Can the Last Glacial Maximum constrain climate sensitivity?

Previous work demonstrated a significant correlation between tropical surface air temperature and equilibrium climate sensitivity (ECS) in PMIP (Paleoclimate Modelling Intercomparison Project) phase 2 model simulations of the last glacial maximum (LGM). This implies that reconstructed LGM cooling in this region could provide information about the climate system ECS value. We analyze results from new simulations of the LGM performed as part of Coupled Model Intercomparison Project (CMIP5) and PMIP phase 3. These results show no consistent relationship between the LGM tropical cooling and ECS. A radiative forcing and feedback analysis shows that a number of factors are responsible for this decoupling, some of which are related to vegetation and aerosol feedbacks. While several of the processes identified are LGM specific and do not impact on elevated CO2 simulations, this analysis demonstrates one area where the newer CMIP5 models behave in a qualitatively different manner compared with the older ensemble. The results imply that so‐called Earth System components such as vegetation and aerosols can have a significant impact on the climate response in LGM simulations, and this should be taken into account in future analyses.

Results are presented from the Community Climate System Model, version 4 (CCSM4), simulation of the Last Glacial Maximum (LGM) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) at the standard 1° resolution, the same resolution as the majority of the CCSM4 CMIP5 long-term simulations for the historical and future projection scenarios. The forcings and boundary conditions for this simulation follow the protocols of the Paleoclimate Modeling Intercomparison Project, version 3 (PMIP3). Two additional CCSM4 CO2 sensitivity simulations, in which the concentrations are abruptly changed at the start of the simulation to the low 185 ppm LGM concentrations (LGMCO2) and to a quadrupling of the preindustrial concentration (4×CO2), are also analyzed. For the full LGM simulation, the estimated equilibrium cooling of the global mean annual surface temperature is 5.5°C with an estimated radiative forcing of −6.2 W m−2. The radiative forcing includes the effects of the reduced LGM greenhouse gases, ice sheets, continental distribution with sea level lowered by approximately 120 m from the present, and orbital parameters, but not changes to atmospheric aerosols or vegetation biogeography. The LGM simulation has an equilibrium climate sensitivity (ECS) of 3.1(±0.3)°C, comparable to the CCSM4 4×CO2 result. The LGMCO2 simulation shows a greater ECS of 4.2°C. Other responses found at the LGM in CCSM4 include a global precipitation rate decrease at a rate of ~2% °C−1, similar to climate change simulations in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4); a strengthening of the Atlantic meridional overturning circulation (AMOC) with a shoaling of North Atlantic Deep Water and a filling of the deep basin up to sill depth with Antarctic Bottom Water; and an enhanced seasonal cycle accompanied by reduced ENSO variability in the eastern Pacific Ocean’s SSTs.

<strong class="journal-contentHeaderColor">Abstract.</strong> The use of paleoclimates to constrain the equilibrium climate sensitivity (ECS) has seen a growing interest. In particular, the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period have been used in emergent constraint approaches using simulations from the Paleoclimate Modelling Intercomparison Project (PMIP). Despite lower uncertainties regarding geological proxy data for the LGM in comparison with the Pliocene, the robustness of the emergent constraint between LGM temperature and ECS is weaker at both global and regional scales. Here, we investigate the climate of the LGM in models through different PMIP generations, and how various factors contribute to the spread of the model ensemble. Certain factors have large impact on an emergent constraint, such as state-dependency in climate feedbacks or model-dependency on ice sheet forcing. Other factors, such as models being out of energetic balance and sea-surface temperature not responding below -1.8 &deg;C in polar regions have a limited influence. We quantify some of the contributions and find that they mostly have extratropical origins. Contrary to what has previously been suggested, from a statistical point of view, the PMIP model generations do not differ substantially. Finally, we show that the lack of high or low ECS models in the ensembles critically limits the strength and reliability of the emergent constraints.

Dynamical downscaling of simulations of the Last Glacial Maximum (LGM) and late twentieth century (20C) were conducted using the Weather Research and Forecasting (WRF) model with the aim of (1) understanding how the downscaled kinematic and thermodynamic variables influence simulated tropical cyclone (TC) activity over the western North Pacific during the LGM and the 20C periods and (2) to test the relevance of TC genesis factors for the colder LGM climate. The results show that, despite the lower temperatures during the LGM, the downscaled TC climatology over the western North Pacific in the LGM simulation does not differ significantly from that in the 20C simulation. Among the TC environmental factors, the TC potential intensity, mid‐tropospheric entropy deficit, and vertical wind shear during the LGM were consistent with previous analyses of TC genesis factors in LGM global climate model simulations. Changes in TC genesis density between the LGM and the 20C simulations seem to be well represented by the ventilation index, a nondimensional measure of the combined effects of vertical wind shear, and thermodynamic properties, suggesting the potential applicability of those factors for TC activity evaluation during the LGM and possibly other climates.

The climate during the Last Glacial Maximum (LGM) has been simulated using the UK Universities Global Atmospheric Modelling Programme (UGAMP) general circulation model (GCM) with both prescribed sea surface temperatures (SSTs) based on the CLIMAP reconstruction and computed SSTs with a simple thermodynamic slab ocean. Consistent with the Paleoclimate Modelling Intercomparison Project (PMIP), the other boundary conditions include the large changes in ice-sheet topography and geography, a lower sea level, a lower concentration of CO2 in the atmosphere, and a slightly different insolation pattern at the top of the atmosphere. The results are analysed in terms of changes in atmospheric circulation. Emphasis is given to the changes in surface temperatures, planetary waves, storm tracks and the associated changes in distribution of precipitation. The model responds in a similar manner to the changes in boundary conditions to previous studies in global mean statistics, but differs in its treatment of regional climates. Results also suggest that both the land ice sheets and sea ice introduce significant changes in planetary waves and transient eddy activity, which in turn affect regional climates. The computed SST simulations predict less sea ice and cooler tropical temperatures than those based on CLIMAP SSTs. It is unclear as to whether this is a model and/or a data problem, but the resulting changes in land temperatures and precipitation can be large. Snow mass budget analysis suggests that there is net ice loss along the southern edges of the Laurentide and Fennoscandian ice sheets and net ice gain over other parts of the two ice sheets. The net accumulation is mainly due to the decrease in ablation in the cold climate rather than to the changes in snowfall. The characteristics of the Greenland ice-sheet mass balance in the LGM simulations is also quite different from those in the present-day (PD) simulations. The ablation in the LGM simulations is negligible while it is a very important process in the ice mass budget in the PD simulations.

The quasi-biennial oscillation (QBO) and sudden stratospheric warmings (SSWs) during the Last Glacial Maximum (LGM) are investigated in simulations using the Whole Atmosphere Community Climate Model version 6 (WACCM6). We find that the period of QBO, which is 27 months in the preindustrial and modern climate simulations, was 33 months in the LGM simulation using the proxy sea surface temperatures (SSTs) and 41 months using the model-based LGM SSTs. We show that the longer QBO period in the LGM is due to weaker wave forcing. The WACCM6 simulations of the LGM, preindustrial, and modern climates do not support previous modeling work that suggests that the QBO amplitude is smaller (larger) in a warmer (colder) climate. We find that SSWs in the LGM occurred later in the year, as compared to the preindustrial and modern climate, but that time of the final warming was similar. The difference in SSW frequency is inconclusive.

Equilibrium climate sensitivity (ECS) and hydrological sensitivity describe the global mean surface temperature and precipitation responses to a doubling of atmospheric CO2. Despite their connection via the Earth’s energy budget, the physical linkage between these two metrics remains controversial. Here, using a global climate model with a perturbed mean hydrological cycle, we show that ECS and hydrological sensitivity per unit warming are anti-correlated owing to the low-cloud response to surface warming. When the amount of low clouds decreases, ECS is enhanced through reductions in the reflection of shortwave radiation. In contrast, hydrological sensitivity is suppressed through weakening of atmospheric longwave cooling, necessitating weakened condensational heating by precipitation. These compensating cloud effects are also robustly found in a multi-model ensemble, and further constrained using satellite observations. Our estimates, combined with an existing constraint to clear-sky shortwave absorption, suggest that hydrological sensitivity could be lower by 30% than raw estimates from global climate models. The connections between global mean temperature and precipitation responses to CO2 doubling (equilibrium climate and hydrological sensitivity) are driven through low-cloud responses to surface warming, according to MIROC5 perturbation experiments.

&amp;lt;p&amp;gt;There is much current debate about the way in which the earth's climate and temperature are responding to anthropogenic and natural forcing. Current forecasts are dependent on the accuracy of how Equilibrium Climate Sensitivity (ECS) is evaluated. In the recent wide-ranging ECS sensitivity review and assessments of Sherwood et al (2020, &amp;lt;em&amp;gt;Rev. Geophys.&amp;lt;/em&amp;gt; &amp;lt;strong&amp;gt;58&amp;lt;/strong&amp;gt;, e2019RG000678), they report a baseline ECS 5%&amp;amp;#8211;95% with a range of 2.3 &amp;amp;#176;C to 4.7 &amp;amp;#176;C. While this is an important and much needed quantification &amp;amp;#8211; this wide ECS uncertainty range, based largely on uncertainty around total radiative forcing (TRF), is also problematic for our geophysical based community. The ECS value as viewed by policy and global strategy negotiators is currently only understood to a 70% (= 2.4 / 3.5) physically resolved level of resolution. An open question (to discuss in this talk) is it possible to resolve this physical measurement more accurately and what are the current main issues that need to be accommodated? As part of this discussion we present the work of Young, Allen and Bruun (2021): &amp;lt;em&amp;gt;a re-evaluation of the Earth's surface temperature response to radiative forcing &amp;lt;/em&amp;gt;(2021, &amp;lt;em&amp;gt;ERL&amp;lt;/em&amp;gt;, &amp;lt;strong&amp;gt;16&amp;lt;/strong&amp;gt;, 054068). In that paper we have re-assessed the current evidence at the globally averaged level by adopting a generic 'data-based mechanistic' modelling strategy that incorporates statistically efficient parameter estimation. This identifies a low order, differential equation model that explains how the global average surface temperature variation responds to the influences of total radiative forcing. The model response includes a novel, stochastic oscillatory component with a period of about 55 years (range 51.6&amp;amp;#8211;60 years) that appears to be associated with heat energy interchange between the atmosphere and the ocean. These 'quasi-cycle' oscillations, which account for the observed pauses in global temperature increase around 1880, 1940 and 2001, appear to be related to ocean dynamic responses, particularly the Atlantic multidecadal oscillation. The model explains 90% of the variance in the global average surface temperature anomaly and yields estimates of the equilibrium climate sensitivity (ECS) (2.29 &amp;amp;#176;C with 5%&amp;amp;#8211;95% range 2.11 &amp;amp;#176;C to 2.49 &amp;amp;#176;C) and the transient climate response (TCR) (1.56 &amp;amp;#176;C with 5%&amp;amp;#8211;95% range 1.43 &amp;amp;#176;C to 1.68 &amp;amp;#176;C), both of which are smaller than most previous estimates. When a high level of uncertainty in the TRF is taken into account, the ECS and TCR estimates are unchanged but the ranges are increased to 1.43 &amp;amp;#176;C to 3.14 &amp;amp;#176;C and 0.99 &amp;amp;#176;C to 2.16 &amp;amp;#176;C, respectively. This then gives the 70% physical resolution limit in ECS mentioned above. Current work is in progress to test this ECS re-evaluation approach using the CMIP6 models. We will discuss some on-going findings of these model signal assessments which include a specific focus on resolution of Atlantic and Pacific Ocean pentedecadal modes. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Peter C Young, P Geoffrey Allen and John T Bruun (2021). A re-evaluation of the Earth's surface temperature response to radiative forcing, &amp;lt;em&amp;gt;Environ. Res. Lett.&amp;lt;/em&amp;gt; &amp;lt;strong&amp;gt;16&amp;lt;/strong&amp;gt; 054068, https://iopscience.iop.org/article/10.1088/1748-9326/abfa50.&amp;lt;/p&amp;gt;

A numerical study of the South Atlantic circulation at the Last Glacial Maximum

Relationships between climate variability and climate sensitivity are to be expected where the damping of a climatic anomaly is due to a change in the energy balance of the planet, such that the Fluctuation-Dissipation theorem heuristically applies [Leith, 1975]. A recent attempt to relate Equilibrium Climate Sensitivity (ECS) to global temperature variability over the historical period suggested a surprisingly tight emergent constraint on ECS [Cox et al., 2018]. However, the sensitivity-variability relationship in that study was partially hidden by anthropogenic forcing over the historical period. Here we examine instead CMIP5 control runs. These runs have no external forcing and therefore provide a much cleaner test of proposed links between internal variability and sensitivity. It has been noted before that there is a positive correlation between decadal temperature variability and climate sensitivity across climate models [Colman & Power, 2018]. Questions remained however as to how robust this relationship is across different model ensembles, what mechanisms are responsible for it, and whether it can be used as an emergent constraint on climate sensitivity. We examine the relationship between decadal variability and ECS using models of varying complexity, including CMIP5 control runs and a range of conceptual energy balance models for which analytical solutions are presented. Based on these results, a general mechanism becomes apparent and the shape of the relationship is determined to be more quadratic than linear. The nonlinearity has implications for using this relationship as an emergent constraint, where an incorrect assumption of linearity might lead to biased estimates. A further surprising implication of the study is that a slowdown in global warming does not necessarily imply that climate sensitivity is lower than previously estimated. Models with a higher sensitivity, but which broadly reproduce the long-term record of global warming, are actually more likely to have slow-down periods than models with lower sensitivity.

Cloud feedback is a dominant source of uncertainty in climate model estimates of equilibrium climate sensitivity (ECS). Cloud controlling factor analysis can observationally constrain cloud feedback. For the first time, we use separate rather than unified frameworks to assess high- and low-cloud feedbacks and constrain the net cloud feedback and subsequently, the ECS. We find a robustly positive cloud feedback (i.e., a negative feedback is &lt;0.5% probable), indicating that clouds amplify global warming. We assess the individual and combined impacts of our cloud feedback constraints on ECS using three approaches. Two indicate an upward ECS shift with reduced uncertainty, preserving ECS–feedback correlations but using cloud feedback as a single line of evidence. The third, a Bayesian framework combining multiple lines of evidence, also suggests a higher ECS but with a smaller increase and broader confidence range.

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

The equilibrium climate sensitivity of a climate model is usually defined as the globally averaged equilibrium surface temperature response to a doubling of carbon dioxide. This is virtually always estimated in a version with a slab model for the upper ocean. The question is whether this estimate is accurate for the full climate model version, which includes a full-depth ocean component. This question has been answered for the low-resolution version of the Community Climate System Model, version 3 (CCSM3). The answer is that the equilibrium climate sensitivity using the full-depth ocean model is 0.14°C higher than that using the slab ocean model, which is a small increase. In addition, these sensitivity estimates have a standard deviation of nearly 0.1°C because of interannual variability. These results indicate that the standard practice of using a slab ocean model does give a good estimate of the equilibrium climate sensitivity of the full CCSM3. Another question addressed is whether the effective climate sensitivity is an accurate estimate of the equilibrium climate sensitivity. Again the answer is yes, provided that at least 150 yr of data from the doubled carbon dioxide run are used.

Cloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof‐of‐concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere‐only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present‐day runs using multiple satellite climatologies and by comparing simulated δ18O of precipitation (δ18Op), known to be sensitive to parameterization schemes, with a global database of speleothem δ18O records covering the Last Glacial Maximum (LGM), mid‐Holocene (MH) and pre‐industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ18Op values across all time periods, along with LGM and MH δ18Op anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non‐stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations.

Abstract. The equilibrium climate sensitivity (ECS) – the equilibrium global mean temperature response to a doubling of atmospheric CO2 – is a high-profile metric for quantifying the Earth system's response to human-induced climate change. A widely applied approach to estimating the ECS is the “Gregory method” (Gregory et al., 2004), which uses an ordinary least squares (OLS) regression between the net radiative flux, N, and surface air temperature anomalies, ΔT, from a 150 year experiment in which atmospheric CO2 concentrations are quadrupled. The ECS is determined by extrapolating the linear fit to N=0, i.e. the ΔT-intercept, indicating the point at which the system is back in equilibrium. This method has been used to compare ECS estimates across the CMIP5 and CMIP6 ensembles and will likely be a key diagnostic for CMIP7. Despite its widespread application, there is little consistency or transparency between studies in how the climate model data is processed prior to the regression, leading to potential discrepancies in ECS estimates. We identify 32 alternative data processing pathways, varying by differences in global mean weighting, net radiative flux variable, anomaly calculation method, and linear regression fit. Using 44 CMIP6 models, we systematically assess the impact of these choices on ECS estimates and calculate uncertainty ranges using two bootstrap approaches. While the inter-model ECS range is insensitive to the data processing pathway, individual outlier models exhibit notable differences. Approximating a model's native grid cell area (if irregular) with cosine of the latitude can decrease the ECS by 11 %, the choice of N-variable can change the ECS by 6 %, and some anomaly calculation methods can introduce spurious temporal correlations in the processed data. Beyond data processing choices, we also evaluate an alternative linear regression method – total least squares (TLS) – which has a more statistically robust basis than OLS. However, for consistency with previous literature, and given TLS may reduce the ECS compared to OLS (by up to 24 %), thereby making a known bias in the Gregory method worse, we do not feel there is sufficient clarity to recommend a transition to TLS in all cases. To improve reproducibility and comparability in future studies, we recommend a standardised Gregory method: weighting the global mean by cell area, using the top of the atmosphere (as opposed to the top of model) N-variable, and calculating anomalies by first applying a rolling average to the preindustrial control timeseries then subtracting from the raw CO2 quadrupling experiment. This approach accounts for model drift while reducing noise in the data to best meet the pre-conditions of the linear regression. While CMIP6 results of the multi-model mean ECS appear insensitive to these processing choices, similar assumptions may not hold for CMIP7, underscoring the need for standardised data preparation in future climate sensitivity assessments.