Bio

Caroline Famiglietti is a PhD candidate working with Prof. Alexandra Konings. She studies the terrestrial carbon cycle, focusing on understanding and reducing uncertainties in model projections of its behavior. In 2017, Caroline graduated summa cum laude from UCLA with a B.S. in Applied Mathematics and a minor in Geography/Environmental Studies. Her prior research experience includes work in the Carbon Cycle & Ecosystems group at NASA JPL from 2017-2018 and in the UC Berkeley Department of Civil & Environmental Engineering in 2016.

Honors & Awards

  • FINESST Award, NASA (2021-2024)
  • ARCS Fellowship, ARCS Foundation, Stanford University (2021-2022)
  • Stanford Graduate Fellowship, Stanford University (2018-2021)

Education & Certifications

  • M.S., Stanford University, Earth System Science (2022)
  • B.S., UCLA, Applied Mathematics (2017)

Contact

  • Academic
    cfamigli@stanford.edu
    University - Student Department: Department of Earth System Science Position: Graduate

Additional Info

  • Mail Code: 2210

Links

Work Experience

  • Research Intern (Carbon Cycle & Ecosystems Group), NASA Jet Propulsion Laboratory, California Institute of Technology (2017 - 2018)

    Location

    Pasadena, CA

  • Project Lead, Sustainable L.A. Grand Challenges Program, UCLA (2016 - 2017)

    Location

    Los Angeles, CA

  • Undergraduate Research Assistant, Department of Civil & Environmental Engineering, UC Berkeley (2016)

    Location

    Berkeley, CA

  • Environmental Education Network Intern, TreePeople (2014)

    Location

    Beverly Hills, CA

All Publications

  • Attributing Past Carbon Fluxes to CO2 and Climate Change: Respiration Response to CO2 Fertilization Shifts Regional Distribution of the Carbon Sink Global Biogeochemical Cycles Quentin, G. R., Famiglietti, C. A., et al 2023

    View details for DOI 10.1029/2022GB007478

  • Global net biome CO2 exchange predicted comparably well using parameter-environment relationships and plant functional types. Global change biology Famiglietti, C. A., Worden, M., Quetin, G. R., Smallman, T. L., Dayal, U., Bloom, A. A., Williams, M., Konings, A. G. 2022

    Abstract

    Accurate estimation and forecasts of net biome CO2 exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)-based approach against a novel top-down, machine learning-based "environmental filtering" (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model-data fusion framework of the terrestrial carbon cycle (CARDAMOM) at global scale. In the PFT-based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF-based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby "true" parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF-based approach matches or outperforms the PFT-based approach at 55% of pixels-a narrow majority. However, NBE estimates from the EF-based approach are susceptible to compensation between errors in component flux predictions, and predicted parameters can align poorly with the assumed "true" values. Overall, though, the EF-based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between TBM performance and parametric uncertainty, informing efforts to improve model parameterization via PFT-free and trait-based approaches.

    View details for DOI 10.1111/gcb.16574

    View details for PubMedID 36560840

  • Extreme wet events as important as extreme dry events in controlling spatial patterns of vegetation greenness anomalies ENVIRONMENTAL RESEARCH LETTERS Famiglietti, C. A., Michalak, A. M., Konings, A. G. 2021; 16 (7)

    View details for DOI 10.1088/1748-9326/abfc78

    View details for Web of Science ID 000667939300001

  • Optimal model complexity for terrestrial carbon cycle prediction BIOGEOSCIENCES Famiglietti, C. A., Smallman, T., Levine, P. A., Flack-Prain, S., Quetin, G. R., Meyer, V., Parazoo, N. C., Stettz, S. G., Yang, Y., Bonal, D., Bloom, A., Williams, M., Konings, A. G. 2021; 18 (8): 2727-2754

    View details for DOI 10.5194/bg-18-2727-2021

    View details for Web of Science ID 000646696300003

  • Plant responses to volcanically elevated CO2 in two Costa Rican forests BIOGEOSCIENCES Bogue, R. R., Schwandner, F. M., Fisher, J. B., Pavlick, R., Magney, T. S., Famiglietti, C. A., Cawse-Nicholson, K., Yadav, V., Linick, J. P., North, G. B., Duarte, E. 2019; 16 (6): 1343–60

    View details for DOI 10.5194/bg-16-1343-2019

    View details for Web of Science ID 000462963200002

  • Global Validation of MODIS Near-Surface Air and Dew Point Temperatures Geophysical Research Letters Famiglietti, C. A., Fisher, J. B., Halverson, G., Borbas, E. E. 2018

    View details for DOI 10.1029/2018GL077813

  • Ecosystem Responses to Elevated CO2 Using Airborne Remote Sensing at Mammoth Mountain, California Biogeosciences Cawse-Nicholson, K., Fisher, J. B., Famiglietti, C. A., et al 2018

    View details for DOI 10.5194/bg-2018-73