Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational datasets. Variability exists not just among the data products, but also within the creation of individual ones. This includes significant variations among ensemble members within a single data product. Using the optimal fingerprint approach combined with Bayesian updating, we quantify the uncertainties in ECS and TCR estimates arising from both individual datasets and their various groupings. Our methodology, utilizing both spatial and temporal data, reveals impacts on the estimates of ECS and TCR. As we assess different groupings of observational data products, we observe that using products sharing identical Sea Surface Temperatures (SST) introduces a discernible biases. Furthermore, these results highlight that the variations among ensemble members within a single data product are as influential as the disparities across multiple data products.

Addressing climate change is ultimately a challenge of risk management, which requires an understanding of the likelihood of potential outcomes. We provide integrated, probabilistic socio-economic and climate projections obtained using updated estimates of probability distributions for key parameters in both the human and Earth system components of the MIT Integrated Global System Model (IGSM). The Reference scenario results in median end-of-century warming of 3.5°C and a 90% range of 2.8–4.3°C, which is lower than the median of 5.7°C from a prior study using a previous version of the IGSM. About 0.5°C of the difference is due to updated estimates in the human system and the rest of the difference is explained by changes in Earth system estimates. Our results show that climate policy lowers the upper tail of temperature change distributions more than the median, and that even relatively modest policies can significantly reduce the likelihood of high global temperature outcomes. Human system uncertainties contribute more to uncertainty in projected CO2 concentrations and total radiative forcing, while Earth system uncertainties have the greatest influence on temperature and precipitation. Including additional uncertain inputs does not automatically increase the outcome range because uncertainties can offset one another. Results also show how policy costs can vary greatly among regions. As we improve understanding of underlying technology and economic factors as well as Earth system response to human forcing, further updating of these estimates of uncertainty can make an important contribution to decision-making about mitigation and adaptation.

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.

Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for 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 % year−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.

The Massachusetts Institute of Technology Integrated Global System Model (IGSM) is designed for analyzing the global environmental changes that may result from anthropogenic causes, quantifying the uncertainties associated with the projected changes, and assessing the costs and environmental effectiveness of proposed policies to mitigate climate risk. The IGSM consists of the Massachusetts Institute of Technology Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model. This paper documents the current version of the MESM, which includes a two‐dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly‐diffusing ocean model.

Surface temperature, upper‐air temperature, and ocean heat content data are used to constrain the distributions of the parameters that define three climate system properties in the MIT Integrated Global Systems Model: effective climate sensitivity, the rate of ocean heat uptake into the deep ocean, and net anthropogenic aerosol forcing. Five different surface temperature data records are used to show that the resulting parameter distribution functions are sensitive to the dataset used to estimate the likelihood of model output given the observed climate records. Estimates of effective climate sensitivity mode and mean differ by as much as 1 K between the datasets, with an overall range of 1.2 to 5.3 K. Ocean effective diffusivity distributions are poorly constrained by any dataset. The overall range of net aerosol forcing values, −0.19 to −0.83 Wm−2, is small compared to other uncertainties in climate forcings. Transient climate response (TCR) estimates derived from these distributions range between 0.87 and 2.41 K and the shapes of individual TCR distributions depend on the surface dataset. Understanding the differences in parameter distributions and climate system properties derived from them is critical for understanding the full range of uncertainty involved in climate model calibration and prediction results.