ArticleNature communications2024
Process-evaluation of forest aerosol-cloud-climate feedback shows clear evidence from observations and large uncertainty in models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Climate and plant traits drive a cross-continental imbalance in atmospheric Hg uptake.Nature communications · 2026Article
- The key role of nanoparticle concentration gradient in aerosol initial growth.Nature communications · 2026Article
- Changes in Secondary Organic Aerosol Composition and Volatility Going from a Low to a High HOACS ES&T air · 2026Article
- Improved modelling of biogenic emissions in human-disturbed forest edges and urban areas.Nature communications · 2025Article
- Ten new insights in climate science 2024.One earth (Cambridge, Mass.) · 2025Review
Corrections and comments
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural aerosol feedbacks are expected to become more important in the future, as anthropogenic aerosol emissions decrease due to air quality policy. One such feedback is initiated by the increase in biogenic volatile organic compound (BVOC) emissions with higher temperatures, leading to higher secondary organic aerosol (SOA) production and a cooling of the surface via impacts on cloud radiative properties. Motivated by the considerable spread in feedback strength in Earth System Models (ESMs), we here use two long-term observational datasets from boreal and tropical forests, together with satellite data, for a process-based evaluation of the BVOC-aerosol-cloud feedback in four ESMs. The model evaluation shows that the weakest modelled feedback estimates can likely be excluded, but highlights compensating errors making it difficult to draw conclusions of the strongest estimates. Overall, the method of evaluating along process chains shows promise in pin-pointing sources of uncertainty and constraining modelled aerosol feedbacks.
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Registered trials
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