Predicting Lower Stratospheric Water Vapor from Chemistry-Climate Models Using a Multivariate Linear Regression
Abstract
Climate models predict that tropical lower stratospheric humidity will increase as
the climate warms, with important implications for the chemistry and climate of the atmosphere.
We analyze this trend in 21st-century simulations from 12 state-of-the-art
chemistry-climate models (CCMs) using a linear regression model to determine the factors
driving the trends. The trend in humidity in the CCMs is driven by warming of the
troposphere. This is partially offset in most CCMs by an increase in the strength of the
Brewer-Dobson circulation, which tends to cool the tropopause layer. We also apply the
regression model to individual decades from the 21st century CCM runs and compared
them to the results from a regression of a decade of lower stratospheric humidity observations.
Many of the CCMs, but not all, compare well with observations, lending credibility
to their predictions. One notable deficiency in most CCMs is that they underestimate the
impact of the quasi-biennial oscillation on lower stratospheric humidity. Our analysis provides
a new way to evaluate model trends in lower stratospheric humidity.
Subject
Chemistry-Climate ModelsClimate Change
Lower Stratosphere
Water Vapor
Multivariate Linear Regression
Citation
Smalley, Kevin (2016). Predicting Lower Stratospheric Water Vapor from Chemistry-Climate Models Using a Multivariate Linear Regression. Master's thesis, Texas A & M University. Available electronically from https : / /hdl .handle .net /1969 .1 /158953.