With the increasing prevalence of large wildfire events, especially near urban areas, the identification and quantification of combustion-derived air pollutants is critical. The collection, quantification, and identification of particulate air pollutants, also known as aerosols, provide important insights into air pollution sources, evolution, and health effects. To improve capabilities in this area, with funding and support from the Beckman Foundation, we have designed and developed an aerosol collector that uses high-voltage Dielectric Barrier Discharge plasma and Electrostatic Precipitation (DBD-ESP) to collect and desorb target nanosized aerosols (30 nm – 800 nm) containing components typical of woodsmoke, such as levoglucosan and phenolic compounds, among others. The DBD-ESP is integrated with an Ultra-Portable Time-of-Flight (TOF) Mass Spectrometer (UP-ToFMS) to produce online, near real-time analysis of the chemical composition, mass, and size of target aerosols. Based on preliminary collection and desorption testing, we expect to have a 75% collection and desorption efficiency of submicron aerosol particles within 5 minutes. The DBD-ESP has an internal volume of less than 2 cm3 and an overall external volume of around 1 L. Once coupled with the UP-ToFMS, the overall ultra-portable system will be no larger than 0.095 m3 and weigh around 35 kg, enabling the remote sampling of aerosols from wildfire events while still yielding high-quality mass spectral data.

In the Earth system, land-atmosphere interactions play a crucial role in the development of weather and climate. Rising buoyant air parcels in convective environments create thunderstorms and clouds and can be influenced by energy fluxes at the land surface. However, the role of soil moisture in convective development is still a topic of ongoing debate.  A recent study by Zhang et al. (2023) investigated how soil moisture drydown periods affect the convective available potential energy (CAPE) and precipitation patterns in different regions using satellite data and statistical modeling. Other studies have predicted that CAPE will increase in humid regions and decrease in arid regions due to anthropogenic warming (Diffenbaugh et al., 2013; Taszarek et al., 2021). In our study, we use time series from global climate model (GCM) simulations to compare interstorm CAPE in different regions across current and future warming scenarios. Our research will result in the enhanced understanding of land-atmosphere coupling and how severe weather will respond to a CO2-driven warming climate.

The Pacific Northwest (PNW) saw an unprecedented heatwave between June 25 to July 3 of 2021, with temperatures reaching up to 15℃ above the climatological mean. Previous studies have focused on this event’s impacts on plants in Western Washington and Oregon through direct observations, or have focused on the economic implications from poor crop turnout. We used remote sensing data to take a holistic approach and examined how all plants throughout the PNW fared during and after this historical heatwave. We found that solar induced fluorescence (SIF) and near-Infrared reflectance of vegetation (NIRv), two remotely sensed vegetation health markers, had regionally dependent plant responses to the extreme heat. In particular, anomalously high SIF regions coincided with anomalously high photosynthetically active radiation (PAR) regions due to low cloud cover. As SIF has been used as a proxy for gross primary productivity (GPP), our findings begs the question: was the elevated SIF during the heatwave indicative of higher GPP, or was the SIF response an artifact of the higher radiation? Our study aims to further our understanding of how extreme events impact plant health, which is increasingly important as heatwaves become more intense and frequent in the future.

Nitrous oxide (N₂O) is an important greenhouse gas that depletes stratospheric ozone and is 300 times more potent than carbon dioxide (CO₂) over 100 years. Emissions have increased by 40% since 1980, and N₂O has been accumulating in the atmosphere at an unprecedented rate due to its long lifetime. The rapid rise of N₂O emissions primarily come from soil microbes that respond to the increased usage of agricultural fertilizers which help supply global food demand. Other notable sources include combustion, wastewater treatment, and industrial processes such as nitric acid production. Despite the importance of N₂O, atmospheric observations have limited spatial coverage. Remote sensing presents an attractive solution to dramatically increase spatial sampling. Here we assess the feasibility of using remote sensing to measure N₂O concentrations from sub-orbital platforms. Sub-orbital remote sensing platforms provide a testbed to determine the future viability of space-borne measurements. Our work uses an airborne instrument: the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS). AVIRIS is a full spectral range airborne imaging spectrometer that measures the radiance of the Earth’s atmosphere from 380 - 2510 nm wavelengths. We hypothesize that band ratios from AVIRIS can be used to detect N₂O plumes. We begin by selecting the highest emitting point-source facilities in cloud-free flight tracks. Preliminary plumes will be verified by shape and direction according to meteorological data and consistency with facility layouts. We first test this methodology on CO₂, as previous studies have demonstrated successful detections with AVIRIS. CO₂ will serve as a proof of concept before applying our method to N₂O, which is more challenging to detect due to its lower atmospheric abundance and weaker spectral signature. 

Freezing rain events in the Pacific Northwest are rare but can be highly disruptive. Proximity to the warm northeast Pacific Ocean typically keeps low elevation temperatures above freezing, while the Olympic and Cascade mountain ranges receive large amounts of snowfall. However, certain synoptic setups can create a “perfect storm” that leads to widespread precipitation transition events. When upper-level troughs pass over the region, arctic air masses at the surface can follow close behind. This often results in a deep cold pool becoming entrenched east of the Cascade mountains. Cyclogenesis associated with troughing and cold air outbreaks creates strong pressure gradients that drive this cold pool through mountain gaps. In metropolitan areas like Seattle, this cold air can undercut warm air aloft by continuously replenishing cold air at the surface, setting the stage for impactful mixed precipitation, including freezing rain. In this analysis, we document two high-impact freezing rain events across the Pacific Northwest through detailed synoptic analyses, using the High Resolution Rapid Refresh (HRRR) model to characterize atmospheric conditions behind these events at high spatial and temporal resolution. Model algorithms often struggle with predicting these transitions accurately, as they rely on simplified methodologies that fail to capture the nuances of lower-level temperature profiles and critical dynamical processes. To diagnose these model shortcomings, we developed innovative diagnostic maps visualizing the interplay between warm nose strength aloft and cold air at the surface, derived from HRRR analysis data. These maps provide forecasters with a cutting-edge tool to pinpoint areas prone to precipitation-type transitions with unprecedented clarity, enhancing forecast capabilities in anticipating mixed precipitation across the Pacific Northwest.

Rapid-growth wildfires disproportionately contribute to loss of life and destruction of property. Further improving our understanding of longer-term signals of impending fire-associated weather is crucial if we are to mitigate future destruction. Recent work compared local conditions, including surface wind and 100-hour dead fuel moisture (FM100) to fire growth (Murphy and Mass 2025). We investigate the evolution of larger scale weather patterns prior to rapid wildfire growth. Using two individual-fire-growth datasets, Fire Events Data Suite (FEDS) and Fire Events Delineation (FIRED), we separate fires by season, growth rate, and region. We conduct analyses of several meteorological variables for periods preceding maximum growth in rapid-growth wildfires. Using the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset, we compare weather patterns at different heights in the atmosphere prior to maximum growth for fires of different growth rates and in different seasons, to identify any signals comporting to eventual fire extremity. We also consider how the patterns affect FM100 and near fire winds and the impacts of region of wildfire within California.

Sulfate aerosols cause pollution and affect climate by influencing cloud properties and incoming solar radiation. Emissions and abundances of sulfur-containing aerosols are one of the largest sources of uncertainties in global climate modeling. The largest biogenic and most uncertain emission source of sulfur aerosols is from phytoplankton in the form of dimethyl sulfide (DMS). In the atmosphere, DMS is oxidized to methanesulfonic acid (MSA) and other compounds that can form sulfate. Historical emissions of DMS are studied by measuring MSA concentrations in ice cores as a proxy for DMS oxidation. Declining levels of MSA have been found in ice core records, implying that production of DMS has also been decreasing; however, anthropogenically driven changes in atmospheric chemistry have altered the ratio of MSA to sulfate produced from DMS over time. To better understand DMS oxidation mechanisms and its relationship to the production of MSA and sulfate aerosols, we need more recent ice core records of MSA and sulfur isotopes of sulfate (δ³⁴S(SO₄^2–)) at higher temporal resolution. To measure δ³⁴S(SO₄^2–) at seasonal resolution in an ice core, rather than an annual resolution, the measurement size is smaller than previously measured by an order of magnitude, at about 1 µg S per sample. We will develop a new method to isolate samples containing less than 1 µg of sulfur from an ice core sample by separating SO₄^2– from other major ions in the sample using an ion chromatograph. We will quantify the isotopic ratio of sulfur in our samples by using an Orbitrap mass spectrometer. Quantifying sulfur isotopes at this resolution will provide information about the seasonality and change in phytoplankton sulfate production.