LLNL is a world leader in climate model evaluation, analysis and intercomparison. Since 1989, PCMDI (originally known as the Program for Climate Model Diagnosis and Intercomparison) has been closely examining and comparing the results of nationally and internationally developed climate models with observed changes in the climate system. Livermore pioneered the development of standard benchmark simulations for many different components of climate models. These standard simulations enable scientists to identify errors among different models, to investigate the possible causes of these errors, to track changes in model performance over time, to make informed statements about the quality of different models, to quantify uncertainties in model projections of future climate change, and to estimate the sizes of human and natural contributions to observed climate change. PCMDI’s ongoing efforts are helping to reduce uncertainty in climate predictions.
PCMDI plays a significant role in the coordination and implementation of the Coupled Model Intercomparison Project (CMIP) , and the analysis and provisioning of model data for the global research community. Analysis of CMIP simulations by PCMDI and the climate community provides much of the scientific foundation for assessment reports by the Intergovernmental Panel on Climate Change (IPCC). PCMDI has been a major contributor to all six IPCC reports. After the fourth assessment, more than 40 Livermore researchers were recognized when the IPCC was co-awarded the 2007 Nobel Peace Prize for its efforts to “build up and disseminate greater knowledge about man-made climate change, and to lay the foundations for the measures that are needed to counteract such change.”
Livermore’s high-performance computing capabilities are an essential enabler of climate model analysis efforts, particularly for fingerprint detection, a signature LLNL capability. Although differences in modeling approaches will lead to some degree of variation among climate models and differences with normal variation of weather, multiple runs from multiple models usually reveal consistent trends when the results are combined into an ensemble. When plotted, each set of ensemble paths forms an envelope of climate trajectories. Researchers examine the evolution of the envelopes and compare these envelopes and their averages to actual data gathered from satellite sources. This approach helps scientists to differentiate between human-caused signals and noise—natural internal variability in climate—and to better understand how and when human activities first began to affect the climate.
Learn more about PCMDI
- PCMDI website
- Climate Change Comes into Focus (LLNL magazine article, 2021)
