Using PCA and Global Smoothing to Explore Differences between Global Vegetation Models
Summary, in English
Using the output from different GCMs under different emission scenarios LPJ-GUESS can be used to generate global vegetation and carbon uptake patterns that are specific to each forcing climate scenario. We investigate if important regional and global differences exist between the vegetation patterns from different GCMs and emission scenarios. An important question is if potential differences are primarily due to the different emission scenarios or to the different GCMs.
In order for us to carry out the above analysis we need to both reduce the noise in the LPJ-GUESS predictions and reduce the vast amount of data. To accomplish both these goals we compute smooth principal components. A problem when computing the PCA and the smoothing is that LPJ-GUESS output is generated on a regular longitude-latitude grid, implying that both the size and distance between grid cells vary. To handle this irregular data on a sphere we use a Gaussian Markov random field (GMRF) approximation of Thin Plate Splines (TPS) that generalises the TPS to general manifolds (such as a sphere). The well known computational advantages of GMRFs greatly aids the analysis, given the large amount of data obtained from LPJ-GUESS.
- Mathematical Statistics
- Dept of Physical Geography and Ecosystem Science
- MERGE: ModElling the Regional and Global Earth system
Proceedings of the 58th World Statistics Congress of the International Statistical Institute (ISI 2011)
International Statistical Institute
- Probability Theory and Statistics
- Physical Geography
58th World Statistics Congress of the International Statistical Institute (ISI 2011)
2011-08-21 - 2011-08-26
- ISBN: 978-90-73592-33-9