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 ecosystems 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 climate models 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 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 (PCA). 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 GMRF:s greatly aids the analysis, given the large amount of data obtained from LPJ-GUESS.
Keywords: Principal component analysis; Global vegetation and climate; Spatial smoothing; Gaussian Markov Random Fields
Biography: Got his Phd from Lund, Sweden in 2008, and then spent some time as a post doc at the University of Washington in Seattle. He is now an assistant professor in Lund. He works with spatial and spatio-temporal modelling of environmental data, and is involved in Lund's MERGE and BECC programs for modelling of regional and global climate.