Chapter Nineteen Model-Data Fusion in Studies of the Terrestrial Carbon Sink

• Published in: Developments in Integrated Environmental Assessment Vol. 3; pp. 329 - 344
• Main Authors: Alexandrov, G.A., Chan, D., Chen, M., Gurney, K., Higuchi, K., Ito, A., Jones, C.D., Komarov, A., Mabuchi, K., Matross, D.M., Veroustraete, F., Verstraeten, W.W.
• Format: Book Chapter
• Language: English
• Published: Elsevier Science & Technology 2008
• Subjects: carbon cycle; Kalman filtering; model-data fusion; residual terrestrial uptake; Kalman filtering; carbon cycle; residual terrestrial uptake; model-data fusion
• ISBN: 9780080568867; 0080568866
• ISSN: 1574-101X
• DOI: 10.1016/S1574-101X(08)00619-4

Current uncertainty in quantifying the global carbon budget remains a major contributing source of uncertainty in reliably projecting future climate change. Furthermore, quantifying the global carbon budget and characterising uncertainties have emerged as critical to a successful implementation of the United Nations Framework Convention on Climate Change and its Kyoto Protocol. Beyond fundamental quantification, attribution of the processes responsible for the so-called ‘residual terrestrial uptake’ is important to the carbon cycle communities' ability to simulate the future response of the terrestrial biosphere to climate change and intentional sequestration activities. The objective of this chapter is to describe the approaches to model-data fusion enabling continued advances in quantifying carbon cycling and the terrestrial mechanisms at work. The major impediments to advances in this field include accounting for climate variability and uncertainties in model outcomes. One proposed solution to overcome these obstacles is the use of data from the FLUXNET network to characterise the relative strength of climate impact on plant productivity and respiration. Other solutions involve the use of atmospheric CO2 concentration measurements for model validation and the use of remote sensing data.