PhD project offered by the IMPRS-gBGC in July 2025

Leveraging machine learning to refine leaf phenology representation in land surface models

Phillip Papastefanou, Christian Frankenberg, Jeff Dukes, Sönke Zaehle

Project description

Climate change is altering environmental conditions and directly impacting the carbon update and storage of terrestrial ecosystems. To understand these complex interactions, Terrestrial Biosphere Models (TBMs) such as QUINCY can be used to improve our understanding of the current and future feedbacks of climate change.

One key process that remains challenging for Terrestrial Biosphere Models (TBMs) to accurately simulate is leaf phenology, which strongly influences photosynthesis and overall plant growth. Leaf phenology encompasses the seasonal cycle of foliage expansion and senescence, both of which are key processes that can be critically influenced by environmental stressors like drought and are projected to be altered under future climate change scenarios. The impact of phenology extends beyond the simple effect of carbon dynamics, as it influences water and energy partitioning at the land surface.

The aim of this PhD thesis is to develop a data-driven algorithm to represent observed trends in phenology for different vegetation types into the QUINCY TBM, and evaluate the effect on land carbon, water and energy fluxes under current and future climates. The project will use machine-learning approaches to identify climate and ecosystem drivers of phenology based on existing leaf/ecosystem phenology data sets, e.g. at site-level via phenocams, or large-scale estimates from multiple satellite products such as MODIS or SENTINEL-2. Specifically the research programme will include the following steps:

Research program

Analysing and combining phenology data from different sources, e.g. phenocams; FLUXNET in-situ observations, and remote-sensing products, e.g MODIS and SENTINEL-2
Developing and training machine-learning based algorithms that predict leaf phenology across species and scales (site level to global)
Coupling the developed algorithm to the QUINCY biosphere models
Applying the enhanced QUINCY model at regional and global scales to assess patterns in phenology under current climate and future climate projections

Working group & collaborations

The PhD candidate will join the Terrestrial Biosphere modelling (TBM) at the Department of Biogeochemical Signals. The TBM group consists of skilled process based modelers and (amongst other things) is developing the ecosystem model QUINCY. This position is associated with the Max Planck Centre for Earth and intensive collaboration with experts from the center is expected.

Requirements for the PhD project are

Applications to the IMPRS-gBGC are open to well-motivated and highly-qualified students from all countries. Prerequisites for this PhD project are:

Master's degree in environmental science, Earth system science, physics, mathematics, or related disciplines
Background in biogeoscience, ecosystem modelling, machine learning and/or remote sensing
Statistical skills or programming skills (e.g. Python, R, Mathematica)
Experience with "higher" scientific languages such as Fortran or C++
Excellent written and communication skills in English

The Max Planck Society (MPS) strives for gender equality and diversity. The MPS seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply. The MPS is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals.