| Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence M Amos, PJ Young, JS Hosking, JF Lamarque, NL Abraham, H Akiyoshi, ... Atmospheric Chemistry and Physics 20 (16), 9961-9977, 2020 | 29 | 2020 |
| Ensembling geophysical models with Bayesian neural networks U Sengupta, M Amos, JS Hosking, CE Rasmussen, M Juniper, PJ Young Advances in Neural Information Processing Systems 33, 2020 | 25 | 2020 |
| The Bristol CMIP6 data hackathon DM Mitchell, EJ Stone, OD Andrews, JL Bamber, RJ Bingham, J Browse, ... Weather 77 (6), 218-221, 2022 | 7 | 2022 |
| Update on Global Ozone: Past, Present, and Future B Hassler, PJ Young, WT Ball, R Damadeo, J Keeble, E Maillard Barras, ... | 5 | 2022 |
| Flyway‐scale analysis reveals that the timing of migration in wading birds is becoming later TO Mondain‐Monval, M Amos, JL Chapman, A MacColl, SP Sharp Ecology and evolution 11 (20), 14135-14145, 2021 | 3 | 2021 |
| A continuous vertically resolved ozone dataset from the fusion of chemistry climate models with observations using a Bayesian neural network M Amos, U Sengupta, P Young, JS Hosking EarthArXiv, 2021 | 2 | 2021 |
| LancasterAQ: A High Resolution Street Level Dataset of Ultrafine Particles M Amos, D Booker, R Duncan, L Gouldsbrough, T Pinder, PJ Young, ... EarthArXiv, 2022 | | 2022 |
| Improving the robustness, accuracy, and utility of chemistry-climate model ensembles M Amos PQDT-Global, 2022 | | 2022 |
| Fusing model ensembles and observations together with Bayesian neural networks M Amos, U Sengupta, S Hosking, P Young EGU General Assembly Conference Abstracts, EGU21-11905, 2021 | | 2021 |
| Identifying latent climate signals using sparse hierarchical Gaussian processes M Amos, T Pinder, PJ Young | | |
| Ensembling geophysical models with Bayesian Neural U Sengupta, M Amos, JS Hosking | | |
