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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2023)

Freshwater exchanges between the ocean--ice--atmosphere system play a crucial role in the global climate system. This study provides an analysis of the local impact of freshwater fluxes both off the coast of California and in the Arctic. Studies are carried out using observations and numerical and statistical models. We show that freshwater exchanges between the ocean and atmosphere in the form of precipitation from atmospheric rivers (ARs) over the ocean in the California Current System (CCS) have impacts on the surface ocean salinity on event and seasonal timescales. In the upper ocean, precipitation from ARs can produce long-lasting layers of freshwater, the extent of which are dependent on atmospheric forcing from precipitation and wind. We conclude that upper ocean salinity changes due to ARs are within the limits of detectability of ocean instruments.

We also examine the extent to which wind acts as a driving force for ice motion in the Arctic. To accomplish this, we build a sequence of machine learning (ML) models that make one-day predictions of present-day zonal and meridional sea-ice velocity components from inputs of present-day wind velocity, previous-day sea-ice velocity, and previous-day sea-ice concentration. We analyze the performance of these models, and implement explainable machine learning (XML) methods to understand how they are making their predictions. One of these methods, layerwise relevance propagation (LRP), was developed for ML models that make classification rather than regression predictions. This study is the first known implementation of a global LRP for a regression problem in geosciences. We therefore provide a comparative study of several different XML methods to bolster trustworthiness in the use of LRP for this particular application. A convolutional neural network (CNN) has improved performance compared conventional persistence (PS) and linear regression (LR) models. Outputs from local LRP studies are shown to be consistent with other XML methods. However global implementations of LRP are highly sensitive to choices made during processing. We analyze the coefficients of the LR model to understand the relationship between ice motion and wind speed. We show that the ice is becoming more responsive to wind forcing, and link this to decreasing ice concentration.

    Cover page: Freshwater Processes in the Upper Ocean
    • Article
    • Peer Reviewed
    UC Merced Previously Published Works (2024)
    Metallic nanoparticles have been demonstrated to be versatile photocatalysts, as exemplified by those made from noble and precious metals. Transitioning from precious to earth-abundant metals for sustainable photocatalysis requires benchmarking their catalytic performance. In this work, we attempt to compare the photocatalytic activities of Au, Pd, and Co-B nanoparticles in the reduction of nitrobenzene by hydrazine. Despite their different morphologies and surface structures, Co-B nanoparticles offer the highest catalytic enhancement when comparing their reaction rates under irradiation to those under nonirradiation conditions. The trend of improved photocatalytic performance when transitioning from Au to Pd, and then to Co-B, can be explained by the nature of their d-band positions and corresponding hot carriers photogenerated from interband transitions.
      Cover page: From Precious to Earth-Abundant Metallic Nanoparticles: A Trend of Interband Transitions in Photocatalyzed Nitrobenzene Reduction
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2022)
      Abstract Atmospheric rivers (ARs) result in precipitation over land and ocean. Rainfall on the ocean can generate a buoyant layer of freshwater that impacts exchanges between the surface and the mixed layer. These “fresh lenses” are important for weather and climate because they may impact the ocean stratification at all time scales. Here we use in situ ocean data, collocated with AR events, and a one-dimensional configuration of a general circulation model, to investigate the impact of AR precipitation on surface ocean salinity in the California Current System (CCS) on seasonal and event-based time scales. We find that at coastal and onshore locations the CCS freshens through the rainy season due to AR events, and years with higher AR activity are associated with a stronger freshening signal. On shorter time scales, model simulations suggest that events characteristic of CCS ARs can produce salinity changes that are detectable by ocean instruments (≥0.01 psu). Here, the surface salinity change depends linearly on rain rate and inversely on wind speed. Higher wind speeds (U > 8 m s−1) induce mixing, distributing freshwater inputs to depths greater than 20 m. Lower wind speeds (U ≤ 8 m s−1) allow freshwater lenses to remain at the surface. Results suggest that local precipitation is important in setting the freshwater seasonal cycle of the CCS and that the formation of freshwater lenses should be considered for identifying impacts of atmospheric variability on the upper ocean in the CCS on weather event time scales. Significance Statement Atmospheric rivers produce large amounts of rainfall. The purpose of this study is to understand how this rain impacts the surface ocean in the California Current System on seasonal and event time scales. Our results show that a greater precipitation over the rainy season leads to a larger decrease in salinity over time. On shorter time scales, these atmospheric river precipitation events commonly produce a surface salinity response that is detectable by ocean instruments. This salinity response depends on the amount of rainfall and the wind speed. In general, higher wind speeds will cause the freshwater input from rain to mix deeper, while lower wind speeds will have reduced mixing, allowing a layer of freshwater to persist at the surface.
        Cover page: Ocean Surface Salinity Response to Atmospheric River Precipitation in the California Current System
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2023)
        Abstract: Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea-ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea-ice motion. The ML models are built to predict present-day sea-ice velocity given present-day wind velocity and previous-day sea-ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea-ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and convolutional neural network (CNN). We quantify the spatio-temporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea-ice velocity with a correlation up to 0.81 between predicted and observed sea-ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally; lower values occur in shallow coastal regions and during times of minimum sea-ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea-ice velocity on one-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.
          Cover page: Machine learning for daily forecasts of Arctic sea-ice motion: an attribution assessment of model predictive skillCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2019)
          The 2nd Annual Symposium on Hidradenitis Suppurativa Advances (SHSA) took place on 03-05 November 2017 in Detroit, Michigan, USA. This symposium was a joint meeting of the Hidradenitis Suppurativa Foundation (HSF Inc.) founded in the USA, and the Canadian Hidradenitis Suppurativa Foundation (CHSF). This was the second annual meeting of the SHSA with experts from different disciplines arriving from North America, Europe and Australia, in a joint aim to discuss most recent innovations, practical challenges and potential solutions to issues related in the management and care of Hidradenitis Suppurativa patients. The last session involved clinicians, patients and their families in an effort to educate them more about the disease.
            Cover page: Proceeding report of the Second Symposium on Hidradenitis Suppurativa Advances (SHSA) 2017
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