Improving Global Climate Models

Developing a uniform set of software tools suitable for evaluation of high-end climate models

Rao Kotamarthi (project webpage)
Argonne National Laboratory

Data collected at the Atmospheric Radiation Measurement Program Climate Research Facility (ACRF) sites are employed mainly in column radiation models, to validate the models and develop new parameterizations. The ACRF observatories are designed to simulate the grid size of a climate model and to support collection of sparsely distributed surface and profile measurements over an area of more than 300 square kilometers. Similarly, a vast majority of the data collected in the DOE Terrestrial Carbon Processes program, such as at AmeriFlux sites, are widely distributed and are very specific to local soil type, vegetation, climatology, and hydrology. Such data are employed primarily to constrain model calculations, often with a single measurement assumed to represent the average for the domain. In a few instances (e.g., in the Single Column Atmospheric Model), measured data sets are used for four-dimensional data assimilation of meteorological variables such as wind velocities, temperature, and humidity, to constrain the dynamics in the model and improve on the boundary forcing derived from the National Centers for Environmental Prediction. However, no single methodology can be used with data collected at the spatial scale of the ACRF sites or for specific AmeriFlux locations, to derive suitable grid average or column mean values of measured variables for model evaluation and data assimilation in climate models. Such a tool would generate statistical error estimates of the mean quantities when averaged from the observation grid to the model grid, as well as correlations in errors across space and time. This project will develop such methods and implement a novel approach for generating data ensembles, by using the latest available statistical modeling tools and knowledge of relevant physical and chemical process to develop climatologically aware methods for processing ACRF and other spatially sparse data sets. The software tools generated will be documented and distributed under the name Data Domain to Model Domain Conversion Package (DMCP).

The PIs for this project will work closely with the Scalable and Extensible Earth System Model for Climate Change Science (Drake et al.) SciDAC project so that the data sets generated in this project will be used for climate model evaluation work being done by that group.

The Departmentís Atmospheric Radiation Measurement (ARM) program was created to help resolve scientific uncertainties related to global climate change, with a specific focus on the crucial role of clouds and their influence on radiative feedback processes in the atmosphere. The primary goal of the ARM program is to improve the treatment of cloud and radiation physics in global climate models in order to improve the climate simulation capabilities of these models. ARM scientists research a broad range of issues that span remote sensing, physical process investigation and modeling on all scales. This project will advance these efforts by developing methods for processing the spatially sparse climate data sets generated by the program.

Science Application: Climate Modeling and Simulation

Project Title: A Data Domain to Model Domain Conversion Package (DMCP) for Sparse Climate Related Process Measurements

Project Webpage:

Principal Investigator: Rao Kotamarthi
Affiliation: Argonne National Laboratory

Participating Institutions and Co-Investigators:
University of Chicago - Michael Stein
Argonne National Laboratory - Rao Kotamarthi (PI), Richard Coulter, Robert Jacob, and Jay Larson

Funding Partners: Office of Science ó Office of Biological and Environmental Research

Budget and Duration: Approximately $0.25 million per year for five years 1

Other SciDAC climate efforts

1Subject to acceptable progress review and the availability of appropriated funds


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