Scientists use supercomputers to determine how reliably a popular Earth system model represents precipitation regionally and globally.
Floods, droughts, and superstorms affect people around the world. To study these and other events, scientists develop cutting-edge computer models that can tackle the most demanding climate research imperatives. For example, how will precipitation evolve over the next 40 years? To answer such questions, researchers need to know how well a model performs. A team conducted extensive simulations and determined why certain mismatches occur in the model between predicted and measured data.
Small changes in the water cycle can have wide-ranging societal and environmental impacts. To study those changes, scientists need accurate simulations. With use, these simulations offer insights on the changes. This work offers insights into how models handle precipitation.
One of the most basic quantities of the atmosphere water cycle is the global-mean precipitation rate. The team conducted atmosphere-only simulations spanning the years 1980–2005 to examine how the precipitation rate changes when the horizontal resolution is increased from 1 to 0.25 degrees. The team used supercomputers, such as Theta at the Argonne Leadership Computing Facility, extensively to run the recently released Energy Exascale Earth System Model (E3SM) code, which comprises component models for atmosphere, ocean, sea ice, and land. The efficient execution of each simulation involves an optimal balance of compute nodes among the various component models. The simulations showed that the more frequent heavy precipitation, the decrease in precipitable water, and the shift from convective to large-scale precipitation are predominantly due to resolution changes. Like similar models, all E3SM resolutions of Energy Exascale Earth System Model overestimate the global mean precipitation rate. In particular, the models all show a tendency to lightly rain too frequently. Future work will address these aspects of the water cycle and reconcile differences between the model and observations, with the goal of improving the model to help researchers predict and understand how precipitation will evolve in the next 40 years.
Christopher R. Terai
Lawrence Livermore National Laboratory
Advanced Scientific Computing Research Allocation Principal Investigator
Sandia National Laboratories
The efforts of C. R. Terai, P. M. Caldwell, Q. Tang, and M. L. Branstetter are supported as part of the Accelerated Climate Modeling for Energy program, funded by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research. The efforts of S.A. Klein are supported by the Regional and Global Climate Modeling program of DOE’s Office of Science. The research used computing resources at the Argonne Leadership Computing Facility, the Oak Ridge Leadership Computing Facility, and the National Energy Research Scientific Computing Center, all of which are supported by the DOE Office of Science.
Terai, C. R., P. M. Caldwell, S. A. Klein, Q. Tang, and M. L. Branstetter. “The atmospheric hydrologic cycle in the ACME v0.3 Model.” Climate Dynamics 50, 3251 (2018). [DOI:10.1007/s00382-017-3803-x]
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