Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Page not in menu
This is a page not in th emain menu
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
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
Bayesian History Matching Applied to the Calibration of a Gravity Wave Parameterization
Published in JAMES, 2024
Citation: King, R. C., Mansfield, L. A., & Sheshadri, A. (2024). Bayesian history matching applied to the calibration of a gravity wave parameterization. Journal of Advances in Modeling Earth Systems, 16, e2023MS004163. https://doi.org/10.1029/2023MS004163
Uncertainty Quantification of a Machine Learning Subgrid-Scale Parameterization for Atmospheric Gravity Waves
Published in JAMES, 2024
Citation: Mansfield, L. A., & Sheshadri, A. (2024). Uncertainty quantification of a machine learning subgrid-scale parameterization for atmospheric gravity waves. Journal of Advances in Modeling Earth Systems, 16, e2024MS004292. https://doi.org/10.1029/2024MS004292
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
LearnBayesStats podcast
Published:
Listen to my podcast episode on LearnBayesStats!
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
Palo Alto school
Published:
I visited a local high school to talk about career paths
New position!
Published:
as a Schmidt AI in Science fellow.
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.