documented seasonal cycle of aerosols, comparing favorably with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) space-based lidar and the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) model reanalysis. By leveraging the unique orbit of the International Space Station, we find that CATS can additionally resolve the diurnal cycle of aerosol altitude as observed by ground-based instruments over the Maritime Continent of Southeast Asia.

Recent fire seasons have featured volcanic-sized injections of smoke aerosols into the stratosphere where they persist for many months. Unfortunately, the aging and transport of these aerosols are not well understood. Using space-based lidar, the vertical and spatial propagation of these aerosols can be tracked and inferences can be made as to their size and shape. In this study, space-based CATS and CALIOP lidar were used to track the evolution of the stratospheric aerosol plumes resulting from the 2019–2020 Australian bushfire and 2017 Pacific Northwest pyrocumulonimbus events and were compared to two volcanic events: Calbuco (2015) and Puyehue (2011). The pyrocumulonimbus and volcanic aerosol plumes evolved distinctly, with pyrocumulonimbus plumes rising upwards of 10 km after injection to altitudes of 30 km or more, compared to small to modest altitude increases in the volcanic plumes. We also show that layer-integrated depolarization ratios in these large pyrocumulonimbus plumes have a strong altitude dependence with more irregularly shaped particles in the higher altitude plumes, unlike the volcanic events studied.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Developing predictive capability for future atmospheric oxidation capability requires a detailed analysis of model uncertainties and sensitivity of the modeled oxidation capacity to model input variables. Using oxidant mixing ratios modeled by the GEOS-Chem chemical transport model and measured on the NASA DC8 aircraft, uncertainty and global sensitivity analyses were performed on the GEOS-Chem chemical transport model for the modeled oxidants hydroxyl (OH), hydroperoxyl (HO&lt;sub&gt;2&lt;/sub&gt;), and ozone (O&lt;sub&gt;3&lt;/sub&gt;). The sensitivity of modeled OH, HO&lt;sub&gt;2&lt;/sub&gt;, and ozone to modeled inputs perturbed simultaneously within their respective uncertainties were found for the period of NASA's Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) A &amp; B campaigns (2008) in the North American Arctic. For the spring deployment (ARCTAS-A), ozone is most sensitive to the photolysis rate of NO&lt;sub&gt;2&lt;/sub&gt;, the NO&lt;sub&gt;2&lt;/sub&gt; + OH reaction rate, and various emissions, including methyl bromoform (CHBr&lt;sub&gt;3&lt;/sub&gt;). OH and HO&lt;sub&gt;2&lt;/sub&gt; were overwhelmingly sensitive to aerosol particle uptake of HO&lt;sub&gt;2&lt;/sub&gt; with this one factor contributing upwards of 75&amp;#8201;% of the uncertainty in HO&lt;sub&gt;2&lt;/sub&gt;. For the summer deployment (ARCTAS-B), ozone was most sensitive to emissions factors, such as soil NOx and isoprene. OH and HO&lt;sub&gt;2&lt;/sub&gt; were most sensitive to biomass emissions and aerosol particle uptake of HO&lt;sub&gt;2&lt;/sub&gt;. With modeled HO&lt;sub&gt;2&lt;/sub&gt; showing a factor of 2 underestimation compared to measurements in the lowest 2 kilometers of the troposphere, lower uptake rates (&amp;#947;HO&lt;sub&gt;2&lt;/sub&gt; &lt; 0.04), regardless of whether or not the product of the uptake is H&lt;sub&gt;2&lt;/sub&gt;O or H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;, produced better agreement between modeled and measured HO&lt;sub&gt;2&lt;/sub&gt;.&lt;/p&gt;

Abstract. Making sense of modeled atmospheric composition requires not only comparison to in situ measurements but also knowing and quantifying the sensitivity of the model to its input factors. Using a global sensitivity method involving the simultaneous perturbation of many chemical transport model input factors, we find the model uncertainty for ozone (O3), hydroxyl radical (OH), and hydroperoxyl radical (HO2) mixing ratios, and apportion this uncertainty to specific model inputs for the DC-8 flight tracks corresponding to the NASA Intercontinental Chemical Transport Experiment (INTEX) campaigns of 2004 and 2006. In general, when uncertainties in modeled and measured quantities are accounted for, we find agreement between modeled and measured oxidant mixing ratios with the exception of ozone during the Houston flights of the INTEX-B campaign and HO2 for the flights over the northernmost Pacific Ocean during INTEX-B. For ozone and OH, modeled mixing ratios were most sensitive to a bevy of emissions, notably lightning NOx, various surface NOx sources, and isoprene. HO2 mixing ratios were most sensitive to CO and isoprene emissions as well as the aerosol uptake of HO2. With ozone and OH being generally overpredicted by the model, we find better agreement between modeled and measured vertical profiles when reducing NOx emissions from surface as well as lightning sources.

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