U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Detecting Modeling Problems Early and Quickly
Published: February 03, 2017
Posted: January 26, 2018

Researchers develop a new method for testing the reproducibility of atmosphere model results.

The Science
Weather and climate models are large computer code structures that solve complex systems of mathematical equations. Researchers developed a new, objective, and computationally efficient method to determine whether simulations performed with these codes on new computers or built with new computer software are essentially similar when they don’t match exactly.


The Impact
Weather and climate models that provide important predictions for society are often developed and maintained by a large team of scientists working collaboratively. These teams require objective and efficient testing methods to assure the codes are producing expected behavior as they are being developed in ever-changing computing environments.

Summary
As computer codes are revised, or software and hardware environment are changed, there may be times when it is no longer possible to obtain numbers identical “digit for digit” to previous results. In these situations it is very important, and non-trivial, to distinguish whether these differences are just “noise” or discrepancies caused by unintended coding errors or computing-environment problems. Existing methods that evaluate these discrepancies using long-term statistics of model results are too computationally expensive to use for daily testing during phases of very active model development. A team of researchers led by scientists at Pacific Northwest National Laboratory developed a new method just as robust as existing methods, but hundreds of times cheaper. The new test identifies when simulations performed in a new model or computing environment are considered “changed beyond noise level” by recognizing when the numerical error calculated against a benchmark is found to be inconsistent with previously verified values. The team showed that the new method was effective when applied in the Community Atmosphere Model, and they expect that the underlying concept is generally applicable to atmosphere and geophysical models.

Contacts (BER PM)
Dorothy Koch
Earth System Modeling Program
Dorothy.Koch@science.doe.gov

(PNNL Contacts)
Hui Wan
Pacific Northwest National Laboratory
Hui.Wan@pnnl.gov

Phil Rasch
Pacific Northwest National Laboratory
Phillip.Rasch@pnnl.gov

Funding
This research was supported as part of the Accelerated Climate Modeling for Energy (ACME) program, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (BER). High-performance computing resources were provided by the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, supported by the Office of Science, and the National Center for Atmospheric Research (NCAR) Computational and Information Systems Laboratory.

Publication
Wan, H., K. Zhang, P. J. Rasch, B. Singh, X. Chen, and J. Edward. “A new and inexpensive non-bit-for-bit solution reproducibility test based on time step convergence (TSC1.0).” Geoscientific Model Development, 10, 537-552 (2017). [DOI: 10.5194/gmd-10-537-2017]
(Reference link)

Related Links
The design of the new test method was inspired by a previous study that evaluated numerical errors related to time evolution in the Community Atmosphere Model. PNNL Highlight: Tracking Down Time Missteps April 2015

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

Aug 24, 2019
New Approach for Studying How Microbes Influence Their Environment
A diverse group of scientists suggests a common framework and targeting of known microbial processes [more...]

Aug 08, 2019
Nutrient-Hungry Peatland Microbes Reduce Carbon Loss Under Warmer Conditions
Enzyme production in peatlands reduces carbon lost to respiration under future high temperatures. [more...]

Aug 05, 2019
Amazon Forest Response to CO2 Fertilization Dependent on Plant Phosphorus Acquisition
AmazonFACE Model Intercomparison. The Science Plant growth is dependent on the availabi [more...]

Jul 29, 2019
A Slippery Slope: Soil Carbon Destabilization
Carbon gain or loss depends on the balance between competing biological, chemical, and physical reac [more...]

Jul 15, 2019
Field Evaluation of Gas Analyzers for Measuring Ecosystem Fluxes
How gas analyzer type and correction method impact measured fluxes. The Science A side- [more...]

List all highlights (possible long download time)