Aim and objectives – University of Copenhagen

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Aim and objectives

ClimMamiThe aim of the Action is to improve the quality and appropriateness of experiments, data and modelling in climate change research, facilitate better use of experimental data for scenario building and process understanding and promote the training of the next generation of climate change researchers in experimentation and modelling.

This ambitious goal will be achieved by bringing together a community of researchers designing and conducting climate change experiments and measuring and modelling the experimental impacts.

This community will have a highly multidisciplinary profile including areas like plant-, soil- and community ecology, ecophysiology, microbiology, hydrology, genetics, biogeochemistry, biometeorology, physics, technical design, modelling and data management and therefore have the capabilities to provide holistic views and assessments of climate change impacts on terrestrial ecosystems and how this can/should be studied.

Climate change research is particularly challenged with the complexity in individual drivers as well as their combination and with providing interaction between data and modelling.

This Action will deliver scientific syntheses of the current understanding of climate change impacts, identify gaps in knowledge and provide guidelines for future experimentation, especially focusing on new emerging challenges like multifactor pressure and extreme events. Further, the Action will address the key scientific challenges and produce guidelines for improved data and meta data facilities and better linkages between experimentalists and modellers.

The Action has four main objectives:

  1. To produce frameworks and best practice guidance for future climate change manipulation experiments in terrestrial ecosystem responses with particular focus on extreme events.
  2. To synthesise and promote the use of observations along natural climatic gradients as a proxy for climatic experiments and as a tool to obtain mechanistic understanding.
  3. To provide progress in data sharing through creation of meta databases and guidelines for data bases and data sharing.
  4. To integrate ecosystem modellers and experimentalists in order to facilitate and improve the use of models in experimental design, evaluation and extrapolation of extreme events experiments, increase the awareness of the issues of scaling and transfer of data and knowledge.

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