Investigating the Effects of Co-Occurring Weather Phenomena on Extreme Precipitation in Reanalysis, E3SM, and CMIP6

Investigating the Effects of Co-Occurring Weather Phenomena on Extreme
Precipitation in Reanalysis, E3SM,

Energy Exascale Earth System Model (E3SM)

The Energy Exascale Earth System Model (E3SM) project includes Earth System Models, simulations, and predictions to investigate energy-relevant science using code optimized for DOE's advanced supercomputers.

Source: NVCL Web Page Source

and CMIP6
Travis O’Brien, Indiana University (Principal Investigator)
Jennifer Catto, University of Exeter (Co-Investigator)
William Boos, University of California, Berkeley (Co-Investigator)
Ruby Leung, Pacific Northwest National Laboratory (Co-Investigator)
Alan Rhoades, Lawrence Berkeley National Laboratory (Co-Investigator)
Yang Zhou, Lawrence Berkeley National Laboratory (Co-Investigator)
J. David Neelin, University of California, Los Angeles (collaborator)
Fiaz Ahmed, University of California, Los Angeles (collaborator)
Paul Ullrich, University of California, Davis (collaborator)
This collaborative project, under the RGMA – Water Cycle and Associated Extremes
topic of DOE DE-FOA-0002593, aims to advance understanding of co-occurring weather
phenomena and their effects on precipitation in nature and in climate model simulations by
studying the co-occurrence of weather phenomena–specifically, tropical low-pressure systems
(LPS), fronts, mesoscale convective systems

mesoscale convective system (MCS)

A collection of thunderstorms that act as a system. An MCS can spread across an entire state and may appear as a solid line, a broken line, or a cluster of cells that can last more than 12 hours.

Source: NOAA Source

(MCS), and atmospheric rivers

atmospheric river

Long, narrow regions in the atmosphere – like rivers in the sky – that transport most of the water vapor outside of the tropics.

Source: NOAA Source

(AR)–in reanalyses and
climate model simulations, to enhance a predictive understanding

predictive understanding

Predictive understanding refers to the ability of a system, typically an artificial intelligence or machine learning model, to anticipate future events or outcomes based on historical data and patterns.

Source: Source

of the Earth system (FOA, pg 7)
. This will enable a thorough examination of how biases in the representation of the water
cycle...affect predictability of the Earth system (FOA, pg 7).
There is evidence that the co-occurrence of weather phenomena can lead to more extreme
precipitation: higher intensities, larger areas, and longer durations. This project builds on existing
studies of co-occurring weather phenomena to systematically assess the ability of climate models to
simulate co-occurring weather phenomena, specifically weather phenomena (e.g., MCS and ARs)
that occur close enough in space and time to influence their development.
Phenomenon-focused research on precipitation has just emerged to a point where multiple climate
models can be evaluated based on their ability to simulate multiple types of weather phenomena. A
recent paper led by members of this project team (Leung et al. 2022; doi:10.1175/JCLI-D-21-0590.1),
which emerged from an ad hoc international collaboration following the 2019 DOE Workshop on
Benchmarking Simulated Precipitation in Climate Models, examines the ability of High Resolution
Model Intercomparison Project (HighResMIP) models to simulate tropical LPS, fronts, MCS, and
ARs. This project addresses a number of unanswered questions arising from that working group:
Q1a How do the meteorological characteristics of weather phenomena (e.g., wind and precipitation
in fronts) vary when they are or are not associated with another weather phenomenon (e.g.,
ARs) and how does this vary across phenomena, in space, and in time?
Q1b Does the co-occurrence of phenomena alter the statistical characteristics of precipitation (e.g.,
is precipitation more extreme when phenomena co-occur)?
Q2 How well does the Energy Exascale Earth System Model (E3SM) simulate co-occurring
phenomena compared to individually-occurring phenomena?
Q3 What types of meteorological situations and combinations of weather phenomena do climate
models simulate effectively?
We aim to answer questions Q1a–Q3 through the use of objective methods for identifying fronts,
tropical LPS, MCS, and ARs in observational datasets and climate model simulations. As outcomes
and deliverables of this proposed project, we will:
1. advance the understanding of water cycle extremes and their relationship to co-occurring
phenomena in observations and climate models, leading to the publication of at least 4
peer-reviewed manuscripts (corresponding to Q1a–Q3);
2. lower the barrier-to-entry for future research on this topic by publishing 3 open-access
catalogues of the occurrence (and co-occurrence) of LPS, fronts, MCS, and ARs in ERA5,
E3SMv1 HR, and HighResMIP; and
3. enable multi-metric-based evaluation of precipitation in E3SM and HighResMIP by
disseminating our metrics via the a public web-based model evaluation system.