Articles | Volume 10, issue 4
Geosci. Model Dev., 10, 1817–1833, 2017
Geosci. Model Dev., 10, 1817–1833, 2017

Development and technical paper 27 Apr 2017

Development and technical paper | 27 Apr 2017

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

Daniel Rothenberg and Chien Wang Daniel Rothenberg and Chien Wang
  • Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

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.

Short summary
Climate models include descriptions of how cloud droplets form from particles in the atmosphere. We have developed an efficient parameterization of this process by building an emulator of a detailed model, which can accurately predict cloud droplet number concentrations and potentially include additional physics and chemistry. We further show that using different parameterizations could influence droplet number estimates in global models and their aerosol indirect effect on climate.