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.