Skip to main content

(gc3562)Superparameterization: A New Paradigm for Climate Modeling


EMSL Project ID
3562

Abstract

Global climate simulation is perhaps the most demanding computation problem in geosciences today. Current atmospheric global climate models (AGCM) are typically limited to horizontal resolutions of several hundred kilometers due to computer time limitations. All processes that occur at scales smaller than this are parameterized, i. e., diagnosed from the variables predicted on the large scale grid. The sub grid parameterizations of cloud properties are the single largest source of uncertainty in model simulations. Recently, a new approach to the cloud problem, a superparameterization model (SPM), has been proposed and implemented. All the cloud related parameterizations are removed from the AGCM and replaced in each AGCM grid with a 2D (height and longitude) nested cloud model consisting of 64 elements, each 4km in horizontal extent, that computes cloud properties explicitly. The SPM computes cloud properties directly from physical equations at a scale that is consistent with observations. It runs roughly 200 times slower than the parent AGCM, but is highly efficient on massively parallel machines. To date, only limited climate simulations with an SPM have been carried out.

A global SPM has been developed and implemented by the climate research group at the Colorado State University (CSU). Our team will port this existing model to the MSCF. In addition, we will port the Weather Research and Forecast model (WRF) from NCAR to the MSCF. This model is designed for high-resolution (1-10 km) applications over a limited area. We will then carry out a series of runs with these models designed to meet the following objectives:
a. Simulate current climate for at least a decade using the SPM
b. Compare the results of that SPM with data, with results from a high-resolution 3D model and with results from GCMs that use classical cloud parameterizations to assess whether the SPM produces an improved simulation of climate
c. Evaluate the sensitivity of SPM results to model configuration changes in two specific areas: the resolution of the embedded cloud model and the formulation of the radiation-cloud properties interaction

These comparisons and sensitivity tests will enable us to determine quantitatively how well the SPM model simulates actual clouds and cloud-radiative interactions and whether it offers a significant improvement over existing GCMs in this. We think that the SPM will prove to be a significant improvement. If so, the results from this project will provide the basis to evaluate the SPM as the modeling system of the future for the investigation of hydrologic forcing and feedbacks in the climate system and to lay the groundwork for the next generation of climate modeling.

Project Details

Project type
Capability Research
Start Date
2003-10-01
End Date
2006-10-08
Status
Closed

Team

Principal Investigator

Thomas Ackerman
Institution
University of Washington

Team Members

Konstantin Ovchinnikov
Institution
Pacific Northwest National Laboratory

Sally Mcfarlane
Institution
Pacific Northwest National Laboratory

John Michalakes
Institution
National Center for Atmospheric Research

Timothy Shippert
Institution
Pacific Northwest National Laboratory

Jason Cole
Institution
Pennsylvania State University

Eugene Clothiaux
Institution
Pennsylvania State University

Robert Pincus
Institution
National Oceanic and Atmospheric Administration (NOAA)

Marat Khairoutdinov
Institution
Colorado State University

Jimmy Voyles
Institution
Pacific Northwest National Laboratory

Roger Marchand
Institution
Pacific Northwest National Laboratory

Lai-Yung Ruby Leung
Institution
Pacific Northwest National Laboratory

Annette Koontz
Institution
Pacific Northwest National Laboratory

David Randall
Institution
Colorado State University

Larry Berg
Institution
Pacific Northwest National Laboratory

Related Publications

Marchand, R., J. Haynes, G. G. Mace, T. Ackerman, and G. Stephens (2009), A comparison of simulated cloud radar output from the multiscale modeling framework global climate model with CloudSat cloud radar observations, J. Geophys. Res., 114, D00A20, doi:10.1029/2008JD009790.
Zhang, Y., S. A. Klein, C. Liu, B. Tian, R. T. Marchand, J. M. Haynes, R. B. McCoy, Y. Zhang, and T. P. Ackerman (2008), On the diurnal cycle of deep convection, high-level cloud, and upper troposphere water vapor in the Multiscale Modeling Framework, J. Geophys. Res., 113, D16105, doi:10.1029/2008JD009905.