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Abstract. Historical time series of surface temperature and ocean heat content changesare commonly used metrics to diagnose climate change and estimate propertiesof the climate system. We show that recent trends, namely the slowing ofsurface temperature rise at the beginning of the 21st century and theacceleration of heat stored in the deep ocean, have a substantial impact onthese estimates. Using the Massachusetts Institute of Technology Earth SystemModel (MESM), we vary three model parameters that influence the behavior ofthe climate system: effective climate sensitivity (ECS), the effective oceandiffusivity of heat anomalies by all mixing processes (Kv), and the netanthropogenic aerosol forcing scaling factor. Each model run is compared toobserved changes in decadal mean surface temperature anomalies and the trendin global mean ocean heat content change to derive a joint probabilitydistribution function for the model parameters. Marginal distributions forindividual parameters are found by integrating over the other two parameters.To investigate how the inclusion of recent temperature changes affects ourestimates, we systematically include additional data by choosing periods thatend in 1990, 2000, and 2010. We find that estimates of ECS increase inresponse to rising global surface temperatures when data beyond 1990 areincluded, but due to the slowdown of surface temperature rise in the early21st century, estimates when using data up to 2000 are greater than when dataup to 2010 are used. We also show that estimates of Kv increase inresponse to the acceleration of heat stored in the ocean as data beyond 1990are included. Further, we highlight how including spatial patterns of surfacetemperature change modifies the estimates. We show that including latitudinalstructure in the climate change signal impacts properties with spatialdependence, namely the aerosol forcing pattern, more than properties definedfor the global mean, climate sensitivity, and ocean diffusivity.

Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates.

Abstract. Historical time series of surface temperature and ocean heat content changesare commonly used metrics to diagnose climate change and estimate propertiesof the climate system. We show that recent trends, namely the slowing ofsurface temperature rise at the beginning of the 21st century and theacceleration of heat stored in the deep ocean, have a substantial impact onthese estimates. Using the Massachusetts Institute of Technology Earth SystemModel (MESM), we vary three model parameters that influence the behavior ofthe climate system: effective climate sensitivity (ECS), the effective oceandiffusivity of heat anomalies by all mixing processes (Kv), and the netanthropogenic aerosol forcing scaling factor. Each model run is compared toobserved changes in decadal mean surface temperature anomalies and the trendin global mean ocean heat content change to derive a joint probabilitydistribution function for the model parameters. Marginal distributions forindividual parameters are found by integrating over the other two parameters.To investigate how the inclusion of recent temperature changes affects ourestimates, we systematically include additional data by choosing periods thatend in 1990, 2000, and 2010. We find that estimates of ECS increase inresponse to rising global surface temperatures when data beyond 1990 areincluded, but due to the slowdown of surface temperature rise in the early21st century, estimates when using data up to 2000 are greater than when dataup to 2010 are used. We also show that estimates of Kv increase inresponse to the acceleration of heat stored in the ocean as data beyond 1990are included. Further, we highlight how including spatial patterns of surfacetemperature change modifies the estimates. We show that including latitudinalstructure in the climate change signal impacts properties with spatialdependence, namely the aerosol forcing pattern, more than properties definedfor the global mean, climate sensitivity, and ocean diffusivity.

For over 20 years, the Massachusetts Institute of Technology Earth System Model (MESM) has been used extensively for climate change research. The model is under continuous development with components being added and updated. To provide transparency in the model development, we perform a baseline evaluation by comparing model behavior and properties in the newest version to the previous model version. In particular, changes resulting from updates to the land surface model component and the input forcings used in historical simulations of climate change are investigated. We run an 1800-member ensemble of MESM historical climate simulations where the model parameters that set climate sensitivity, the rate of ocean heat uptake, and the net anthropogenic aerosol forcing are systematically varied. By comparing model output to observed patterns of surface temperature changes and the linear trend in the increase in ocean heat content, we derive probability distributions for the three model parameters. Furthermore, we run a 372-member ensemble of transient climate simulations where all model forcings are fixed and carbon dioxide concentrations are increased at the rate of 1 % yearĝ'1. From these runs, we derive response surfaces for transient climate response and thermosteric sea level rise as a function of climate sensitivity and ocean heat uptake. We show that the probability distributions shift towards higher climate sensitivities and weaker aerosol forcing when using the new model and that the climate response surfaces are relatively unchanged between model versions. Because the response surfaces are independent of the changes to the model forcings and similar between model versions with different land surface models, we suggest that the change in land surface model has limited impact on the temperature evolution in the model. Thus, we attribute the shifts in parameter estimates to the updated model forcings.

This paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The focus is on long-term climate projections designed to 1) quantify the climate change commitment of different radiative forcing trajectories and 2) explore the extent to which climate change is reversible on human time scales. All commitment simulations follow the four representative concentration pathways (RCPs) and their extensions to year 2300. MostEMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year-2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near-preindustrial values in most models for RCPs 2.6-6.0. The MOC weakening is more persistent for RCP8.5. Elimination of anthropogenic CO2 emissions after 2300 results in slowly decreasing atmospheric CO2 concentrations. At year 3000 atmospheric CO2 is still at more than half its year-2300 level in all EMICs forRCPs 4.5-8.5. Surface air temperature remains constant or decreases slightly and thermosteric sea level rise continues for centuries after elimination ofCO2 emissions in allEMICs.Restoration of atmosphericCO2 fromRCPto preindustrial levels over 100-1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to preindustrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2. © 2013 American Meteorological Society.

After many years with little change in community views on Equilibrium Climate Sensitivity (ECS), in 2021 the IPCC concluded that it was much better known than previously, supported by a major assessment the previous year. This development underpinned increased confidence in long-term climate changes in that report. Here, we place this development in historical context, briefly assess progress since then, and discuss the challenges and opportunities for further improving our knowledge of this iconic concept. We argue that the probability distributions published in those assessments are still approximately valid; while various subsequent studies have claimed further narrowing, they have omitted important structural uncertainties that should be included. The distributions could nonetheless be narrowed in the future, particularly through better understanding of certain climate processes and paleoclimate proxies. Not all touted strategies are truly helpful, however. We also note that as increasingly strong mitigation (i.e., "net-zero") scenarios are considered, ECS becomes less informative about future climate change compared to other factors such as aerosol radiative forcing, carbon cycle and vegetation processes, and influences on regional change such as ocean dynamics. [ABSTRACT FROM AUTHOR]

Decisions on how to manage future flood risks are frequently informed by both sophisticated and computationally expensive models. This complexity often limits the representation of uncertainties and the consideration of strategies. Here we use an intermediate complexity model framework that enables us to analyze a richer set of strategies, a wider range of objectives, and greater levels of uncertainty than are typically considered by more sophisticated and computationally expensive models. We find that allowing for more combinations of risk mitigation strategies can help expand the solution set, help explain synergies and trade‐offs, and point to strategies that can improve outcomes. [ABSTRACT FROM AUTHOR]

In formulating a response to I the risk of future human influence on the climate, it is important to understand what has happened in the recent past. Has the climate [...]

We derive joint probability density distributions for three key uncertain properties of the climate system, using an optimal fingerprinting approach to compare simulations of an intermediate complexity climate model with three distinct diagnostics of recent climate observations. On the basis of the marginal probability distributions, the 5 to 95% confidence intervals are 1.4 to 7.7 kelvin for climate sensitivity and -0.30 to -0.95 watt per square meter for the net aerosol forcing. The oceanic heat uptake is not well constrained, but ocean temperature observations do help to constrain climate sensitivity. The uncertainty in the net aerosol forcing is much smaller than the uncertainty range for the indirect aerosol forcing alone given in the Intergovernmental Panel on Climate Change Third Assessment Report., Estimation of the uncertainty in long-term climate projections requires estimates of the probability density functions (pdfs) of key properties of the climate system. Attempts thus far (1, 2) have used [...]

We present a method for deducing paleoaltitudes that incorporates basic physical principles of atmospheric science and inferences of paleoclimates from plant leaf physiognomy. We exploit the average west-to-east flow of the atmosphere at mid-latitudes and a thermodynamically conserved variable in the atmospheremoist static energy (the combined internal, latent heat, and gravitational potential energy of moist air)to develop a method that relies on a parameter that varies with height in the atmosphere in a predictable fashion. Because the surface distribution of moist static energy is constrained by atmospheric dynamics and thermodynamics, the combined internal and latent heat energies, also known as the moist enthalpy, should only vary with altitude provided we know the distribution of moist static energy. Thus, we avoid having to make assumptions about the mean annual temperature lapse rate, which varies spatially and temporally owing to unpredictable variations in atmospheric water vapor. To estimate a paleoaltitude, therefore, we require (1) a prior knowledge of the spatial distribution of moist static energy for the paleoclimate and (2) the ability to estimate paleoenthalpy for two isochronous locations: one at sea level, the other at some unknown elevation. To achieve this, we investigated the spatial distribution of moist static energy for the present-day climate of North America to estimate the deviations in moist static energy from zonal invariance. In parallel, we quantified the relation between moist enthalpy and plant leaf physiognomy of modern forests. Assuming that such deviations from zonal invariance and such relationships between physiognomy and enthalpy apply to ancient climates and fossil leaves, these investigations yield an uncertainty estimate of +/-910 m in the paleoaltitude difference between two isochronous fossil assemblage locations.

Comparisons of the physiognomy of leaves from modern vegetation of known climates with that of Eocene and Oligocene fossil leaf assemblages from middle latitudes of western North America indicate paleoaltitudes comparable or higher than those at present. Using canonical correspondence analysis, a multivariate statistical approach that includes nonlinear relationships between characters and environmental parameters, we relate leaf physiognomy of fossil samples to modern vegetation of known environmental parameters, including moist enthalpy of the atmosphere, a thermodynamically conserved quantity of the atmosphere that varies with altitude. By estimating enthalpy values for both lowland leaf assemblages and coeval interior leaf assemblages, altitudes for the interior assemblages can be estimated to [+ or -]890 m. In northeastern Washington and southern British Columbia, the high Eocene altitudes indicate subsidence, or collapse, of an area undergoing crustal extension and may reflect an immediately preceding period of uplift that triggered the extension. Similarly, other areas that have undergone Cenozoic crustal extension appear to have been at least as high as they are at present. Three sites from the Rocky Mountains also indicate elevations at least as high as at present, and therefore suggest subsidence, either resulting from cooling of a hot upper mantle or erosion and isostatic compensation of surrounding terrain. High altitudes during Eocene and Oligocene time in western North America appear to have been normal, even in areas such as the Green River basin, and therefore cast doubt on the commonly inferred late Cenozoic uplift of that region.

Leaf physiognomy provides estimates of environmental parameters, including mean annual enthalpy, which is a thermodynamic parameter of the atmosphere that varies with altitude. Analyses of 12 mid-Miocene floras from western [...]

A new alternative paleobotanical method is developed to study the past changes in the average elevations of large continental areas. The method is based on energy conservation in the atmosphere, where the enthalpy is determined instead of the temperature. The influences exerted by the paleoclimate does not affect the results of paleoelevation obtained by this method.