Estimating Sea Surface Temperature Measurement Methods Using Characteristic Differences in the Diurnal Cycle

Lack of reliable observational metadata represents a key barrier to understanding sea surface temperature (SST) measurement biases, a large contributor to uncertainty in the global surface record. We present a method to identify SST measurement practice by comparing the observed SST diurnal cycle from individual ships with a reference from drifting buoys under similar conditions of wind and solar radiation. Compared to existing estimates, we found a larger number of engine room-intake (ERI) reports post-World War II and in the period 1960-1980. Differences in the inferred mixture of observations lead to a systematic warmer shift of the bias adjusted SST anomalies from 1980 compared to previous estimates, while reducing the ensemble spread. Changes in mean field differences between bucket and ERI SST anomalies in the Northern Hemisphere over the period 1955-1995 could be as large as 0.5 degrees C and are not well reproduced by current bias adjustment models. Plain Language Summary The sea surface temperature (SST) is an important indicator of climate change but its uncertainty affects our confidence in estimates of global surface temperature change. A main systematic component of SST uncertainty (or bias) is caused by changes in SST observational practice onboard ships. Historically, SST measurements have been made either using buckets to collect water samples or recording the temperature of pumped seawater used to cool the ship engines (engine room-intake (ERI)). Because SST observational biases vary by measurement method, empirical models to quantify SST biases must be applied separately to bucket and ERI SSTs. This approach is hampered by the lack of reliable information on the adopted measurement method. We present an independent assessment of SST measurement practice derived comparing diurnal SST variations from individual ships with those computed from drifting buoys under similar conditions. The newly inferred mixture of observations leads to a systematic warmer shift of the bias-adjusted SST anomalies from 1980 compared to previous estimates, while reducing the uncertainty in these estimates. Changes in bucket-ERI mean SST anomaly differences are not fully reproduced by current bias adjustment models. These results have important implications for estimates of uncertainty in SST trends and variability changes.