Marybeth Arcodia

• Doctor of Philosophy
• Research Scientist at Colorado State University

Extreme heat is the deadliest weather-related hazard in the United States. Furthermore, it is increasing in intensity, frequency, and duration, making skillful forecasts vital to protecting life and property. Traditional numerical weather prediction (NWP) models struggle with extreme heat for medium-range and subseasonal-to-seasonal (S2S) timescale...

Global water cycling plays a fundamental role in the climate system, with the majority of terrestrial water ultimately sourced from the ocean. As oceanic moisture evaporates, it leaves a signature on sea surface salinity, allowing the salinity fields to be a predictor of terrestrial precipitation. This research is among the first in the published l...

As oceanic moisture evaporates, it leaves a signature on sea surface salinity. Roughly 10% of the moisture that evaporates over the ocean is transported over land, allowing the salinity fields to be a predictor of terrestrial precipitation. This research is among the first in published literature to assess the role of sea surface salinity for impro...

Climate variability affects sea levels as certain climate modes can accelerate or decelerate the rising sea level trend, but subseasonal variability of coastal sea levels is underexplored. This study is the first to investigate how remote tropical forcing from the MJO and ENSO impact subseasonal U.S. coastal sea level variability. Here, composite a...

Identifying predictable states of the climate system allows for enhanced prediction skill on the generally low-skill subseasonal timescale via forecasts with higher confidence and accuracy, known as forecasts of opportunity. This study takes a neural network approach to explore decadal variability of subseasonal predictability, particularly during...

Climate variability affects sea levels as certain climate modes can accelerate or decelerate the rising sea level trend, but subseasonal variability of coastal sea levels is under-explored. This study is the first to investigate how remote tropical forcing from the MJO and ENSO impact subseasonal U.S. coastal sea level variability. Here, composite...

Simple dynamical models are used to understand fundamental processes of how ENSO modulates subseasonal teleconnections associated with tropical imprints of the MJO by stripping away complex phenomena. Both a dry linear baroclinic model and a dry nonlinear baroclinic model are employed to (1) assess how much of the MJO teleconnection pattern in a pa...

Identifying predictable states of the climate system allows for enhanced prediction skill on the generally low-skill subseasonal timescale via forecasts with higher confidence and accuracy, known as forecasts of opportunity. This study takes a neural network approach to explore decadal variability of subseasonal predictability, particularly during...

Cities around the world are experiencing the effects of climate change via increasing extreme heat worsened by urbanization. Within cities, there are disparities in extreme heat exposure that are apparent in various surface and remotely-sensed observations, as well as in the health impacts. There are, however, large data gaps in our ability to quan...

Some simple dynamical models have proven to be useful tools for understanding tropical-extratropical teleconnections. Here, we use simple linear and non-linear dynamical models to understand how ENSO modulates subseasonal teleconnections associated with the tropical imprint of the MJO. Both a dry linear baroclinic model (LBM) and a dry nonlinear ba...

A composite analysis reveals how the Madden-Julian Oscillation (MJO) impacts North American rainfall through perturbations in both the upper-tropospheric flow and regional low-level moisture availability. Upper-level divergence associated with the MJO tropical convection drives a quasi-stationary Rossby wave response to the mid-latitudes. This forc...