Atmospheric River Detection Under Changing Seasonality and Mean-State
Climate: ARTMIP Tier 2 Paleoclimate Experiments
Abstract
Atmospheric rivers (ARs) are filamentary structures within the
atmosphere that account for a substantial portion of poleward moisture
transport and play an important role in Earth’s hydroclimate. However,
there is no one quantitative definition for what constitutes an
atmospheric river, leading to uncertainty in quantifying how these
systems respond to global change. This study seeks to better understand
how different AR detection tools (ARDTs) respond to changes in climate
states utilizing single-forcing climate model experiments under the
aegis of the Atmospheric River Tracking Method Intercomparison Project
(ARTMIP). We compare a simulation with an early Holocene orbital
configuration and another with CO2 levels of the Last Glacial
Maximum to a pre-industrial control simulation to test how the ARDTs
respond to changes in seasonality and mean climate state, respectively.
We find good agreement among the algorithms in the AR response to the
changing orbital configuration, with a poleward shift in AR frequency
that tracks seasonal poleward shifts in atmospheric water vapor and
zonal winds. In the low CO2 simulation, the algorithms generally
agree on the sign of AR changes but there is substantial spread in their
magnitude, indicating that mean-state changes lead to larger
uncertainty. This disagreement likely arises primarily from differences
between algorithms in their thresholds for water vapor and its transport
used for identifying ARs. These findings warrant caution in ARDT
selection for paleoclimate and climate change studies in which there is
a change to the mean climate state, as ARDT selection contributes
substantial uncertainty in such cases.