CGD's Atmospheric Modeling & Predictability Section

Modeling Research

Scientific activities in modeling research have generally been focused on the improvement, analysis and documentation of the CCSM Community Atmosphere Model (CAM), fundamental studies aimed at improving the understanding of key processes in the climate system, and contributing directly to national and international activities focused on advancing climate science by coordinating and conducting broad community initiatives. In addition to their contributions to CAM development, AMP staff play an integral role in the development and improvement of the Whole Atmosphere Community Model (WACCM), a comprehensive model of the atmosphere from the Earth's surface to about 150 km, which includes interactive chemistry and physical processes throughout the model column.

The standard CAM3 and CCSM3 simulations and contributions to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) have been extensively analyzed and documented in the peer reviewed literature by members of the AMP Staff.


Predictability Research

AMP develops the scientific understanding of the dynamics and predictability of large-scale atmospheric variability and coupled variability on time scales of days to decades. This process will allow construction of the scientific basis for predicting the transient, global circulation in the atmosphere beyond the present practical limits. AMP scientists take three approaches to their research:

    (1) numerical and theoretical experimentation with a hierarchy of physical models ranging from the non-divergent, barotropic model to coupled atmosphere-ocean models,
    (2) diagnostic analyses of the cause of atmospheric climatic variability and its theoretical and practical predictability in simulation and forecast experiments using the NCAR Community Atmosphere Model (CAM) and Community Climate System Model (CCSM), and
    (3) sensitivity analyses of numerical prediction models to atmospheric initial and boundary conditions using ensemble techniques that will aid in the design of improved methods of data assimilation, for both conventional and non-conventional meteorological data, e.g., precipitation and sea surface temperature (SST).