Impact of Physics Parameterization Ordering in a Global Atmosphere Model

A study of the inter-process dependence of sub-grid scale physics parameterizations and process ordering impact.

The Science

DOE scientists explored how the order in which physics process parameterizations are handled within a global atmosphere model impact the model climate and climate sensitivity, i.e., how much the model will warm in response to increased greenhouse gas forcing.  Through a complete set of physics re-orderings for four physics processes, they were able to show that (a) process order impacts the model predicted climate sensitivity, and (b) there exist two distinct solution regimes which are dependent on the interaction between shallow convection and stratiform cloud processes.

The Impact

This new understanding suggests that periodic assessment of the physics process order should be an integral part of global atmosphere model development.  The study also demonstrates the importance that process order has on model accuracy and predicted climate sensitivity, suggesting that inter-model comparisons would benefit from an understanding of how individual models handle physics process ordering. It also determines a list of best practices which will be helpful for future model design efforts.


Atmospheric models consist of several process parameterizations.  The standard modeling approach is to apply each individual parameterization sequentially such that each parameterization feels the effects of all the parameterizations preceding it.  This approach is noncommutative, in that changing the parameterization order will change the model solution.  Researchers from DOE have recently analyzed the impact of rearranging the physics parameterization order in the Energy Exascale Earth System Model (E3SM).  They discovered that the process order impacts model climate and climate sensitivity. The study demonstrates that there is a bifurcation in the modeled climate dependent on the interaction between the shallow convection and stratiform cloud processes.  Using these results, it is possible to determine a set of best practices for process ordering that will improve the credibility of future modeling efforts.  This study also demonstrated that differences in inter-model studies for climate sensitivity could be attributed to how individual models handle the coupling of physics parameterizations.

Principal Investigator(s)

Peter Caldwell
Atmospheric, Earth, and Energy Division, Lawrence Livermore National Laboratory


This work was performed with support from the BER-Scientific Discovery Through Advance Computing (SciDAC) project titled Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System and the Climate Model Development and Validation project ‘‘ACME-SM: A Global Climate Model Software Engineering Surge.”


Donahue, A. S. and P.M. Caldwell. “Impact of physics parameterization ordering in a global atmosphere model.” J. Adv. Model. Earth Syst. 10(2): 481-499 (2018). [DOI:10.1002/2017MS001067].