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Impact of the Hunga eruption on polar stratospheric clouds as seen by the lidar observatory at Concordia Station

Published online by Cambridge University Press:  26 March 2026

Federico Serva
Affiliation:
Institute of Marine Science, Consiglio Nazionale delle Ricerche , Italy
Luca Di Liberto
Affiliation:
Institute of Atmospheric Sciences and Climate, Consiglio Nazionale delle Ricerche , Italy
Francesco Colao
Affiliation:
ENEA Agenzia Nazionale per Le Nuove Tecnologie l’Energia e lo Sviluppo Economico , Italy
Alessandro Bracci
Affiliation:
Institute of Marine Science, Consiglio Nazionale delle Ricerche , Italy
Francesco Cairo
Affiliation:
Institute of Marine Science, Consiglio Nazionale delle Ricerche , Italy
Michael Pitts
Affiliation:
NASA , USA
Marcel Snels*
Affiliation:
Institute of Marine Science, Consiglio Nazionale delle Ricerche , Italy
*
Corresponding author: Marcel Snels; Email: marcellinus.snels@cnr.it
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Abstract

Concordia Station is a long-term lidar observatory in Antarctica. Its main purpose is to detect and classify polar stratospheric clouds (PSCs). In September 2023, water ice PSCs were observed for a period of 6 days. This has never occurred during the month of September in the 10 years of observations of PSCs at Concordia Station. In addition, the space-borne CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) lidar observed a rare occurrence of ice PSCs over Concordia Station during the first half of June. It is well known that the January 2022 eruption of the Hunga submarine volcano (20°32’S, 175°23’W) injected vast amounts of water vapour into the stratosphere. The Hunga hydration flooded southern high latitudes at the end of the 2022 austral winter, and the moist air was then entrained into the developing vortex in the austral autumn (April–May) of 2023. The increased water vapour from Hunga was reported to promote PSC formation by cooling the stratosphere and raising PSC formation temperatures. Here, we explore the impact of the Hunga eruption on the PSCs observed at Concordia Station.

Information

Type
Earth Sciences
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of Antarctic Science Ltd
Figure 0

Figure 1. The formation temperatures for nitric acid trihydrate (NAT) and ice have been calculated following the formulas presented by Hanson & Mauersberger (1988) for NAT and by Murphy & Koop (2005) and Romps (2021) for ice. A HNO3 mixing ratio of 5 ppbv has been assumed. Blue and magenta lines represent the formation temperatures for ice and NAT, respectively, calculated for pressure levels of 30 and 100 mbar. The green line at 190 K shows a typical stratospheric temperature during winter.

Figure 1

Figure 2. Nine years of ground-based polar stratospheric cloud (PSC) observations at Concordia Station. The different PSC classes are indicated with the following colours: orange = supercooled ternary solution; green = nitric acid trihydrate mixtures; red = enhanced nitric acid trihydrate mixtures; dark blue = ice. The small circles at the bottom of each panel indicate that measurements were taken but no PSCs were observed. It is possible for a given day to be characterized by both open circles and coloured squares in cases when multiple measurement sessions that yielded different PSC detection outcomes at different times were performed. Unshaded areas with no circles represent times when the ground-based lidar was deliberately non-operational; for example, measurements in a given year typically do not commence until mid-June. Shaded (light blue) areas indicate periods of instrument failures that precluded data collection.

Figure 2

Figure 3. The polar stratospheric clouds (PSCs) observed by CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) within 300 km of Concordia Station from 2014 to 2023 for the months of May and June. The different PSC classes are indicated with the following colours: orange = supercooled ternary solution; green = nitric acid trihydrate mixtures; red = enhanced nitric acid trihydrate mixtures; dark blue = ice. The small circles at the bottom of each panel indicate that measurements were taken but no PSCs were observed. Shaded (light blue) areas indicate periods when CALIOP was not operational.

Figure 3

Figure 4. Upper panel: temperatures in Kelvin at Concordia Station from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications) for the period 1 June until the end of September, from 12 to 30 km. Lower panel: temperature anomalies in Kelvin at Concordia Station from MERRA-2 for the period 1 June until the end of September, from 12 to 30 km. The temperature anomalies were obtained from the difference between the 2023 values with respect to the 2014–2022 average values. Both panels: the red and white circles indicate where ice polar stratospheric clouds were observed by CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) and ground-based lidar, respectively. The hatched areas indicate periods when ground-based lidar data are unavailable due to instrumental issues.

Figure 4

Figure 5. Upper panel: water vapour mixing ratios in parts per million (ppm) as observed by Aura Microwave Limb Sounder (MLS) at Concordia Station in 2023. Lower panel: water vapour anomalies in ppm in 2023 with respect to the average values over the 2014–2022 period. The water vapour mixing ratios (ppm) have been taken from MLS and correspond with the closest point on the latitude-longitude grid with respect to Concordia Station. The black and white circles represent the ice polar stratospheric clouds as observed by CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) and ground-based lidar, respectively. The hatched areas correspond with periods of instrumental issues of the ground-based lidar, resulting in the absence of data.