· Climate dynamics, including atmosphere–ocean interactions and the role of the ocean in climate variability
· Climate modeling, ranging from simple models to general circulation models (GCMs)
· Clouds and convection
Below are the more specific topics I worked on during my M.S. and Ph.D:
Quantifying The Role of Ocean Dynamics in Ocean Mixed Layer Temperature Variability
It is understood that ocean dynamical processes are fundamental to the El Niño- Southern Oscillation, but the role of ocean heat transport in climate variability outside of the Tropical Pacific is relatively less well-understood.
In order to provide fundamental insights into the role of ocean dynamics in driving climate variability, we used observations, reanalyses, and a state-of-the-art ocean state estimate, ECCO (Estimating the Circulation and Climate of the Ocean), to quantify the role of ocean dynamics in mixed-layer temperature variability on monthly to decadal timescales across the global oceans.
Consistent with previous studies, the results indicate that the ocean dynamical contribution to temperature variance is largest over the western boundary currents, their extensions, and regions of equatorial upwelling (Figure 1, top panel). However, In contrast to many previous studies, the results suggest that ocean dynamics reduce the variance of Northern Hemisphere mixed-layer temperatures on timescales longer than a few years (Figure 2, bottom panel).
Understanding The Role of Ocean Dynamics in Midlatitude Sea Surface Temperature Variability using a Simple Stochastic Climate Model
As a follow-up study to Patrizio and Thompson (2021), we further explored the role of midlatitude ocean dynamics in sea-surface temperature (SST) variability using a simple stochastic climate model. The model is based off of one of the simplest models of climate variability, whereby the ocean-mixed layer converts random atmospheric variability (white-noise) to slow fluctuations in SSTs (red-noise) because of its large heat capacity (Hasselmann 1976, Frankignoul and Hasselmann, 1977).
In this study, we considered two different configurations of the simple stochastic model to provide further insights into the relative roles of atmospheric and oceanic processes in midlatitude SST variability. The simplest configuration included the forcing and damping of SST variability by observed surface heat fluxes only, and the more complex configuration included forcing and damping by ocean processes, which were estimated indirectly from monthly observations.
It was found that the simple model driven only by the observed surface heat fluxes generally produced midlatitude SST power spectra that are too red compared to observations (Figure 2; green lines). Including ocean processes in the model reduced this discrepancy by whitening the midlatitude SST spectra (Figure 2; black lines) . In particular, ocean processes generally increased the SST variance on <2-yr time scales and decreased it on >2-yr time scales. This is because oceanic forcing increases the midlatitude SST variance across many time scales, but oceanic damping outweighs oceanic forcing on >2-yr time scales, particularly away from the western boundary currents.
We also found that the whitening of midlatitude SST variability by ocean processes operates in NCAR’s Community Earth System Model (CESM). In particular, the low-frequency SST variance in the midlatitudes is generally larger when the same atmospheric model is coupled to a slab rather than dynamically active ocean model (Figure 3). Overall, the results suggest that forcing and damping by ocean processes play essential roles in driving midlatitude SST variability.
Sensitivity of Convective Self-Aggregation to Domain Size
During my M.S. degree, we performed idealized simulations of the tropical atmosphere wherein the clouds spontaneously cluster; a process that is often referred to as "convective self-aggregation". The simulations were performed across wide range of domain sizes, ranging from ~700-6000km across, to better understand how the properties of the convective aggregation change as the domain size is increased. A movie illustrating the evolution of precipitation and precipitable water in the simulation with the largest domain size (6144km x 6144km) is shown below.
The results reveal many important domain-size sensitivities, such the emergence of a 25-30 day oscillation between deep convection and congestus clouds when the model domain size is made sufficiently large. This oscillation of convective aggregation at large spatial scales may provide insights into the largest intraseasonal oscillation in the tropical atmosphere, the Madden-Julian Oscillation.