Reducing the uncertainty on regional and local climate induced by land-atmospherefeedbacks

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Land use and land cover change is recognized as an important forcing on local, regional and global climate, and consequently alter the productivity of agriculture. Continued pressure on agricultural land, food insecurity and required adaptation to climate change have made integrated assessment and modelling of future land surface development increasingly important. Recent reviews revealed that neither regional climate modelling approaches nor the analysis tools are fully up to the task. There is a need for reducing the uncertainty on climate introduced by land-atmosphere feedbacks by integrating relevant processes interactively into regional climate models, which are important for ecosystem-atmosphere interactions on seasonal, inter-decadal and climate time scales. Furthermore, missing processes need to be included, which are relevant for convection-permitting simulations. Generally, a change of the simulated climate elements through integration of these climate relevant processes is expected. Although vague are the direction and strength of these feedbacks and their mutual influence. The novelty of this project is to account for transient anthropogenic land use, for dynamical vegetation, and for areas of agricultural fields, which cover major land parts of the world, on regional and local scale. The focus needs to be on analysis of natural variability and sensitivity of different plant ecosystems to changes of environmental and climatic conditions. Furthermore, the influence of vegetation and land use/cover changes on land-atmosphere coupling and climate needs to be estimated. The key challenges to consider the inherent uncertainty are to advance regional climate modelling for improved assessment of climate change impacts on extremes and food security. Importantly, the work strategy includes both to stimulate excellent science and to support capacity and network building including demonstration of improved impact assessments with links to decision makers.