Nitrogen limitation in the Earth System: merging top-down and bottom-up

How does nitrogen availability limit carbon sequestration over long time scales, in real and model ecosystems, and for a changing global climate system?


The terrestrial carbon sink has absorbed 1/3 of the anthropogenic carbon dioxide (CO2) emitted since the start of the Industrial Revolution, primarily through increased primary production. However, nitrogen (N) availability constrains primary productivity in many temperate and boreal ecosystems. The extent to which N might limit the ability of terrestrial ecosystems to sequester current, and in particular, future CO2 emissions remains highly uncertain. To predict future terrestrial carbon storage capacity, and to identify which ecosystems are most likely to store carbon without N limitation, we need to know where N limitation occurs, over what time scale and how this is affected by ecological and environmental contexts. Dendro-ecological records provide a largely untapped yet powerful resource for information on long-term plant-N interactions and how these have varied under different climatic and biotic contexts. This project proposal is to develop novel model-data assimilation techniques to entrain these  measurements, and critically improve the representation of nitrogen limitation within global land surface models with longterm observations from existing tree-ring records spanning the last few decades.

Aims of the Project

Use dendro-ecological data to constrain contemporary land surface models, and assess the implications of such constraint on future projections of terrestrial carbon stores.

Project Description

The standard method of projecting how climate will change into the future is through the use of Earth System Models (ESMs). These estimate the statistics of meteorological change expected for different potential levels of future atmospheric greenhouse gas concentrations. ESMs have capitalised on nearly 60 years of research in weather forecasting, and so most features of atmospheric dynamics are calibrated well. However, on the decadal-centennial timescale, global environment attributes that can be considered fixed for weather forecasts of just days ahead (e.g. ice-cover), are instead evolving. Hence, more recently, these other parts of the Earth system have needed to be modelled, and this includes vegetation carbon stores. With less lead time of development, these components still need substantial advancement. Besides being an integral part of the coupled climate-carbon cycle system, there is much policy interest as to whether terrestrial carbon stores can continue to offset ~33% of emissions. Furthermore, policymakers ask whether deliberate land-use change could even increase current CO2 offset level, to raise the chances that climate change can be constrained to key thermal thresholds such as two degrees of global warming.

A fundamental uncertainty in modelling terrestrial ecosystem stores is the extent to which the N (nitrogen) cycle impacts on vegetation carbon cycling. The JULES land surface model has only recently included a framework to describe this process, but it is only in provisional form, and many parameters are little more than initial estimates. Yet one form of data that has huge potential to inform the calibration of the N part of JULES are dendro-ecological measurement of plant biomass and N availability dynamics. Mechanistic modelling of these data have been used to infer the extent to which N availability constrains biomass dynamics and identify the presence of plant-soil feedbacks, but to date, such research has only been operating on local scales. A fundamental question is “How generalizable, to global scales, are the insights gleaned from studies at smaller scales?”. The student will answer that question by applying a flexible regression approach to learn the equations that best describe plant-N relationships at the regional to continentalscale from existing dendro-ecological time-series data from multiple locations. They will uncover the biological and physical covariates that best explain differences in plant-N relationships and will translate these insights into improved representation of biogeochemical cycling in the JULES land surface model.

This initiative will generate multiple outputs. However of particular interest is that it will contribute towards an overarching assessment of whether N-effects will downregulate future fertilisation as atmospheric CO2 rises. Hence it will aid improvement in models of global climate-carbon cycle interactions.

There is a strong training element to this project. The student will be exposed to the full end-to-end scientific procedure of using models to infer ecological processes from data, through to performing projections of large-scale geochemical cycle change under climate change. They will have access to existing dendroecological datasets and the opportunity – if they choose – to collect additional records where required. Coaching will be provided on the modelling and data analysis methods to be employed in the study. The student will join the Ecosystem Ecology group in the Department of Zoology and be a part of the Oxford Long Term Ecology Laboratory. They will also benefit from access to nearby Centre for Ecology and Hydrology, Wallingford, as well as visits to Exeter University and the Meteorological Office (also in Exeter).

Specialised skills required

The student will encounter quite substantial involvement with statistical and process modelling, with attendant calculus. Hence a minimum of an Alevel in mathematics is advisable. Ideally the student will also have some experience of – or be willing to learn – developing, testing,  managing and operating computer code in FORTRAN and/or Python.

If you are interested in this project please email