Understanding Long-Term Variations in Stratospheric Water Vapor Grant uri icon

abstract

  • This project seeks to understand changes in tropical stratospheric water vapor seen in observations and in model simulations of future climate change. Simulations of future climate consistently show an increasing trend in tropical stratospheric water vapor due to greenhouse gas increases, but satellite data since the 1980s show interannual and decadal variability but little trend. In addition, comparison of monthly stratospheric water vapor and 100hPa heating rate are negatively correlated in observational data, but positively correlated in future climate simulations. Negative correlations are expected if changes in water vapor and heating rates are linked to variations in the Brewer-Dobson circulation, as a stronger BD circulation is associated with stronger tropical heating (i.e. stronger diabatic upwelling) and colder tropical stratospheric temperatures. But future climate simulations show increases in both water vapor and the strength of the BD circulation. Moreover, increases in surface temperature are expected to produce increases in stratospheric water vapor, but surface temperatures rose over the observed record while tropical stratospheric water vapor did not. This project uses a suite of models including a domain-filling trajectory model, the Whole Atmosphere Community Climate Model (WACCM), and a one-dimensional radiative transfer model, to understand the underlying processes responsible for the changes in observed and simulated water vapor changes.The work has broader impacts due to the climatic effects of stratospheric water vapor, and the relationship between stratospheric ozone and water vapor. In addition, the PI will conduct outreach to general audiences through new-media outlets, thereby working to increase public understanding of the phenomena addressed in this research. The project also supports and trains a graduate student, thereby providing for the development of the scientific workforce in this research area.

date/time interval

  • 2013 - 2017