UC Davis

Climate change will push the weather experienced by people affected outside the bounds of historic norms, resulting in unprecedented weather events. But people and firms should be able to learn from their experience of unusual weather and adjust their expectations about the climate distribution accordingly. The efficiency of this learning process gives an upper bound on the rate at which adaptation can occur and is therefore important in determining the adjustment costs associated with climate change. Learning about climate change requires people to infer the state of a changing probability distribution (climate) given annual draws from that distribution (weather). If the climate is stationary, it can be inferred from the distribution of historic weather observations, but if it is changing, the inference problem is more challenging. This paper first develops different learning models, including an efficient hierarchical Bayesian model in which the observer learns whether the climate is changing and, if it is, the functional form that describes that change. I contrast this with a less efficient but simpler learning model in which observers react to past changes but are unable to anticipate future changes. I propose a general metric of learning costs based on the average, discounted squared difference between beliefs and the true climate state and use climate model output to calculate this metric for two emissions scenarios, finding substantial relative differences between learning models and scenarios but small absolute values. Geographic differences arise from spatial patterns of warming rates and natural weather variability (noise). Finally, I present results from an experimental game simulating the adaptation decision, which suggests that people are able to learn about a trending climate and respond proactively.