Cirrus clouds are endemic to Southeast Asia and are a source of potential bias in regional passive aerosol remote sensing datasets. Here, performance of the cloud-screening algorithm for the ground-based Aerosol Robotic Network (AERONET) sun photometer data is evaluated for cirrus cloud contamination at Singapore (1.30
N, 103.77
E). Using twelve months of concurrent AERONET Level 1.5 and 2.0 cloudscreened aerosol optical depth (AOD) data, and collocated Level 1.0 Micro-Pulse Lidar Network (MPLNET) measurements, we investigate the baseline AOD bias due to cirrus cloud presence. Observations are considered for a primary sample of all data and a secondary sample where AERONET data are restricted to a zenith viewing angle  45
. Cirrus clouds are present in zenith-viewing MPL profiles for 34% and 23% of these samples respectively. Based on approximations of cirrus cloud optical properties necessary to estimate cloud optical depth from the single-channel lidar signal, and assuming partial forward scattering of diffuse light by cirrus clouds into the sun photometer’s field of view, we estimate a range in AOD bias due to unscreened cloud presence of 0.034 to 0.060 and 0.031 to 0.055  0.01 for the primary and secondary sample respectively. From the analysis of AERONET AOD for the angle-limited subset alone, we also derive a positive AOD bias of 0.034, which is comparable to the lower bounds for the estimated cloud bias reported for our datasets. These findings, which we attribute to the prevalence of cirrus clouds present from regional convection, are higher than previous reports of global AOD bias in the Moderate Resolution Infrared Spectroradiometer (MODIS) satellite-borne measurements due to residual cirrus cloud presence.