Land Surface Carbon Constellation Study (Carbon Constellation)
With atmospheric CO2 the main cause of the enhanced greenhouse effect, the fraction of carbon emissions by human activities that is taken up by the land is a decisive quantity for climate predictions and climate policy targets. However, uncertainties remain high (Friedlingstein et al. 2019), and many questions remain about how different processes act out across different spatial and temporal scales, and how they act together to result in the actual global land carbon sink. For example, the carbon cycle on land is intimately coupled with the water cycle because of the necessity of plants to regulate their water loss in order to maximise carbon gains. Models bring in process understanding but exhibit uncertainties, partly due to lack of observational constraints. Hence, purely model-based quantification of carbon fluxes and dynamics of stocks is difficult.
There is therefore a need to constrain land surface models with observations and to improve understanding of the processes described by these models. Directly constraining models with field observations has the disadvantage that these are much too sparse across space for generating meaningful regional carbon budgets that could flow into a global-scale understanding of the land carbon cycle. On the other hand, satellite data have large temporal gaps or can only measure indirect quantities, so that interpretation is often difficult. Individual EO missions may provide information on particular aspects of the carbon cycle; models provide links between these different aspects observed from space (e.g. fluxes and stocks). Also data have gaps, and interpretation of signals is sometimes difficult without a process-based understanding of what is measured. Furthermore, satellite data from different instruments and different measurement techniques (e.g. using different domains of the electromagnetic spectrum) often have vastly different footprints, temporal coverage, as well as uncertainties.
In this situation, the best strategy is to optimally combine the best available knowledge to provide the best possible estimate of carbon and water fluxes and description of the underlying processes.
- Lunds Universitet
- Lunds Universitet
- FMI - Finnish Meteorological Institute
- University of Edinburgh
- Universitè Paul Sabatier, Toulouse III
- Delft University of Technology
- Universitat Politecnica de Valencia
- The Inversion Lab
- Max Planck Institute for Biogeochemistry