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Starting in late 2013, the Northeast (NE) Pacific Ocean experienced anomalously warm sea surface temperatures (SSTs) that persisted for over 2 years. This marine heatwave, known as “the Blob,” produced many devastating ecological impacts with socioeconomic implications for coastal communities. The warm waters observed during the NE Pacific 2013/2016 marine heatwave altered the surface energy balance and disrupted ocean–atmosphere interactions in the region. In principle, ocean–atmosphere interactions following the formation of the marine heatwave could have perpetuated warm SSTs through a positive SST‐cloud feedback. The actual situation was more complicated. While reanalysis data show a decrease in boundary layer cloud fraction and an increase in downward shortwave radiative flux at the surface coincident with warm SSTs, this was accompanied by an increase in longwave radiative fluxes at the surface, as well as an increase in sensible and latent heat fluxes out of the ocean mixed layer. The result is a small negative net heat flux anomaly (compared to the anomalies of the individual terms contributing to the net heat flux). This provides new information about the midlatitude ocean–atmosphere system while it was in a perturbed state. More specifically, a mixed layer heat budget reveals that anomalies in both the atmospheric and oceanic processes offset each other such that the anomalously warm SSTs persisted for multiple years. The results show how the atmosphere–ocean system in the NE Pacific is able to maintain itself in an anomalous state for an extended period of time. During the 2013–2016 NE Pacific marine heatwave, a decrease in low cloud fraction and increase in surface radiative fluxes were observedConcurrently, anomalous turbulent fluxes and ocean processes offset a positive SST‐cloud feedback to maintain high SST anomaliesA balance of anomalous processes allows the atmosphere–ocean system in the NE Pacific to maintain an anomalous state for multiple years

Atmospheric reanalyses are valuable tools for studying the atmosphere, as they provide temporally and spatially complete coverage of atmospheric variables. However, some regions are susceptible to large biases in reanalysis products due to the scarce data available to assimilate into the reanalyses. Consequently, evaluation of reanalyses using available measurements is essential for quantifying regional errors. Here we use NASA's CERES satellite estimates to evaluate surface radiative fluxes and total cloud fraction in the Northeast Pacific from five reanalysis products—ERA‐Interim, MERRA2, JRA‐55, NCEP2, and CFSR—from years 2001 to 2015. Results show that biases of surface incident shortwave radiative flux in reanalyses compared to satellite estimates range from 3.8 (CFSR) to 21.2 Wm‐2 (NCEP2), with significant biases in JRA‐55 and NCEP2. Mean surface downward longwave radiative flux in the reanalysis products is biased by −8.9 (MERRA2) to 3.9 Wm−2(JRA‐55), with significant biases in MERRA2 and NCEP2. Errors in the surface radiative fluxes are partially linked to differences in total cloud fraction in the satellite estimates and reanalyses, which show significant negative biases ranging from −8% (CFSR) to −21.7% (NCEP2). There is not one reanalysis that outperforms the rest in the NE Pacific. The most appropriate data set depends on the variables of interest, subregion of the NE Pacific being studied, time period of interest, and whether the reanalysis data will be used to study long‐term or short‐term climate processes. Using the errors presented for each reanalysis data set can help guide appropriate use and bound uncertainty for the five reanalysis products analyzed. An evaluation of radiative fluxes and total cloud fraction from five reanalysis products is performed using CERES EBAF satellite estimatesShortwave radiative flux is mostly overestimated in reanalysis data sets, while longwave radiative flux has both negative and positive biases, depending on the reanalysisThere is no gold standard reanalysis—the most appropriate data set depends on variables, time period, and subregion of interest