PhD project offered by the IMPRS-gBGC in Jan 2025

Extracting ecosystem properties and legacy effects at the Amazon Tall Tower Observatory (ATTO)

Santiago Botía, Shujiro Komiya, Cléo Quaresma Días-Júnior, Sönke Zaehle, Anke Hildebrandt

Project description

The Amazon rainforest represents more than 50% of the tropical rainforest area and stores between 150-200 PgC in aboveground biomass. The stability of this biome is under several stressors, such as deforestation, fires, forest degradation and climate variability, that can lead to major droughts and flooding. The future trajectory of ecosystem functioning, biodiversity and carbon storage under such threats is unknown. Monitoring the forest's response to climate variability in the absence of anthropogenic disturbance could provide insights on ecosystem resilience and the ability of the ecosystem to recover after an extreme weather event. The Amazon Tall Tower Observatory (ATTO, Andreae et al., (2015)) is a research station located in well-preserved old-growth forest in the central Brazilian Amazon (-2.1441, -58.999) and has been monitoring carbon fluxes, CO₂ mole fractions and environmental variables for more than 10 years recording two major droughts, the one in 2015-2016 and more recently in 2023-2024.

The objective of this PhD project is to characterise the main drivers of seasonal variability in the carbon fluxes at ATTO, together with the long-term record of CO₂ mole fractions and identify the main causes for seasonal departures in years of major droughts or above-average rainfall. To achieve this, the candidate will use eddy covariance flux observations, ancillary data and other data streams, to assess the uncertainties associated with the NEE calculation (e.g. storage flux and height above the canopy) and with the partitioning into ecosystem respiration (Reco) and gross primary productivity (GPP). Once the uncertainties are well-characterised, the R package bigleaf (Knauer et al., 2018) will be used to derive physiological ecosystem properties and investigate seasonal drivers and inter-annual variability. In addition, legacy effects and recovery time-scales of carbon use efficiency, canopy conductance and net ecosystem exchange (NEE) will be investigated. To assess the representativity of the local fluxes of a larger spatial scale, the onset of local flux anomalies will be compared to the regional CO₂ signal (CO₂, using the STILT model (Lin et al., 2003) as in Botía et al., 2022), after accounting for the effect of fire and other disturbances not present in the flux record at ATTO.

Working group & collaborations

The candidate will be part of the Biogeochemical Signals department and will have the opportunity to collaborate with researchers from multiple institutions in Brazil and Europe.

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 bio(geo)chemistry, environmental science, geosciences, physics, atmospheric science, engineering, remote sensing or other disciplines related to environmental sciences.
Computational skills

Experience with programming languages like R or python
Experience with gridded datasets like ERA5 or similar

Experience dealing with eddy covariance datasets and instrumentation
Knowledge on statistics and micrometeorology
Excellent oral and written communication skills in English
Willingness to travel to South America if needed

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.

References

Andreae, M. O., Acevedo, O. C., Araújo, A., Artaxo, P., Barbosa, C. G., Barbosa, H. M. J., ... & Yáñez-Serrano, A. M. (2015). The Amazon Tall Tower Observatory (ATTO): overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols. Atmospheric Chemistry and Physics, 15(18), 10723-10776.

Botía, S., Komiya, S., Marshall, J., Koch, T., Gałkowski, M., Lavric, J., . . . & Gerbig, C. (2022). The CO₂ record at the Amazon Tal Tower Observatory: A new opportunity to study processes on seasonal and inter-annual scales. Global Change Biology, 28(2), 588-611.

Knauer, J., EI-Madany, T. S., Zaehle, S., & Migliavacca, M. (2018). Bigleaf -An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PloS one, 13(8), e0201114.

Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis, K. J., & Grainger, C. A. (2003). A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model. Journal of Geophysical Research: Atmospheres, 108(D16).