Search for: All records

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. 

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). 

Abstract Earth's hydrological cycle is expected to intensify in response to global warming, with a “wet‐gets‐wetter, dry‐gets‐drier” response anticipated over the ocean. Subtropical regions (∼15°–30°N/S) are predicted to become drier, yet proxy evidence from past warm climates suggests these regions may be characterized by wetter conditions. Here we use an integrated data‐modeling approach to reconstruct global and zonal‐mean rainfall patterns during the early Eocene (∼56–48 million years ago). The Deep‐Time Model Intercomparison Project (DeepMIP) model ensemble indicates that the mid‐ (30°–60°N/S) and high‐latitudes (>60°N/S) are characterized by a thermodynamically dominated hydrological response to warming and overall wetter conditions. The tropical band (0°–15°N/S) is also characterized by wetter conditions, with several DeepMIP models simulating narrowing of the Inter‐Tropical Convergence Zone. However, the latter is not evident from the proxy data. The subtropics are characterized by negative precipitation‐evaporation anomalies (i.e., drier conditions) in the DeepMIP models, but there is surprisingly large inter‐model variability in mean annual precipitation (MAP). Intriguingly, we find that models with weaker meridional temperature gradients (e.g., CESM, GFDL) are characterized by a reduction in subtropical moisture divergence, leading to an increase in MAP. These model simulations agree more closely with our new proxy‐derived precipitation reconstructions and other key climate metrics and imply that the early Eocene was characterized by reduced subtropical moisture divergence. If the meridional temperature gradient was even weaker than suggested by those DeepMIP models, circulation‐induced changes may have outcompeted thermodynamic changes, leading to wetter subtropics. This highlights the importance of accurately reconstructing zonal temperature gradients when reconstructing past rainfall patterns.