Disagreement among global cloud distributions from CALIOP,
passive satellite sensors and general circulation
models
Abstract
Cloud detection is the first step of complex satellite-based cloud
retrievals. No instrument detects all clouds, and analyses using a given
satellite climatology can only describe a specific subset of clouds. We
attempt to clarify which subsets of clouds are detected in a robust way
by passive sensors, and which require active sensors. To do so, we
identify where retrievals of Cloud Amounts (CAs), based on numerous
sensors and algorithms, differ the most. We investigate large
uncertainties, and confront retrievals from the CALIOP lidar, which
detects semitransparent clouds and directly measures their vertical
distribution, whatever the surface below. We document the cloud vertical
distribution, opacity and seasonal variability where CAs from passive
sensors disagree most.
CALIOP CAs are larger than the passive average by +0.05 (AM) and +0.07
(PM). Over land, the +0.1 average difference rises to +0.2 over the
African desert, Antarctica and Greenland, where large passive
disagreements are traced to unfavorable surface conditions. Over oceans,
CALIOP retrievals are closer to the average of passive retrievals except
over the ITCZ (+0.1). Passive CAs disagree more in tropical areas
associated with large-scale subsidence, where CALIOP observes a specific
multi-layer cloud population: optically thin, high-level clouds and
opaque, shallow boundary layer clouds (altitude 0.5-2km).
We evaluate the CA and cloud vertical distribution from 8 General
Circulation Models where passive retrievals disagree and CALIOP provides
new information. We find that modeled clouds are not more realistic
where cloud detections from passive observations have long been robust,
than where active sensors are required.