| The art and science of climate model tuning F Hourdin, T Mauritsen, A Gettelman, JC Golaz, V Balaji, Q Duan, D Folini, ... Bulletin of the American Meteorological Society 98 (3), 589-602, 2017 | 492 | 2017 |
| History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble D Williamson, M Goldstein, L Allison, A Blaker, P Challenor, L Jackson, ... Climate dynamics 41, 1703-1729, 2013 | 207 | 2013 |
| Identifying and removing structural biases in climate models with history matching D Williamson, AT Blaker, C Hampton, J Salter Climate dynamics 45, 1299-1324, 2015 | 102 | 2015 |
| Ice-free Arctic at 1.5° C? JA Screen, D Williamson Nature Climate Change 7 (4), 230-231, 2017 | 69 | 2017 |
| Early epidemiological signatures of novel SARS-CoV-2 variants: establishment of B. 1.617. 2 in England R Challen, L Dyson, CE Overton, LM Guzman-Rincon, EM Hill, HB Stage, ... MedRxiv, 2021.06. 05.21258365, 2021 | 68 | 2021 |
| Uncertainty quantification for computer models with spatial output using calibration-optimal bases JM Salter, DB Williamson, J Scinocca, V Kharin Journal of the American Statistical Association, 2019 | 68 | 2019 |
| Process‐based climate model development harnessing machine learning: I. A calibration tool for parameterization improvement F Couvreux, F Hourdin, D Williamson, R Roehrig, V Volodina, ... Journal of Advances in Modeling Earth Systems 13 (3), e2020MS002217, 2021 | 66 | 2021 |
| Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model DB Williamson, AT Blaker, B Sinha Geoscientific Model Development 10 (4), 1789-1816, 2017 | 59 | 2017 |
| The art and science of climate model tuning, B. Am. Meteorol. Soc., 98, 589–602 F Hourdin, T Mauritsen, A Gettelman, JC Golaz, V Balaji, Q Duan, D Folini, ... | 59 | 2017 |
| A comparison of statistical emulation methodologies for multi‐wave calibration of environmental models JM Salter, D Williamson Environmetrics 27 (8), 507-523, 2016 | 47 | 2016 |
| Fast linked analyses for scenario-based hierarchies D Williamson, M Goldstein, A Blaker Journal of the Royal Statistical Society Series C: Applied Statistics 61 (5 …, 2012 | 44 | 2012 |
| Process‐based climate model development harnessing machine learning: II. Model calibration from single column to global F Hourdin, D Williamson, C Rio, F Couvreux, R Roehrig, N Villefranque, ... Journal of Advances in Modeling Earth Systems 13 (6), e2020MS002225, 2021 | 43 | 2021 |
| Exploratory ensemble designs for environmental models using k‐extended Latin Hypercubes D Williamson Environmetrics 26 (4), 268-283, 2015 | 42 | 2015 |
| Evolving Bayesian emulators for structured chaotic time series, with application to large climate models D Williamson, AT Blaker SIAM/ASA Journal on Uncertainty Quantification 2 (1), 1-28, 2014 | 33 | 2014 |
| Deep Gaussian process emulation using stochastic imputation D Ming, D Williamson, S Guillas Technometrics 65 (2), 150-161, 2023 | 26 | 2023 |
| How are emergent constraints quantifying uncertainty and what do they leave behind? DB Williamson, PG Sansom Bulletin of the American Meteorological Society 100 (12), 2571-2588, 2019 | 26 | 2019 |
| Efficient uniform designs for multi-wave computer experiments D Williamson, I Vernon arXiv preprint arXiv:1309.3520, 2013 | 26 | 2013 |
| Diagnostics-driven nonstationary emulators using kernel mixtures V Volodina, D Williamson SIAM/ASA Journal on Uncertainty Quantification 8 (1), 1-26, 2020 | 24 | 2020 |
| Bayesian policy support for adaptive strategies using computer models for complex physical systems D Williamson, M Goldstein Journal of the Operational Research Society 63 (8), 1021-1033, 2012 | 21 | 2012 |
| Smart cities and behavioural change:(Un) sustainable mobilities in the neo-liberal city S Barr, S Lampkin, L Dawkins, D Williamson Geoforum 125, 140-149, 2021 | 20 | 2021 |
