- Historical Changes in Wind-Driven Ocean Circulation Can Accelerate Global Warming , GEOPHYSICAL RESEARCH LETTERS (2023)
- Impacts of a weakened AMOC on precipitation over the Euro-Atlantic region in the EC-Earth3 climate model , CLIMATE DYNAMICS (2023)
- Taxation and citizen choice: The effect of a county charter on property taxes , PUBLIC BUDGETING AND FINANCE (2023)
- Destructive Interference of ENSO on North Pacific SST and North American Precipitation Associated with Aleutian Low Variability , JOURNAL OF CLIMATE (2022)
- ENSO Explains the Link Between Indian Ocean Dipole and Meridional Ocean Heat Transport , GEOPHYSICAL RESEARCH LETTERS (2022)
- Hadley Cell Edge Modulates the Role of Ekman Heat Flux in a Future Climate , GEOPHYSICAL RESEARCH LETTERS (2022)
- Java-Sumatra Ni (n)over-tilde no/Ni (n)over-tilde na and Its Impact on Regional Rainfall Variability , JOURNAL OF CLIMATE (2022)
- Role of ocean dynamics in equatorial Pacific decadal variability , CLIMATE DYNAMICS (2022)
- Ocean Dynamics are Key to Extratropical Forcing of El Nino , JOURNAL OF CLIMATE (2021)
- Pacific Meridional Modes without Equatorial Pacific Influence , JOURNAL OF CLIMATE (2021)
Despite substantial progress in recent years in our ability to simulate and to a lesser degree predict ENSO, a clear picture of what processes limit predictability remains elusive. Indeed, many studies argue that predictability is determined by the strength of the buildup of heat content along the subsurface equatorial Pacific Ocean, or preconditioning, whereas others assert that uncoupled atmospheric noise both internal and external to the tropical Pacific ultimately determines the limit of predictability. Yet outstanding questions remain about where, when, and how preconditioning is most effective in increasing predictability, how preconditioning and uncoupled noise impact predictability in concert, and to what extent low frequency changes in the background state alter predictability estimates. Confronting these questions is critical for assessing and improving our prediction systems, identifying forecasts of opportunity, and will affect how we design observing systems in the future. To address these questions, we propose a suite of ENSO predictability experiments that leverage state-of-the-art Earth system modeling frameworks previously developed by the research team. This approach not only facilitates a complete assessment of the role of preconditioning in ENSO amplitude and evolution, but also allows us to quantify the role of uncoupled atmospheric noise and changes in the background state in our predictability estimates. This work will advance our understanding of the physical mechanisms limiting our ability to predict ENSO events, with direct applications to both basic ENSO theory and real-time prediction.
Recent research highlights a wide range of hypotheses for expected changes in ENSO variability in a future climate. These hypotheses do not necessarily agree on how ENSO characteristics such as phase asymmetry, amplitude, spatial pattern, or frequency will change, and this uncertainty has implications for determining how ENSO teleconnections and impacts will be altered. Furthermore, even if no changes in ENSO characteristics were to occur, future ENSO events are expected to have intensified impacts due to the warmer mean state and projected increases in atmospheric water vapor. Understanding how ENSO teleconnections and terrestrial impacts over North America will change in a future climate is hence compounded by these two factors: changes in the mean state and changes in ENSO characteristics themselves. Given that present-day ENSO impacts are global and immense, improved understanding of the different possible outcomes for changes in ENSO-related teleconnections is crucial to understanding the potential impacts on terrestrial climate anomalies and extremes. The proposed work has two primary objectives: Objective 1) Determine to what extent and through what physical mechanisms diversity in ENSO characteristics modulate North American terrestrial impacts in a future climate, and Objective 2) Quantify how different ENSO characteristics alter the potential predictability of North American terrestrial impacts in a future climate. Given the discrepancy in the literature, we will not assume a priori knowledge of how ENSO will change in the future, but rather rigorously test multiple hypothesized scenarios through model experiments. We will leverage a novel coupled model framework in which ENSO variability can be manually modulated while leaving variability outside the tropical Pacific fully coupled and unconstrained. All experiments will be performed under pre-industrial and future climate conditions, which will allow us to quantify how changes in ENSO characteristics and the mean state, separately and together, modify North American precipitation and surface air temperature in a future climate. We will also calculate how ENSO characteristics and changes in the mean state modify predictability estimates of temperature and precipitation over North America to determine how ENSO-driven impacts in a future climate may manifest in a more realistic prediction setting.
Observations and models indicate that Atlantic sea surface temperatures (SSTs) exhibit significant low-frequency (interannual to decadal) variability, and a significant portion of these variations are related to internal variations of the climate system (rather than external forcing). However, the origin of these internal Atlantic SST variations is yet to be fully understood. The goal of our proposed work is to disentangle the roles of atmospheric forcing and various ocean dynamics (including both one-dimensional and three-dimensional processes) in Atlantic SST variability and predictability on interannual-to-decadal time scales. In order to achieve this goal, we propose to develop a hierarchy of coupled models using the Community Earth System Model version 2 (CESM2). The model hierarchy will include ocean model components of varying complexity and comparing model pairs from the hierarchy will enable us to quantitatively determine the roles of specific aspects of ocean dynamics, including one-dimensional processes (mixing, interannual mixed layer depth variations, entrainment) and three-dimensional ocean dynamics (including wind and buoyancy-driven processes) on driving SST variability. We will apply a rigorous statistical technique, called covariance discriminant analysis, to diagnose the leading differences in Atlantic SST variance between model pairs in the hierarchy, thus elucidating the impact of specific ocean processes on Atlantic SST variability. Additionally, we will compare predictability of Atlantic SST between models in the hierarchy, to elucidate the role of oceanic processes in predictability.
Air-sea mechanisms are critical to establishing the timescales, patterns, and amplitudes of much of the climate variations we observe and simulate in models. And yet, the individual roles of different air-sea processes in driving climate variability and change are especially elusive due to sparse ocean observations and complicated dynamics. This issue is exacerbated by a wide gap in the model hierarchy, between fully coupled models with a complete dynamical representation of both the ocean and atmosphere and models that represent the ocean as a slab, a motionless layer that is only thermally coupled to the atmosphere. Due to this gap in the hierarchy, many open questions remain about the importance of winds versus buoyancy forcing (including radiative heat fluxes) as pathways through which the atmosphere communicates intrinsic and externally forced variations to the ocean. The proposed research will decompose these relative effects using rigorous climate model experiments, with emphasis on two features expected to substantially change in a future climate, surface temperature and the Atlantic Meridional Overturning Circulation (AMOC). This work will be implemented by closing said gap in the model hierarchy via leveraging a mechanically decoupled (MD) version of a climate model, which isolates variations in the ocean circulation that are driven solely by buoyancy from the wind-driven variability captured by fully coupled models. With the MD as an intermediate step between fully coupled and slab ocean models, a clearer mechanistic understanding of the drivers of climate variations is possible. These 3 model versions will be used together to complete the following objectives: identify the relative roles of buoyancy and wind variations in driving 1) intrinsic sea surface temperature variability, 2) anthropogenic trends in surface temperature, and 3) intrinsic and anthropogenically forced AMOC variations. The PI is ideally suited to perform such tasks, as she is the developer of the MD model.