Project:	Optimality-Based Model Development and Calibration for CliMA-Land
Discipline:	Environmental Science and Engineering
Mentor:	Tapio Schneider, Professor, (GPS), tapio@caltech.edu, Phone: 6263956920
Mentor URL:	https://climate-dynamics.org/people/tapio-schneider/  (opens in new window)
AO Contact:	Renato Braghiere, renatob@caltech.edu
Background:	The accelerating pace of climate change highlights the need for advanced and efficient climate modeling systems. The Climate Modeling Alliance (CliMA) integrates physics, applied mathematics, and machine learning to improve understanding and prediction of climate systems. CliMA-Land specifically models land-surface processes, from stomatal conductance in vegetation to the global carbon cycle. Traditional climate models rely on rigid, parameter-heavy approaches that limit computational efficiency and accuracy. This project leverages novel techniques, such as hybrid modeling and optimality-based algorithms, to enhance the flexibility and precision of land-climate interaction modeling.
Description:	The project focuses on implementing an optimality-based approach for parameterizing key processes in CliMA-Land. Specifically, the student will: 1. Literature Review: Study existing research on optimality principles, machine learning applications in parameter calibration, and the Julia programming language within CliMA-Land. 2. Model Development: Develop and implement optimality algorithms to estimate critical plant physiological parameters like Vcmax (maximum carboxylation rate), integrating data-driven techniques with mechanistic formulations. 3. Data Calibration: Use observational data (e.g., FLUXNET) to calibrate the model and align its outputs with real-world patterns. Hybrid modeling methods combining machine learning and physics-based approaches will be explored for robust calibration. 4. Model Analysis and Validation: Conduct a comparative analysis of the new model against current benchmarks, such as TRENDY and CMIP simulations, to evaluate accuracy, predictive capability, and computational efficiency. 5. Reporting and Presentation: Compile a comprehensive report detailing the model development process, calibration methodologies, challenges, and results. Prepare findings for potential publication and presentations at scientific conferences.
References:	1. Harrison et al. (2021). Eco-evolutionary optimality as a means to improve vegetation and land-surface models. New Phytologist, 231(6), 2125–2141. 2. Collier et al. (2018). The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation. Journal of Advances in Modeling Earth Systems, 10(11), 2731–2754. 3. Braghiere et al. (2023). Tipping point in North American Arctic-Boreal carbon sink persists in new generation Earth system models despite reduced uncertainty. Environmental Research Letters, 18(2), 025008. 4. ElGhawi et al. (2023). Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning. Environmental Research Letters, 18(3), 034039. 5. Rogers et al. (2017). A roadmap for improving the representation of photosynthesis in Earth system models. New Phytologist, 213(1), 22–42. 6. Schneider et al. (2023). Harnessing AI and computing to advance climate modelling and prediction. Nature Climate Change, 13, 887–889. 7. Dou & Yang (2018). Estimating forest carbon fluxes using four different data-driven techniques. Science of the Total Environment, 627, 78–94.
Student Requirements:	-Strong programming skills, particularly in Julia or Python. -Background in climate science, environmental modeling, or Earth system processes. -Familiarity with machine learning techniques and hybrid modeling. Coursework or experience in applied mathematics and data assimilation. -Ability to work with large datasets and perform statistical analysis (e.g., FLUXNET data). -Experience with model validation and comparative analysis is a plus.
Programs:	This AO can be done under the following programs:   Program    Available To        Amgen Scholars    Non-Caltech students only          SURF    both Caltech and non-Caltech students  Click on a program name for program info and application requirements.