Research projects
Here we want to give you a brief introduction of the scope of each project. You will be able to further define the project with your ideas and skill set once you start your PhD.
Fast feedbacks in the Earth system: land surface fluxes’ influence on low-cloud formation and the Earth energy balance
(Project 01, responsible: Alexander Winkler ; Markus Reichstein)
Key points
- The land surface controls energy and water fluxes to the atmosphere and thereby modulates the boundary layer dynamics including low-cloud formation on short time-scales.
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Global changes in land surface processes, e.g., through changes in vegetation cover / compositions or physiological responses to climate change, thus potentially propagate into changes in cloud cover and the Earth energy balance.
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This project aims to use machine learning to retrieve land surface responses from observational data and to use state-of-the-art Earth system modeling to quantify these fast feedbacks.
Involved partners: Tapio Schneider (Caltech) & Pierre Gentine (Columbia University)
Slow carbon-climate feedback? How fast microbial processes affect the long-term soil carbon-climate feedback
(Project 02, responsible: Bernhard Ahrens; Alexander Winkler)
Key points:
- Fast microbial processes, such as potential microbial acclimation or substrate depletion may be crucial aspects for soil carbon models that can provide a self-regulating feedback controlling soil carbon losses in a warmer world.
- Increased vegetation productivity may increase the carbon inputs to the soil and offset the stabilizing effect of substrate depletion and prime microbial decomposition of existing soil carbon.
- This project aims to understand how fast microbial processes affect the long-term soil carbon-climate feedback by combining soil microbial models and vegetation models with machine learning from large-scale soil warming datasets and global soil carbon databases.
Involved partners: Alexis Renchon (Caltech)
Controls on long-term land carbon dynamics
(Project 03, responsible: Susan Trumbore, Nuno Carvalhais, Carlos Sierra)
Key points:
- Vegetation disturbance-regrowth and soil decomposition dynamics are controlled by spatio-temporal processes that shape the responses and feedbacks of terrestrial carbon cycle to climate variability from decades to centuries.
- The role of terrestrial ecosystems in the future Carbon cycle depends on how changes in vegetation and soil processes will alter how much and also how long Carbon is stored. Remaining and large uncertainties in such dynamics still limit our prediction ability.
- This project aims to leverage hybrid modeling and existing data on ecosystem fluxes and stocks of carbon, radiocarbon measurements and satellite Earth observations, from regional to global scales (e.g. ATTO, FLUXNET, ISRaD, SIF), to improve the representation of land feedbacks in the Earth system.
Involved partners: Jeffrey Dukes (Carnegie) & Anthony Bloom (Caltech & JPL)
Leveraging machine learning to refine leaf phenology and mortality representations in land surface models
(Project 04, responsible: Sönke Zaehle, Phillip Papastefanou)
Key points:
- Land Surface Modelling (LSM) is crucial for understanding current and future feedbacks of climate change on the global carbon cycle.
- Accurate representation of leaf phenology, which drives photosynthesis and plant growth, and mortality, which influences vegetation dynamics under changing climatic conditions, is essential yet challenging in current LSM.
- This project will leverage machine learning and novel data sources to enhance the predictive capabilities of land surface models.
Involved partners: Christian Frankenberg (Caltech) & Jeffrey Dukes (Carnegie)