Originally published in our May 2023 newsletter (Issue 20)
David’s research is focused on understanding the influence of climate uncertainty in models of coupled human-environment systems. Specifically, the research aims to identify the uncertainties and potential biases associated with using downscaled climate model projections to provide high-resolution and accurate information about future climate and weather conditions at regional and local scales. The outcomes of this research will help impact modelers and decision-makers better manage the risks of climate change.
David Lafferty is a Ph.D. candidate working with Professor Ryan Sriver in the Department of Atmospheric Sciences at the University of Illinois Urbana-Champaign. David’s research seeks to understand the influence of climate uncertainty in models of coupled human-environment systems. Before his graduate studies, David earned a bachelor’s degree in physics from the University of Glasgow, UK, and a master’s degree in physics from Ruprecht-Karls-Universität Heidelberg, Germany.
Quantifying and managing the risks of a changing climate requires a robust understanding of how climate change will manifest at regional and local scales. To this end, impact modelers and decision-makers in both the public and private spheres often rely on downscaled climate model projections to provide high-resolution and accurate information about future climate and weather conditions. David’s Ph.D. research focuses on the uncertainties and potential biases of this approach. Working with collaborators as part of the Program on Coupled Human and Earth Systems (PCHES), David incorporates the expertise of hydrologists, economists, and engineers into his research to facilitate uncertainty analyses that are targeted at specific sectoral outcomes. For example, in one paper, David and coauthors show how potential biases in projections of US corn yields are driven by the representation of seasonal extreme heat in the underlying climate projections [1].
In more recent work, David and his advisor, Ryan Sriver, leverage the outputs of several different downscaled ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to understand how different sources of uncertainty combine to affect the representation of decision-relevant climate hazards [2]. By applying a simple variance decomposition approach at global scale, they partition projection spread among four factors to show that the uncertainty associated with downscaling can often be more important than other sources such as emissions scenario uncertainty and climate model uncertainty. Their results show that downscaling is particularly important for near-term projections, projections involving precipitation, or projections of climate extremes, and suggest that sampling from more than one downscaled ensemble may be advisable in these cases.
Since 2021, David has also been a member of the MSD Working Group on Uncertainty Quantification and Scenario Development. He was part of a multidisciplinary team of authors that contributed to the Working Group’s recent review paper outlining the challenges and opportunities of uncertainty analysis for multi-sector systems [3]. In addition to discussing the climate-related uncertainties that form the central theme of David’s Ph.D. research, the paper covers a broad array of topics relevant to multi-sector analysis, including endogenous model calibration and uncertainty, and scenario discovery for high-dimensional output spaces.
For his remaining Ph.D. research, David is working with PCHES collaborators to extend the CMIP6 uncertainty characterization approach to analyze soil moisture and crop yield outcomes in the central US. He is also involved in a parallel PCHES project aiming to develop a stylized multi-sector modeling framework that can serve as an uncertainty quantification method testbed as well as for didactic purposes.
Highlighted Articles:
[1] Lafferty, D.C., Sriver, R.L., Haqiqi, I., Hertel, T.W., Keller, K., Nicholas, R.E. (2021). Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields. Commun Earth Environ 2, 196. https://doi.org/10.1038/s43247-021-00266-9
[2] Lafferty, D.C. and Sriver, R.L. (2023) Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6. ESS Open Archive. April 30, 2023. https://doi.org/10.22541/essoar.168286894.44910061/v1
[3] Srikrishnan, V., Lafferty, D.C., Wong, T.E., Lamontagne, J.R., Quinn, J.D., Sharma, S., et al. (2022). Uncertainty analysis in multi-sector systems: Considerations for risk analysis, projection, and planning for complex systems. Earth’s Future, 10, e2021EF002644. https://doi.org/10.1029/2021EF002644
Website: david0811.github.io
Twitter:@DavidCLafferty