Atmospheric sciences

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/4893

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The influence of historical sea-surface temperature patterns on regional precipitation trends
(2026-02-05) Pillai, Jaydeep Rajeev; Armour, Kyle C.; Battisti, David S.
State-of-the-art coupled global climate models (GCMs) fail to simulate key features of observed seasonal precipitation trends since 1980, including drying of the southwestern US, the southeastern US, East Africa, and subtropical South America, as well as wetting of the Maritime Continent and the Amazon. They also fail to simulate the sea-level pressure (SLP) trends since 1980 associated with a poleward shift of the North Pacific storm track in the mid-latitudes and a strengthened Pacific Walker Circulation. We show that state-of-the-art atmosphere-only climate model ensembles driven by observed sea-surface temperatures (SSTs) simulate historical precipitation and SLP trends that are more similar to those observed in the regions noted above, suggesting that the observed pattern of SST changes has shaped regional precipitation and SLP trends. Analysis of the coupled and atmosphere-only model ensembles reveals that multidecadal SST patterns similar to those of the interannual El-Ni\~no Southern Oscillation are responsible for some of the regional trends simulated. A strengthening tropical Pacific zonal SST gradient is found to have contributed to observed drying over the southwestern US, subtropical South America, and the southeastern US, as well as observed wetting over the Maritime Continent, signifying a key role for tropical Pacific warming patterns in future precipitation trends in these regions.
New Space-Based Perspectives on Blowing Snow Over Arctic Sea Ice
(2026-02-05) Robinson, Joseph; Jaegle, Lyatt
Blowing snow over Arctic sea ice is an important but poorly constrained process connecting the cryosphere and atmosphere. Sublimation of blowing snow is a significant removal pathway for snow from the sea ice surface, thereby affecting the surface mass balance of sea ice as well as the surface energy and radiation budgets. While model parameterizations of blowing snow have been developed, the resulting predictions can be highly uncertain due to the scarcity of observations to evaluate them. However, active remote sensing satellite platforms provide a novel and unique opportunity to constrain blowing snow processes on an Arctic-wide scale. The ultimate goal for this dissertation is to better constrain blowing snow occurrence, its properties, and role in the hydrology of snow on Arctic sea ice using satellite observations, thereby providing a framework to evaluate model predictions of sea ice mass balance, polar chemistry, and Arctic climate. This dissertation first evaluates and optimizes an algorithm for detecting blowing snow over Arctic sea ice using observations from the NASA Ice Cloud and land-Elevation Satellite-2 (ICESat-2) satellite. In particular, refinements were made to the algorithm to account for the presence of clouds which could be mis-identified as blowing snow. To conduct this optimization, ICESat-2 orbits coincident with the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign were examined for a six-month period (November 2019 through April 2020). Both ICESat-2 and MOSAiC suggested that blowing snow occurred frequently (17-18%) during the campaign associated with passing storms. This analysis also showed that ICESat-2 inferred blowing snow number and mass concentrations were broadly consistent with in-situ observations made during MOSAiC. ICESat-2 data near MOSAiC suggest that blowing snow sublimation may explain a substantial portion of precipitation mass loss during the campaign, offering important context to the observed snowfall and snow depth. Using the optimized blowing snow detection algorithm, this dissertation then examines ICESat-2 observations across a multi-year period (2018-2023). Retrieved blowing snow occurrence was found to average 20% across the sea ice during the cold season (November through April), with some regions of the Central Arctic reaching as high as 35%. Blowing snow occurrence detected by ICESat-2 shows substantial interannual variability that is related to large-scale climate variability including the Arctic Oscillation (AO). The ICESat-2 observations confirm windspeed plays the strongest role in modulating blowing snow occurrence, height, and optical depth, with all increasing by more than a factor of five across the 4-15 m s⁻¹ range. By combining retrieved ICESat-2 blowing snow properties with reanalysis meteorology this chapter shows that sublimation averages 1.63 ± 0.74 cm snow-water-equivalent (SWE) per cold season, corresponding to a mean precipitation mass loss of 13.6 ± 5.9 %. Predictions from two models of ranging complexity are consistent with these totals (1.66-2.07 cm SWE equaling a 14.1-16.9% precipitation mass loss). Blowing snow sublimation is more than a factor of 3 larger than surface sublimation (0.3-0.5 cm SWE per season), highlighting that it exerts considerable forcing on snowpack evolution during the Arctic cold season. Critically, the findings from this chapter provide the first multi-year satellite-based constraints on blowing snow processes over Arctic sea ice and underscore its importance in the Arctic sea ice snow budget.
From Local- to Large-Scale, the Meteorology Associated with Rapid-Growth California Wildfires
(2026-02-05) Murphy, Patrick; Mass, Clifford F
This dissertation serves to clarify the connection between short periods of meteorology (near surface weather conditions and upper air pressure patterns) and periods of rapid growth in California wildfires at a comprehensive scale not yet seen in the literature—across thousands of wildfires. In doing so, the dissertation also explores how the atmosphere produces periods of fire-relevant weather and considers the ability of a commonly used meteorological dataset to capture that meteorology. Our results indicate that periods of large growth in California wildfires occur when fuels are driest, trailing longer periods of atmospheric dryness. Particularly rapid growth—during which fires grow a great amount from a relatively smaller size in a short period of time—may occur in the presence of strong, dry (usually downslope) winds. The circulation patterns that produce these downslope winds are consistent from wind event to wind event in the lower-troposphere but vary substantially in the mid-troposphere. These circulation patterns can be generated as a result of numerous different antecedent atmospheric pathways (e.g., downslope wind events can follow the buildup of a high pressure ridge or the passage of an upper-level trough) but differences in antecedent drying from different evolutions are found to have little effect on ensuing fires. For describing fire-relevant meteorology, the ERA5—the most heavily used weather dataset in the world—is generally found to be a useable, self-consistent option. However, it struggles to reproduce the magnitude of strong winds observed by surface stations which may limit its ability to be used for other wildfire-related purposes (e.g., modeling fire spread).
Extreme Climates and How to Avoid Them
(2025-10-02) Poletti, Alyssa N; Frierson, Dargan
Climate change poses an existential threat. In this dissertation, I explore the uncertainties within, atmospheric changes due to, adaptations for, and mitigation pathways to avoid catastrophic warming. I begin with an analysis of extreme warming in Global Climate Model simulations into 2300, continue to an analysis of Washington State energy assistance policy in a warming world, and end with a coupled economic-climate model to bridge the topics of atmospheric science and climate policy. I find that prioritizing the reduction of fossil fuel emissions rather than scaling carbon capture technologies provides the best chance of avoiding local- to global-scale climatological changes. Ultimately, future research must prioritize equity in climate modeling to better guide us to a greener future.
Tides, Winds, and Reactive Halogens A Study of Multiphase Chemistry in the Marine Boundary Layer
(2025-08-01) Rund, Philip; Thornton, Joel
Reactive halogens in the atmosphere (inorganic compounds containing chlorine, bromine, and/or iodine)have the ability to catalytically destroy ozone (O3) as well as react with NOx (= NO + NO2) , influencing concentrations and ratios of important tropospheric pollutants. They can also react with and change the ratio of OH and HO2 ( = HOx), altering the oxidative capacity of the atmosphere. Furthermore, heterogeneous reactions involving halogenated species can change the chemical composition of particulates, especially for sea-salt aerosols (SSAs). Reactive bromine (Bry) specifically is known to have a chemical mechanism unique from that of chlorine and iodine, and is more efficient in the catalytic destruction of ozone on a per-molecule basis compared to chlorine. There are generally fewer in-situ observations of Bry species because they are globally expected to be present at much lower concentrations compared to reactive chlorine. In this work we present a new transverse Ion-Molecule reaction Region, the so-called ”t-IMR”, for use with a Time-of-Flight Chemical Ionization Mass Spectrometer (ToF-CIMS). The t-IMR samples ambient air at a laminar high volume flow (at a rate of 10 L/min), and demonstrates a reduction in artificial signals/background from wall effects by multiple orders of magnitude across compound volatilities compared to previous low-pressure designs. The t-IMR utilizes an applied electric potential to accelerate ions and subsequent clusters across the IMR cavity, the strength of which is optimized to retain both high instrument sensitivity and total ion flux to the mass spectrometer. The t-IMR CIMS is calibrated directly to obtain sensitivity values for Br2 and (experimentally) for HOBr, the results of which confirm the use of theoretical ion cluster binding enthalpies to generate sensitivities for other Bry components. A dynamic water-vapor-dependent sensitivity for Br2 is also developed and applied. Five reactive bromine components are observed at the Tudor Hill Marine Atmospheric Observatory (THMAO) located in Bermuda as part of the Bermuda boundary Layer Experiment on the Atmospheric Chemistry of Halogens (BLEACH) campaign, including Br2, BrCl, BrO, HOBr, and for the first time to our knowledge, BrONO2. Local air masses originating from over the ocean, as well as those influenced by anthropogenic activity and pollution from the island of Bermuda, provide multiple environments in which to investigate Bry concentrations and partitioning. HOBr and BrONO2 are observed above detection limits almost exclusively during the day, evidencing the currently understood formation mechanisms for both which require active photochemistry and the presence of BrO. BrONO2 shows a clear dependence on NO2 concentrations, and a diurnal profile shape that qualitatively aligns with previous modeling studies. Comparison with the GEOS-Chem model output (with reactive halogen chemistry and SSA debromination mechanisms included) shows that the detailed atmospheric chemistry model over-predicts levels of the five observed Bry constituents, most of which by an order of magnitude or higher. The model is in much better agreement with observed bulk particulate sodium, bromine, and chlorine concentrations, suggesting that the SSA mass concentrations in the model are likely not the source of the discrepancy, but rather partitioning among reactive bromine. The relationship of total measured reactive bromine (Bry* = 2 Br2 + BrCl + BrO + HOBr + BrONO2) with local wind speed, tide height, and other measurements at THMAO is examined. The magnitude of the most recent low tide height and wind speed are found to be independently significant predictors of Bry* concentrations. This implies a local coastal source of reactive bromine at THMAO in addition to that expected from SSAs.
Impacts of giant cloud condensation nuclei on precipitation formation in marine low clouds
(2025-08-01) Mifsud, Katherine Elise; Wood, Rob
Precipitation in low clouds strongly affects cloud responses to aerosol perturbations, thereby impacting aerosol radiative forcing of climate, a significant but indeterminate contribution to uncertainty in future warming. The onset of drizzle formation in marine low clouds may be influenced by the presence of coarse-mode sea salt aerosols with dry diameters exceeding 1 um. These particles can, under some circumstances, act as giant cloud condensation nuclei (GCCN) that benefit from rapid condensational growth to a size that initiates collision-coalescence. Aircraft-based in-situ observations of cloud microphysical properties and aerosol size distributions from the Aerosol-Cloud-Meteorology Interactions Over the Western Atlantic Experiment (ACTIVATE) are analyzed from January to June 2022. ACTIVATE focused on making detailed in situ microphysical measurements of aerosols and clouds using a low-flying research aircraft. The western Atlantic region is ideal for this study due to its location in the midlatitudes, where diverse cloud types can be observed under varying meteorological conditions. Unlike many aerosol-cloud interaction studies focused on subtropical regions, ACTIVATE’s emphasis on the midlatitudes provides a unique opportunity to examine aerosol impacts on precipitation formation in an area characterized by complex cloud structures and dynamic atmospheric processes. Observations from a cloud aerosol spectrometer (CAS) and a cloud droplet probe (CDP) in clear air from just below the cloud base are used to quantify size distributions of haze droplets. Clear-sky conditions for accurate interpretation of the size distributions are identified by strictly filtering for liquid water content below 0.0025 g/mÂ³ to remove contamination from cloud droplets and total precipitation number concentration below 100 particles per cubic meter (#/mÂ³), as measured by a two-dimensional stereo probe. These thresholds ensure that the size distributions measure haze droplets without cloud or drizzle droplet contamination. Haze droplet size distributions reveal modest correlations with near surface-level wind speeds when averaged together into wind speed bins, a result consistent with other recent studies. This is seen by a strong correlation coefficient between wind speed and total concentration of R =0.90 for the CAS and R =0.84 for the CDP. And consequently, I conclude, as near surface wind speed increases, the production of giant cloud condensation nuclei increases. Observed GCCN distributions and cloud thicknesses are used to drive simulations with an explicit microphysics parcel model, exploring the conditions under which the observed range of GCCN induces a first-order impact on precipitation rates. Analysis of droplet size distributions using a 1-dimensional kinematic super-droplet cloud model (PySDM) demonstrates that even the addition of a small number of GCCN accelerates the onset of drizzle and increases drizzle rate in the marine boundary layer. This effect was analyzed under conditions with liquid water paths similar to those in the thicker parts of low cloud fields over the Western Atlantic region. Similarly, analysis with observationally derived rainwater content as a function of total concentration and liquid water content for high and low GCCN concentrations reveals an increased ratio of condensate in precipitation drops to that in cloud drops for flights with higher measured concentrations of GCCN. These results suggest that GCCN can meaningfully influence precipitation in marine low clouds.
Atmospheric sulfur sources and chemistry from preindustrial to present
(2025-08-01) Jongebloed, Ursula Anne; Alexander, Becky
Atmospheric aerosols affect climate by scattering radiation and influencing cloud properties. Sulfate aerosols are estimated to have a large but uncertain cooling effect on global climate over the industrial era. A substantial portion of this uncertainty is caused by the dependence of anthropogenic aerosol radiative forcing on the preindustrial (natural) aerosol abundance due to the nonlinear relationship between aerosol abundance and cloud albedo. This nonlinear relationship motivates study of the relative importance of sulfate sources, sulfur oxidation chemistry, and how sulfate aerosols change over time. In this dissertation, I investigate the sources and chemistry of sulfate aerosols over the industrial era using ice core records of the sulfur isotopic composition of sulfate. Chapter 1 provides background and motivation for the research questions explored in this dissertation. Chapter 2 quantifies anthropogenic influence on Greenland sulfate using sulfur isotopes in a Summit, Greenland ice core. Chapter 3 shows that a change in DMS oxidation chemistry in regions influenced by anthropogenic pollution causes an industrial-era decline in methanesulfonic acid and an increase in biogenic sulfate in a Greenland ice core. Chapter 4 compares industrial-era changes in ice core MSA and biogenic sulfate to trends in changes in DMS oxidation chemistry simulated in a global model. Chapter 5 examines sulfate sources in the southern high latitudes, which are quantified using sulfur isotopes in Antarctic ice cores and compared to a global model. Conclusions and opportunities for future work are summarized in Chapter 6.
Characterizing and Forecasting Severe Convective Storms using Deep Learning
(2025-08-01) Hua, Zhanxiang; Anderson-Frey, Alexandra
This dissertation investigates the dynamics and predictability of tornadic supercell environments, leveraging advanced data analysis and machine learning techniques. Initial research established a foundation by examining the spatial and temporal variability of tornado-favorable parameters using reanalysis data. This work revealed substantial regional and temporal variations in these parameters, highlighting the limitations of using universal environmental thresholds for tornado prediction. Further investigation quantified the statistically significant differences between tornadic supercell and baseline environments, demonstrating that tornado-favorable conditions can exist well before storm formation, particularly outside of peak tornado seasons. This emphasizes the challenge of distinguishing between environments that produce tornadoes and those that do not. Building upon these foundational analyses, this research explored two interconnected avenues: the impact of climate change on severe convective storms (SCSs), and the application of deep learning (DL) for forecasting tornadic supercell environments. This research evaluated the performance of the deep learning model Pangu-Weather in forecasting tornadic environments one day in advance. Pangu-Weather's skill in predicting convective available potential energy (CAPE), 0-6 km shear, and 0-3 km storm-relative helicity was assessed and compared to the operational Global Forecast System (GFS). Results indicate that Pangu-Weather generally outperforms the GFS in predicting wind shear and helicity at the time and location of tornado reports, but tends to underpredict CAPE in the hours leading up to the event. To further enhance operational severe weather prediction, a novel neural network post-processing framework using decoder-only transformer was developed. This framework integrates forecasts from multiple models, including both numerical and AI-based models, and has the potential of accommodating various lead times from different models, improving the accuracy of SCS predictions. To investigate future SCS activity, a novel approach was developed using hierarchical clustering to identify potentially convective atmospheric profiles in both historical and future climate simulations. These profiles were then analyzed to understand the spatial and seasonal variations in potential SCS activity, providing a more nuanced analysis compared to previous studies relying solely on composite parameters. By combining data-driven analyses with advanced modeling techniques, this research provides valuable insights into the complex nature of tornadic supercell environments and their predictability in both present and future climates. These findings contribute to a deeper understanding of tornado dynamics and pave the way for improved forecasting capabilities, ultimately enhancing public safety and resilience to severe weather.
Extreme Southwest U.S. and Northern Mexico Vapor Pressure Deficit Events in CMIP6 Climate Model Projections
(2025-08-01) Lopez, David Gregory; Frierson, Dargan
This study characterizes the most extreme events of vapor pressure deficit (VPD) in CMIP6 SSP5-8.5 projections for nine different climate models in the greater Southwest U.S. and Northern Mexico region. VPD is a strong predictor of wildfire area burned in Southwestern North America. Model base states are in spatial agreement for temperature and moisture to observational studies. Two areas of VPD are present with differing dominant terms in VPD formulation. Individual event extremities also have differing dominant terms. Precipitation hinders the evolution and magnitude of VPD extremities. Maximum yearly VPD takes place prior to maximum yearly solar insolation in all models, just prior to precipitation of the expected North American Monsoon. Among all extreme events, a minimal precipitation signal is present in the period prior.
A Paradigm Shift in Precipitation Modeling: Moving Beyond Numerical Models
(2025-08-01) Moreno, Raul Antonio; Durran, Dale
Accurately representing surface precipitation in weather and climate modeling is crucial to practical operational use of these models. Presently, global numerical weather prediction (NWP) models struggle to recreate the precipitation variable due to unresolved physical processes at the subgrid level as well as use of poorly constrained microphysical parameterizations. The advent of machine learning in the field of weather and climate prediction has proven to be beneficial to advance modeling efforts and has the potential to also target weak points in NWP such as estimating the precipitation field. Training a deep learning model using satellite data, we can bypass the parameterizations traditionally used by NWP to produce precipitation, achieving a field that more closely matches observations than the widely used ERA5 reanalysis dataset. The resulting model can compute precipitation from only ten ERA5 input fields and is able to better capture extremes while also improving the issue of overproduction of light precipitation in the ERA5 product when evaluated against the IMERG satellite dataset. The machine learning model is also used to produce precipitation from NWP forecast fields, improving on the forecasted precipitation of the NWP model. This work supports future development of deep learning models that meet the needs of current weather and climate modeling.
Hydrometeorological Drivers of Western US Summertime Temperature Variability in Global Climate Models
(2025-08-01) Zhang, Lily Ning; Battisti, David S.
Interannual variations in summertime temperature have a large impact on drought, fire, and extreme heat across the Western United States. We investigate the influence of antecedent hydrological conditions on the leading pattern of Western US summertime temperature variability in global climate model (GCM) simulations and find that Western US summertime heating is associated with antecedent Southwest US springtime soil moisture deficit and wintertime precipitation deficits across all six of the CMIP6 models in our analysis. Furthermore, the relationship between Western US summertime temperature and Southwest US wintertime precipitation is disrupted in experiments where soil moisture variations were removed. Our results suggest that springtime soil moisture anomalies in the Southwest US drive variations in summertime temperature throughout the Western US and that land-atmosphere coupling in this region can impart predictability at seasonal time scales.
Revisiting the Stratosphere Troposphere Exchange of Air Mass and Ozone Based on Reanalyses and Observations
(2025-08-01) Hall, Anna; Fu, Qiang
Wang and Fu (2021) examined the stratosphere-troposphere exchange (STE) of air mass and ozone using ERA5 and MERRA2 reanalysis data and observations for 2007-2010. Their analysis applied a lower stratosphere mass budget approach, with the 380 K isentropic surface serving as the upper boundary of the lowermost stratosphere. This study employs a dynamic isentropic surface fitted to the tropical tropopause, providing an update to the results using the static 380 K boundary. Additionally, we improve the numerical scheme for deriving the mass of the lowermost stratosphere. Under this new framework, the air mass upward flux at the isentropic surface in the tropics increases from 19.3 x109, 19.3x109, and 22.0x109 kg s-1 in Wang and Fu (2021) to 21.9x109, 20.9x109, and 26.3x109 kg s-1 in the present study for ERA5, MERRA2, and observations, respectively. The global ozone fluxes across the fitted isentrope become -347.6, -362.5 and -368.4 Tg yr-1 as compared to -345.7, -359.5 and -335.6 Tg yr-1 at the 380 K level from Wang and Fu (2021) for ERA5, MERRA2 and observations, respectively. The increased role of tropical cirrus clouds near the tropopause is also highlighted under the updated framework in observations. The contribution of cloud heating to tropical air mass flux increases from 2.0% in Wang and Fu (2021) to 8.2% in the present analysis, while for ozone, the corresponding contribution increases from 1.8% to 8.1%. We further show that the improved estimate of the change rate of mass in the lowermost stratosphere have impact on seasonal ozone STE results from chemistry climate models presented in Wang and Fu (2023). These findings provide new insights into the processes governing stratosphere-troposphere exchange.
Paleoclimate and Historical Perspectives on Modern Climate Sensitivity
(2025-08-01) Cooper, Vincent; Armour, Kyle; Hakim, Gregory
Determining modern climate sensitivity, i.e., the global surface warming from doubling prein-dustrial concentrations of CO2, is an urgent task as it controls how much the planet will warm from greenhouse-gas emissions. The upper bound on estimates of climate sensitivity has been highly uncertain for decades, but paleoclimates now provide a strong constraint. In this dissertation, we combine proxy data from paleoclimate data assimilation with atmospheric general circulation mod- els to show that the climate sensitivity inferred from paleoclimates is systematically higher than the climate sensitivity that applies to modern warming from CO2. This difference in climate sensitiv- ity arises because (a) ice sheets, topography, and vegetation changes drive atmospheric stationary waves that alter the spatial patterns of sea-surface temperature (SST) over distant oceans during both the cold Last Glacial Maximum and the warm Pliocene; and (b) these paleoclimate SST pat- terns are associated with amplifying cloud feedbacks that make past climates more sensitive than the modern climate. Accounting for these differences between climates leads to a substantial re- duction (∼1.0Â°C) in the upper bound on modern climate sensitivity compared to recent community assessments, such as IPCC AR6 (Forster et al., 2021). The leading role of spatial patterns of temperature change in determining climate sensitivity alsocompels a re-evaluation of the historical climate record (c. 1850–present). Previous studies have identified major discrepancies in radiative feedbacks due to differences in the patterns of sea-surface temperature across instrumental datasets. These discrepancies result from statistical infilling of the expansive gaps between sparse SST observations over the global oceans. In this dissertation, we use coupled data assimilation, which optimally combines observational and dynamical constraints from all climate fields simultaneously, to reconstruct monthly and globally resolved SST, near-surface air temperature, sea ice, and sea-level pressure over 1850–2023. The reconstruction provides a novel and internally consistent perspective on coupled climate variability and recent trends, which informs investigation of radiative feedbacks in the historical record. Chapter 1 introduces the research topics addressed in this dissertation. Chapter 2 quantifiesLast Glacial Maximum pattern effects and their impacts on modern climate sensitivity. Chapter 3 quantifies Pliocene pattern effects and provides stronger constraints on both modern climate sensitivity and 21st-century warming. Chapter 4 presents a reconstruction of the historical climate record (1850–2023) using linear inverse models and coupled data assimilation. Chapter 5 reviews the conclusions of the dissertation.
Modeling atmospheric perchlorate and chlorate
(2025-08-01) Chan, Yuk Chun; Alexander, Becky; Catling, David C.
Naturally occurring perchlorate (ClO4–) and chlorate (ClO3–) have been observed in different terrestrial and extraterrestrial environments. On Mars, per/chlorate may play important roles in driving oxidation of near-surface minerals, degrading chemical fossils of extinct life, and creating potentially habitable liquid brines for extant life. To date, it is still very unclear how most of the natural per/chlorate form on Earth and Mars. In this work, I study how the atmospheric oxidation of chlorine-containing species contributes to natural per/chlorate formation via a modeling approach. I use the GEOS-Chem global 3-D chemical transport model to simulate the production, loss, transport, and deposition of atmospheric per/chlorate. The model predictions are compared to observations of aerosol per/chlorate concentration, per/chlorate deposition flux, and isotopic composition of per/chlorate in remote desert soils. In Chapter 1, I give an overview of the atmospheric chemistry of chlorine species and the significance of per/chlorate in Astrobiology. In Chapter 2, I present the first global 3-D simulation for atmospheric perchlorate and find that gas-phase production in the stratosphere alone cannot explain measurements of 17O excess in perchlorate in remote deserts. In Chapter 3, I develop the first-ever model of atmospheric chlorate and show that chlorate likely decomposes in acidic aerosols. In Chapter 4, I demonstrate that multiphase oxidation of chlorine species on aerosols and polar stratospheric clouds can be an important source of per/chlorate in the atmosphere based on an extensive review of the literature and model experiments. In Chapter 5, I advocate for new research in laboratory and field measurements of per/chlorate via novel mass spectrometry techniques, model re-interpretation of ice-core records of perchlorate, and the development of atmospheric models for simulating halogen chemistry on Mars.
Sea Surface Temperature and Convection in Tropical Radiative Convective Equilibrium
(2025-08-01) Dygert, Brittany; Hartmann, Dennis
Tropical convection has significant implications for the global climate, and it is helpful to study convection in an idealized framework. This work uses a general circulation model in tropical radiative convective equilibrium, a popular idealized framework for studying the tropics in which convection is approximately balanced by radiative cooling, to explore the interactions between sea surface temperature and convection. This work is divided into three chapters. The first chapter explores inter-annual variability in these idealized tropical model experiments and how this cycle is fueled by the coupling between sea surface temperature and convection. The second chapter focuses on how ocean heat transport could impact the climate's response to increased forcing. Finally, the third chapter explores the role of sea surface temperature and the sea surface temperature gradient in setting the vertical distribution of convection and circulation.
The Radar and Microphysical Properties in the “Dendritic Growth” Layer of Winter Storms: Findings from the IMPACTS Field Campaign
(2025-08-01) Garcia Gallegos, Valeria; McMurdie, Lynn A
Radar scans of winter storms frequently show enhancements in equivalent radar reflectivity factor (Ze) within the -18˚C to -12˚C cloud layer, often referred to as the “Dendritic Growth Layer” (DGL). However, the microphysical processes responsible for these radar signatures remain poorly understood due to limited in-cloud in situ validation. This study leverages coordinated airborne radar and in situ observations collected during the NASA Investigation of Microphysics and Precipitation in Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign over the Northeastern U.S. We analyzed 591 vertical profiles of Ku-band Ze gradient (dZku/dz), each averaged over 10 km (~1 minute) segments and grouped into five clusters using a k-means clustering algorithm. Two of the five clusters exhibited local maxima in the magnitude of dZku/dz ≥ 10 dBZe/km and corresponding increases in Ku-Ka dual-frequency ratio (DFR) ≥ 1.5 dB within the DGL. Coincident in situ observations revealed enhanced particle growth with temperature for these clusters. However, habit imagery and relative humidity measurements showed that dendrites were largely absent and that the RHw supersaturation criterion for dendritic growth was unmet across all clusters. Instead, side planes and polycrystalline plates dominated with RHw subsaturated in the DGL. These findings suggest that the growth of plate-like polycrystals, not dendrites, was primarily responsible for observed radar enhancements in these IMPACTS cases, offering new insight into the microphysical drivers of radar signatures in winter storms.
Empirical Orthogonal Teleconnections as a Framework for Regional Climate Analysis: Insights from Pacific Northwest Temperature Extremes
(2025-08-01) Sandhu, Satveer; Salathe, Eric P
As the climate warms, understanding whether heat waves will intensify following mean temperature trends or undergo dynamic changes in their progression leading to changes in intensity is critical for regional climate adaptation. This study investigates heat wave dynamics in Washington and Oregon using Empirical Orthogonal Teleconnections (EOT) and climate composites, with a focus on setting historical baselines for use in further study with future projections. We analyze daily 2-meter maximum temperature data during the heat wave season (May 1–September 30) from ERA5 (1951–2020) and CMIP6 (historical forcing, 1981–2010) downscaled with WRF. EOT analysis reveals distinct spatial modes of teleconnectivity of maximum temperature, and this study primarily focuses on four: the Whole Region, Northeast, Southeast, and Coastal domains, which experience heat waves distinctly from one another. The composite analysis of ERA5 fields during days in the top ten percent for 2-meter maximum temperature shows both local forcings and a degree of progression from the Coastal to Whole Region, while the Northeast and Southeast modes represent regions of remote forcing during heat waves. This is supported by the results of the heat wave selection criteria used at the defining spatial point of each mode. This work provides a novel framework for the analysis of temperature regimes on a regional scale, having implications for understanding the synoptic and mesoscale conditions necessary for heat waves in different parts of the region.
The Multiscale Nature of Tropical Convection in Observations and Models
(2025-08-01) Angulo-Umana, Pedro; Blossey, Peter N.
This dissertation seeks to improve understanding of tropical convection's multiscale nature. By utilizing high-resolution observations, global reanalysis, and convection-permitting numerical models, this dissertation examines the multiscale structure of tropical convection. The physical processes that couple different scales of motion to one another are also examined. In this dissertation we will: use satellite observations to show that the multiscale structure of tropical precipitation features impacts the likelihood of the feature generating a local-scale, intense rain rate event; use high-resolution idealized models to explore the possible mechanism behind this coupling, namely the interaction between convective updrafts via turbulent mixing; and use a global, storm-resolving model to separate large-scale and small-scale convective motions, and characterize their co-evolutions and mutual influences on one another.
From Surface to Stratosphere: Understanding Interannual Climate Variability and Decadal Changes
(2025-05-12) Sweeney, Aodhan; Fu, Qiang
This thesis investigates key processes governing interannual and decadal variability in the stratosphere and troposphere and emphasizes their implications for climate projections. Arctic Amplification (AA), the disproportionate warming of the Arctic relative to global mean temperatures, is a robust feature of climate change. Using machine learning and CMIP6 models, this work demonstrates that internal variability has amplified AA by 38% since 1980, reconciling discrepancies between observed and simulated AA. These inflated values of AA are made possible by a unique pattern of multi-decadal internal variability which warms the Barents and Kara Sea, while cooling the Tropical Eastern Pacific and Southern Ocean. These results highlight the critical role of internal variability in shaping observed climate trends. Focusing on the tropical tropopause layer (TTL), this thesis quantifies the drivers of interannual variability in temperature, water vapor, and cirrus clouds, revealing the central role of stratospheric processes in determining variability of this region. The QBO's impact on tropical clouds is further explored, identifying a seasonally synchronized response in cloud fraction, temperature, and even the radiative budget of the tropics. Finally, this work addresses the rapid warming of the Southern Hemisphere subtropical lower stratosphere, linked to changes in the SH BDC. These circulation changes reconcile observed temperature and ozone trends with simulations, shedding light on the dynamical processes influencing stratospheric variability and ozone recovery. Together, these studies advance understanding of how internal variability contributes to observed changes from the surface to the stratosphere, informing model validation and projections of future climate.
Convective Upscale Growth in Central Argentina: Environmental Conditions and the Role of the South American Low-level Jet
(2025-05-12) Sasaki, Clayton; McMurdie, Lynn
Convective upscale growth, the processes by which initially discrete convection grows into large, organized mesoscale convective systems (MCS), impacts convective mode which determines the primary severe weather hazard and plays a key role in redistributing water and energy in the atmosphere. Convection near the Sierras de Córdoba (SDC) in central Argentina frequently experiences rapid upscale growth in the presence of complex terrain as well as the South American low-level jet (SALLJ). This upscale growth occurs over shorter distances than for convection over the U.S. Great Plains which allowed for unprecedented observations of rapid upscale growth. We use targeted field campaign observations along with a high-resolution Weather Research and Forecasting (WRF) simulation to describe the environments under which upscale growth occurs and identify environmental differences across multiple scales that influence the growth rate.We show that the WRF simulation produces a reasonable SALLJ and therefore use the simulation to investigate the impact of the SALLJ on the convective environment. We also compare two cases of observed upscale growth with differing rates and degrees of convective organization to document the environmental conditions and produce hypotheses of how meso-synoptic scale features impact the rate of upscale growth. Lastly, we take a broader look, using the many tracked MCSs in the WRF simulation to compare environments of slow and rapid growth and identify parameters that could differentiate the rate of upscale growth.

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