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Equilibrium climate sensitivity (ECS) quantifies the amount of warming resulting from a doubling of the atmospheric CO2 forcing. Despite recent advancements in climate simulation capabilities and global observations, there remains large uncertainty on the degree of future warming. To help alleviate this uncertainty, past climates provide a valuable insight into how the Earth will respond to elevated atmospheric CO2. However, there is evidence to suggest that ECS is dependent on background climate warmth, which may interfere with the direct utilization of paleo-ECS to understand present-day ECS. Thus, it is important that a range of different climate states are considered to better understand the factors modulating the relationship between CO2 and temperature. In this study, we focus on three time intervals: the mid-Pliocene Warm Period (3.3 – 3.0 Ma), the mid-Miocene (16.75 – 14.5 Ma), and the early Eocene (~50 Ma), in order to sample ECS from Cenozoic coolhouse to hothouse climates. Here, we combine the Bayesian framework of constraining the ECS and its uncertainty with several published methods to estimate the global mean surface temperature (GMST) from sparse proxy records. This framework utilizes an emergent constraint between the simulated GMST changes and climate sensitivities across the model ensemble. For each time interval, we employ a combination of parametric and non-parametric functions, coupled with a probabilistic approach to derive a refined estimate. Preliminary results for the Pliocene indicate a GMST reconstruction of approximately 19.3°C, which is higher than previous estimates that were derived using only marine records. Using this estimate, we calculate an ECS that is also higher than previously published values, especially due to the inclusion of high-latitude terrestrial temperature records into our estimates. Intriguingly, using the consistent methodology, our calculated ECS for the early Eocene is lower than that of the mid-Pliocene. This result does not support an amplified ECS in hothouse climate, and points to a potentially important role of ice albedo feedback in amplifying the ECS in coolhouse climate. Ongoing work will apply the same methodology to the mid-Miocene and further investigate the source for the estimated ECS state dependency between these climate intervals. 

The response of the terrestrial biosphere to warming remains one of the most poorly understood and quantified aspects of the climate system. One way to test the behavior of the Earth system in warm climate states is to examine the geological record. The abundance, distribution, and/or isotopic composition of source-specific organic molecules (biomarkers) have been used to reconstruct terrestrial paleoenvironmental change over a range of geological timescales. Here, we review new or recently improved biomarker approaches for reconstructing ( a) physical climate variables (land temperature, rainfall), ( b) ecosystem state variables (vegetation, fire regime), and ( c) biogeochemical variables (soil residence time, methane cycling). This review encompasses a range of key compound classes (e.g., lipids, lignin, and carbohydrates). In each section, we explore the concept behind key biomarker approaches and discuss their successes as paleoenvironmental indicators. We emphasize that analyzing several biomarkers in tandem can provide unique insights into the Earth system. ▪ Biomarkers can be used to reconstruct terrestrial environmental change over a range of geological timescales. ▪ Analyzing several biomarkers in tandem can provide unique insights into the Earth system. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 50 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

The latitudinal temperature gradient is a fundamental state parameter of the climate system tied to the dynamics of heat transport and radiative transfer. Thus, it is a primary target for temperature proxy reconstructions and global climate models. However, reconstructing the latitudinal temperature gradient in past climates remains challenging due to the scarcity of appropriate proxy records and large proxy–model disagreements. Here, we develop methods leveraging an extensive compilation of planktonic foraminifera δ 18 O to reconstruct a continuous record of the latitudinal sea-surface temperature (SST) gradient over the last 95 million years (My). We find that latitudinal SST gradients ranged from 26.5 to 15.3 °C over a mean global SST range of 15.3 to 32.5 °C, with the highest gradients during the coldest intervals of time. From this relationship, we calculate a polar amplification factor (PAF; the ratio of change in >60° S SST to change in global mean SST) of 1.44 ± 0.15. Our results are closer to model predictions than previous proxy-based estimates, primarily because δ 18 O-based high-latitude SST estimates more closely track benthic temperatures, yielding higher gradients. The consistent covariance of δ 18 O values in low- and high-latitude planktonic foraminifera and in benthic foraminifera, across numerous climate states, suggests a fundamental constraint on multiple aspects of the climate system, linking deep-sea temperatures, the latitudinal SST gradient, and global mean SSTs across large changes in atmospheric CO 2 , continental configuration, oceanic gateways, and the extent of continental ice sheets. This implies an important underlying, internally driven predictability of the climate system in vastly different background states. 

Abstract. Accurate estimates of past global mean surface temperature (GMST) help tocontextualise future climate change and are required to estimate thesensitivity of the climate system to CO2 forcing through Earth's history.Previous GMST estimates for the latest Paleocene and early Eocene(∼57 to 48 million years ago) span a wide range(∼9 to 23 ∘C higher than pre-industrial) andprevent an accurate assessment of climate sensitivity during this extremegreenhouse climate interval. Using the most recent data compilations, weemploy a multi-method experimental framework to calculate GMST during thethree DeepMIP target intervals: (1) the latest Paleocene (∼57 Ma), (2) the Paleocene–Eocene Thermal Maximum (PETM; 56 Ma), and (3) the earlyEocene Climatic Optimum (EECO; 53.3 to 49.1 Ma). Using six differentmethodologies, we find that the average GMST estimate (66 % confidence)during the latest Paleocene, PETM, and EECO was 26.3 ∘C (22.3 to28.3 ∘C), 31.6 ∘C (27.2 to 34.5 ∘C), and27.0 ∘C (23.2 to 29.7 ∘C), respectively. GMST estimatesfrom the EECO are ∼10 to 16 ∘C warmer thanpre-industrial, higher than the estimate given by the Intergovernmental Panel on Climate Change (IPCC) 5thAssessment Report (9 to 14 ∘C higher than pre-industrial).Leveraging the large “signal” associated with these extreme warm climates,we combine estimates of GMST and CO2 from the latest Paleocene, PETM,and EECO to calculate gross estimates of the average climate sensitivitybetween the early Paleogene and today. We demonstrate that “bulk”equilibrium climate sensitivity (ECS; 66 % confidence) during the latestPaleocene, PETM, and EECO is 4.5 ∘C (2.4 to 6.8 ∘C),3.6 ∘C (2.3 to 4.7 ∘C), and 3.1 ∘C (1.8 to4.4 ∘C) per doubling of CO2. These values are generallysimilar to those assessed by the IPCC (1.5 to 4.5 ∘C per doublingCO2) but appear incompatible with low ECS values (<1.5 perdoubling CO2). 

As the world warms, there is a profound need to improve projections of climate change. Although the latest Earth system models offer an unprecedented number of features, fundamental uncertainties continue to cloud our view of the future. Past climates provide the only opportunity to observe how the Earth system responds to high carbon dioxide, underlining a fundamental role for paleoclimatology in constraining future climate change. Here, we review the relevancy of paleoclimate information for climate prediction and discuss the prospects for emerging methodologies to further insights gained from past climates. Advances in proxy methods and interpretations pave the way for the use of past climates for model evaluation—a practice that we argue should be widely adopted.