Abstract
Abstract. We describe an emulator of a detailed cloud parcel model which has been trained to assess droplet nucleation from a complex, multimodal aerosol size distribution simulated by a global aerosol–climate model. The emulator is constructed using a sensitivity analysis approach (polynomial chaos expansion) which reproduces the behavior of the targeted parcel model across the full range of aerosol properties and meteorology simulated by the parent climate model. An iterative technique using aerosol fields sampled from a global model is used to identify the critical aerosol size distribution parameters necessary for accurately predicting activation. Across the large parameter space used to train them, the emulators estimate cloud droplet number concentration (CDNC) with a mean relative error of 9.2 % for aerosol populations without giant cloud condensation nuclei (CCN) and 6.9 % when including them. Versus a parcel model driven by those same aerosol fields, the best-performing emulator has a mean relative error of 4.6 %, which is comparable with two commonly used activation schemes also evaluated here (which have mean relative errors of 2.9 and 6.7 %, respectively). We identify the potential for regional biases in modeled CDNC, particularly in oceanic regimes, where our best-performing emulator tends to overpredict by 7 %, whereas the reference activation schemes range in mean relative error from −3 to 7 %. The emulators which include the effects of giant CCN are more accurate in continental regimes (mean relative error of 0.3 %) but strongly overestimate CDNC in oceanic regimes by up to 22 %, particularly in the Southern Ocean. The biases in CDNC resulting from the subjective choice of activation scheme could potentially influence the magnitude of the indirect effect diagnosed from the model incorporating it.
Highlights
Aerosols play a critical role in the climate system by interacting with radiation through several different mechanisms
The first parameterization, by Abdul-Razzak and Ghan (2000) (ARG), uses a pseudoanalytical solution to an integro-differential equation derived from the original adiabatic parcel model system; the second, by Morales Betancourt and Nenes (2014b) (MBN), applies an iterative scheme to partition the aerosol population into two subsets and uses different limits on the underlying analytical formulas to derive a maximum supersaturation
Existing activation schemes, we studied a sample of n = 10 000 aerosol and meteorology parameter sets drawn directly from the Model of Aerosols for Research of Climate (MARC) simulation previously used to study the distributions of simulated aerosol parameters, in contrast with the previous Latin hypercube sampling (LHS) sample
Summary
Aerosols play a critical role in the climate system by interacting with radiation through several different mechanisms. Despite decades of focused research by the scientific community, the radiative forcing produced through this second pathway, known as aerosol–cloud interactions, remains one of the largest uncertainties in understanding contemporary and future climate change on both regional and global scales (Boucher et al, 2013) To include this second pathway, contemporary Earth system models predict cloud droplet number concentration (CDNC) by evaluating the nucleation of droplets (aerosol activation) from their simulated aerosol fields. Compared to two other physically based schemes, the emulator-activation parameterizations tended to predict CDNC more accurately versus a reference parcel model, in regimes with weak updraft speeds and high aerosol number concentration, where the traditional parameterizations performed the most poorly In this present work, we extend this approach to develop a set of metamodels trained for the aerosol and meteorology parameter space simulated by a global aerosol–climate model. We evaluate the performance of our emulator parameterizations versus two physically based schemes which are used in the vast majority of contemporary global models (see Table 3 of Ghan et al, 2011) and assess the impact of including each of these schemes on the computational expense of our global model
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