Models meet data: Challenges and opportunities in implementing land management in Earth System Models

Besides the burning of fossil fuels it is land use that drives man-made climate change. But to capture land use in climate models we require a sufficient basis of process understanding and data. In a new study a team of scientists led by Julia Pongratz and including Kim Naudts, both from the department "The Land in the Earth System" at the Max Planck Institute for Meteorology (MPI-M), show that some forms of land use are much easier to implement in Earth system models than others.

Land use such as agriculture or forestry covers about three quarters of the ice-free land surface. Earth system models have long been used to understand how much carbon dioxide has been emitted to the atmosphere due to the clearing of forests or how the surface reflectivity is altered when a dark coniferous forest is replaced by bright pasture. But most models so far dealt with a very specific form of land use only, namely conversions in the type of vegetation cover, such as transforming a forest to cropland or shrubland to pastures. Most of the land used by humans, however, has not undergone a change in vegetation type, but it nevertheless is heavily managed, for example because wood is harvested from a forest or grasslands are fertilized. Observational evidence revealed that changes in such land management can be as important as conversions of vegetation type. Earth system models are therefore not just missing major processes through which humans influence climate and the carbon cycle. They also need to implement land management because they are increasingly applied to questions of mitigation and adaptation to climate change through land use change.

The author team around Julia Pongratz and Kim Naudts covered three communities that investigate land use effects on the Earth system: Beside the modeling community that of Earth observation and land system science. Together, they tried to prioritize the implementation of the many different land management practices that exist. Specifically, the authors looked at forestry harvest, tree species selection, grazing and mowing harvest, crop harvest, crop species selection (see photo), irrigation, wetland drainage, fertilization, tillage, and fire as management tool.



Land management, such as the choice which crop species to plant (shown are wheat and maize fields in Switzerland), may have substantial influence on surface climate: the brightness of the field determines how much solar radiation is absorbed, the amount of leaves influences transpiration. Photo courtesy: Julia Pongratz.

The author team then evaluated the ten management practices for several criteria: (1) their importance on the Earth system, that is, the strength of effects and spatial extent, (2) the possibility of implementing them in state-of-the-art ESMs, and (3) availability of required input data from Earth observations. Matching these criteria, the authors identified "low-hanging fruits" for the inclusion in Earth system models, such as basic implementations of crop and forestry harvest and fertilization. They also identified research requirements for specific communities to address the remaining land management practices. Most notably, data availability severely hampers modeling the most extensive land management practice, grazing and mowing harvest. By contrast other practices, such as tillage, lack adequate process understanding.

The implementation of land management into Earth system models will in the near future lead to model divergence as the planned paths of model development and prioritization differ between modeling groups. Eventually, however, it will allow for a more accurate description and projection of the human impact on the Earth system.

Original publication:

Pongratz, J., H. Dolman, A. Don, K.-H. Erb, R. Fuchs, M. Herold, Ch. Jones, T. Kuemmerle, S. Luyssaert, P. Meyfroidt, and K. Naudts (2017). Models meet data: Challenges and opportunities in implementing land management in Earth System Models. Global Change Biology. doi:10.1111/gcb.13988

Contact:

Dr Julia Pongratz
Max Planck Institute for Meteorology
Phone: +49 (0) 40 41173 255
Email: julia.pongratz@we dont want spammpimet.mpg.de

Dr Kim Naudts
Max Planck Institute for Meteorology
Phone:+49 (0) 40 41173 550
Email: kim.naudts@we dont want spammpimet.mpg.de