In this study we identify a global seasonal bias in ocean color remote sensing reflectances (R rs , λ) using data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder) satellite, in addition to Argo floats and in-water reflectance from the Marine Optical BuoY (MOBY) site. The seasonal bias in R rs is present in the VIIRS (Visible Infrared Imaging Radiometer Suite), SeaWIFS (Sea-viewing Wide Field-of-view sensor), and MODIS (Moderate resolution imaging spectrometer) satellites at all visible wavelengths and is larger at longer wavelengths. Products derived from Rrs are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The seasonal bias is prominent in areas of low biomass (i.e., gyres) and is not easily discernable in areas of high biomass. We found that the seasonal bias in Rrs is not caused by Raman scattering choice or implementation, nor is it due to differences with satellite viewing angle. Biases in particulate backscattering are not affected by specific assumptions used within Rrs inversion models. Changing the specific space/time averaging window in different processing levels of remote sensing data and matchups were not the cause either. While we have eliminated several candidates which could cause the bias, there are still outstanding questions about the role atmospheric correction plays. We provide evidence that the Bidirectional Reflectance Distribution Function correction factor may control the observed seasonal bias to some extent, but does not preclude the effect of the aerosol correction. We provide recommendations for work to be conducted in the near-future. In particular, the use of CALIOP aerosol data may help improve the aerosol model used in atmospheric correction and the execution of more simulations to discern the relative influence of atmospheric correction parameters. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data will be affected until a solution is implemented.