An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model
Other literature type
(issn: 1991-9603, eissn: 1991-9603)
In order to simulate an aerosol indirect effect, most global aerosol-climate models utilize an activation scheme to physically relate the ambient aerosol burden to the droplet number nucleated in newly-formed clouds. While successful in this role, activation schemes are becoming frequently called upon to handle chemically-diverse aerosol populations of ever-increasing complexity. As a result, there is a need to evaluate the performance of existing schemes when interfacing with these complex aerosol populations and to consider ways to incorporate additional processes within them.
We describe an emulator of a detailed cloud parcel model which can be used to assess aerosol activation, and compare it with two activation parameterizations used in global aerosol models. The emulator is constructed using a sensitivity analysis approach (polynomial chaos expansion) which reproduces the behavior of the parent parcel model across the full range of aerosol properties simulated by an aerosol-climate model. Using offline, iterative calculations with aerosol fields from the Community Earth System Model/Model of Aerosols for Research of Climate (CESM/MARC), we identify subsets of aerosol parameters to which diagnosed aerosol activation is most sensitive, and use these to train metamodels including and excluding the influence of giant CCN for coupling with the model. Across the large parameter space used to train them, the metamodels estimate droplet number concentration with a mean relative error of 9.2 % for aerosol populations without giant CCN, and 6.9 % when including them. Using offline activation calculations with CESM/MARC aerosol fields, the best-performing metamodel has a mean relative error of 4.6 %, which is comparable with the two widely-used activation schemes considered here (which have mean relative errors of 2.9 % and 6.7 %, respectively). We identify the potential for regional biases to arise when estimating droplet number using different activation schemes, particularly in oceanic regimes where our best-performing emulator tends to over-predict by 7 %, whereas the reference activation schemes range in mean relative error from −3 % to 7 %. In these offline calculations, the metamodels which include the effects of giant CCN are accurate in continental regimes (mean relative error of 0.3 %), but strongly over-estimate droplet number in oceanic regimes by up to 22 %, particularly in the Southern Ocean. The biases in cloud droplet number resulting from the subjective choice of activation scheme could potentially influence the magnitude of the indirect effect diagnosed from the model incorporating it.