Adam Bauer:
Learning Climate Sensitivity from Future Observations, Fast and Slow
We develop a model to explore the learning rate about the equilibrium climate sensitivity (ECS) and the transient climate response (TCR) conditional on (hypothetical) future observations of the climate. We show how TCR uncertainty is expected to decline in the near-term regardless of its true value, whereas ECS uncertainty may remain persistent even with an additional 80 years of observations. We close by using a two-layer energy balance model to understand the physics of ECS/TCR learning rates, and why they are so dissimilar.
Jessica Skocinski:
Tornado Track Deviation: A Possible Approach to Statistical Warnings
Current warning strategies for tornadoes have significant spatial inefficiencies owing to uncertainty in tornado movement. Quantifying the historical deviation of tornado tracks in a given storm environment may allow for multiple warning probabilities and could decrease false alarm ratios while increasing effective lead time in warnings. Prior literature on this topic has been limited to small sample sizes investigating singular tornadoes or tornado outbreaks. Tornado tracks obtained from the Tornado Archive are correlated with storm mode and meteorological model data and plotted in a novel method allowing for statistical comparisons of tracks. Preliminary results suggest that tornadoes in environments with low storm relative helicity move more erratically and therefore less predictably than tornadoes in environments with high storm relative helicity."
