Research

Extreme weather and climate

Climate events disproportionately impact health and welfare if they are both rare and severe. Generally, these extremes (such as tropical cyclones, severe weather outbreaks, snowstorms, atmospheric rivers, etc.) are critically under-resolved, or completely unresolved, in standard climate assessments. Computational advances and contemporary numerical techniques now allow adequate simulation of these phenomena in a discrete sense. This facilitates direct projection of climate extremes without the need for traditional downscaling that may introduce biases by requiring a non-unified or physically inconsistent modeling framework.

Much of my work centers around simulating and understanding these extremes in a climate framework. What large-scale patterns impact the frequency, intensity, and spatial patterns of landfalling tropical cyclones? How does climate change transform how the east coast of the United States manages winter storms? Can improvements in our treatment of moist processes in climate models extend skill in severe weather prediction to seasonal scales?

(Pictured: 20-year North Atlantic tropical cyclone genesis locations (color-coded by maximum lifetime intensity) for the default 1° version of the Community Earth System Model (a.), for a regionally-refined, 0.25° version of the same model (b.), and observations (c.). Note that the variable-resolution model produces not only a more realistic number of cyclogenesis events but a much more realistic distribution of storm intensity when compared to the low-resolution model. From Zarzycki and Jablonowski, 2014, JAMES.)

Objective detection of weather extremes in climate data

One area contributing to uncertainty in understanding the role of atmospheric extremes within the climate system (and their simulated future changes) is the multitude of automated detection algorithms used to find and track storms in model data. For example, the combination of the under-resolution of tropical cyclones in traditional climate models in addition to the relatively low number of storms per year (approximately 90 globally) makes quantifying the number of cyclones in a given model simulation difficult. Storm counts are sensitive to choices in threshold parameters such as surface pressure minimum, warm core anomaly, and wind speed. Ongoing research seeks to understand the uncertainty in these calculations and provide more unified algorithms for the climate community. This framework is not only tropical cyclone specific, but is extendable to the detection of wintertime storms, mesoscale convective systems, atmospheric rivers, heat waves, droughts, and other climate extremes. Current detection methods are also handicapped by 'big data.' As grid spacings grow finer and models increase in complexity, the burden of processing output grows larger due to computational limitations exposed by colossal quantities of information. Some of my work aims to eliminate this bottleneck.

(Pictured: Schematic of an extratropical snowstorm tracking algorithm. The algorithm first tracks local minima in the sea level pressure field and then integrates snowfall associated with the cyclone before categorizing the storm with a northeastern U.S. Regional Snowfall Index (RSI) value.)

Next-generation global weather and climate models

Simulating the global atmosphere at high spatial resolution is computationally burdensome and inhibits the use of these models for either fine-scale or long-term analysis of weather and climate. To alleviate these issues, limited area models (or regional climate models) have become popular, although they suffer from issues such as lack of conservation properties, mathematically (or physically) inconsistent lateral boundary conditions, and additional biases "adopted" from coarser driving models. Variable-resolution models can serve to bridge this gap, providing spatial "targeting" of computing resources to a specific region or feature of interest while maintaining a unified modeling framework. Variable-resolution models are central to both the strategic plans of the Department of Energy and National Science Foundation and are a required capability of the next generation global forecast system for the National Centers for Environmental Prediction.

(Pictured: a grid from the Community Earth System Model (CESM) with regional refinement over the eastern two-thirds of the continental United States.)

Tropical cyclone dynamics

As the resolutions used for global modeling continue to increase, the ability of these models to discretely resolve tropical cyclones correspondingly improves. At horizontal resolutions of 0.25° and finer, features such as calm eyes, spiral rainbands, and tilted eyewalls are noticeable in simulated storms. This provides new insight into how tropical cyclones not only are influenced by the global climate system but also how they impact the transport of moisture, heat, and momentum from the tropics. Additionally, we are now capable of investigating potential impacts of different climates on aspects of storms such as surface wind fields, rainfall, and storm motion. Given that observational (and limited area modeling) studies have shown that certain features in tropical cyclone cores occur at spatial scales on the order of a single kilometer, the dynamical response of cyclones in climate models serve as interesting test beds for understanding the relative role of resolved versus parameterized processes driving their genesis, maintenance, and lysis.

(Pictured: Differences in radial (top) and tangential (bottom) wind flow for identically-initialized tropical cyclones using two different sub-grid parameterizations of the planetary boundary layer, macrophysics, and shallow convection.)

Subseasonal to Seasonal (S2S) prediction

While numerical weather prediction has historically been focused on lead times out to seven to ten days, information regarding projected weather conditions on the scales of weeks to months is beneficial for many stakeholders. Computationally-scalable, variable-resolution models offer potential improvements in this area through increased regional refinement, longer forecast integration times, and additional ensemble members, given an available computing allowance. Skill advances in these areas may provide tangible benefit for energy projections, agriculture and water resource planning, and disaster risk reduction.

(Pictured: Estimate of the current state of forecast skill as a function of lead time. S2S forecasting is shown in orange. Courtesy of Columbia IRI.)

Aerosols and climate forcing

The impact of aerosols (short-lived forcers) on climate impacts remains poorly constrained. For example, absorbing aerosols (such as black carbon) can have dramatically different heating impacts based on their spatial location in the atmosphere. Black carbon above highly reflective (high albedo) surfaces (such as snow and ice) can have nearly double the amount of forcing (per unit mass) due to incident shortwave radiation not only coming from above, but also below. This same behavior holds true for aerosols that can be lofted above highly reflective clouds. Additional work in terms of both modeling and observations are needed to reduce the uncertainty in how aerosols are emitted, where they move in the atmosphere, and how they are removed.

(Pictured: A schematic demonstrating how aerosols that absorb shortwave radiation (such as black carbon) can have a significantly larger direct radiative forcing if lofted above reflective clouds. Based on Zarzycki and Bond, 2010, GRL.)