Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals
are commonly combined into gridded SST analyses and climate data records (CDRs). Differential
biases between SSTs from different sensors cause errors in such products, including feature artefacts.
We introduce a new method for reducing differential biases across the SST constellation, by reconciling
the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the
Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature
Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer
(AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a
range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from
the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals
by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined,
including BT bias corrections and observation error covariance matrices as functions of water-vapor
path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor
SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable
across the reference-sensor gap. We discuss that this method is suitable to improve consistency across
the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future
SST CDRs, as well as having application to other domains of remote sensing
