The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here:

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Anders Ahlström

Anders Ahlström

Senior lecturer

Anders Ahlström

Using PCA and Global Smoothing to Explore Differences between Global Vegetation Models


  • Johan Lindström
  • Anders Ahlström
  • Emma Blom

Summary, in English

A common method for comparing the result of different global circulation models (GCMs) under different emission scenarios is to study global climate response variables, such as mean temperature. An interesting alternative measure of climate sensitivity is to study the biosphere’s response to the different climate scenarios. The Lund-Postdam-Jena (LPJ) global vegetation model and its extension LPJ-GUESS is a dynamic global vegetation model that can be coupled to GCMs and used to explore the effect of varying climates on vegetation and carbon uptake.

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

Publishing year







Proceedings of the 58th World Statistics Congress of the International Statistical Institute (ISI 2011)

Document type

Conference paper


International Statistical Institute


  • Probability Theory and Statistics
  • Physical Geography

Conference name

58th World Statistics Congress of the International Statistical Institute (ISI 2011)

Conference date

2011-08-21 - 2011-08-26

Conference place

Dublin, Ireland




  • ISBN: 978-90-73592-33-9