Sensitivity of Ocean Heating Rate Estimate to Time Series Processing Methods

Ocean heat content (OHC) is a key indicator of global warming, and the OHC rate can be used to quantify Earth's energy imbalance (EEI), as ~90% of the EEI is stored in the ocean. However, when estimating the global ocean heating rate, defined as the first derivative of OHC (dOHC/dt), various time series processing methods have been used, including smoothing, differentiation, and annual mean definition, which introduced differences in the resultant dOHC/dt estimate. This study utilized nine OHC datasets to analyze the impact of the above-mentioned approaches on dOHC/dt estimation and compared them with the EEI observation at the top of the atmosphere. The results show that the choice between the January–December and July to the following June (July–June) annual means introduces a 14% relative trend uncertainty into the estimation of the long-term trend of dOHC/dt. Different time derivative methods affect the interannual signal phase and amplitude, where the center difference for dOHC/dt and Clouds and the Earth’s Radiant Energy System (CERES) has the strongest EEI consistency. The smoothing process further enhanced the consistency between dOHC/dt and CERES. These results indicate that the choice of time series processing techniques introduces a non-negligible uncertainty in the OHC-based EEI estimate. Moreover, this study shows that different OHC datasets show larger differences compared with the methodology choices, suggesting that data uncertainty is still the primary source of OHC-based EEI estimation errors. This study provides a methodological basis for improving EEI estimation and also for the evaluation of climate models with observations.