Admissions > PhD by research > Research Projects > Constraining the land carbon and water cycles with stable carbon isotope measurements in a Carbon Cycle Data Assimilation System

Constraining the land carbon and water cycles with

stable carbon isotope measurements in a Carbon Cycle

Data Assimilation System

Supervisors: Dr Marko Scholze and Dr Peter Rayner (University of Melbourne, Australia)

The increasing realization of the potential for significant climate change has led to a focus on those processes which regulate the concentrations of key greenhouse gases in the atmosphere. CO2 is the major anthropogenic greenhouse gas and so understanding and predicting the cycling of this gas through natural and human-controlled systems is a matter of special importance. Despite considerable advances, major questions remain about the distribution of present sources and sinks of this gas as well as their evolution, especially their response to climate change. Recent work suggest the possibility of substantial changes in the terrestrial carbon cycle [1], however, these projections are highly diverse and uncertain [2].

Carbon isotope data has been used for many years in classical inversion studies of carbon sources and sinks and its potential for adding to process-level understanding has been shown by a protoype study [3]. One of the problems with the use of isotope data in previous studies has been that the impact of biospheric processes on the atmospheric abundance of carbon isotopes is a function of climate. It is this abundance which can be used to estimate the biospheric source and so it is important to include climatic effects in the study. This has generally not been done leading to incorrect estimates based on isotopic data.

The student will use the most advanced methodology available in this field - assimilation of observations using a variational approach - to estimate parameter values and resulting carbon fluxes complete with uncertainties. The student will develop a method for assimilating measurements of atmospheric delta 13CO2 in addition to the existing assimilation of CO2 observations in the free atmosphere.

The student's work will build on the existing Carbon Cycle Data Assimilation System (CCDAS, for further information see www.ccdas.org and [4]). CCDAS is a modelling tool that uses atmospheric CO2 concentration observations and remotely sensed vegetation activity to constrain process parameters in the terrestrial ecosystem model BETHY [5]. The inferred parameters (some spatially explicit, some global) and their uncertainty estimates are carried forward within the system to quantities of interest, such as terrestrial carbon fluxes for past, present, or future periods. CCDAS is currently the only tool of its kind that is able to quantify terrestrial carbon fluxes complete with error bars fully consistent with the major global-scale observations. BETHY represents the processes of photosynthesis, plant and soil respiration but, currently, does not include a model for carbon isotope fractionation. Hence, for assimilating atmospheric delta 13C data into the BETHY model a process-based description of carbon isotope fractionation has to be included into the photosynthesis compartment.

The project involves a novel combination of methods and data, although the contributing elements are well tested. The innovation lies in the combination of new data streams and new modelling techniques to gain process level understanding from atmospheric composition data. The student will work within an international consortium on a project that will improve the understanding of the coupling between the terrestrial carbon and hydrological cycles on seasonal, interannual and decadal time scales. Good numerical skills and knowledge of at least one computer programming language, as well as interest in climate change and terrestrial ecology, are required. The project, once finished, will be of particular interest to climate change institutions (government agencies, NGOs), but is also important basic science. The modules and insights derived from this project can be applied to the newest generation of global climate models to improve predictions of the future climate.



References

  1. Cox, P. et al., 2000, Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature 408, 184-187.
  2. Friedlingstein, P. et al., 2006, Climate–Carbon Cycle Feedback Analysis: Results from the C4MIP Model Intercomparison, Journal of Climate 19, 3337-3353.
  3. Randerson, J. et al., 2002, Carbon isotope discrimination of arctic and boreal biomes inferred from remote atmospheric measurements and a biosphere-atmosphere model, Global Biogeochemical Cycles 16.
  4. Rayner, P., et al., 2005, Two decades of terrestrial carbon fluxes from a Carbon Cycle Data Assimilation System (CCDAS), Global Biogeochemical Cycles 19, doi:10.1029/2004GB002,254.
  5. Knorr, W., 2000, Annual and interannual CO2 exchanges of the terrestrial biosphere: process based simulations and uncertainties, Global Ecology and Biogeography 9, 225-252.
Last updated: 2/11/11