GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 291-7
Presentation Time: 9:40 AM


WILLIAMS, John W., Department of Geography, University of Wisconsin-Madison, 550 N Park St, Madison, WI 53706, GORING, Simon, Department of Geography, University of Wisconsin, 550 N Park St, Madison, WI 53706, GRIMM, Eric, Department of Geosciences, University of Minnesota, Minneapolis, MN 55455 and MCLACHLAN, Jason, Department of Biological Sciences, University of Notre Dame, 100 Galvin Life Sciences, Notre Dame, IN 46556,

Our understanding of past climates and their principal modes of variation, external forcings, feedbacks, and teleconnections have been transformed by the advent of global-scale interdisciplinary data-model syntheses. Herb Wright, as in other arenas, was an early pioneer, particularly through his leadership, with others, of COHMAP. COHMAP was revolutionary with respect to both its scientific discoveries (Milankovitch control of subtropical monsoons and high-latitude climate dynamics) and its establishment of large, coordinated teams of scientists, working across many facets of the earth system. The many children of COHMAP include the NOAA National Center for Environmental Informatics (Paleoclimatology), BIOME 6000, the Paleoclimate Modeling Intercomparison Project, PAGES 2k, the Neotoma Paleoecology Database, and the Paleoecological Observatory Network (PalEON).

Neotoma and PalEON are part of a new wave of data-model comparisons, primarily focused on testing and improving ecological forecasting models. This new wave is driven by three main factors: 1) the recognition that anthropogenic climate change is underway and the need to develop robust science-based adaptation strategies; 2) the on-going assembly of paleoecological and paleoclimatic data, originally launched by COHMAP, into well-organized and community-curated data resources such as Neotoma (; and 3) advances in statistical methods for paleoenvironmental inference and data-model assimilation. These advances are enabling increasingly integrated scientific workflows from data repositories to statistical models and are opening up new kinds of data-model integration, in which paleodata and models can be fused to better estimate latent state variables (e.g. aboveground biomass; an unobservable quantity in the paleorecord) and improve model parameterization of slow processes, thereby (hopefully) improving their ability to predict future ecosystem dynamics at decadal to centennial timescales.