Cooperative Agreement To Analyze variabiLity, change and predictabilitY in the earth SysTem (CATALYST)

The “Cooperative Agreement To Analyze variabiLity, change and predictabilitY in the earth SysTem” (CATALYST) proposes to perform foundational coordinated research in a team-oriented collaborative effort aimed at advancing a robust understanding of modes of Earth system variability and change using models, observations, machine learning,

machine learning (ML)

Machine Learning (ML) refers to the field and practice of using algorithms that are able to “learn” by extracting patterns from a large body of data.

Source: Center of Excellence Source

and process studies. The research addresses the DOE/BER mission by exploring the limits of predictability, identifying fundamental underlying mechanisms, quantifying interactions among modes of variability (MOV), and exploring the role of MOV in triggering tipping points in the Earth system, with the goal of understanding the current and future impacts of these phenomena on regional and global climate.

We identify four fundamental gaps in our understanding of MOV in the Earth system that are posed as four core science questions:

GAP 1: What are the limits of predictability for MOV across timescales?

GAP 2: What are the interactions among MOV in the Earth system?

GAP 3: How well do new generation Earth System Models

earth system model (ESM)

A model that simulates how chemistry, biology, and physical forces work together. These models are similar to, but much more comprehensive than, global climate models.

Source: U.S. Department of Energy Source

(ESMs) represent MOV and how might these respond to future changes in external forcing, and what are the tipping points involved with those changes?

GAP 4: How are high-impact events connected to MOV and how may they change in the future?

The CATALYST experimental design addresses GAPs 1 through 4 in four aligned research objectives (ROs). Each RO poses testable hypotheses that we address through analysis of simulations with ESMs (specifically the Energy Exascale Earth System Model (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 the Community Earth System Model (CESM)), Coupled Model Intercomparison Project (CMIP) multi-model data sets, machine

learning (ML), a hierarchy of simpler models, and numerous observational data sets.

Research Objective 1 (RO1) addresses GAP 1 by proposing to predict MOV and their limits of predictability on subseasonal to decadal timescales using ESMs and ML.

Research Objective 2 (RO2) addresses GAP 2 and proposes to use a hierarchy of models to understand the mechanisms, processes and feedbacks related to how MOV interact with each other.

Research Objective 3 (RO3) addresses GAP 3 by benchmarking MOV in ESMs, investigating the role of external forcings on MOV, and analyzing the likelihood and predictability of tipping points and irreversible changes.

Research Objective 4 (RO4) addresses GAP 4, using high-resolution ESMs and ML methods to investigate the relationships between high-impact events (e.g. flash droughts and precipitation extremes, 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

(ARs), tropical cyclones (TCs), Mesoscale

mesoscale

Size scale referring to weather systems smaller than synoptic-scale systems but larger than storm-scale systems. Horizontal dimensions generally range from around 50 miles to several hundred miles. Squall lines, MCCs, and MCSs are examples of mesoscale weather systems

Source: NOAA Source

Convective Complexes (MCSs), fronts, low centers) and MOV, and how these relationships might change in future climate.

Collectively, these four ROs represent an integrated scientific research plan with testable hypotheses. RO1 concentrates on predictions of MOV and understanding the limits of their predictability. To inform those predictions, RO2 focuses on understanding the processes, mechanisms and interactions among MOV. RO3 benchmarks those MOV in model simulations and identifies characteristics that may change in a future warmer climate, including those that may trigger tipping points. Finally, RO4 connects MOV examined in the first three ROs to high-impact events and their possible future changes.

To achieve these research goals CATALYST conducts new E3SM and CESM simulations while leveraging existing model simulations, including the CMIP archive, to characterize MOV in large ensembles, single forcing large ensembles, high-resolution and regionally-refined simulations, initialized subseasonal to decadal hindcasts, and a hierarchy of models (from aquaplanet to fully coupled simulations). In addition, we use ML techniques in concert with physical models to explore the limits of MOV predictability and to establish connections between MOV and high-impact events.