Assessing the Impact of Parameterized Gravity Wave Drag on Climate Change Forecasts: A Systematic Investigation with Global Circulation Models

New York University, New York NY

Investigators

Abstract

This project seeks to understand and quantify the uncertainty associated with gravity wave drag (GWD) parameterization in climate models, particularly for climate change forecasts. Gravity waves influence the global circulation of the atmosphere by transporting momentum between and within the troposphere and stratosphere, but are too small to be adequately resolved in state-of-the-art climate models. Hence their effects are estimated, or parameterized, based on the resolved variables of the model. Recent studies have demonstrated that uncertainty in how GWD is parameterized has a first order impact on both the tropospheric and stratospheric response to anthropogenic forcing. Existing studies of parameterizations, largely performed "offline" with fixed background winds, are insufficient to quantify these uncertainties, as the impact of GWD is amplified by interaction with synoptic and planetary waves. To redress these concerns, the investigators will establish a framework for testing GWD parameterizations in an idealized general circulation model (GCM). The idealized GCM captures the stratospheric and tropospheric circulation with fidelity, but with computational efficiency that permits us to systematically assess the impact of GWD parameters on a changing climate, driven both by trends in the troposphere (i.e. tropical upper tropospheric warming) and stratosphere (i.e. the cooling/heating of the polar vortex associated with ozone loss/recovery). The investigators will use the model to understand the influence of GWD on (1) trends in the tropospheric circulation, in particular shifts in the extratropical jet streams, (2) trends in the stratospheric circulation, in particular the Brewer-Dobson Circulation, and (3) stratospheric climate and variability. The broader impacts of the research are (1) to help improve understanding and confidence in climate change forecasts, (2) to educate and mentor students at the undergraduate and graduate level, and (3) to provide a more general understanding of climate forecasting. The inherent uncertainty in climate prediction is a significant problem for the scientific community in communicating with the public. This project provides a case study in how models (in particular GCMs) can be used to quantify uncertainty, and used for outreach directed at undergraduate students.