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Aerosol particles influence cloud formation and properties. Hence climate models that aim for a physical representation of the climate system include aerosol modules. In order to represent more and more processes and aerosol species, their representation has grown increasingly detailed. However, depending on one's modelling purpose, the increased model complexity may not be beneficial, for example because it hinders understanding of model behaviour. Hence we develop a simplification in the form of a climatology of aerosol concentrations. In one approach, the climatology prescribes properties important for cloud droplet and ice crystal formation, the gateways for aerosols to enter the model cloud microphysics scheme. Another approach prescribes aerosol mass and number concentrations in general. Both climatologies are derived from full ECHAM-HAM simulations and can serve to replace the HAM aerosol module and thus drastically simplify the aerosol treatment. The first simplification reduces computational model time by roughly 65 %. However, the naive mean climatological treatment needs improvement to give results that are satisfyingly close to the full model. We find that mean cloud condensation nuclei (CCN) concentrations yield an underestimation of cloud droplet number concentration (CDNC) in the Southern Ocean, which we can reduce by allowing only CCN at cloud base (which have experienced hygroscopic growth in these conditions) to enter the climatology. This highlights the value of the simplification approach in pointing to unexpected model behaviour and providing a new perspective for its study and model development. [ABSTRACT FROM AUTHOR]

Aerosol particles influence cloud formation and properties. Hence climate models that aim for a physical representation of the climate system include aerosol modules. In order to represent more and more processes and aerosol species, their representation has grown increasingly detailed. However, depending on one's modeling purpose, the increased model complexity may not be beneficial, for example because it hinders understanding of model behaviour. Hence we develop a simplification in the form of a climatology of aerosol concentrations. In one approach, the climatology prescribes properties important for cloud droplet and ice crystal formation, the gateways for aerosols to enter the model cloud microphysics scheme. Another approach prescribes aerosol mass and number concentrations in general. Both climatologies are derived from full ECHAM-HAM simulations and can serve to replace the HAM aerosol module and thus drastically simplify the aerosol treatment. The first simplification reduces computational model time by roughly 65 %. However, the naive mean climatological treatment needs improvement to give results that are satisfyingly close to the full model. We find that mean CCN concentrations yield an underestimation of CDNC in the Southern Ocean, which we can reduce by allowing only CCN at cloud base (which have experienced hygroscopic growth in these conditions) to enter the climatology. This highlights the value of the simplification approach in pointing to unexpected model behaviour and providing a new perspective for its study and model development. [ABSTRACT FROM AUTHOR]

In a quest to represent the Earth system, climate models have become more and more complex. This generates problems, for example, hindering model interpretability. This study contributes to a regain of model understanding and proposes simplifications to decrease scheme complexity. We reflect on the reasons for model complexity and the problems it generates or deepens, connecting perspectives from atmospheric science and the philosophy of climate science. Using an emulated perturbed parameter ensemble of the cloud microphysics (CMP) process efficiencies, we investigate the sensitivity of the model to process perturbations. The sensitivity analysis characterizes the scheme and model behavior, contrasting it to physical process understanding as well as an alternative CMP formulation (comparing the 2M (Lohmann et al., 2007, https://doi.org/10.5194/acp-7-3425-2007) to the P3 scheme (Morrison & Milbrandt, 2015, https://doi.org/10.1175/JAS-D-14-0065.1; Dietlicher et al., 2018, https://doi.org/10.5194/gmd-11-1557-2018)). For the 2M scheme, ice crystal autoconversion dominates the model sensitivity in the ice phase. The P3 scheme removes this artificial process and thus shows more balanced sensitivities. Model behavior sometimes aligns with process understanding, but many process sensitivities are masked by other more dominant processes or the model finally responds differently due to adjustments. We identify processes that the model is not sensitive to and test their simplification. For example, heterogeneous freezing or secondary ice production are drastically simplifiable. Depending on one's modeling vision one may interpret this study's findings as pointing to simplification potential in the CMP scheme or the need for process representation improvements where the model behavior does not tally with our physical understanding. Plain Language Summary: Climate models are supposed to help us to understand or project Earth system behavior. In order to represent many aspects of this system, modelers have added more and more detail to the models. This makes the models complex and difficult to understand, even for the modelers themselves. We want to gain more understanding of the model behavior. For this we use a methodology where we manipulate the effectiveness of a process in the model to test how the model reacts to that change. Some of how the model reacts is surprising and different from what our physical understanding of a process suggests. In particular, some processes, like the freezing of cloud droplets at temperatures warmer than −35°C, are negligible in the model, even though atmospheric scientists think that this is an important process in the real atmosphere. Now one could either simplify the representation of such a process, or try to improve the model to reflect physical reality better. What one decides for depends on the purpose and concept of the model. Key Points: Model scheme complexity brings about a bouquet of problems, from hindering interpretability to hiding unforced modeler choicesUsing sensitivity analysis on an emulated perturbed parameter ensemble we characterize the two cloud microphysics schemes of ECHAM‐HAMWe find potential for process simplifications as well as instances where model sensitivities deviate from a priori physical understanding [ABSTRACT FROM AUTHOR]

Cloud properties and their evolution influence Earth's radiative balance. The cloud microphysical (CMP) processes that shape these properties are therefore important to represent in global climate models. Historically, parameterizations in these models have grown more detailed and complex. However, a simpler formulation of CMP processes may leave the model results mostly unchanged while enabling an easier interpretation of model results and helping to increase process understanding. This study employs sensitivity analysis of an emulated perturbed parameter ensemble of the global aerosol–climate model ECHAM-HAM to illuminate the impact of selected CMP cloud ice processes on model output. The response to the perturbation of a process serves as a proxy for the effect of a simplification. Autoconversion of ice crystals is found to be the dominant CMP process in influencing key variables such as the ice water path and cloud radiative effects, while riming of cloud droplets on snow has the most influence on the liquid phase. Accretion of ice and snow and self-collection of ice crystals have a negligible influence on model output and are therefore identified as suitable candidates for future simplifications. In turn, the dominating role of autoconversion suggests that this process has the greatest need to be represented correctly. A seasonal and spatially resolved analysis employing a spherical harmonics expansion of the data corroborates the results. This study introduces a new application for the combination of statistical emulation and sensitivity analysis to evaluate the sensitivity of a complex numerical model to a specific parameterized process. It paves the way for simplifications of CMP processes leading to more interpretable climate model results. [ABSTRACT FROM AUTHOR]

In this paper, we present the implementation and evaluation of the aerosol microphysics module SALSA2.0 in the framework of the aerosol-chemistry-climate model ECHAM-HAMMOZ. It is an alternative microphysics module to the default modal microphysics scheme M7 in ECHAM-HAMMOZ. The SALSA2.0 implementation is evaluated against the observations of aerosol optical properties, aerosol mass, and size distributions. We also compare the skill of SALSA2.0 in reproducing the observed quantities to the skill of the M7 implementation. The largest differences between SALSA2.0 and M7 are evident over regions where the aerosol size distribution is heavily modified by the microphysical processing of aerosol particles. Such regions are, for example, highly polluted regions and regions strongly affected by biomass burning. In addition, in a simulation of the 1991 Mt Pinatubo eruption in which a stratospheric sulfate plume was formed, the global burden and the effective radii of the stratospheric aerosol are very different in SALSA2.0 and M7. While SALSA2.0 was able to reproduce the observed time evolution of the global burden of sulfate and the effective radii of stratospheric aerosol, M7 strongly overestimates the removal of coarse stratospheric particles and thus underestimates the effective radius of stratospheric aerosol. As the mode widths of M7 have been optimized for the troposphere and were not designed to represent stratospheric aerosol the ability of M7 to simulate the volcano plume was improved by modifying the mode widths decreasing the standard deviations of the accumulation and coarse modes from 1.59 and 2.0, respectively, to 1.2. Overall, SALSA2.0 shows promise in improving the aerosol description of ECHAM-HAMMOZ and can be further improved by implementing methods for aerosol processes that are more suitable for the sectional method, e.g size dependent emissions for aerosol species and size resolved wet deposition. [ABSTRACT FROM AUTHOR]