Conference Agenda

Land Cover (LC) and its bio-geo-physical feedbacks are important for the understanding of climate and its vulnerability to changes on the surface of the Earth. Recently ESA has published a new LC map derived by combining remotely sensed surface reflectance and ground-truth observations. For each grid-box at 300m resolution, an estimate of confidence is provided. This LC data set can be used in climate modelling to derive land surface boundary parameters for the respective Land Surface Model (LSM). However, the ESA LC classes are not directly suitable for LSMs, therefore they need to be converted into the model specific surface presentations. Due to different design and processes implemented in various climate models they might differ in the treatment of artificial, water bodies, ice, bare or vegetated surfaces. Nevertheless, usually vegetation distribution in models is presented by means of plant functional types (PFT), which is a classification system used to simplify vegetation representation and group different vegetation types according to their biophysical characteristics. The method of LC conversion into PFT is also called “cross-walking” (CW) procedure. The CW procedure is another source of uncertainty, since it depends on model design and processes implemented and resolved by LSMs. These two sources of uncertainty, (i) due to surface reflectance conversion into LC classes, (ii) due to CW procedure, have been studied by Hartley et al 2016 to investigate their impact on LSM state variables (albedo, evapotranspiration (ET) and primary productivity) by using three standalone LSMs. The present study is a follow up to that work and aims at quantifying the impact of these two uncertainties on climate simulations performed with the Max Planck Institute for Meteorology Earth System Model (MPI-ESM) using prescribed sea surface temperature and sea ice. The main focus is on the terrestrial water cycle, but the impacts on surface albedo, wind patterns, 2m temperatures, as well as plants productivity are also examined.

The analysis of vegetation covered area indicates that the range of uncertainty might be about the same order of magnitude as the estimated historical anthropogenic LC change. For example, the area covered with managed grasses (crops and pasture in MPI-ESM PFT classification) varies from 17 to 26 million km², and area covered with trees ranges from 15 million km² up to 51 million km². These uncertainties in vegetation distribution lead to noticeable variations in atmospheric temperature, humidity, cloud cover, circulation, and precipitation as well as local, regional and global climate forcing. For example, the amount of terrestrial ET ranges from 73 to 77 × 10³ km³yr^-1in MPI-ESM simulations and this range has about the same order of magnitude as the current estimate of the reduction of annual ET due to recent anthropogenic LC change. This and more impacts of LC uncertainty on the near surface climate will be presented and discussed in the context of LC change.

Hartley, A.J., MacBean, N., Georgievski, G., Bontemps, S.: Uncertainty in plant functional type distributions and its impact on land surface models (in review with Remote Sensing of Environment Special Issue)

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. In this study, we assess the largest plausible range of PFT uncertainty derived from land cover maps produced by the European Space Agency (ESA) Land Cover Climate Change Initiative (LC_CCI) on simulations of land surface fluxes using 3 leading LSMs. PFT maps used in LSMs can be derived from a land cover (LC) class map and cross-walking (CW) table that allocates the fraction of each PFT that occurs within each LC class. We evaluate the impact of uncertainty due to both LC classification algorithms, and CW procedure.

We examined the impact of this 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 and regional scales. 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.

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. We suggest that both issues can be significantly improved through the integration of very high spatial resolution satellite observations with more frequent and thematically detailed medium resolution observations.