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Preprints
https://doi.org/10.5194/acp-2020-509
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-509
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jun 2020

10 Jun 2020

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A revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

Arshad Arjunan Nair and Fangqun Yu Arshad Arjunan Nair and Fangqun Yu
  • Atmospheric Sciences Research Center, State University of New York, Albany, New York 12203, USA

Abstract. Cloud condensation nuclei (CCN) number concentrations are an important aspect of aerosol–cloud interactions and the subsequent climate effects; however, their measurements are very limited. We use a machine learning tool, random decision forests, to develop a Random Forest Regression Model (RFRM) to derive CCN at 0.4 % supersaturation ([CCN0.4]) from commonly available measurements. The RFRM is trained on the long-term simulations in a global size-resolved particle microphysics model. Using atmospheric state and composition variables as predictors, through associations of their variabilities, the RFRM is able to learn the underlying dependence of [CCN0.4] on these predictors, which are: 8 fractions of PM2.5 (NH4, SO4, NO3, secondary organic aerosol (SOA), black carbon (BC), primary organic carbon (POC), dust, and salt), 7 gaseous species (NOx, NH3, O3, SO2, OH, isoprene, and monoterpene), and 4 meteorological variables (temperature (T), relative humidity (RH), precipitation, and solar radiation). The RFRM is highly robust: median mean fractional bias (MFB) of 4.4 % with ~ 96.33 % of the derived [CCN0.4] within a good agreement range of −60 % < MFB < +60 % and strong correlation of Kendall's τ coefficient ≈ 0.88. The RFRM demonstrates its robustness over 4 orders of magnitude of [CCN0.4] over varying spatial (such as continental to oceanic, clean to polluted, and near surface to upper troposphere) and temporal (from the hourly to the decadal) scales. At the Atmospheric Radiation Measurement Southern Great Plains observatory (ARM SGP) in Lamont, Oklahoma, United States, long-term measurements for PM2.5 speciation (NH4, SO4, NO3, and organic carbon (OC)), NOx, O3, SO2, T, and RH, as well as [CCN0.4] are available. We modify, optimise, and retrain the developed RFRM to make predictions from 19 → 9 of these available predictors. This retrained RFRM (RFRM-ShortVars) shows a reduction in performance due to the unavailability and sparsity of measurements (predictors); it captures the [CCN0.4] variability and magnitude at SGP with ~ 67.02 % of the derived values in the good agreement range. This work shows the potential of using the more commonly available measurements of PM2.5 speciation to alleviate the sparsity of CCN number concentrations' measurements.

Arshad Arjunan Nair and Fangqun Yu

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Interactive discussion

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Arshad Arjunan Nair and Fangqun Yu

Arshad Arjunan Nair and Fangqun Yu

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Latest update: 20 Oct 2020
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Short summary
Small particles in the atmosphere can affect cloud formation and properties and thus, Earth's energy budget. These Cloud Condensation Nuclei (CCN) contribute the largest uncertainties in climate change modeling. To reduce these uncertainties, it is important to quantify CCN numbers accurately, measurements of which are sparse. We propose and evaluate a machine learning method to estimate CCN, in the absence of their direct measurements, using more common measurements of weather and air quality.
Small particles in the atmosphere can affect cloud formation and properties and thus, Earth's...
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