Dr Lindsay Lee
PhD
Advanced Manufacturing Research Centre
Technical Fellow for Data Science
l.a.lee@sheffield.ac.uk
Factory 2050
Full contact details
Dr Lindsay Lee
Advanced Manufacturing Research Centre
Factory 2050
Sheffield Business Park
Sheffield
S9 1ZA
Advanced Manufacturing Research Centre
Factory 2050
Sheffield Business Park
Sheffield
S9 1ZA
- Profile
-
Lindsay holds the role of Technical Fellow for Data Science at AMRC, where she leverages her extensive 15+ years of expertise in statistics and machine learning. Her proficiency lies in applying these techniques to address intricate challenges in manufacturing and climate science domains. Lindsay's impactful work, showcasing the potential of statistics and machine learning in enhancing domain knowledge, has been featured in renowned publications such as Nature, The Proceedings of the National Academy of Sciences, and the RSS Significance Magazine. She is deeply enthusiastic about demystifying the realms of statistics and machine learning and has achieved this through engaging public lectures delivered for esteemed organizations like the Royal Statistical Society and the Royal Meteorological Society.
- Research interests
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Statistics
Machine Learning
Explainable AI
Uncertainty Quantification of Computer Simulation
Experimental Design
Data Quality and Management
- Publications
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Journal articles
- Exploring a Stratocumulus-to-Cumulus Transition: A Perturbed Parameter Ensemble of Large-Eddy Simulations.
- Unknown eruption source parameters cause large uncertainty in historical volcanic radiative forcing reconstructions. Journal of Geophysical Research: Atmospheres, 126(13). View this article in WRRO
- Effect of aerosol radiative forcing uncertainty on projected exceedance year of a 1.5 °C global temperature rise. Environmental Research Letters, 15(9).
- Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition. Earth System Science Data, 12(3), 1649-1677.
- Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity. Atmospheric Chemistry and Physics, 20(7), 4047-4058.
- Exploring the impact of aerosol radiative forcing uncertainty on shifts in ITCZ position and tropical rainfall in the near-term future.
- Constraining direct aerosol radiative forcing using remote sensing and in-situ constraints.
- Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition.
- Ensembles of global climate model variants designed for the quantification and constraint of uncertainty in aerosols and their radiative forcing. Journal of Advances in Modeling Earth Systems, 11(11), 3728-3754.
- Global sensitivity analysis of chemistry-climate model budgets of tropospheric ozone and OH: Exploring model diversity.
- Exploring how eruption source parameters affect volcanic radiative forcing using statistical emulation. Journal of Geophysical Research: Atmospheres, 124(2), 964-985. View this article in WRRO
- The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing. Atmospheric Chemistry and Physics, 18(17), 13031-13053.
- Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output. Geoscientific Model Development, 11(8), 3131-3146.
- Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF. Atmospheric Chemistry and Physics, 18(13), 9975-10006.
- The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design. Geoscientific Model Development, 11(7), 2581-2608.
- Acceleration of northern ice sheet melt induces AMOC slowdown and northern cooling in simulations of the early last deglaciation. Paleoceanography and Paleoclimatology, 33(7), 807-824.
- The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing.
- Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF.
- Supplementary material to "Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF".
- Climate models are uncertain, but we can do something about it. Eos, 99.
- Supplementary material to "The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): Motivation and experimental design".
- The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): Motivation and experimental design.
- Fast sensitivity analysis methods for computationally expensive models with multidimensional
output.
- Supplementary material to "Fast sensitivity analysis methods for computationally expensive models with multidimensional
output".
- The Global Aerosol Synthesis and Science Project (GASSP): measurements and modeling to reduce uncertainty. Bulletin of the American Meteorological Society, 98(9), 1857-1877.
- Potential negative consequences of geoengineering on crop production: a study of Indian groundnut. Geophysical Research Letters, 43(22), 11,786-11,795.
- On the relationship between aerosol model uncertainty and radiative forcing uncertainty. Proceedings of the National Academy of Sciences (PNAS), 113(21), 5820-5827. View this article in WRRO
- Corporate reporting on corruption: An international comparison. Accounting Forum, 39(4), 349-365.
- The Climatic Importance of Uncertainties in Regional Aerosol–Cloud Radiative Forcings over Recent Decades. Journal of Climate, 28(17), 6589-6607.
- On the effectiveness of private transnational governance regimes—Evaluating corporate sustainability reporting according to the Global Reporting Initiative. Journal of World Business, 50(2), 312-325.
- Evaluating uncertainty in convective cloud microphysics using statistical emulation. Journal of Advances in Modeling Earth Systems, 7(1), 162-187.
- Uncertainty in the magnitude of aerosol‐cloud radiative forcing over recent decades. Geophysical Research Letters, 41(24), 9040-9049.
- Occurrence of pristine aerosol environments on a polluted planet. Proceedings of the National Academy of Sciences, 111(52), 18466-18471.
- Evaluation of a regional air quality model using satellite column NO<sub>2</sub>: treatment of observation errors and model boundary conditions and emissions.
- Intercomparison and evaluation of aerosol microphysical properties among AeroCom global models of a range of complexity.
- Large contribution of natural aerosols to uncertainty in indirect forcing. Nature, 503(7474), 67-71.
- Uncertainties in Climate Models: Living with Uncertainty in an Uncertain World. Significance, 10(5), 34-39.
- The magnitude and sources of uncertainty in global aerosol. Faraday Discussions, 165, 495-495.
- The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei.
- Evaluation of a regional air quality model using satellite column NO<sub>2</sub>: treatment of observation errors and model boundary conditions and emissions. Atmospheric Chemistry and Physics, 15(10), 5611-5626.
- Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity. Atmospheric Chemistry and Physics, 14(9), 4679-4713.
- Corrigendum to "The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei" published in Atmos. Chem. Phys., 13, 8879–8914, 2013. Atmospheric Chemistry and Physics, 13(18), 9375-9377.
- The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei. Atmospheric Chemistry and Physics, 13(17), 8879-8914.
- Boundary layer nucleation as a source of new CCN in savannah environment. Atmospheric Chemistry and Physics, 13(4), 1957-1972.
- No statistically significant effect of a short-term decrease in the nucleation rate on atmospheric aerosols. Atmospheric Chemistry and Physics, 12(23), 11573-11587.
- Mapping the uncertainty in global CCN using emulation. Atmospheric Chemistry and Physics, 12(20), 9739-9751.
- Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model. Atmospheric Chemistry and Physics, 12(10), 4449-4476.
- Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters. Atmospheric Chemistry and Physics, 11(23), 12253-12273.
- Primary versus secondary contributions to particle number concentrations in the European boundary layer. Atmospheric Chemistry and Physics, 11(23), 12007-12036.
Chapters
- Overview of Sensitivity Analysis Methods in Earth Observation Modeling, Sensitivity Analysis in Earth Observation Modelling (pp. 3-24). Elsevier
Conference proceedings papers
- New approaches to quantifying the magnitude and causes of uncertainty in global aerosol models. AIP Conference Proceedings
Preprints
- An emulator of stratocumulus cloud response to two cloud-controlling factors accounting for natural variability., Authorea, Inc..
- No statistically significant effect of a short-term decrease in the nucleation rate on atmospheric aerosols, Copernicus GmbH.
- Mapping the uncertainty in global CCN using emulation, Copernicus GmbH.
- Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model, Copernicus GmbH.
- Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters, Copernicus GmbH.
- Exploring a Stratocumulus-to-Cumulus Transition: A Perturbed Parameter Ensemble of Large-Eddy Simulations.
- Grants
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EUROPEAN COMMISSION - HORIZON 2020, FORCeS Oct 2019-Mar 2023, GBP 8,497 as UoS PI, Led by Stockholm University with 23 partners from 12 countries.
- Teaching activities
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PhD supervision (completed):
- Rachel Sansom (University of Leeds) - Statistical methods to quantify and visualise the complex behaviour of clouds in the climate system
- Amy Peace - Quantifying the effect of uncertainties in aerosols on near-term climate projections
- Leighton Regayre - Quantifying and interpreting the climatic effects of uncertainty in aerosol radiative forcing
Previously led teaching in Time Series Analysis for BSc Applied Statistics students at Sheffield Hallam University and Statistics for Environmental Science at University of Leeds.
- Professional activities and memberships
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Fellow of Royal Statistical Society