SPRING 2023
Wednesdays at 3:00 pm, Seminar Room SLAB 103 / Virtual SLAB 103
(unless stated otherwise)
Jan 18: NO SEMINAR
Jan 25: NO SEMINAR
Feb 01: NO SEMINAR (SLAB 103 not available)
Feb 08: Dr. David Nolan
Department of Atmospheric Sciences, Rosenstiel School
Spiral Gravity Waves Radiating From Tropical Cyclones
at the Surface, Mid-Levels, and in the Outflow
Recording Available at COMPASS ON DEMAND
Gravity waves, also known as internal waves, are ubiquitous in the atmosphere, with their primary sources being flow interactions with topography and deep moist convection. While it has been known for some time that tropical cyclones (TCs) produce gravity waves that propagate upward into the stratosphere, a recent study brought new attention to small-scale waves that radiate outward in the troposphere, with their phase lines wrapped into tight spirals. The vertical motions caused by the waves can be found in flight-level data obtained from research aircraft flying in tropical cyclones, and the waves can be detected in wind and pressure anomalies observed by surface instruments. The waves are caused by convective asymmetries rotating around the TC eyewall, that are amplified as they pass through the "downshear-left" quadrant and are suppressed in the opposite quadrant. This hypothesis is tested by using rotating and pulsing heat sources in a linear dynamical model of perturbations to stationary, balanced, TC-like vortices. The linear model shows that such forcing produces two types of waves: a fast, deep wave with a strong signal at the surface, followed by a packet of slower waves with shorter radial and vertical wavelengths. The results suggest that the observed peak in the power spectrum of surface pressure anomalies, corresponding to periods of 1000-3000 seconds, is connected to the pulsation frequency of the rotating convective anomalies in the eyewall. Careful examination of TC simulations with increasing horizontal resolution shows that the while the mid-level vertical velocity waves are only marginally resolved, the surface pressure waves are accurately depicted. In the upper-level TC outflow we find evidence for two new classes of slow-moving spiral waves that are distinct from gravity waves. Finally, we discuss our efforts to relate the intensity of remotely observed gravity waves to TC intensity.
Feb 09 (Thursday): Dr. Laur Ferris
Environmental & Information Systems, Applied Physics Laboratory, UW, Seattle
Toward Improved Understanding and Prediction of
Dynamical Instabilities and Mixing in Extreme Environments
As high-latitude oceans become increasingly ice-free, the importance of simulating these regions also increases. The sub-polar oceans are defined by energetic storms, strong fronts, intense seasonal cycles, and close connectivity with complex littoral topography. Predicting ocean structure leverages a combination of resolved and parameterized dynamics. In this seminar we combine glider data from the Antarctic Circumpolar Current (ACC) and high North Atlantic with modeling and theory to inform the adaptation of parameterized dynamics for parts of the ocean with intense forcing. We focus on submesoscale frontal instabilities, which may be significant sources of turbulent kinetic energy (TKE) for mixing in the global ocean. While criteria for diagnosing and parameterizing these instabilities were largely derived in a geostrophic and inviscid framework, they often occur in the presence of wind, topographic boundaries, and pre-existing turbulence. Our results indicate that model parameterizations for submesoscale frontal instabilities would benefit from the inclusion of ageostrophic and viscous effects.
Feb 15: Haley Royer
Department of Atmospheric Sciences, Rosenstiel School
(one-hour ATM student seminar)
Aerosol Mixing State and Its Effects on
Air Quality, Cloud Formation, and Nutrient Deposition
Recording Available at COMPASS ON DEMAND
Atmospheric aerosols are ubiquitous components of the atmosphere that impact air quality, cloud formation, ecosystem health, and climate. To understand the role that aerosols play in many of these aspects of the atmosphere, it is essential to determine their chemical and physical properties. The distribution of chemical and physical properties across individual particles in an aerosol population, also referred to as aerosol mixing state, has been previously overlooked in the literature as most research assumes aerosols are homogenous in composition and shape. However, recent work shows that individual particles are complex and combinations of these complex particles make for extremely unique aerosol populations. In this work, we present the advantages of considering aerosol mixing state when understanding the effects of an aerosol population on air quality, cloud formation, and nutrient deposition. First, we explore the role of aerosol mixing state on ozone production from lakebed dust. Then, we determine how African aerosols transported to the Caribbean and collected at the Ragged Point field station in Barbados affect cloud formation in the tropical Atlantic during the boreal winter. Finally, we explore the nutrient content of aerosols transported from Africa to the Caribbean to determine the potential for long-range transported aerosols to enhance ecosystem productivity. To conduct these studies, we utilize laboratory techniques that analyze individual particles in an aerosol population (single-particle techniques) as well as techniques that look at the total composition of the aerosol population (bulk techniques) to understand the complexity of the aerosol population and the gaps that bulk techniques create in this understanding. Results of all studies emphasize the importance of aerosol mixing state for determining the effects of aerosols in the Earth system.
Feb 22: NO SEMINAR
Feb 27 (Monday, 10:30): Dr. Yiming Qin
Invited Speaker of the Department of Atmospheric Sciences
University of California Irvine
Sources and Transformation of Atmospheric Aerosol Particles
Atmospheric aerosol particles significantly impact air pollution, human health, and the global climate. However, due to the chemical complexity of aerosol particles and associated reactivity and physical properties, a comprehensive understanding of the sources, transformation, and overall impacts of atmospheric particles still needs improvement. In this talk, I will discuss the important atmospheric processes that affect aerosol particle chemical composition. Firstly, I will present the results from the aerosol mass spectrometer and machine learning to identify critical environmental variables in the source and formation of atmospheric organic aerosols from field measurements. Secondly, the dynamic transformation of aerosol particles by the uptake of organic vapors and the growth of the particle in the environmental chamber will be discussed. I will also discuss my current work in developing an online surface-sensitive mass spectrometry method to probe the chemical heterogeneity of aerosol particles and labile organic molecules. This method provides a path forward for understanding surface chemistry in determining the health and climate impacts of particles. Finally, I will conclude my talk with future research plans on applying these novel techniques in emerging atmospheric aerosol particles such as atmospheric micro- and nano-plastics and their impacts on air pollution and climate change.
Mar 01: Dr. Gage Bonner
Department of Atmospheric Sciences, Rosenstiel School
Transition Path Theory on Real Data Sets With Applications to Sargassum Motion
Recording Available at COMPASS ON DEMAND
Transition Path Theory (TPT) provides a rigorous statistical characterization of the ensemble of trajectories connecting directly, i.e., without detours, two disconnected (sets of) states in a Markov chain, a stochastic process that undergoes transitions from one state to another with probability depending only on the state attained in the previous step. We will first discuss the basic results of TPT and show how they are applied to small examples where the transition probability matrix of the Markov chain is given. For real data sets, such as buoy trajectories obtained from the NOAA Global Drifter Program, Markov chains can be constructed using trajectory data via counting of transitions between cells covering the domain spanned by these trajectories. A natural choice for the cells is a regular grid of squares. We will show that this kind of covering leads to unstable estimates of the total duration of transition paths. Using Voronoi cells resulting from k-means clustering of the trajectory data, we obtain stable estimates of this TPT statistic, which is generalized to compute the remaining duration of transition paths, a new TPT statistic suitable for investigating connectivity. This is framed in the context of the problem of studying the motion of Sargassum in the tropical Atlantic.
Mar 06 (Monday, 10:00): Dr. Mingyi Wang
Invited Speaker of the Department of Atmospheric Sciences
California Institute of Technology, Pasadena
Atmospheric Aerosol Formation From Anthropogenic Pollution
The major remaining uncertainty in climate projections stems from the aerosol-cloud-climate interactions in the atmosphere. Therefore, an accurate representation of aerosol particles and clouds is the foundation of any effort to forecast long-term climate change. Aerosol particles in the urban atmosphere are especially important, in part because air pollution constitutes a public health crisis for over half of the world's population, but also because the regional climate forcing associated with urban haze can be large. In this seminar, I will first show how aerosol particles are formed in cities, and how unregulated pollutants greatly increase their survival probability and contribution to urban clouds. Then, I will discuss my latest work on how anthropogenic pollution drives aerosol formation in the cold upper troposphere, which in turn may exert a broad climate impact across the Northern Hemisphere.
Mar 08: Dr. Serge Guillas
Department of Statistical Science, University College London, UK
Uncertainty Quantification for Tsunami Hazard Modelling
Recording Available at COMPASS ON DEMAND
Building an emulator (i.e. a statistical surrogate) of a computer model, greatly alleviates the computational burden to carry out the uncertainty quantification of future events. We first present some illustrations of emulation to hazard modelling for earthquake-generated tsunamis over the Indian Ocean (Makran subduction zone), the Pacific Northwest (Cascadia), and Indonesia (South Java), and to landslide-generated tsunamis in Indonesia (Makassar strait). We also present the open computational Turing platform Multi-Output Gaussian Process Emulator to carry out rapid emulation over large outputs of the model. We then introduce a design strategy that allows us to efficiently allocate limited computational resources over simulations of different levels of fidelity. We provide an illustration to tsunami hazard assessment for the city of Cilacap in Indonesia. Using our multi-level method, tsunami hazard assessments can be achieved with higher precision using the highest spatial resolutions, and for impacts over larger regions. Finally, we present novel risk quantification using catastrophe modelling, based upon the losses in household assets, to demonstrate how such techniques can be used for humanitarian disaster financing.
Serge Guillas is Professor of Statistics at University College London (UCL) in the UK since 2007, and since 2020 he holds the Met Office Joint Chair in Data Sciences for Weather and Climate. He has been a Leverhulme fellow over 2011-2013 and fellow and group leader at the Alan Turing Institute over 2018-2020, UK's national institute for data science and AI, where he now leads the interest group on Uncertainty Quantification. He is also currently Associate Director of the UCL Centre for Advanced Research Computing. He was Vice-Chair of SIAM UQ over 2015-16. Previously he was Assistant Professor at the Georgia Institute of Technology (2004-2007) where he received the 2005 joint EPA Stratospheric Ozone Protection Award and an NSF SAMSI fellowship in 2006. He was beforehand a postdoc at the University of Chicago (2002-2004), and received his PhD in 2001 from the University Pierre-et-Marie-Curie in Paris, France.
Mar 15: Samantha Furtney
Department of Ocean Sciences, Rosenstiel School
(one-hour OCE student seminar)
Effects of Oceanic Internal Waves on Momentum Flux at the Air-Sea Interface
Recording Available at COMPASS ON DEMAND
Oceanic internal waves are subsurface oscillations that impact the exchange of nutrients, heat, and other properties between the open-ocean and the continental shelf. The orbital currents of internal waves create convergent and divergent regions at the sea surface, which can cause a modulation of the surface roughness and become visible in radar images. Traditionally, researchers studying internal waves have limited their focus to the oceanic side of the boundary layer. However, it has been speculated that internal waves can drive wind velocity and stress variance resulting in the enhancement of air-sea momentum flux. We investigate the possible implications of momentum transfer associated with internal waves using data from the Coastal Land Air-Sea Interaction 2021 field campaign in Monterey Bay, California. The land-based X-band radar swath contains one of the eight Air-Sea Interaction Spar (ASIS) buoys deployed in the Bay. Using the radar to identify the presence of an internal wave packet, we determine the duration of the event at the ASIS buoy site and estimate the wave period / frequency. We derive the turbulent fluctuations of the three components of wind velocity from the ultrasonic anemometer mounted on ASIS. Combining time series and wavelet analysis, we relate the radar detected internal wave to the wind information from the ASIS buoy. From this case study, we determine that the narrow internal wave frequency band made a dominant contribution to the covariance, but believe the internal waves redistribute the momentum flux among spatial scales without changing the total vertical transport.
Mar 22: NO SEMINAR
Mar 29: Dr. Alicia Karspeck
Invited Speaker of the Department of Atmospheric Sciences
SilverLining, Washington, DC
Working as a Climate Scientist Across Sectors:
A Path Through Research, Government, Commercial, and Non-Profit
Recording Available at COMPASS ON DEMAND
In the last half-decade, the explosion in commercial and philanthropic interest in climate mitigation, adaptation, and risk management has created (for arguably the first time) career paths for trained geophysical scientists to work on climate issues outside the academic and research spheres. In this talk, I will walk the audience through my career path (to date!) through research, government, commercial, and non-profit sectors. Researchers with deep knowledge of the physical science of climate, oceanography, land processes, and weather are in high demand – a trend that most folks believe will continue as the commercial sector grows and matures and climate adaptation / risk management is integrated into business and governance decision-making. I provide a few examples (from my personal experience) of high-value areas of expertise and talk about emerging areas of attention.
Apr 05: Dr. Flavio Lehner
Invited Speaker of the students of Atmospheric Sciences
College of Agriculture and Life Sciences, Cornell University, Ithaca, NY
Known Unknowns or Unknown Unknowns?
Climate Projection Uncertainty and Its Implications
Recording Available at COMPASS ON DEMAND
Climate is changing and is expected to continue to do so for the foreseeable future. By how much exactly is however unknown because many different sources of uncertainty contribute to cloud our outlook, especially at regional scales. At the same time, progress in understanding sources of uncertainty starts to enable constraints on the wide range of plausible futures. I will discuss potential progress and outstanding challenges in projection uncertainty at examples ranging from global temperature to regional water resource management.
Apr 12: NO SEMINAR (SLAB 103 not available)
Apr 17 (Monday, 10:30 am): Dr. Maike Sonnewald
Invited Speaker of the Department of Ocean Sciences
Atmospheric and Oceanic Sciences Program, Princeton University, NJ
Elucidating Driving Mechanisms in North Atlantic and Southern Ocean Dynamics:
Physics-Informed and Trustworthy Machine Learning for Ocean Science
Recording Available at COMPASS ON DEMAND
The global ocean plays a central role in maintaining the health of our planet and regulating various critical factors, such as heat and carbon levels, biological productivity, and sea level. Despite its importance, there are still unresolved questions regarding the driving forces behind even major circulation features, which limits our ability to monitor and understand ongoing changes. In this presentation, I will discuss the basin-scale circulation in the North Atlantic and Southern Ocean. First I will use a physics-guided machine learning methodology to construct hypotheses regarding the balance of drivers in the global ocean. This approach provides a new way to understand the primitive equations. Looking at the Southern Ocean, I will reveal a new unifying framework to understand the gyre circulation, which is critical to the upwelling that is key to climate. Secondly, I will present a groundbreaking methodology to infer subsurface circulation by using a trustworthy neural network that reasons using geophysical fluid dynamics. When used on climate models, this methodology can detect changes in dynamics associated with the Atlantic Meridional Overturning Circulation and reveal differences in model physics that could explain the roots of climate projection uncertainties. Finally, I will discuss the problems of using machine learning as a black box and present solutions. Throughout this talk I will emphasize the use of data science for discovery, which opens doors to gain new knowledge.
Maike Sonnewald is an Associate Research Scholar at Princeton University and an Affiliate Assistant Professor at the University of Washington. Focused on the ocean and climate, she uncovers underlying principles that govern ocean dynamics from small to global scales. She has pioneered methods for physics-informed machine learning models, automated regime identification, and trustworthy machine learning applications. Dr. Sonnewald's core motivation is to deliver actionable information to decision-makers. Her focus is on the physical and biological ocean, and she is passionate about bringing the different branches of oceanography together. Her influence spans national and international policy, fundamental scientific discovery, and climate model development. Her work has been featured prominently in the NOAA Artificial Intelligence strategy (2021-2025) and informed the basis for New Zealand's Marine Protected Area legislation. Her review articles include an invited contribution on machine learning applications in oceanography. She has given over 60 invited talks, including to the NOAA Research senior management, the Department of Energy, colloquia, and major conferences. She serves as Associate Editor at the journal Artificial Intelligence for the Earth System (American Meteorological Society) and is affiliated with the NOAA Geophysical Fluid Dynamics Laboratory.
Apr 17 (Monday, 3:30 pm): Dr. Damian Rouson
Guest of Milan Curcic, Department of Ocean Sciences
Computer Languages and Systems Software Group, Lawrence Berkeley National Laboratory, CA
Language-Based Parallel Programming for Earth System Modeling
Recording Available at COMPASS ON DEMAND
Language-based parallel programming models offer the promise of improved portability, programmability, and performance along with reduced maintenance costs relative to some alternative approaches. Fortran 2018 delivers on this promise by incorporating and expanding upon Coarray Fortran, a feature set originally defined as a syntactically small extension to Fortran 95. Coarray Fortran supports single-program, multiple data (SPMD) programming compatible with other SPMD models such as the widely employed Message Passing Interface (MPI). Fortran also provides array statements and concurrent loop iterations, "do concurrent", that a compiler can exploit for multithreading, vectorization, or offloading computation to accelerators such as graphics processing units (GPUs). Four widely available compilers now support the aforementioned language features, making the time ripe for exploring language-based parallelism in Fortran. This talk will demonstrate the use of coarrays for advection prediction in the Intermediate Complexity Atmospheric Research (ICAR) model and the use of array statements and "do concurrent" in Inference-Engine. Developed at the National Center for Atmospheric Research (NCAR) and initially motivated by the precipitation input requirements of hydrological models, ICAR facilitates downscaling to predict the regional impacts of global climate change. Developed at Berkeley Lab, the new deep learning framework Inference-Engine targets the large-batch inference needs of applications such as ICAR, which will need to infer multiple variables at each grid point at each time step if we succeed in training a neural network to serve as the cloud microphysics module. The talk will highlight the challenges and benefits of the language-based approach in the context of ICAR and Inference-Engine from the standpoint of portability, programmability, performance, and maintenance.
Apr 19: Manish Devana
Department of Ocean Sciences, Rosenstiel School
(one-hour MPO student seminar)
A Multi-Scale Investigation of the Dynamics, Variability, and Evolution
of the Iceland Scotland Overflow
Recording Available at COMPASS ON DEMAND
The Iceland Scotland Overflow Water (ISOW) is a major component of the Atlantic Meridional Overturning Circulation's (AMOC) lower limb. Overflowing deep waters formed in the Nordic Seas mix with upper ocean Atlantic Waters in the Iceland Basin to form ISOW, which makes up a quarter of North Atlantic Deep Water. Newly available observations from the OSNAP (Overturning in the Subpolar North Atlantic Program) mooring array across the Iceland Basin show that ISOW is highly variable in terms of both its transport and water mass properties. The mechanisms behind this variability and their downstream impacts on the AMOC are largely unknown. This 3-part study examines ISOW properties and dynamics across a range of spatial and temporal scales. First, we show that a major observed decline in ISOW salinity was linked to entrainment of a broad upper salinity anomaly at key junctions int the ISOW formation pathway. The results highlight how large-scale patterns of circulation combined with entrainment processes allow upper ocean hydrographic signals to rapidly modify North Atlantic abyssal water properties on subdecadal time scales. Next, we use OSNAP mooring observations to assess the transport variability of the ISOW plume as it is exported from the Iceland Basin into North Atlantic Deep Water. We show that transport is split between dynamically distinct flows along the ridge flank and the basin interior. The bulk of transport occurs along the ridge where EOF decomposition shows that variability is largely governed by an intense flow through an axial rift valley. We show that one third of the along ridge transport is ageostrophic and directly linked to the intense flow within the valley. Finally, we quantify the role of small-scale turbulent processes within the ISOW layer. CTD and mooring based estimates of turbulent dissipation show that mixing increases 1-2 orders of magnitude in the ISOW layer as the flow encounters ridge fracture zone topography. The bottom intensified mixing alters the mean potential vorticity balance of the ISOW layer and leads to an eastward and downslope rotation of the flow. These results suggest that turbulent mixing processes generate a "PV shield" that inhibits westward ISOW export and may provide a dynamical explanation for recent findings that show a significant fraction of ISOW remains east of the Mid-Atlantic Ridge.
Apr 26: NO SEMINAR
May 23 (Tuesday, 10:00 am): Dr. Milan Curcic
Invited Speaker of the Departments of Atmospheric Sciences and Ocean Sciences
Department of Ocean Sciences, Rosenstiel School
Advancing Earth System Prediction With Machine Learning and
State-of-the-Art Measurements
Recording Available at COMPASS ON DEMAND
Weather, ocean wave, and ocean circulation models have improved tremendously over the past few decades, both in terms of prediction skills and the physical processes that they resolve. They provide crucial information that reduces risk to human life and property, while also reducing costs in trillion-dollar industries that rely on weather and ocean forecasts. Historically, atmosphere and ocean models have been developed in isolation from one another. Although we have made significant strides toward unified, fully-coupled, Earth System modeling capability over the past 15 years, many coupled physical processes remain insufficiently observed, poorly understood, or simply missing from operational Earth System models. Recent advances in machine learning promise to become an essential component of model physics and data analysis workflows alike. In this talk, I will review our progress toward comprehensive Earth System modeling capability and recent laboratory and field observations that are necessary to better understand and implement Earth System model components. I will also discuss recent advances in deep learning methods to emulate and significantly accelerate components of Earth System models. Finally, I will emphasize the importance of integrating theory, observations, numerical modeling, and machine learning for the Rosenstiel School to remain at the forefront of Earth System science.
Milan Curcic is an atmospheric and ocean scientist. He uses theory, observations, and numerical models to better understand air-sea interaction processes and their implications for Earth System prediction. He has worked with teams from US Navy, NASA, DOE, and many universities to develop and improve Earth System model components. Milan created a high-performance machine-learning framework that has been successfully used to accelerate various numerical models from chemistry and combustion to atmospheric radiative transfer. He has published a best-selling computer programming book and co-founded a large and rapidly-growing open source community. Milan received his Ph.D. in Meteorology and Physical Oceanography from the University of Miami in 2015. He is currently an Assistant Scientist in the Ocean Sciences department here at the Rosenstiel School.
