Simulating Earth’s climate system is a computationally daunting task. Predicting future climate as accurately as possible means modeling atmospheric, oceanic, and land surface processes at scales ranging from the microscopic to the global and spanning microseconds to decades. Such an approach remains infeasible because of its substantial computational cost.
Hannah et al. present an alternative strategy that operates within the limits of today’s computational practicalities to improve climate predictions generated by the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model (E3SM).
The new approach uses a technique called superparameterization, in which a second model—in this case, one that simulates cloud formation and dynamics—is embedded within the main climate model. The embedded cloud model provides the main model with predictions of cloud attributes and behavior that are more precise than those achieved by conventional cloud parameterizations used in climate models.
The new, superparameterized E3SM (SP-E3SM) outperforms the standard E3SM by some metrics, such as in correctly re-creating the daily timing of peak rainfall and in the representation of tropical waves—atmospheric features associated with storms. These results are in line with improvements seen in other superparameterized models.
SP-E3SM also runs very fast, simulating about 1.2–1.4 years of data per day of computing, compared with roughly 0.2 year of data per day in similar superparameterized models. (SP-E3SM is still slower than the standard E3SM, however, which can produce 5–7 years of data per day.) The researchers achieved this acceleration in large part by restructuring the model’s code to run on DOE’s powerful graphics processing unit computing hardware.
Despite the improvements, SP-E3SM suffers from a unique problem known as grid imprinting, which introduces errors into the pattern of rainfall the model simulates. The researchers noted that they are addressing the grid imprinting problem as they refine the new model. (Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2019MS001863, 2020)
—Sarah Stanley, Science Writer