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The spatial distribution and fractional cover of plant functional types (PFTs) is a key uncertainty in land surface models (LSMs) that is closely linked to uncertainties in global carbon, hydrology and energy budgets. Land cover is considered to be an Essential Climate Variable because changes in it can result in local, regional or global scale impacts on climate. In LSMs, land cover (LC) class maps are converted to PFT fractional maps using a cross walking (CW) table by prescribing the fraction of each PFT that occurs within each LC class. In this study we assess the largest plausible range of PFT uncertainty derived from remotely sensed LC maps produced under the European Space Agency Land Cover Climate Change Initiative on simulations of land surface fluxes using 3 leading LSMs. We evaluate the impact of uncertainty due to both LC classification algorithms, and CW procedure, on energy, moisture and carbon fluxes in LSMs. We investigate the maximum plausible range of uncertainty deriving from both LC and CW within the context of a potential biomass scale (bare ground-grass shrub-tree), representing a gradient from low to high biomass PFTs. More specifically, plausible alternative land cover maps and associated PFT fractional distributions were produced to prioritise low or high biomass vegetation in the LC classification (uncertainty in LC), and subsequently in the assignment of PFT fractions for each LC class (uncertainty in CW), relative to a reference PFT distribution.

We examined the impact of PFT uncertainty on 3 key variables in the carbon, water and energy cycles (gross primary production (GPP), evapo-transpiration (ET), and albedo), for 3 LSMs (JSBACH, JULES and ORCHIDEE) at global scale. Results showed a greater uncertainty in PFT fraction due to CW as opposed to LC uncertainty, for all three variables. CW uncertainty in tree fraction was found to be particularly important in the northern boreal forests for simulated LSM albedo. Uncertainty in the balance between grass and bare soil fraction in arid parts of Africa, central Asia, and central Australia was also found to influence albedo and ET in all models. The spread due to PFT uncertainty for albedo was between 30 and 105% of inter-model uncertainty, for GPP between 20 and 90%, and for ET 0-30%. Each model had a different sensitivity to PFT uncertainty, for example, GPP in JSBACH was found to have a much higher sensitivity to PFT uncertainty in the tropics than JULES and ORCHIDEE, whereas the inverse was true for ET.

These results show that inter-model uncertainty for key variables in LSMs can be reduced by more accurate representation of PFT distributions. Future efforts in land cover mapping should therefore be focused on reducing CW uncertainty through better understanding of the fractional cover of PFTs within a land cover class. Efforts to reduce LC uncertainty should particularly be focused on more accurate mapping of grass and bare soil fractions in arid areas. In the context of Land Surface Models, these results demonstrate that prescribed vegetation distribution in models is a key source of uncertainty that is comparable to the spread between models for key model state variables.