Simulation of sea surface temperature changes in the Middle Pliocene warm period and comparison with reconstructions

The Middle Pliocene (ca 3.12–2.97 Ma) is a recent warm period in the Earth’s history. In many respects, the warmth of the Middle Pliocene is similar to the probable warm situation of the late 21st century predicted by climate models. Understanding the Middle Pliocene climate is important in predicting the future climate with global warming. Here, we used the latest reconstructions for the Middle Pliocene—Pliocene Research Interpretation and Synoptic Mapping (PRISM) version 3—to simulate the Middle Pliocene climate with a fully coupled model Fast Ocean Atmosphere Model. From comparison of the results of simulations with reconstructions, we considered two important scientific topics of Middle Pliocene climate modeling: extreme warming in the subpolar North Atlantic and a permanent El Ni?o in the tropical Pacific. Our simulations illustrate that the global annual mean sea surface temperature (SST) in the Middle Pliocene was about 2.3°C higher than that in the pre-industrial era. The warming was stronger at midand high latitudes than at low latitudes. The simulated SST changes agree with SST reconstructions in PRISM3 data, especially for the North Atlantic, North Pacific and west coast of South America. However, there were still discrepancies between the simulation of the SST and reconstructions for the subpolar North Atlantic and tropical Pacific. In the case of the Atlantic, the weakened meridional overturning circulation in the simulation did not support the reconstruction of the extremely warm condition in the subpolar North Atlantic. In the case of the tropical Pacific, the whole ocean warmed, especially the eastern tropical Pacific, which did not support the permanent El Ni?o suggested by the reconstruction. From evaluation of the modeling and reconstruction, we suggest that the above discrepancies were due to uncertainties in reconstructions, difficulties in paleoclimate modeling and deficiencies of climate models. The discrepancies should be reduced through consideration of both the modeling and data.