Evidence map›Paper›PMID 41326580›Full record

ArticleScientific reports2025

Model simulations capture seasonal Arctic Haze and clean-air cycle better than satellite and reanalysis.

Basudev Swain, Marco Vountas, Aishwarya Singh, Rui Song, Upasana Panda, Heiko Schellhorn, Linus Andrae, Adrien Deroubaix, Luca Lelli, Ankit Tandon and 2 more

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Basudev SwainDepartment of Physics, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK. basudev@iup.physik.uni-bremen.de.
Marco VountasInstitute of Environmental Physics, University of Bremen, Bremen, Germany.
Aishwarya SinghAerosol Chemistry Department, Max-Planck Institute for Chemistry, Mainz, Germany.
Rui SongDepartment of Physics, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK.
Upasana PandaKalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, India.
Heiko SchellhornInstitute of Environmental Physics, University of Bremen, Bremen, Germany.
Linus AndraeInstitute of Environmental Physics, University of Bremen, Bremen, Germany.
Adrien DeroubaixMax-Planck-Institut für Meteorologie, Hamburg, Germany.
Luca LelliRemote Sensing Technology Institute, German Aerospace Centre (DLR), Wessling, Germany.
Ankit TandonDepartment of Environmental Sciences, Central University of Jammu, Jammu, India.
Akshaya NikumbhDepartment of Climate Studies, Indian Institute of Technology Bombay, Maharashtra, India.
Sachin S GuntheDepartment of Civil Engineering, Indian Institute of Technology Madras, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Arctic is heating far more rapidly than the global mean, and clarifying the influence of aerosols in this intensification demands accurate and reliable observational records. The Arctic exhibits a distinct seasonal aerosol cycle, springtime "Arctic Haze" with elevated AOD and summertime "Clean Air" with low AOD. Thus, it is critical to evaluate how well various datasets capture this seasonality relative to ground-based observations. This study analyzes spring and summer AOD variability using CAMSRA and MERRA-2 reanalyses, MODIS Terra and Aqua satellite observations, AERONET measurements, AEROSNOW retrievals, and GEOS-Chem model simulations. Results show that satellite-derived and satellite-assimilated reanalyses are far from capturing the expected seasonal Arctic Haze and Clean Air pattern, except at Bonanza Creek and Yakutsk, where anthropogenic pollution alters it. The inability of reanalyses to capture Arctic aerosol seasonality likely stems from the assimilation of satellite retrievals influenced by cloud contamination and surface reflection from snow and ice, as well as inherent biases in the underlying models used to generate these datasets. In contrast, AERONET observations and GEOS-Chem simulations consistently capture Arctic Haze in spring, driven by long-range transport, and Clean Air in summer, associated with efficient wet removal of aerosols. CAMSRA further underestimates emissions from Arctic forest fires and inadequately represents long-range pollution transport. These findings suggest that independent model simulations align more closely with ground-based observations than satellite products or reanalyses, and that adjusting wet-scavenging parameters to fit such reanalyses may misrepresent aerosol processes and their contribution to Arctic warming. Incorporating advanced retrieval algorithms like AEROSNOW into reanalyses offers a pathway to reduce these biases and improve representation of Arctic aerosol seasonality.

Identifiers

PMID41326580
PMCPMC12672705

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