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Climate Process Team (CPT): Projects at NCAR


Comparison of the NCAR and GFDL models in subsidence regions

GCSS Pacific Cross-section Intercomparison (GPCI)

    Weather and climate prediction models are analyzed along a cross-section in the Pacific Ocean, from California to the Equator. The cross-section over the Pacific Ocean encompasses several fundamental cloud regimes such as stratocumulus, shallow cumulus and deep cumulus, as well as the transitions between them. The model outputs are collected every 3 hours (JJA 1998 and 2003), which allows for a better understanding of issues associated with the diurnal cycle of clouds and cloud related processes in the tropics and subtropics. Presently, GPCI has collected output from 6 models from GFDL, NCAR, UKMO, MeteoFrance, JMA and KNMI.

Subgrid scale cloud variability: subcolumn generator and McICA method implementation

    Collaboration with Robert Pincus (CDC), Mike Iacono (AER)

    To take into account the subgrid scale cloud variability in CAM, we have implemented a subcolumn column generator. The idea of the cloud generator is that the Subgrid scale cloud variability can be represented by a set of subcolumns that are generated randomly.
    We are testing the subcolumn generator on radiation calculations using the Monte Carlo Independant Column Approximation (McICA). The McICA is a method developed to improve the treatment of cloud variability in climate models. This method is essentially a sampling strategy that yields unbiased estimates of heating rate profiles with respect to the full ICA, but that speeds the calculation up compared to the full ICA.

Forecast runs with the UW scheme

    Collaboration with Dave Williamson (NCAR), Jerry Olson (NCAR), Dani Coleman (NCAR), Steve Klein (LLNL), Jim Boyle (LLNL)

    We are testing the UW atmospheric boundary layer scheme with CAM3 runs in forecast mode. The goal is to provide insight into parameterization errors which ultimately could lead to model improvements. Our effort follows the CAPT protocol. In the CAPT protocol, we realistically initialize CAM3 with NWP analyses, and we then run the model in forecast mode to determine the drift from the NWP analyses and/or from available field data. This method allows us to diagnose model parameterization deficiencies. This project is a collaboration with the LLNL.




Acknowledgments

This research is supported by the National Science Foundation.


Last modified: Aug 9 2010   by hannay@ucar.edu