Global cloud distribution: Reconciling the active and passive points of view
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Passive remote sensors have been monitoring the state of the atmosphere from space for several decades. Their measurements have led to reliable climatologies of clouds distribution on a global scale. Unfortunately, these retrievals were penalized by significant uncertainties above certain surface types (ice, snow, deserts, and more generally everything that is not ocean). The ability to detect clouds was moreover strongly dependent on their optical depth, with thinnest clouds often escaping detection. Along the vertical dimension, retrievals were often limited to the cloud top temperature, which required strong assumptions on cloud density that were rarely realistic and led to underestimations by several kilometers (Sherwood 2004). Considerable effort and ingenuity went into solving these problems and reducing uncertainties as much as possible, but the error bars remain significant. They mean that passive observations cannot answer specific questions about how the cloud cover reacts to changes in its atmospheric environment [Chepfer et al., in review].
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In the past few years, several active instruments put in orbit have led to significant insights regarding the spatial distribution of clouds. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) (Winker 2010), bla bla. CALIOP cannot penetrate optically thick clouds, and thus cannot always document the entire vertical profile of cloud fraction. However, this does not impair its ability to retrieve whether a given column of atmosphere contain clouds or not, with a high horizontal resolution and a sensitivity to optically thin clouds unmatched by most passive sensors. CALIOP is therefore a reliable source for documenting the overall cloud fraction and it has been often used so in recent years [bla bla et al. 2012, 2013 etc].
Here we attempt to contrast how active and passive remote sensing observations see cloud amounts on a global scale. We present how passive sensors disagree on the retrieved cloud amounts in Sect. 2, and compare their averaged cloud amounts with those retrieved from three datasets derived from CALIOP data in Sect. 3. In Sect. 4, we focus on geographic areas where passive sensors disagree most among themselves or with active datasets.
As our baseline, we used the most up to date and comprehensive cloud retrievals based on spaceborne passive remo