A Multivariate Space-Time Dynamic Model for Characterizing the Atmospheric Impacts Following the Mt Pinatubo Eruptio

TL;DR

This paper introduces a multivariate space-time dynamic linear model to analyze the complex atmospheric impacts of the 1991 Mt. Pinatubo eruption, capturing spatial and temporal variations and relationships among atmospheric parameters.

Contribution

The paper presents a novel multivariate space-time dynamic linear model with a VAR structure for basis coefficients, estimated via a customized MCMC approach, to analyze atmospheric impacts of volcanic eruptions.

Findings

Model effectively captures spatial and temporal variations in atmospheric parameters.

Quantifies relationships between atmospheric variables before and after eruption.

Demonstrates advantages over univariate models in analyzing complex atmospheric data.

Abstract

The June 1991 Mt. Pinatubo eruption resulted in a massive increase of sulfate aerosols in the atmosphere, absorbing radiation and leading to global changes in surface and stratospheric temperatures. A volcanic eruption of this magnitude serves as a natural analog for stratospheric aerosol injection, a proposed solar radiation modification method to combat a warming climate. The impacts of such an event are multifaceted and region-specific. Our goal is to characterize the multivariate and dynamic nature of the atmospheric impacts following the Mt. Pinatubo eruption. We developed a multivariate space-time dynamic linear model to understand the full extent of the spatially- and temporally-varying impacts. Specifically, spatial variation is modeled using a flexible set of basis functions for which the basis coefficients are allowed to vary in time through a vector autoregressive (VAR) structure. This novel model is caste in a Dynamic Linear Model (DLM) framework and estimated via a customized MCMC approach. We demonstrate how the model quantifies the relationships between key atmospheric parameters prior to and following the Mt. Pinatubo eruption with reanalysis data from MERRA-2 and highlight when such model is advantageous over univariate models.