Preprints
https://doi.org/10.5194/amt-2021-371
https://doi.org/10.5194/amt-2021-371

  29 Nov 2021

29 Nov 2021

Review status: this preprint is currently under review for the journal AMT.

The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data

Edward Gryspeerdt1,2, Daniel T. McCoy3, Ewan Crosbie4,5, Richard H. Moore4, Graeme J. Nott6, David Painemal4,5, Jennifer Small-Griswold7, Armin Sorooshian8, and Luke Ziemba4 Edward Gryspeerdt et al.
  • 1Space and Atmospheric Physics Group, Imperial College London, UK
  • 2Grantham Institute for Climate Change and the Environment, Imperial College London, UK
  • 3Department of Atmospheric Science, University of Wyoming, Laramie, WY, USA
  • 4NASA Langley Research Center, Science Directorate, Hampton VA, USA
  • 5Science Systems and Applications Inc., Hampton, VA, USA
  • 6FAAM, Cranfield, UK
  • 7University of Hawai‘i at M¯anoa, Honolulu, HI, USA
  • 8Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA

Abstract. Cloud droplet number concentration (Nd) is of central importance to observation-based estimates of aerosol indirect effects, being used to quantify both the cloud sensitivity to aerosol and the base state of the cloud. However, the derivation of Nd from satellite data depends on a number of assumptions about the cloud and the accuracy of the retrievals of the cloud properties from which it is derived, making it prone to systematic biases.

A number of sampling strategies have been proposed to address these biases by selecting the most accurate Nd retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved Nd, using a selection of insitu measurements. In stratocumulus regions, the MODIS Nd retrieval is able to achieve a high precision (r2 of 0.5–0.8). This is lower in other cloud regimes, but can be increased by appropriate sampling choices. Although the Nd sampling can have significant effects on the Nd climatology, it produces only a 20 % variation in the implied radiative forcing from aerosol-cloud interactions, with the choice of aerosol proxy driving the overall uncertainty. The results are summarised into recommendations for using MODIS Nd products and appropriate sampling.

Edward Gryspeerdt et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-371', Anonymous Referee #1, 10 Jan 2022 reply
  • RC2: 'Comment on amt-2021-371', Anonymous Referee #1, 11 Jan 2022 reply

Edward Gryspeerdt et al.

Edward Gryspeerdt et al.

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Short summary
The droplet number concentration is a key property of clouds, influencing a variety of cloud processes. It is also used for estimating the cloud response to aerosols. The satellite retrieval depends on a number of assumptions – different sampling strategies are used to select cases where these assumptions are most likely to hold. Here we investigate the impact of these strategies on the agreement with insitu data, the droplet number climatology and on estimates of the indirect radiative forcing.