Vegetation demographics in Earth System Models: A review of progress and priorities.
Rosie A Fisher, Charles D Koven, William R L Anderegg, Bradley O Christoffersen, Michael C Dietze, Caroline E Farrior, Jennifer A Holm, George C Hurtt, Ryan G Knox, Peter J Lawrence, Jeremy W Lichstein, Marcos Longo, Ashley M Matheny, David Medvigy, Helene C Muller-Landau, Thomas L Powell, Shawn P Serbin, Hisashi Sato, Jacquelyn K Shuman, Benjamin Smith, Anna T Trugman, Toni Viskari, Hans Verbeeck, Ensheng Weng, Chonggang Xu, Xiangtao Xu, Tao Zhang, Paul R Moorcroft
Author Information
Rosie A Fisher: National Center for Atmospheric Research, Boulder, CO, USA. ORCID
Charles D Koven: Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
William R L Anderegg: Department of Biology, University of Utah, Salt Lake City, UT, USA. ORCID
Bradley O Christoffersen: Los Alamos National Laboratory, Los Alamos, NM, USA.
Michael C Dietze: Department of Earth and Environment, Boston University, Boston, MA, USA. ORCID
Caroline E Farrior: Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA.
Jennifer A Holm: Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
George C Hurtt: Department of Geographical Sciences, University of Maryland, College Park, MD, USA.
Ryan G Knox: Lawrence Berkeley National Laboratory, Berkeley, CA, USA. ORCID
Peter J Lawrence: National Center for Atmospheric Research, Boulder, CO, USA.
Jeremy W Lichstein: Department of Biology, University of Florida, Gainesville, FL, USA.
Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.
