DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

Abstract

<div> <div> <div>We present results from an ensemble of eight climate models, each of which has carried out simulations of theearly Eocene climate optimum (EECO, ∼50 million years ago). These simulations have been carried out in the framework of DeepMIP (www.deepmip.org), and as such all models have been configured with the same paleogeographic and vegetation boundary conditions. The results indicate that these non-CO<sub>2</sub> boundary conditions contribute between 3 and 5<sup>o</sup>C to Eocene warmth. Compared to results from previous studies, the DeepMIP simulations show in general reduced spread of global mean surface temperature response across the ensemble for a given atmospheric CO<sub>2</sub> concentration, and an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with preindustrial arises mostly from decreases in emissivity due to the elevated CO<sub>2</sub> (and associated water vapour and long-wave cloud feedbacks), whereas in terms of the meridional temperature gradient, the reduction in the Eocene is primarily due to emissivity and albedo changes due to the non-CO<sub>2</sub> boundary conditions (i.e. removal of the Antarctic ice sheet and changes in vegetation). Three of the models (CESM, GFDL, and NorESM) show results that are consistent with the proxies in terms of global mean temperature, meridional SST gradient, and CO<sub>2</sub>, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale the models lack skill. In particular, in the southwest Pacific, the modelled anomalies are substantially less than indicated by the proxies; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxiesor models in this region. Our aim is that the documentation of the large scale features and model-data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability; and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols</div> </div> </div>

Maastrichtian–Rupelian paleoclimates in the southwest Pacific – a critical re-evaluation of biomarker paleothermometry and dinoflagellate cyst paleoecology at Ocean Drilling Program Site 1172

“…Absolute air temperature reconstructions from soil-derived lipid biomarkers (e.g., using brGDGT-based paleothermometry; e.g., Weijers et al, 2007a;De Jonge et al, 2014a) have also yielded MAAT reconstructions for the Eocene (Pancost et al, 2013;Bijl et al, 2013a;Huurdeman et al, 2020;Lauretano et al, 2021). The resulting MAAT records are more in line with numerical model simulations (Lunt et al, 2021) but considerably colder than the SSTs from the same sections. This is difficult to reconcile in a coastal climate setting, where SST and adjacent air temperature should be broadly consistent.…”

“…Variations in atmospheric CO 2 concentrations (Anagnostou et al, 2016(Anagnostou et al, , 2020Foster et al, 2017) are likely the primary driver of these multi-million-year climatic trends (Cramwinckel et al, 2018). While equatorial proxy-based sea surface temperatures (SSTs) and deep-sea temperatures, assumed to reflect high-latitude SSTs, show good correspondence with numerical model simulations under Eocene boundary conditions and with varying CO 2 forcing (Cramwinckel et al, 2018), proxy-based SST reconstructions of the southwest (SW) Pacific remain warmer than those from model simulations (Bijl et al, 2009;Cramwinckel et al, 2018;Hollis et al, 2019;Crouch et al, 2020;Lunt et al, 2021), despite proposed zonal heterogeneity (Douglas et al, 2014). Specifically, numerical climate models are currently unable to simulate a paleoclimate in which the annual SST difference between the equatorial Atlantic Ocean (Cramwinckel et al, 2018) and the SW Pacific Ocean (Hollis et al, 2012;Bijl et al, 2013a) is as small as the proxy data suggest.…”

Simulation of the mid-Pliocene Warm Period using HadGEM3: Experimental design and results from model-model and model-data comparison

“…Model simulations of past climate states are useful because, among other aspects, they allow us to interrogate the mechanisms that have caused past climate change Lunt et al, 2021). They also give us a global picture of past climate variables (such as sea surface temperature, SST) that can only be reconstructed by geological data at specific locations, and of variables (such as upper atmospheric winds) that cannot be reconstructed by geological data at all.…”

Different Pathways to an Early Eocene Climate

“…We construct our models using the Isca climate modeling framework (Vallis et al, 2018) configured with no sea ice, a slab mixed-layer ocean boundary condition, and a simple representation of land and topography following Eocene-like continental outlines taken from comprehensive climate model simulations of the Eocene (D. J. Lunt et al, 2021). Meridional ocean heat transport is represented by imposing a q-flux, as described further in Section 6, although in many simulations this is set to zero.…”

“…The surface albedo is set to 0.075 over ocean and 0.15 over land which is similar to comprehensive model simulations of the Eocene (D. J. Lunt et al, 2021). Land also differs from oceans by its heat capacity, which we set to 0.2 meters equivalent water depth for continents (Merlis et al, 2013) and 20 meters for oceans, by the roughness constant, which is set to be 10 times higher over land than ocean, and by the land evaporative resistance which is set to 0.5 (parameter in equation 10 of Vallis et al (2018)).…”