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Posts by Collection
portfolio
Portfolio item number 2
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publications
Predicting global patterns of long-term climate change from short-term simulations using machine learning
Published in npj Climate and Atmospheric Science volume 3, 44, 2020
Citation: Mansfield, L.A., Nowack, P.J., Kasoar, M. et al. Predicting global patterns of long-term climate change from short-term simulations using machine learning. npj Clim Atmos Sci 3, 44 (2020). https://www.nature.com/articles/s41612-020-00148-5
Machine Learning, Emulation and Bayesian Dimension Reduction for Climate Change Projection
Published in University of Reading, 2021
Citation: Mansfield, L.A., Machine Learning, Emulation and Bayesian Dimension Reduction for Climate Change Projection, University of Reading, UK
Calibration and Uncertainty Quantification of a Gravity Wave Parameterization: A Case Study of the Quasi-Biennial Oscillation in an Intermediate Complexity Climate Model
Published in Journal of Advances in Modeling Earth Systems, 2022
Citation: Mansfield, L. A., & Sheshadri, A. (2022). Calibration and uncertainty quantification of a gravity wave parameterization: A case study of the Quasi-Biennial Oscillation in an intermediate complexity climate model. Journal of Advances in Modeling Earth Systems, 14, e2022MS003245. https://doi.org/10.1029/2022MS003245
Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data-Informed Methods and Climate Change Impacts
Published in Journal of Advances in Modeling Earth Systems, 2023
Citation: Mansfield, L. A., Gupta, A., Burnett, A. C., Green, B., Wilka, C., & Sheshadri, A. (2023). Updates on model hierarchies for understanding and simulating the climate system: A focus on data-informed methods and climate change impacts. Journal of Advances in Modeling Earth Systems, 15, e2023MS003715. https://doi. org/10.1029/2023MS003715
Uncertainty Quantification of a Machine Learning Subgrid-Scale Parameterization for Atmospheric Gravity Waves
Published in in review (preprint available at ESS Open Archive), 2024
Citation: Mansfield, L.A. & Sheshadri, A., Uncertainty Quantification of a Machine Learning Subgrid-Scale Parameterization for Atmospheric Gravity Waves. ESS Open Archive . February 28, 2024. doi:10.22541/essoar.170914524.42466387/v1
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.
updates
PhD
Published:
I completed my PhD!
New job
Published:
I started a new position as a postdoc at Stanford University
Model Hierarchies Workshop
Published:
The model hierarchies workshop is taking place at Stanford.
AGU 2022
Published:
I am co-convening a session at the AGU 2022 Fall Meeting
Gordon Research Conference
Published:
I am session leader for the model hierarchies session at the Gordon Research Conference
AGU 2023
Published:
I am co-convening a session at the AGU 2023 Fall Meeting
visualizations
Climate response emulator
Published:
A video of the emissions - response emulator built during my PhD.
Calibration and Uncertainty Quantification of Gravity Wave Parameterization
Published:
Visualizations of Calibrate, Emulate and Sample method used in Mansfield & Sheshadri, 2022.
Comparison of Calibration Methods for Gravity Wave Parameterization
Published:
Work led by Rob King to compare calibration and uncertainty quantification techniques for a gravity wave parameterization
Uncertainty Quantification of a Machine Learning Parameterization
Published:
Machine learning parameterizations are becoming a popular technique for improving climate models. This work aims to learn the uncertainties associated with them.