The Thompson Lecture Series is one of the the career development services ASP provides to postdoctoral fellows at NCAR. Every year, a Thompson lecturer is selected by the postdocs and invited to NCAR for a 3-day visit to spend time with them and other early-career scientists at NCAR.
While at NCAR, Thompson Lecturers:
The Thompson Lecture Series strives to provide many perspectives and invites speakers from a broad range of scientific disciplines, home institutions, demographic backgrounds, and different stages of their career.
The Thompson Lecture Series started 1998 and is named in honor of Phil Thompson who founded the Advanced Study Program and was NCAR's first associate director.
Having some fun with Doctor Frierson
Associate Professor
Dept. of Atmospheric Science
Colorado State University
More information about Dr. Barnes can be found here, https://www.atmos.colostate.edu/people/faculty/barnes/
General talk: An Unstable State: My Continuous Struggle to Find Work-Life “Balance”
April 22, 2019 at 11:00am - Foothills Lab Large Auditorium
Abstract:
I am a climate scientist. I am also a professor, mentor, mother, partner, sister, daughter, friend, and general member of society. Performing well in any one of these roles is hard work for me, but performing perfectly in all of them is impossible. Here, I will discuss my continuing struggle to find balance between these aspects of my life, and how I am coming to terms with the fact that true “balance” may instead be the continuous movement between unstable states. Throughout this talk I will highlight lessons I have learned and those I am still learning, while sprinkling-in thoughts from colleagues across the field.
Technical talk: Extracting climate signals from the noise with machine learning
April 23, 2019 at 11:00am - Mesa Lab Main Seminar Room
Abstract:
The atmosphere is a noisy and chaotic place, and our job as scientists is to disentangle the signal from the noise, and thus, determine cause and effect. This talk focuses on two signals and the tools employed to extract them from their noise.
The first signal is that of remote Arctic and tropical drivers of subseasonal variations in the midlatitude jet-streams. Daily variations in the midlatitude jet-stream can lead both to devastating extreme events, or mild weather, and thus, understanding the drivers of jet-stream variations on sub-seasonal timescales is one step toward accurate predictions. However, internal variability makes it challenging to determine who is driving whom. The second signal is that of humanity’s influence on climate. Identifying this signal in observations, and distinguishing it from internal variability across a range of timescales, often requires one estimate the signal from climate model simulations. However, uncertainty across model projections introduces yet another layer of noise.
Separating the signal from the noise is no easy task, and success requires that one have the right tools for the job. Here, we apply methods from machine learning, specifically, causal discovery techniques and artificial neural networks, to extract these very different signals from the background of internal climate variability and model uncertainty. While this talk is focused on two specific signals, we will also use it as an opportunity to provide a brief overview of some causal discovery and machine learning methods to highlight their relevance for climate dynamics research as whole.
