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. Clouds present many challenges to climate modelling. Todevelop and verify the parameterisations needed to allow climate models torepresent cloud structure and processes, there is a need for high-qualityobservations of cloud optical depth from locations around the world.Retrievals of cloud optical depth are obtainable from radiances measured byAerosol Robotic Network (AERONET) radiometers in “cloud mode” using atwo-wavelength retrieval method. However, the method is unable to detectcloud phase, and hence assumes that all of the cloud in a profile is liquid.This assumption has the potential to introduce errors into long-termstatistics of retrieved optical depth for clouds that also contain ice.Using a set of idealised cloud profiles we find that, for optical depthsabove 20, the fractional error in retrieved optical depth is a linearfunction of the fraction of the optical depth that is due to the presence ofice cloud (“ice fraction”). Clouds that are entirely ice have positiveerrors with magnitudes of the order of 55 % to 70 %. We derive a simple linearequation that can be used as a correction at AERONET sites where icefraction can be independently estimated. Using this linear equation, we estimate the magnitude of the error for a setof cloud profiles from five sites of the Atmospheric Radiation Measurementprogramme. The dataset contains separate retrievals of ice and liquidretrievals; hence ice fraction can be estimated. The magnitude of the errorat each location was related to the relative frequencies of occurrence inthick frontal cloud at the mid-latitude sites and of deep convection at thetropical sites – that is, of deep cloud containing both ice and liquidparticles. The long-term mean optical depth error at the five locationsspans the range 2–4, which we show to be small enough to allow calculationof top-of-atmosphere flux to within 10 % and surface flux to about15 %.

The impact of neglecting ice phase on cloud optical depth retrievals from AERONET cloud mode observations