Projected changes in atmospheric moisture transport contributions associated with climate warming in the North Atlantic.

Global warming and associated changes in atmospheric circulation patterns are expected to alter the hydrological cycle, including the intensity and position of moisture sources. This study presents predicted changes for the middle and end of the 21st century under the SSP5-8.5 scenario for two important extratropical moisture sources: the North Atlantic Ocean (NATL) and Mediterranean Sea (MED).

Significant increase of global anomalous moisture uptake feeding landfalling Atmospheric Rivers.

One of the most robust signals of climate change is the relentless rise in global mean surface temperature, which is linked closely with the water-holding capacity of the atmosphere. A more humid atmosphere will lead to enhanced moisture transport due to, among other factors, an intensification of atmospheric rivers (ARs) activity, which are an important mechanism of moisture advection from subtropical to extra-tropical regions. Here we show an enhanced evapotranspiration rates in association with landfalling atmospheric river events.

Changes in South American hydroclimate under projected Amazonian deforestation.

Continued deforestation in the Amazon forest can alter the subsurface/surface and atmospheric branches of the hydrologic cycle. The sign and magnitude of these changes depend on the complex interactions between the water, energy, and momentum budgets. To understand these changes, we use the weather research and forecasting (WRF) model with improved representation of groundwater dynamics and the added feature of Amazonian moisture tracers.

Lagrangian coherent structures along atmospheric rivers.

We show that filamentous Atmospheric Rivers (ARs) over the Northern Atlantic Ocean are closely linked to attracting Lagrangian Coherent Structures (LCSs) in the large scale wind field. The detected LCSs represent lines of attraction in the evolving flow with a significant impact on all passive tracers. Using Finite-Time Lyapunov Exponents, we extract LCSs from a two-dimensional flow derived from water vapor flux of atmospheric reanalysis data and compare them to the three-dimensional LCS obtained from the wind flow.