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

<p>Cloud droplet number concentration (<span class="inline-formula"><i>N</i>_d</span>) 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 <span class="inline-formula"><i>N</i>_d</span> 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.</p> <p>A number of sampling strategies have been proposed to address these biases by selecting the most accurate <span class="inline-formula"><i>N</i>_d</span> retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved <span class="inline-formula"><i>N</i>_d</span>, using a selection of in situ measurements. In stratocumulus regions, the MODIS <span class="inline-formula"><i>N</i>_d</span> retrieval is able to achieve a high precision (<span class="inline-formula"><i>r</i>²</span> of 0.5–0.8). This is lower in other cloud regimes but can be increased by appropriate sampling choices. Although the <span class="inline-formula"><i>N</i>_d</span> sampling can have significant effects on the <span class="inline-formula"><i>N</i>_d</span> 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 <span class="inline-formula"><i>N</i>_d</span> products and appropriate sampling.</p>