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We investigate the application of clustering algorithms to represent sub-grid\udscale variability in soil texture for use in a global-scale terrestrial\udecosystem model. Our model, the coupled Canadian Land Surface Scheme –\udCanadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented\udat a coarse spatial resolution (approximately 2. 8° × 2. 8°) due to its use as the land surface component of the\udCanadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as\uda means to represent sub-grid heterogeneity. We first determined that the\udmodel was sensitive to tiling of the soil textures via an idealized test case\udbefore attempting to cluster soil textures globally. To cluster\uda high-resolution soil texture dataset onto our coarse model grid, we use two\udlinked algorithms – the Ordering Points to Identify the Clustering Structure\ud(OPTICS) algorithm (Ankerst et al., 1999; Daszykowski et al., 2002) and the\udalgorithm of Sander et al. (2003) – to provide tiles of representative soil\udtextures for use as CLASS-CTEM inputs. The clustering process results in, on\udaverage, about three tiles per CLASS-CTEM grid cell with most cells having\udfour or less tiles. Results from CLASS-CTEM simulations conducted with the\udtiled inputs (Cluster) versus those using a simple grid-mean soil texture\ud(Gridmean) show CLASS-CTEM, at least on a global scale, is relatively\udinsensitive to the tiled soil textures; however, differences can be large in\udarid or peatland regions. The Cluster simulation has generally lower soil\udmoisture and lower overall vegetation productivity than the Gridmean\udsimulation except in arid regions where plant productivity increases. In\udthese dry regions, the influence of the tiling is stronger due to the general\udstate of vegetation moisture stress which allows a single tile, whose soil\udtexture retains more plant-available water, to yield much higher\udproductivity. Although the use of clustering analysis appears promising as\uda means to represent sub-grid heterogeneity, soil textures appear to be\udreasonably represented for global-scale simulations using a simple grid-mean\udvalue

Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1)