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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Induced Seismicity: Relationships between Earthquake Frequency and Magnitude in the Oklahoma Arbuckle Group
I made an in-depth research inquiry on how to mitigate human-made earthquakes from leftover saltwater injection from oil wells in Oklahoma. I spent 6 months going through Excel spreadsheets full of data from oil and gas companies and the Oklahoma Commission Corporation (OCC) to manipulate the data and graph each of the variables against each other. I made line graphs, scatter plots, bar graphs, maps, and 3D models to compare my variables. Through this project, I gained valuable insight into computer, graphing, and analytical skills. I presented this project at the International Science and Engineering Fair in May of 2019 and was fortunate enough to receive 4th place for my project.
Iceland’s Disappearing Glaciers: How climate change affects the future of the Vatnajökull glacier in terms of dynamics, hydrology, and the consequences on Iceland’s hydroelectric potential and generation
I provided an in-depth investigation and summarization of existing research on the Vatnajökull ice cap and its outlet glaciers, particularly the relationship between common climate change factors, the ice cap, and Iceland’s shifting hydrological system, with emphasis on climate change’s impact on the glaciological melting mechanisms of the Vatnajökull ice cap and the corresponding change in hydrology. This emphasis comes from a need to study glaciology to understand the tie between anthropological and environmental causes of climate change on glaciers and how these effects impact energy, power, and resources. This research provided insight into the planned construction designs that Iceland’s hydropower companies adopt to account for the changes in water flow from the melting Vatnajökull glacier.
Investigating ANU and ICE-6G Ice Model Relative Sea Level Misfits to Determine Viscosity Constraints since the Last Glacial Maximum
Glacial isostatic adjustment (GIA) incorporates glacial rebound and gravitational potential to calculate sea level rise. However, many ice models struggle to accurately represent these effects in relative sea level (RSL) predictions. Two widely used ice models that incorporate GIA effects, ANU (Lambeck et al., 2014, 2017) and ICE-6G (Peltier et al., 2015), require further analysis to gauge their validity. I compared the model predictions of RSL of the ANU and ICE- 6G models against six distinct RSL field datasets using a semi-analytic GIA model. To predict RSL, a GIA model requires an ice model and models of the Earth’s mantle viscosity, density, and elastic structure. While mantle density and elastic structures are reliably constructed from seismology studies, mantle viscosity is not well constrained. I developed 864 simulated mantle viscosity models to run with the ANU and ICE-6G ice models to obtain the predicted RSL data. I analyzed the Root Mean Square (RMS) misfits of the simulated test RSL outputs against the observed RSL field data. I showed that RMS demonstrates error quality of field data is critically important to the GIA modeling process. I also showed the strengths and weaknesses of each ice model through further RMS analysis.
Quantifying the Role of Internal Variability on Multidecadal Springtime Arctic Amplification Using Machine Learning
My research uses machine learning (ML) to partition the role of internal variability in springtime AA. Applying ML to quantify springtime internal variability in observed Arctic and global temperature changes will significantly advance our knowledge in Arctic climate dynamics. This research will further shed light on how much internal variability contributes to the differences between observed and simulated AAs. It will also allow for a direct comparison of externally forced changes between observations and model simulations to inform about various feedback processes and their seasonal dependences. This research will help reshape climate science community thinking towards questions regarding the role of internal variability. The climate science community has struggled for years to characterize internal variability and its impact with a method that doesn’t inherently make unphysical assumptions. The proposed efforts to extract the influence of internal variability in springtime AA informs about how variable the climate system is and how much this variability can inflate or reduce temperature trends. Understanding how internal variability impacts AA aids in model predictions, which will benefit the Arctic climate change mitigation plans and local Arctic communities. AA is critical to study due to its impacts on humans and the environment, within and beyond the Arctic. For spring in particular, enhanced surface warming, sea ice melt, snow melt, and permafrost thawing can negatively impact human and animal activity. In a changing Arctic, local communities are seeing impacts on food and water accessibility and infrastructure damage. On the other hand, shipping routes, tourism, and fishing resources are becoming more accessible (Melia et al., 2016). The springtime Arctic ecosystem is experiencing changes in animal migration patterns and introduction of invasive species (CAFF 2013). Arctic ‘greening’ is becoming more apparent and decreasing the Arctic’s albedo as temperatures increase (Osborne et al., 2018). Springtime boreal wildfire frequency is and will continue to increase due to reduced snow and ice cover which leads to increased heat absorption (Heyblom et al., 2023). Increased seasonal sea ice loss also decreases the albedo while allowing disruptive algae to bloom in areas once protected by sea ice year-round (Osborne et al., 2018).
publications
Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies
Published in The Astrophysical Journal, 2023
Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counterintuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfvén waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, α = 2 as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed >600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: preflare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that α = 1.63 ± 0.03. This is below the critical threshold, suggesting that Alfvén waves are an important driver of coronal heating.
Recommended citation: James Paul Mason et al 2023 ApJ 948 71
talks
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.