Statistical stress testing of the global sea level in the alternative climate scenarios

This study develops a statistical framework to forecast global sea-level change as a function of atmospheric carbon dioxide (CO₂) concentrations and global temperature and to conduct stress testing under alternative climate policy scenarios. Three scenarios are considered: a) an expected scenario reflecting current emission trends, b) a best-case scenario assuming compliance with Kyoto Protocol CO₂ reduction targets, and c) a worst-case scenario assuming CO₂ emissions increase at a rate opposite to the Kyoto targets.

The analysis employs a three-stage modeling approach based on Seasonal Autoregressive Integrated Moving Average models with exogenous variables (SARIMAX). In the first stage, CO₂ dynamics are modeled using a univariate SARIMAX specification. In the second stage, global temperature is modeled with lagged temperature and CO₂ as an exogenous predictor. In the final stage, sea level is modeled as a function of its own dynamics and lagged global temperature. The estimated models are used to generate sea-level projections under the three scenarios.

The results indicate significant sea-level rise under the expected scenario, stabilization under the best-case scenario

stress testing

SARIMAX

global warming

predictive modeling

temperature, CO2

sea level 

Section on Statistics and Data Science Education