Fellow Profile Page

Zachary Espinosa

  • Program Year: 3
  • Academic Institution: University of Washington
  • Field of Study: Atmospheric Science
  • Academic Advisor: Cecilia Bitz
  • Practicum(s):
    Lawrence Livermore National Laboratory (2023)
  • Degree(s):
    M.S. Applied and Engineering Physics, Stanford University, 2021; B.S. Computer Science, Stanford University, 2020
  • Personal URL: https://zacespinosa.github.io/

Summary of Research

Trends in surface air temperature are larger in the Arctic than any other region on Earth - a phenomenon known as Arctic amplification. Unprecedented Arctic warming has coincided with a dramatic decrease in perennial sea ice extent. In my current research, I aim to understand trends in interannual variability of sea ice extent in a warming climate. Characterizing trends in Arctic variability is vital to evaluating forecasting systems, interpreting observations, and attributing future weather events to anthropogenic drivers. My research is conducted using a combination of global climate models, observations, and theory.

Publications

BlanchardWrigglesworth, Edward, et al. "The largest ever recorded heatwave ”Characteristics and attribution of the Antarctic heatwave of March 2022." Geophysical Research Letters 50.17 (2023): e2023GL104910.

Espinosa, Z. I., Sheshadri, A., Cain, G. R., Gerber, E. P., & DallaSanta, K. J. (2022). Machine learning gravity wave parameterization generalizes to capture the QBO and response to increased CO2. Geophysical Research Letters, 49, e2022GL098174. https://doi.org/10.1029/2022GL098174

- American Geophysical Union (AGU) Fall Meeting Talk. Vol. 2021. 2021. (New Orleans, LA, 2021), Espinosa, Zac, et al. "A Machine Learning Parameterization of Gravity Wave Momentum Fluxes Coupled to an Atmospheric Global Climate Model."

- Pacific Northwest National Laboratory (PNNL) Aug 2021 Intern Symposium, Espinosa, Zac, et al. "Drivers of the Seasonal Delay of Rainfall in the Amazon Rainforest."

- European Geosciences Union (EGU) General Assembly 2021, online, 19-30 Apr 2021, EGU21-1398, https://doi.org/10.5194/egusphere-egu21-1398, 2021. "Machine Learning Emulation of Parameterized Gravity Wave Momentum Fluxes in an Atmospheric Global Climate Model", Espinosa, Z., Sheshadri, A., Cain, G., Gerber, E., and DallaSanta, K.

- American Geophysical Union (AGU) Fall Meeting Talk. Vol. 2020. 2020. (Virtual, NA, 2020), Espinosa, Zac, et al. "A Data-Driven, Single Column Gravity Wave Parameterization in an Idealized Model."

- Midwest Student Conference on Atmospheric Research, Sep 2020, virtual, Espinosa, Zac, et al. "A Data-Driven, Single Column Gravity Wave Parameterization in an Idealized Model."

- California Geophysical Fluid Dynamics (CalGFD) Conference, Aug 2020, virtual, Espinosa, Zac, et al. "A Data-Driven, Single Column Gravity Wave Parameterization in an Idealized Model."

- American Physical Society (APS) March Meeting, Mar 2020 (Canceled), Espinosa, Zac, et al. "NetQuil: A Playground for Quantum Networking Simulations"

Awards

1) Achievement Rewards for College Scientists (ARCS) Foundation Scholar (09/2021 - Present)

2) Graduate Opportunities and Minority Achievement Program (GO-MAP) Fellow (09/2021 - Present)

3) The GEM National Consortium Graduate Fellow (06/2021 - Present)