ArticleScientific reports2025
Model simulations capture seasonal Arctic Haze and clean-air cycle better than satellite and reanalysis.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.