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. 2022 Aug 25;12(1):12665.
doi: 10.1038/s41598-022-15794-3.

Australian wildfires cause the largest stratospheric warming since Pinatubo and extends the lifetime of the Antarctic ozone hole

Lilly Damany-Pearce  1 , Ben Johnson  2 , Alice Wells  3 , Martin Osborne  3   2 , James Allan  4 , Claire Belcher  3 , Andy Jones  2 , Jim Haywood  3   2
Affiliations

Australian wildfires cause the largest stratospheric warming since Pinatubo and extends the lifetime of the Antarctic ozone hole

Lilly Damany-Pearce et al. Sci Rep. .

Abstract

Global mean lower stratosphere temperatures rose abruptly in January 2020 reaching values not experienced since the early 1990s. Anomalously high lower stratospheric temperatures were recorded for 4 months at highly statistically significant levels. Here, we use a combination of satellite and surface-based remote sensing observations to derive a time-series of stratospheric biomass burning aerosol optical depths originating from intense SouthEastern Australian wildfires and use these aerosol optical depths in a state-of-the-art climate model. We show that the S.E. Australian wildfires are the cause of this lower stratospheric warming. We also investigate the radiatively-driven dynamical response to the observed stratospheric ozone perturbation and find a significant strengthening of the springtime Antarctic polar vortex suggesting that biomass burning aerosols play a significant role in the observed anomalous longevity of the ozone hole in 2020.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Examples of lidar observations of i) first column) the vertical distribution of the 532 nm total attenuated backscatter and the tropopause height (black line), ii) middle column) the aerosol sub-type identified by the CALIOP sensor. The third column represents the lidar footprint of the corresponding observations. The position of the Punta Arenas AERONET site is represented on the bottom right-hand plot by the blue dot in the extreme south of Chile. The maps are created using the python package, cartopy. Notable aerosol sub-types in the stratosphere include type 6-smoke, and types 9-volcanic ash and 10 – sulfate/other.
Figure 2
Figure 2
Perturbation in the SAOD (532 nm) over latitudes 20 to 70°S, as observed by CALIOP and OMPS-LP following the ANY fires. The OMPS-LP retrievals are scaled from 869 to 532 nm assuming an appropriate Ångstrom exponent. The purple dashed line is a fit that linearly interpolates between the peaks in the CALIOP and OMPS-LP retrievals, illustrating the method used to derive the composite SAOD (COMP) dataset.
Figure 3
Figure 3
Perturbations in aerosol extinction coefficient due to the ANY fires, as a function of latitude and altitude derived by the COMP interpolated dataset. Each image shows the weekly mean distribution, for the week commencing (w/c) the date shown, for the 10-degree latitude intervals from 20 to 70˚S, and 1 km height intervals from 0 to 35 km. The black line shows the average tropopause height from the UKESM1 model climatology.
Figure 4
Figure 4
UTLS ozone anomaly (DU) over the southern hemisphere, retrieved by OMPS-LP. The ozone vertical profiles are vertically weighted using the RSS vertical weighting function (see Eq. (1), Stratospheric Temperature Anomaly Data) then integrated over altitudes 13–22 km.
Figure 5
Figure 5
Three-monthly mean modelled perturbations to atmospheric temperature (K) due to the smoke aerosol forcing (e–h), the ozone anomaly (i–l), and both the smoke aerosol and ozone (a–d). The stippled points show the points at which the experiments were significantly different from the control simulations at a 5% significance level (calculated by a Welch’s t-test). The black dashed line shows the average tropopause height from the UKESM1 model climatology.
Figure 6
Figure 6
Monthly-mean lower stratospheric temperature (LST) anomalies provided by RSS data (a), and the mean of the 10-member ensemble of UKESM1 BBA + O3 simulations (b). Temperature anomalies are calculated relative to a 1979–2019 reference period, and averaged over latitudes 83˚S to 83˚N. The difference in the LST anomalies relative to November 2019 are shown in (c). The BBA, O3 and BBA + O3 temperature anomalies shown in (c) are the differences from the control simulation. In addition, the 95% confidence intervals for the BBA + O3 experiment for each month of 2020, assuming a Student’s t-distribution, are shown in c).
Figure 7
Figure 7
Three-monthly mean modelled perturbations the zonal mean wind (m s−1), over all latitudes, due to the smoke aerosol (e–h), the ozone anomaly (i-l), and both the smoke aerosol and ozone (a–d). The stippled points show the points at which the experiments were significantly different from the control simulations at a 5% significance level (calculated by a Welch’s t-test).
Figure 8
Figure 8
Three-monthly mean modelled perturbations the zonal mean wind (m s-1) at 10 hPa for 2020, over the Antarctic region, indicating the impact on the polar vortex, due to the smoke aerosol (e–h), the ozone anomaly (i–l), and both the smoke aerosol and ozone (a–d). The stippled points show the points at which the experiments were significantly different from the control simulations at a 5% significance level (calculated by a Welch’s t-test). The map overlaid is created using the python package, cartopy.
Figure 9
Figure 9
Weekly average perturbation in the SAOD (532 nm) following the ANY wildfires, over latitudes 50 to 60˚S, as observed by OMPS-LP including retrievals with a single scattering angle greater than 145˚ (purple dashed line) and with these retrievals removed (orange line). All retrievals are scaled from 869 to 532 nm assuming an appropriate Ångstrom exponent. The blue line shows a constant rate of decay of the SAOD, interpolating the filtered data, calculated from an e-folding time of 220 days.
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