Career

Open positions in the Department Biogeochemical Signals

Please refer to the institute’s job portal [https://www.bgc-jena.mpg.de/en/jobs] for open position in the department and the IMPRS for Global Biogeochemical Cycles [https://bgc.iedit.mpg.de/4659318/imprs] for available PhD projects. The department is always interested in recruiting highly motivated PhD students. Applications could be directed to the respective research group leaders.

Bachelor and Master projects

The following projects for Bachelor & Master theses are currently available. Please get in touch directly with the stated person of contact.

Interplay of the Atlantic Multidecadal Oscillation and El Niño Southern Oscillation modulating carbon anomalies in tropical South America

MSc Thesis

Contact: Santiago Botía (sbotia@bgc-jena.mpg.de)

The 2023 drought in the Amazon was not only driven by El Niño event, but was amplified by the anomalous warming in the Atlantic Ocean (Botía et al., 2025). In addition, a recent study (Serrão et al., 2025) demonstrated that among several teleconnections analyzed, the Atlantic Multidecadal Oscillation had the strongest combined influence in Gross Primary Productivity, Precipitation, Evapotranspiration and Ecosystem Water Use Efficiency in the Amazon and the Cerrado biomes. In this project we want to explore this further but from the perspective of net carbon exchange and fire emissions. For this we will explore how sea surface temperature anomalies in the Pacific and Atlantic Oceans modulate Net Ecosystem Exchange (NEE) and fire emissions. To obtain NEE, we will use the output of global atmospheric inversions, which provide a spatially resolved field of Net Biome Exchange (NBE) and with remotely-sensed fire emission products we can obtain Net Ecosystem Exchange (NEE = NBE - Fire). Furthermore, during years of large sea surface temperature anomalies in the Atlantic we will quantify how CO2 outgassing from the sea surface affects the concentrations at the Amazon Tall Tower Observatory (ATTO) and the continental regional signal of CO2.

Usefull skills
-   Programming skills in Python or R
-   Knowledge on environmental physics and atmospheric dynamics
-   Experience or willingness to learn atmospheric transport modeling

References
Botía S., Dias-Junior CQ., …& Bastos A. (2025). Reduced vegetation uptake during the extreme 2023 drought turns the Amazon into a weak carbon source. ESS Open Archive . January 22, 2025

Serrão, E.A.O., Cavalcante, R.B.L., Zanin, P.R. et al. (2025). The effects of teleconnections on water and carbon fluxes in the two South America’s largest biomes. Sci Rep 15, 1395. https://doi.org/10.1038/s41598-025-85272-z

What is causing the increasing trend in fire counts in the footprint of the Amazon Tall Tower Observatory?

MSc Thesis

In a previous master thesis, we found that there is an increasing trend of fire occurrences in the footprint of the Amazon Tall Tower Observatory (ATTO), influencing carbon monoxide concentrations (a proxy of fire emissions) measured at the site. At the seasonal scale, the onset of carbon monoxide (CO) was correlated with fires in the far-field of the tower, whereas the near-field fires influenced the peak of CO concentrations (Oct - Nov). Interestingly, in the absence of fires during the wet season (Feb - May), CO levels were above the background (CO levels outside of the continent), suggesting a non-fire related source of CO, consistent throughout the years (2014-2023). In this project, we want to understand the main drivers of the fire count trend and understand the processes controlling it. For this we will analyze precipitation, cumulative water deficits and land use changes in the footprint. In addition, to better understand the seasonal dynamics of CO, we will explore changes in atmospheric composition due to fires and if there has been change over time. For this, we will track individual fire plumes to investigate relative changes among gas species and study their source regions using atmospheric transport modeling.

Usefull skills
-   Programming skills in Python or R
-   Knowledge on environmental physics and atmospheric dynamics
-   Experience or willingness to learn atmospheric transport modeling

Contact: Santiago Botía (sbotia@bgc-jena.mpg.de)

European forests ablaze: did historical management add fuel to the fire?

MSc level

Description:

Heat extremes, combined with drought and low moisture in dead and live fuels, can lead to extremely destructive forest fires. Moreover, droughts drive reductions in forest canopy that not only increase dead fuels (leaf and branch litterfall) but also affect the land surface’s energy balance. In doing so, droughts may either diminish or intensify heat extremes via land–atmosphere feedback, hence fire risk.

Whilst the conditions conducive to forest fire are reasonably well identified, the extent to which different tree species and species composition contribute to fire risk is less clear. This project will investigate the role played by different European forest species in driving vs. mitigating fire risk. More specifically, the contributions of historical forest conversion choices to an increase in drought intensity and fuels leading to an increase in burnt areas will be probed, using a state-of-the art land-surface model (LSM) that considers plant hydraulics and species-specific responses to atmospheric aridity and water availability (Sabot et al., 2020, 2022; De Kauwe et al., 2022).

Given steady increases in wood harvest intensity over Europe, it is plausible that historical species conversion choices (with little other consideration than a fast wood yield) have increased the risk of forest fire. Model outputs will be analysed within a new conceptual framework (Jézéquel et al., 2024) that links extreme weather impacts to human influence on both hydro-climatic drivers, through anthropogenic forcing, and exposure and vulnerability, in this case historical forest management (i.e., the choice of tree species historically planted across European forests).

For a proof of concept, this MSc project will focus on forested areas within France. Extreme fire events (causing very large burnt areas in recent years) will be identified, as well as the non-endemic tree species affected by these extreme fire events. Once interesting events are identified, the LSM will be run for the weather conditions leading the fires, to quantify the amount of drought stress non-endemic species were under. These results will be contrasted with alternative hypothetical simulations accounting for endemic species and different atmospheric [CO₂] levels.

Contact: Manon Sabot (msabot@bgc-jena.mpg.de)

Getting to the root of vegetation change: linking root growth, root biomass, and aboveground anatomical traits through time

MSc level

Description: Plants exhibit a flexible trade-off between belowground (roots) and aboveground (stem, leaf, etc) growth, which allows them to balance their resource acquisition (e.g., nutrients, water, light, carbon) in response to changing environmental conditions. Plant growth strategies greatly affect the productivity and functioning of vegetation, for example in the face of drought or nutrient stresses. Yet, our understanding of variability in root-to-leaf investments across environmental gradients and plant species is equivocal and limited, and there is considerable uncertainty concerning future adjustments in these investments as the climate continues to change.

This thesis will seek to unlock new understanding on belowground growth patterns in two species of oaks commonly found across Europe (Quercus cerris and Q. robur). Specifically, the project will make use of timeseries of root images, captured by automated underground monitoring devices called robotic minirhizotrons, within the context of a controlled greenhouse experiment involving 104 trees and both a soil and a drought treatment. Measurements of root biomass and aboveground anatomical plant traits will complement these root images. By combining these data, the thesis will ask:

How well can root biomass be predicted from root images?
Does knowledge of above ground plant traits increase root biomass predictability?

The MSc candidate will be called to learn a variety of measurement and analytic methods, from root sampling to image analysis and statistical modelling / machine learning (e.g., Dynamic Generalized Additive Models, Random Forest), the latter which they will also have the opportunity to influence.

Working group: The thesis will contribute to an ambitious controlled greenhouse experiment, and it will be highly collaborative within the Climate and Plant Ecophysiology group of the Max Planck Institute for Biogeochemistry.
Useful skills:

Knowledge of a script programming language such as Python or R
An interest in working with field/experimental data
Basic understanding about carbon and water trade-offs in plants

Contact: Manon Sabot (msabot@bgc-jena.mpg.de)

High-Resolution Soil Moisture Mapping Over Arctic Permafrost Regions Using an Unmanned Aerial Vehicle (UAV)-Based Thermal Imagery

MSc level

Description: The rapid increase in air temperatures (2-4 times as fast as the global average) in high-latitude regions causes enhanced permafrost thaw, and related shifts in uptake and release of carbon. This process might potentially increase the atmospheric concentrations of greenhouse gases (GHGs) such as CO2 and CH4, which might in turn cause an increase in the rate of warming (positive feedback). The permafrost carbon processes and the impact of the environmental conditions need to be investigated to improve our understanding to what extent and how fast this increase will occur. One of the most important environmental factors that governs the permafrost carbon cycle is soil moisture, which affects e.g. microbial activities and organic matter decomposition rates. Therefore, assimilating soil moisture in any upscaling model will improve the estimation accuracy of the obtained carbon budgets.
In the project outlined here, soil moisture will be estimated based on thermal imageries that will be collected by an unmanned aerial vehicle (UAV), which is capable of surveying large areas with high resolution. The produced soil moisture maps will complement the carbon flux measurements of various platforms (i.e., manual chambers, a UAV platform, and an eddy tower) over the Canadian Arctic to provide a better estimation of the carbon budget.
Responsibilities:

Conducting a literature survey on the UAV-based thermal imaging and soil moisture estimation methodologies, including machine learning approaches
Processing and analyzing the UAV-based thermal images to estimate the soil moisture content
Supporting carbon flux upscaling effort

Useful skills:

Basic knowledge of a scripting language like R or python
Good communication skills (oral and written) in English
Basic understanding about statistical analysis

Timeframe: Field work in the Canadian Arctic is planned for late June to July 2024. Participation in this campaign would be beneficiary.
Contact: Abdullah Bolek (abolek@bgc-jena.mpg.de)

Frequency and intensity of extreme drought events in state-of-the-art climate model projections

BSc / MSc level

Description: The CMIP (Coupled Model Intercomparision Project) is a global initiative to better understand past, present and future climate change impacts on ecosystems and vegetation . Therefore, the project coordinates simulations of most available state-of-the-art Earth System Models. The output of these models are usually a collection of global climate variables (e.g. temperature, precipitation) at high resolution time-scales, but also vegetation and ecosystem states (e.g. primary productivity, soil water content). Task of the student is to identify regions of extreme drought stress in these simulations using (1) well established indicators of drought stress, and also (2) more modern machine learning based techniques. The goal of the thesis is to identify agreement and differences between the Earth System Models and to relate these to relevant processes of the model eventually feeding back to the CMIP community.
Useful skills:

Profound knowledge of a script programming language such as Python or R
No fear of working with datasets that can have more than 100GB in size
Basic understanding about the relevant processes and variables of the global carbon cycle

Contact: Phillip Papastefanou (papa@bgc-jena.mpg.de)

Identifying key plant hydraulic traits of European species using the terrestrial ecosystem model QUINCY

MSc level

Description: Terrestrial ecosystem models (TBMs) such are QUINCY are used to investigate impacts of current and future climate projections on forests, corps and vegetation in general. Plant hydraulics, i.e more detailed physical representations of the conducting system of plants have recently been brought into focus of TBMs as they are crucial when understanding impacts of extreme events such as droughts. Such a new plant hydraulic architecture has also been introduced to QUINCY and its capability of reproducing drought induced vegetational responds have been demonstrated. However, the influence of the new physicall parameters have not yet been tested and calibrated for the most important plant/tree species in Europe or Globally. The overall objective of this thesis is to improve our undestanding of drought impacts on vegetation by improving the performance of the QUINCY model. Thereby, the student should (1) test the newly implemented plant hydraulic architecture and (2) perform a sensitivity analysis to esimate parameter ranges and estimates for the most important European tree species.

Useful skills:

Knowledge of a script programming language such as Python or R to perform sensititivy or related analysis
Experiences in sensitivity analysis based methods such as latin hypercube sampling or sobol indices
Basic understanding about the physical processes of plants, tree and ecology in general

Contact: Phillip Papastefanou (papa@bgc-jena.mpg.de)

Stochastic modeling of tundra lakes in permafrost landscapes

Description: The novel stochastic model with which we generate the random Arctic landscapes provides very promising results. However, the statistics of the lake size distribution must be improved. This project is about the stochastic modeling of the Arctic landscape and numerical experiments on the boundary layer in terms of large eddy simulations.
Useful skills:

Solid foundations in mathematics and stochastics
Theoretical atmospheric physics and geophysical fluid dynamics
High performance computing

Contact: Mark Schlutow (mschlutow@bgc-jena.mpg.de)

Exploring the effect of tree mortality on the net ecosystem exchange using LPJ-GUESS

MSc level

Description: Plot based studies suggest that the carbon sink in Amazon old-growth forests is declining. Such decline seems to be driven by an increasing tree mortality that counteracts the productivity in these forests. In this project the student will analyze the output of the biosphere model LPJ-GUESS, which was modified to simulate the mortality trend observed in the old-growth forest plots. The focus is to understand how the mortality trend propagates into the net ecosystem exchange flux and potentially perform atmospheric transport simulations to study if we can eventually observe a signal of the increasing mortality in the atmosphere.
Useful skills:

knowledge of a programming language like R or python
Some knowledge on linux and bash scripting
Basic knowledge of the carbon cycle and ecology
knowledge on environmental physics

Contact: Phillip Papastefanou (papa@bgc-jena.mpg.de) and Santiago Botía (sbotia@bgc-jena.mpg.de)