Sensitivity of bi-spectral retrievals to the fixed effective variance assumption in ship tracks

Boyce, Iarla; Cicirello, Alice; Gryspeerdt, Edward

doi:10.5194/amt-19-5475-2026

Articles | Volume 19, issue 16

https://doi.org/10.5194/amt-19-5475-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-19-5475-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 16

Research article

|

26 Aug 2026

Research article |

| 26 Aug 2026

Sensitivity of bi-spectral retrievals to the fixed effective variance assumption in ship tracks

Iarla Boyce, Alice Cicirello, and Edward Gryspeerdt

Data sets

Assessing retrieval biases in ship tracks I. Boyce et al. https://doi.org/10.5281/zenodo.19699485

Model code and software

pyLRT E. Gryspeerdt and O. Driver https://doi.org/10.5281/zenodo.11626012

Analysis code for: Sensitivity of bi-spectral cloud retrievals to the fixed effective variance assumption I. Boyce et al. https://doi.org/10.5281/zenodo.20328197

Short summary

We tested whether satellites accurately measure clouds affected by ship pollution. Using computer simulations, we found that satellites overestimate how much cloud droplet numbers rise in polluted ship tracks compared to nearby clean clouds. This means the cooling effect of pollution on clouds may be overstated, which matters for strategies like marine cloud brightening that aim to boost this cooling effect deliberately.