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Predicting Lower Stratospheric Water Vapor from Chemistry-Climate Models Using a Multivariate Linear Regression
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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.
Multivariate Linear Regression
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