Next-generation Isoprene Measurements from Space: Quantifying Daily
Variability at High Resolution
Abstract
Isoprene is the dominant non-methane organic compound emitted to the
atmosphere, where it drives ozone and aerosol production, modulates
atmospheric oxidation, and interacts with the global nitrogen cycle.
Isoprene emissions are highly variable and uncertain, as is the
non-linear chemistry coupling isoprene and its primary sink, the
hydroxyl radical (OH). Space-based isoprene measurements can help close
the gap on these uncertainties, and when combined with concurrent
formaldehyde data provide a new constraint on atmospheric oxidation
regimes. Here we present a next-generation machine-learning isoprene
retrieval for the Cross-track Infrared Sounder (CrIS) that provides
improved sensitivity, lower noise, and thus higher space-time resolution
than earlier approaches. The Retrieval of Organics with CrIS Radiances
(ROCR) isoprene measurements compare well with previous space-based
retrievals as well as with the first-ever ground-based isoprene column
measurements, with 20-50% discrepancies that reflect differing sources
of systematic uncertainty. An ensemble of sensitivity tests points to
the spectral background and isoprene profile specification as the most
relevant uncertainty sources in the ROCR framework. We apply the ROCR
isoprene algorithm to the full CrIS record from 2012-2020, showing that
it can resolve fine-scale spatial gradients at daily resolution over the
world’s isoprene hotspots. Results over North America and Amazonia
highlight emergent connections between isoprene abundance and
daily-to-interannual variations in temperature, nitrogen oxides, and
drought stress.