On the Radiative Impact of Biomass-Burning Aerosols in the Arctic: The August 2017 Case Study

Boreal fires have increased during the last years and are projected to become more intense and frequent as a consequence of climate change. Wildfires produce a wide range of effects on the Arctic climate and ecosystem, and understanding these effects is crucial for predicting the future evolution of the Arctic region. This study focuses on the impact of the long-range transport of biomass-burning aerosol into the atmosphere and the corresponding radiative perturbation in the shortwave frequency range. As a case study, we investigate an intense biomass-burning (BB) event which took place in summer 2017 in Canada and subsequent northeastward transport of gases and particles in the plume leading to exceptionally high values (0.86) of Aerosol Optical Depth (AOD) at 500 nm measured in northwestern Greenland on 21 August 2017. This work characterizes the BB plume measured at the Thule High Arctic Atmospheric Observatory (THAAO; 76.53∘N, 68.74∘W) in August 2017 by assessing the associated shortwave aerosol direct radiative impact over the THAAO and extending this evaluation over the broader region (60∘N–80∘N, 110∘W–0∘E). The radiative transfer simulations with MODTRAN6.0 estimated an aerosol heating rate of up to 0.5 K/day in the upper aerosol layer (8–12 km). The direct aerosol radiative effect (ARE) vertical profile shows a maximum negative value of −45.4 Wm−2 for a 78∘ solar zenith angle above THAAO at 3 km altitude. A cumulative surface ARE of −127.5 TW is estimated to have occurred on 21 August 2017 over a portion (∼3.1×106 km2) of the considered domain (60∘N–80∘N, 110∘W–0∘E). ARE regional mean daily values over the same portion of the domain vary between −65 and −25 Wm−2. Although this is a limited temporal event, this effect can have significant influence on the Arctic radiative budget, especially in the anticipated scenario of increasing wildfires.

This research was partially funded by the Italian Ministry of University and Research
(MIUR) within the framework of OASIS-YOPP—Observations of the Arctic Stratosphere In Support
of YOPP (PNRA 2016–2018); CLARA2—CLouds And Radiation in the Arctic and Antarctica (PNRA
2019–2021), and ECAPAC—Effects of changing albedo and precipitation on the Arctic climate (PRA
2021–2023). The work of F. Calì Quaglia and G. Muscari was also partially funded under the INGV
environmental project MACMAP—A Multidisciplinary Analysis of Climate change indicators in the
Mediterranean And Polar regions (2020–2023). The NCAR FTIR observation program at Thule, Greenland
is supported under contract by the National Aeronautics and Space Administration (NASA).
The National Center for Atmospheric Research (NCAR) is sponsored by the U.S. National Science
Foundation (NSF). The Thule work is also supported by the NSF Office of Polar Programs (OPP).

1. Serreze, M.C.; Walsh, J.E.; Chapin, F.S.; Osterkamp, T.; Dyurgerov, M.; Romanovsky, V.; Oechel, W.C.; Morison, J.; Zhang, T.;
Barry, R.G. Observational evidence of recent change in the northern high-latitude environment. Clim. Chang. 2000, 46, 159–207.
[CrossRef]
2. Box, J.E.; Res, E.; Box, J.E.; Colgan, W.T.; Christensen, T.R.; Schmidt, N.M.; Lund, M.; Parmentier, F.J.W.J.W.; Brown, R.; Bhatt, U.S.;
et al. Key indicators of Arctic climate change: 1971–2017 Key indicators of Arctic climate change: 1971–2017. Environ. Res. Lett.
2019, 14, 045010. [CrossRef]
3. Jolly, W.M.; Cochrane, M.A.; Freeborn, P.H.; Holden, Z.A.; Brown, T.J.; Williamson, G.J.; Bowman, D.M.J.S. Climate-induced
variations in global wildfire danger from 1979 to 2013. Nat. Commun. 2015, 6, 7537. [CrossRef]
4. Masrur, A.; Petrov, A.N.; DeGroote, J. Circumpolar spatio-temporal patterns and contributing climatic factors of wildfire activity
in the Arctic tundra from 2001–2015. Environ. Res. Lett. 2018, 13, 014019. [CrossRef]
5. Bret-Harte, M.S.; Mack, M.C.; Shaver, G.R.; Huebner, D.C.; Johnston, M.; Mojica, C.A.; Pizano, C.; Reiskind, J.A. The response
of Arctic vegetation and soils following an unusually severe tundra fire. Philos. Trans. R. Soc. B Biol. Sci. 2013, 368, 20120490.
[CrossRef]
6. Wang, C.;Wang, Z.; Kong, Y.; Zhang, F.; Yang, K.; Zhang, T. Most of the Northern Hemisphere Permafrost Remains under Climate
Change. Sci. Rep. 2019, 9, 3295. [CrossRef]
7. Schuur, E.A.G.; Bockheim, J.; Canadell, J.G.; Euskirchen, E.; Field, C.B.; Goryachkin, S.V.; Hagemann, S.; Kuhry, P.; Lafleur, P.M.;
Lee, H.; et al. Vulnerability of Permafrost Carbon to Climate Change: Implications for the Global Carbon Cycle. BioScience 2008,
58, 701–714. [CrossRef]
8. McGuire, A.D.; Anderson, L.G.; Christensen, T.R.; Dallimore, S.; Guo, L.; Hayes, D.J.; Heimann, M.; Lorenson, T.D.; Macdonald,
R.W.; Roulet, N. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 2009, 79, 523–555. [CrossRef]
9. Young, A.M.; Higuera, P.E.; Duffy, P.A.; Hu, F.S. Climatic thresholds shape northern high-latitude fire regimes and imply
vulnerability to future climate change. Ecography 2017, 40, 606–617. [CrossRef]
10. Higuera, P.E.; Brubaker, L.B.; Anderson, P.M.; Brown, T.A.; Kennedy, A.T.; Hu, F.S. Frequent Fires in Ancient Shrub Tundra:
Implications of Paleorecords for Arctic Environmental Change. PLoS ONE 2008, 3, e0001744. [CrossRef]
11. Yue, X.; Mickley, L.J.; Logan, J.A.; Hudman, R.C.; Martin, M.V.; Yantosca, R.M. Impact of 2050 climate change on North American
wildfire: Consequences for ozone air quality. Atmos. Chem. Phys. 2015, 15, 10033–10055. [CrossRef]
12. Markowicz, K.; Lisok, J.; Xian, P. Simulations of the effect of intensive biomass burning in July 2015 on Arctic radiative budget.
Atmos. Environ. 2017, 171, 248–260. [CrossRef]
13. Evangeliou, N.; Kylling, A.; Eckhardt, S.; Myroniuk, V.; Stebel, K.; Paugam, R.; Zibtsev, S.; Stohl, A. Open fires in Greenland in
summer 2017: Transport, deposition and radiative effects of BC, OC and BrC emissions. Atmos. Chem. Phys. 2019, 19, 1393–1411.
[CrossRef]
14. Veraverbeke, S.; Rogers, B.M.; Goulden, M.L.; Jandt, R.R.; Miller, C.E.; Wiggins, E.B.; Randerson, J.T. Lightning as a major driver
of recent large fire years in North American boreal forests. Nat. Clim. Chang. 2017, 7, 529–534. [CrossRef]
15. Kasischke, E.S.; Turetsky, M.R. Recent changes in the fire regime across the North American boreal region—Spatial and temporal
patterns of burning across Canada and Alaska. Geophys. Res. Lett. 2006, 33, 437–451. [CrossRef]
16. Kharuk, V.I.; Ponomarev, E.I. Spatiotemporal characteristics of wildfire frequency and relative area burned in larch-dominated
forests of Central Siberia. Russ. J. Ecol. 2017, 48, 507–512. [CrossRef]
17. Kharuk, V.I.; Ponomarev, E.I.; Ivanova, G.A.; Dvinskaya, M.L.; Coogan, S.C.P.; Flannigan, M.D. Wildfires in the Siberian taiga.
Ambio 2021, 50, 1953–1974. [CrossRef]
18. Balshi, M.S.; McGuire, A.D.; Duffy, P.; Flannigan, M.; Walsh, J.; Melillo, J. Assessing the response of area burned to changing
climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach. Glob. Chang. Biol.
2009, 15, 578–600. [CrossRef]
19. Flannigan, M.D.;Wotton, B.M.; Marshall, G.A.; de Groot,W.J.; Johnston, J.; Jurko, N.; Cantin, A.S. Fuel moisture sensitivity to
temperature and precipitation: Climate change implications. Clim. Chang. 2016, 134, 59–71. [CrossRef]
20. Scholten, R.C.; Jandt, R.; Miller, E.A.; Rogers, B.M.; Veraverbeke, S. Overwintering fires in boreal forests. Nature 2021, 593, 399–404.
[CrossRef]
21. McCarty, J.L.; Smith, T.E.L.; Turetsky, M.R. Arctic fires re-emerging. Nat. Geosci. 2020, 13, 658–660. [CrossRef]
22. Lutsch, E.; Strong, K.; Jones, D.B.; Blumenstock, T.; Conway, S.; Fisher, J.A.; Hannigan, J.W.; Hase, F.; Kasai, Y.; Mahieu, E.; et al.
Detection and attribution of wildfire pollution in the Arctic and northern midlatitudes using a network of Fourier-Transform
infrared spectrometers and GEOS-Chem. Atmos. Chem. Phys. 2020, 20, 12813–12851. [CrossRef]
23. Thule High Arctic Atmospheric Observatory (THAAO). Available online: https://www.thuleatmos-it.it/ (accessed on 29
November 2021).
24. Zielinski, T.; Bolzacchini, E.; Cataldi, M.; Ferrero, L.; Graßl, S.; Hansen, G.; Mateos, D.; Mazzola, M.; Neuber, R.; Pakszys, P.; et al.
Study of chemical and optical properties of biomass burning aerosols during long-range transport events toward the arctic in
summer 2017. Atmosphere 2020, 11, 84. [CrossRef]
25. Peterson, D.A.; Campbell, J.R.; Hyer, E.J.; Fromm, M.D.; Kablick, G.P.; Cossuth, J.H.; DeLand, M.T. Wildfire-driven thunderstorms
cause a volcano-like stratospheric injection of smoke. npj Clim. Atmos. Sci. 2018, 1, 30. [CrossRef] [PubMed]
26. Christian, K.; Yorks, J.; Das, S. Differences in the evolution of pyrocumulonimbus and volcanic stratospheric plumes as observed
by cats and caliop space-based lidars. Atmosphere 2020, 11, 1035. [CrossRef]
27. Das, S.; Colarco, P.R.; Oman, L.D.; Taha, G.; Torres, O. The long-term transport and radiative impacts of the 2017 British Columbia
pyrocumulonimbus smoke aerosols in the stratosphere. Atmos. Chem. Phys. 2021, 21, 12069–12090. [CrossRef]
28. Ansmann, A.; Baars, H.; Chudnovsky, A.; Mattis, I.; Veselovskii, I.; Haarig, M.; Seifert, P.; Engelmann, R.; Wandinger, U. Extreme
levels of Canadian wildfire smoke in the stratosphere over central Europe on 21–22 August 2017. Atmos. Chem. Phys. 2018,
18, 11831–11845. [CrossRef]
29. Lutsch, E.; Strong, K.; Jones, D.B.A.; Ortega, I.; Hannigan, J.W.; Dammers, E.; Shephard, M.W.; Morris, E.; Murphy, K.; Evans, M.J.;
et al. Unprecedented Atmospheric Ammonia Concentrations Detected in the High Arctic From the 2017 CanadianWildfires. J.
Geophys. Res. Atmos. 2019, 124, 8178–8202. [CrossRef]
30. Haarig, M.; Ansmann, A.; Baars, H.; Jimenez, C.; Veselovskii, I.; Engelmann, R.; Althausen, D. Depolarization and lidar ratios at
355, 532, and 1064 nm and microphysical properties of aged tropospheric and stratospheric Canadian wildfire smoke. Atmos.
Chem. Phys. 2018, 18, 11847–11861. [CrossRef]
31. Di Sarra, A.; Cacciani, M.; Di Girolamo, P.; Fiocco, G.; Fuà, D.; Knudsen, B.; Larsen, N.; Joergensen, T.S. Observations of correlated
behavior of stratospheric ozone and aerosol at Thule during winter 1991–1992. Geophys. Res. Lett. 1992, 19, 1823–1826. [CrossRef]
32. Hannigan, J.W.; Coffey, M.T.; Goldman, A. Semiautonomous FTS Observation System for Remote Sensing of Stratospheric and
Tropospheric Gases. J. Atmos. Ocean. Technol. 2009, 26, 1814–1828. [CrossRef]
33. Network for the Detection of Atmospheric Composition Change (NDACC). Available online: ndacc-uvvis-wg.aeronomie.be/
tools/NDACC_UVVIS-WG_NO2settings_v4.pdf (accessed on 1 December 2021).
34. Holben, B.; Eck, T.; Slutsker, I.; Tanré, D.; Buis, J.; Setzer, A.; Vermote, E.; Reagan, J.; Kaufman, Y.; Nakajima, T.; et al. AERONET—A
Federated Instrument Network and Data Archive for Aerosol Characterization. Remote Sens. Environ. 1998, 66, 1–16. [CrossRef]
35. Smirnov, A.; Holben, B.N.; Eck, T.F.; Dubovik, O.; Slutsker, I. Cloud-screening and quality control algorithms for the AERONET
database. Remote Sens. Environ. 2000, 73, 337–349. [CrossRef]
36. Giles, D.M.; Sinyuk, A.; Sorokin, M.G.; Schafer, J.S.; Smirnov, A.; Slutsker, I.; Eck, T.F.; Holben, B.N.; Lewis, J.R.; Campbell, J.R.;
et al. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database–automated near-real-time quality control
algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmos. Meas. Tech.
2019, 12, 169–209. [CrossRef]
37. Mateos, D.; Pace, G.; Meloni, D.; Bilbao, J.; di Sarra, A.; de Miguel, A.; Casasanta, G.; Min, Q. Observed influence of liquid cloud
microphysical properties on ultraviolet surface radiation. J. Geophys. Res. Atmos. 2014, 119, 2429–2440. [CrossRef]
38. Becagli, S.; Caiazzo, L.; Di Iorio, T.; di Sarra, A.; Meloni, D.; Muscari, G.; Pace, G.; Severi, M.; Traversi, R. New insights on metals
in the Arctic aerosol in a climate changing world. Sci. Total Environ. 2020, 741, 140511. [CrossRef]
39. Winker, D.M.; Pelon, J.R.; McCormick, M.P. The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds. In
Lidar Remote Sensing for Industry and Environment Monitoring III; International Society for Optics and Photonics: Bellingham, WA,
USA, 2003. [CrossRef]
40. Burton, S.P.; Ferrare, R.A.; Hostetler, C.A.; Hair, J.W.; Rogers, R.R.; Obland, M.D.; Butler, C.F.; Cook, A.L.; Harper, D.B.; Froyd,
K.D. Aerosol classification using airborne High Spectral Resolution Lidar measurements—Methodology and examples. Atmos.
Meas. Tech. 2012, 5, 73–98. [CrossRef]
41. Platnick, S.; King, M.; Hubanks, P. MODIS Atmosphere L3 Daily Product; NASA MODIS Adaptive Processing System, Goddard
Space Flight Center: Greenbelt, MD, USA, 2015. [CrossRef]
42. Schaaf, C.; Wang, Z. MCD43A3 MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global—500 m V006 [Data Set]; USGS: Sioux Falls, SD,
USA, 2015.
43. Jin, Z.; Charlock, T.P.; Smith, W.L.; Rutledge, K. A parameterization of ocean surface albedo. Geophys. Res. Lett. 2004, 31, 1–4.
[CrossRef]
44. Berk, A.; Conforti, P.; Kennett, R.; Perkins, T.; Hawes, F.; van den Bosch, J. MODTRAN6: A major upgrade of the MODTRAN
radiative transfer code. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery; SPIE: Bellingham,
WA, USA, 2014. [CrossRef]
45. McClatchey, R.A.; Fenn, R.; Selby, J.; Volz, F.; Garing, J. Optical Properties of the Atmosphere, 3rd ed.; Environmental Research
Papers; Air Force Systems Command, United States Air Force: Baltimore, MD, USA, 1972; Volume 411, p. 108.
46. Dirksen, R.; Dobber, M.; Voors, R.; Levelt, P. Prelaunch characterization of the Ozone Monitoring Instrument transfer function in
the spectral domain. Appl. Opt. 2006, 45, 3972. [CrossRef]
47. Dubovik, O.; Holben, B.; Eck, T.F.; Smirnov, A.; Kaufman, Y.J.; King, M.D.; Tanré, D.; Slutsker, I. Variability of absorption and
optical properties of key aerosol types observed in worldwide locations. J. Atmos. Sci. 2002, 59, 590–608. [CrossRef]
48. Zhuravleva, T.B.; Kabanov, D.M.; Nasrtdinov, I.M.; Russkova, T.V.; Sakerin, S.M.; Smirnov, A.; Holben, B.N. Radiative characteristics
of aerosol during extreme fire event over Siberia in Summer 2012. Atmos. Meas. Tech. 2017, 10, 179–198. [CrossRef]
49. Riihelä, A.; King, M.D.; Anttila, K. The surface albedo of the Greenland Ice Sheet between 1982 and 2015 from the CLARA-A2
dataset and its relationship to the ice sheet’s surface mass balance. Cryosphere 2019, 13, 2597–2614. [CrossRef]
50. Viatte, C.; Strong, K.; Hannigan, J.; Nussbaumer, E.; Emmons, L.K.; Conway, S.; Paton-Walsh, C.; Hartley, J.; Benmergui, J.; Lin, J.
Identifying fire plumes in the Arctic with tropospheric FTIR measurements and transport models. Atmos. Chem. Phys. 2015,
15, 2227–2246. [CrossRef]
51. Moroni, B.; Cappelletti, D.; Crocchianti, S.; Becagli, S.; Caiazzo, L.; Traversi, R.; Udisti, R.; Mazzola, M.; Markowicz, K.; Ritter, C.;
et al. Morphochemical characteristics and mixing state of long range transported wildfire particles at Ny-Ålesund (Svalbard
Islands). Atmos. Environ. 2017, 156, 135–145. [CrossRef]
52. Kalogridis, A.C.; Popovicheva, O.; Engling, G.; Diapouli, E.; Kawamura, K.; Tachibana, E.; Ono, K.; Kozlov, V.; Eleftheriadis, K.
Smoke aerosol chemistry and aging of Siberian biomass burning emissions in a large aerosol chamber. Atmos. Environ. 2018,
185, 15–28. [CrossRef]
53. Bowen, H.J.M. Environmental Chemistry of the Elements; Academic Press: London, UK; New York, NY, USA, 1979.
54. Stone, R.S.; Anderson, G.P.; Shettle, E.P.; Andrews, E.; Loukachine, K.; Dutton, E.G.; Schaaf, C.; Roman, M.O. Radiative impact of
boreal smoke in the Arctic: Observed and modeled. J. Geophys. Res. 2008, 113, D14S16. [CrossRef]
55. Di Biagio, C.; di Sarra, A.; Eriksen, P.; Ascanius, S.E.; Muscari, G.; Holben, B. Effect of surface albedo, water vapour, and
atmospheric aerosols on the cloud-free shortwave radiative budget in the Arctic. Clim. Dyn. 2012, 39, 953–969. [CrossRef]
56. Earth Polychromatic Imaging Camera (EPIC) UVAI. Available online: https://epic.gsfc.nasa.gov/science/products/uv (accessed
on 29 November 2021).
57. Ozone Mapping and Profiler Suite (OMPS) UVAI. Available online: https://acd-ext.gsfc.nasa.gov/People/Seftor/OMPS_AI_
August_2017.html (accessed on 29 November 2021).
58. Warner, M.S. Introduction to PySPLIT: A Python Toolkit for NOAA ARL’s HYSPLIT Model. Comput. Sci. Eng. 2018, 20, 47–62.
[CrossRef]