1 Introduction
Atmospheric Rivers (ARs) can generate extreme precipitation events,
flooding, and contribute ~80% of low to mid latitude
and ~30% of high latitude meridional fluxes of water
and energy (Woods et al., 2013; Zhu & Newell, 1998). Their critical
role in the atmosphere makes it important to understand their life
cycle. Details of their precipitation are well studied (e.g., Lavers &
Villarini, 2013; Mahoney et al., 2016; Waliser & Guan, 2017); however,
less has been published regarding energy and moisture sources within ARs
(e.g., Bao et al., 2006).
ARs could result from organized, local convergence associated with an
extratropical cyclone (ETC) (e.g., Dacre et al., 2015), behave as
‘rivers’ representative of synoptic transports of heat and moisture
(e.g., Shinoda et al., 2019; Sodemann & Stohl, 2013), or, on some
occasions, represent a combination of both. Despite this question being
examined from multiple observational and modeling perspectives, it
remains not fully resolved (Eiras-Barca et al., 2017; Ramos et al.,
2016; Payne et al., 2020). For ARs associated with extratropical
cyclones (~82% of ARs in the North Pacific; Zhang et
al., 2019), the primary energy source is the atmospheric response to
baroclinic instability. However, they also found that only 45% of ETCs
are associate with ARs, and this suggests that the moisture component to
ARs may depend on unique circulation or surface fluxes. Dacre et al.
(2019) posit that moisture transported into the AR via a feeder
airstream is rained out before traveling too far poleward, based on
their ETC-focused moisture budget using reanalysis data. If so, then one
might expect subsequent moisture is added via surface fluxes. As such,
the work herein is focused on the behavior of the ocean surface fluxes
under ARs during the events.
One past constraint in determining AR energy sources was that
observations of surface winds directly beneath ARs are obscured by
precipitation (Bourassa et al., 2019). This impacted air-sea flux
estimates that principally derive from bulk flux equations involving
wind speed. The relatively recently (2017) launched NASA Cyclone Global
Navigation Satellite System (CYGNSS) GeoPositioning Satellite (GPS)
receiver constellation is unique for its ability to measure surface
roughness to estimate wind speeds even under heavily precipitating
clouds (Ruf et al., 2019), allowing air-sea fluxes to be estimated with
higher fidelity (Crespo et al., 2019).
Using CYGNSS air-sea fluxes to analyze ETCs, Naud et al. (2021) show
that the weakest fluxes out of the ocean occur in the storm warm sector
where poleward advection of warm air causes surface air temperature to
be as warm as the ocean below, limiting energy fluxes out of the ocean.
Given that the warm sector of ETCs is often associated with an AR, the
Naud et al. (2021) result suggests energy fluxed from ocean to
atmosphere under AR regions may be small. To test this idea, we examine
CYGNSS surface heat fluxes under ARs.
If not from directly below, where do ARs get their fuel? We supplement
CYGNSS observational surface flux analysis using vapor source
distribution tracers in the NASA GISS climate model to determine
explicitly the provenance of moisture (heat) contained in ARs. Since the
GISS model water tracers do not require a priori definition of
evaporative region, they may be collated and examined in post-processing
for any region of the world facilitating the comparison here to
~7500 ARs over the 2018-2022 time period. These tracers
remain robust across the boundary layer and through time allowing for
far-field moisture sources to be examined across greater distances than
in some back-trajectory investigations. This tracer work allows us to
quantify the relative extent to which AR moisture is provided by fluxes
directly below by calculating climatological transport distances and
sources of moisture in ARs and contrasting that to moisture sources
during other times. Further, we can also analyze conditions during AR
genesis specifically, to determine if there is a change in behavior when
ARs initially form as compared to after formation. Importantly, our
simulated results can be vetted against CYGNSS even when well-organized
AR events rain heavily.