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Senior Scientist

Joint Global Change Research Institute (PNNL)


The use of emulators for uncertainty characterization and integrated modeling 


Abstract: In this talk, I will present some recent work that my colleagues and I, at the Joint Global Change Research Institute/Pacific Northwest National Laboratory, have focused on, in the general area of climate model emulation. The aim of my talk is not to go into minute details of our work, but rather to give a flavor to the TIES audience of a range of questions, related to uncertainty characterizations, that provide motivation for the application of data-driven statistical and machine learning methods.  

In the first example, an emulator of climate model output is developed to serve as the Earth system component in an integrated modeling framework of impacts and mitigation scenarios. (I will also briefly describe such framework that attempts to “close the loop” between Earth and human system modeling.) This emulator exploits the rich archives of Earth system model output that the Coupled Model Inter-comparison Project provides at regular intervals of several years (the most recent being CMIP6). The CMIP effort collects the state-of-the-art simulations of future scenarios by multiple Earth system (climate) models. The CMIP catalogue, however, is limited to a handful of scenarios since climate model simulations are computationally expensive and only a few (well-spaced) can be sensibly prescribed to modeling centers without stretching their resources.  Emulators like the one I’m going to present, STITCHES, are developed to fill-in the gaps in between the available scenarios. This part will take up most of the talk but if time is left, I will quickly describe two more applications of the emulator concept. One uses machine learning tools to simulate daily temperature and precipitation fields mimicking a climate model that the ML tools have been trained on. This work is in the name of enlarging the sample size of daily climate model output to better characterize extremes’ behavior. Lastly, I may be able to talk briefly about the use of statistical surrogate models to estimate the relationship between model parameters and model output (when the model is a complex Earth system model that can be run only a few times by varying its parameters because of the same computational constraints alluded to earlier) in order to identify the best parameter settings according to some validatory metric. These best parameter settings often do not coincide with, but lie between those settings that were actually run by the climate model, making the role of the emulator key to identify them.

Speaker Biography

Claudia Tebaldi is a senior scientist with the Joint Global Change Research Institute (PNNL), and her research interests center around the analysis of observations and climate model output in order to characterize observed and projected climatic changes and their uncertainties, with the goal of making this information useful for the modeling and estimation of socio-economic and environmental impacts. She has published papers on detection and attribution, on extreme value analysis, future projections at regional scales, the use of multiple climate model projections, benefits of mitigation, and impacts of climate change on agriculture and human health. She has participated as a lead author in the last two cycles of the UN’s Intergovernmental Panel on Climate Change assessment activities, and was a chapter author for the US National Climate Assessment just released (NCA5). She co-chairs ScenarioMIP, an international activity that designs future projection experiments for climate models to undertake in a coordinated manner, all over the world. She also collaborates with Climate Central's Climate Science and Impacts group and provides scientific oversight and advice on the organization's programs. She has a Ph.D. in statistics from Duke University and was a researcher at the National Center for Atmospheric Research for most of her career before moving to JGCRI/PNNL in the summer of 2019. 

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