The relationship between the global mean deep-sea and surface temperature during the Early Eocene
Goudsmit-Harzevoort, B.; Lansu, A.; Baatsen, M.L.J.; von der Heydt, A.S.; de Winter, N.J.; Zhang, Y.; Abe-Ouchi, A.; de Boer, A.M.; Chan, W.-L.; Donnadieu, Y.; Hutchinson, D.K.; Knorr, G.; Ladant, J.-B.; Morozova, P.; Niezgodzki, I.; Steinig, S.; Tripati, A.K.; Zhang, Z.; Zhu, J.; Ziegler, M. (2023). The relationship between the global mean deep-sea and surface temperature during the Early Eocene. Paleoceanography and Paleoclimatology 38(3): e2022PA004532. https://dx.doi.org/10.1029/2022PA004532
In: Paleoceanography and Paleoclimatology. American Geophysical Union: Washington DC. ISSN 2572-4525; e-ISSN 2572-4525
Estimates of global mean near-surface air temperature (global SAT) for the Cenozoic era rely largely on paleo-proxy data of deep-sea temperature (DST), with the assumption that changes in global SAT covary with changes in the global mean deep-sea temperature (global DST) and global mean sea-surface temperature (global SST). We tested the validity of this assumption by analyzing the relationship between global SST, SAT, and DST using 25 different model simulations from the Deep-Time Model Intercomparison Project simulating the early Eocene Climatic Optimum (EECO) with varying CO2 levels. Similar to the modern situation, we find limited spatial variability in DST, indicating that local DST estimates can be regarded as a first order representative of global DST. In line with previously assumed relationships, linear regression analysis indicates that both global DST and SAT respond stronger to changes in atmospheric CO2 than global SST by a similar factor. Consequently, this model-based analysis validates the assumption that changes in global DST can be used to estimate changes in global SAT during the early Cenozoic. Paleo-proxy estimates of global DST, SST, and SAT during EECO show the best fit with model simulations with a 1,680 ppm atmospheric CO2 level. This matches paleo-proxies of EECO atmospheric CO2, indicating a good fit between models and proxy-data.
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