Simplifying cloud microphysical process representation to reduce climate model complexity

Earth's climate system is complex. Thus, trying to represent this system as the sum of its parts, climate models have grown increasingly complex as well (Shackley et al., 1998). However, this makes the model results and behaviour difficult to interpret, and increases simulations' computational costs.

Building onto Proske et al. (2022), who demonstrated the potential for simplification in the two moment cloud microphysics (CMP) scheme of the global aerosol climate model ECHAM-HAM, we implement such simplifications for the CMP and activation scheme in ECHAM-HAM. For the CMP the sensitivity of the model to a specific process determines whether it is simplifiable. For example, heterogeneous freezing and secondary ice production in their present implementation can be removed without strong deviations in results. Replacing the sublimation and self-collection of ice with a constant rate, or replacing melting of ice crystals with a climatology has similarly small effects. The deviations that simplifications of other processes such as riming produce are larger, but all simplifications are robust to changing climate states.
For aerosol activation into cloud condensation nuclei, using a climatology of CCN diagnosed from a previous run gives satisfactory results. From the perspective of the CMP this simplification eliminates the need for the whole aerosol module HAM, associated with large computational time savings (for aerosol optical effects, the representation of at least anthropogenic aerosols with a simplified climatology has already been demonstrated successfully (Stevens et al., 2017)).

Of course, the value of simplifications and the evaluation standard for their results depends on one's modelling purpose (Parker, 2009). However, our results show that ECHAM-HAM contains redundancy in model detail and thus question the value of complexity in model representation as a normative principle (Shackley et al., 1998).

 

 

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Proske, U., S. Ferrachat, D. Neubauer, M. Staab, and U. Lohmann. “Assessing the Potential for Simplification in Global Climate Model Cloud Microphysics.” Atmos. Chem. Phys. 22, no. 7 (April 12, 2022): 4737–62. https://doi.org/10.5194/acp-22-4737-2022.

Shackley, S., P. Young, S. Parkinson, and B. Wynne. “Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling: Are GCMs the Best Tools?” Clim. Change 38, no. 2 (1998): 159–205. https://doi.org/10.1023/A:1005310109968.

Stevens, B., S. Fiedler, S. Kinne, K. Peters, S. Rast, J. Müsse, S. J. Smith, and T. Mauritsen. “MACv2-SP: A Parameterization of Anthropogenic Aerosol Optical Properties and an Associated Twomey Effect for Use in CMIP6.” Geosci. Model Dev. 10, no. 1 (2017): 433–52. https://doi.org/10.5194/gmd-10-433-2017.