arXiv subjects

Tiffany Shaw

Publications and source records attributed to Tiffany Shaw.

4 recordsLinked to original sources

arXiv2026

Water vapor buoyancy and the African easterly jet

The African easterly jet (AEJ) is a prominent circulation feature in the tropical atmosphere. It transports mineral dust and generates easterly waves that serve as seeds for hurricanes. Conventional wisdom holds that the AEJ is in thermal wind balance with the positive meridional temperature gradient over North Africa. Here, using reanalysis data, we show that the negative meridional moisture gradient substantially counteracts the effect of the temperature gradient on density in that balance, diagnostically accounting for a 30\% reduction of the AEJ magnitude. Using CMIP6 data, we further show that this effect of vapor buoyancy on the AEJ strengthens under global warming, highlighting the critical role of the spatial distribution of moisture on large-scale circulation. Analysis of the AEJ in CMIP6 models confirms that some models do not include vapor buoyancy in their governing equations, raising questions about the relevance of their projections of climate change in that region.

physics.ao-ph↗

arXiv2025

Benchmarking atmospheric circulation variability in an AI emulator, ACE2, and a hybrid model, NeuralGCM

Physics-based atmosphere-land models with prescribed sea surface temperature have notable successes but also biases in their ability to represent atmospheric variability compared to observations. Recently, AI emulators and hybrid models have emerged with the potential to overcome these biases, but still require systematic evaluation against metrics grounded in fundamental atmospheric dynamics. Here, we evaluate the representation of four atmospheric variability benchmarking metrics in a fully data-driven AI emulator (ACE2-ERA5) and hybrid model (NeuralGCM). The hybrid model and emulator can capture the spectra of large-scale tropical waves and extratropical eddy-mean flow interactions, including critical levels. However, both struggle to capture the timescales associated with quasi-biennial oscillation (QBO, $\sim 28$ months) and Southern annular mode propagation ($\sim 150$ days). These dynamical metrics serve as an initial benchmarking tool to inform AI model development and understand their limitations, which may be essential for out-of-distribution applications (e.g., extrapolating to unseen climates).

physics.ao-ph↗

arXiv2025

Predicting Beyond Training Data via Extrapolation versus Translocation: AI Weather Models and Dubai's Unprecedented 2024 Rainfall

Artificial intelligence (AI) models have transformed weather forecasting, but their skill for gray swan extremes is unclear. Here, we analyze GraphCast, AIFS, and FuXi forecasts of the unprecedented 2024 Dubai storm, which had twice the training set's highest rainfall in that region. Remarkably, GraphCast and AIFS accurately forecast this event up to 8 days ahead. FuXi forecasts the event, but underestimates the rainfall. Fine-tuning and receptive field analyses suggest that these models' success stems from "translocation": learning from comparable/stronger dynamically similar events in other regions during training. Evidence of "extrapolation" (learning from weaker events) is not found. Even events within the global distribution's tail are poorly forecasted, which is not just due to data imbalance (generalization error) but also spectral bias (optimization error). These findings demonstrate the potential of AI models to forecast regional gray swans and the opportunity to improve them through understanding the mechanisms behind their successes/limitations.

physics.ao-ph↗

arXiv2019

The Snowball Stratosphere

According to the Snowball Earth hypothesis, Earth has experienced periods of low-latitude glaciation in its deep past. Prior studies have used general circulation models (GCMs) to examine the effects such an extreme climate state might have on the structure and dynamics of Earth's troposphere, but the behavior of the stratosphere has not been studied in detail. Understanding the snowball stratosphere is important for developing an accurate account of the Earth's radiative and chemical properties during these episodes. Here we conduct the first analysis of the stratospheric circulation of the Snowball Earth using ECHAM6 general circulation model simulations. In order to understand the factors contributing to the stratospheric circulation, we extend the Statistical Transformed Eulerian Mean framework. We find that the stratosphere during a snowball with prescribed modern ozone levels exhibits a weaker meridional overturning circulation, reduced wave activity, stronger zonal jets, and is extremely cold relative to modern conditions. Notably, the snowball stratosphere displays no sudden stratospheric warmings. Without ozone, the stratosphere displays slightly weaker circulation, a complete lack of polar vortex, and even colder temperatures. We also explicitly quantify for the first time the cross-tropopause mass exchange rate and stratospheric mixing efficiency during the snowball and show that our values do not change the constraints on CO$_2$ inferred from geochemical proxies during the Marinoan glaciation ($\sim$635 Ma), unless the O$_2$ concentration during the snowball was orders of magnitude less than the CO$_2$ concentration.

physics.ao-ph↗