Presenter: Andrey David Ramos Ramirez, Universidad Carlos III de Madrid

Title: Quantitative Analysis of Climate Heterogeneity

Abstract: This paper introduces a time-series methodology to quantify heterogeneity in the dynamics of the unconditional temperature distribution and its association with climate drivers. By recognizing that temperature unconditional quantiles represent temperatures at different locations—or seasons—, I establish an equivalence between a structural One-Dimensional Energy Balance Model (1D-EBM) and a statistical reduced-form Vector Error Correction Model (VECM) for a range of distributional characteristics of temperature—mean and quantiles—and total radiative forcing, including radiative forcing from anthropogenic greenhouse gases (GHGs). The VECM is estimated employing time-series methods agnostic about the type of trends (stochastic or deterministic) in climate data, and is utilized to produce the following outcomes of practical interest for economic analyses: i) estimation of long-run responses of temperature distribution to changes in GHGs, ii) long-term temperature density forecasts, iii) conditional projections of temperature distribution under different scenarios of future GHGs emissions, iv) extraction of the common-trending component behind the existing warming trend, and v) identification of distributional climate shocks and impulse-response analysis. An empirical study using station-level temperature records (1880–2021) reveals strong climate heterogeneity at global, hemispheric, and continental (Europe) scales, with important potential implications for damage analysis and integrated assessment modeling. A deeper understanding of global warming dynamics is crucial for informing more efficient adaptation and mitigation policies.

Host: Eric Hillebrand