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We find that part of the uncertainty in the amplitude and pattern of the modeled precipitation response to CO2 forcing traces to tropical condensation not directly involved with parameterized convection. The fraction of tropical rainfall associated with large-scale condensation can vary from a few percent to well over half depending on model details and parameter settings. In turn, because of the coupling between condensation and tropical circulation, the different ways model assumptions affect the large-scale rainfall fraction also affect the patterns of the response within individual models. In two single-model ensembles based on the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM), versions 3.1 and 5.3, we find strong correlations between the fraction of tropical large-scale rain and both climatological rainfall and circulation and the response to CO2 forcing. While the effects of an increasing tropical large-scale rain fraction are opposite in some ways in the two ensembles—for example, the Hadley circulation weakens with the large-scale rainfall fraction in the CAM3.1 ensemble while strengthening in the CAM5.3 ensemble—we can nonetheless understand these different effects in terms of the relationship between latent heating and circulation, and we propose explanations for each ensemble. We compare these results with data from phase 5 of the Coupled Model Intercomparison Project (CMIP5), for which some of the same patterns hold. Given the importance of this partitioning, there is a need for constraining this source of uncertainty using observations. However, since a “large-scale rainfall fraction” is a modeling construct, it is not clear how observations may be used to test various modeling assumptions determining this fraction.

A calibrated single-model ensemble (SME) derived from the NCAR Community Atmosphere Model, version 3.1, is used to test two hypothesized emergent constraints on cloud feedback and equilibrium climate sensitivity (ECS). The Fasullo and Trenberth relative humidity (RH) metric and the Sherwood et al. lower-tropospheric mixing (LTMI) metric are computed for the present-day climate of the SME, and the relationships between the metrics, ECS, and cloud and other climate feedbacks are examined. The tropical convergence zone relative humidity (RHM) and the parameterized lower-tropospheric mixing (LTMIS) are positively correlated to ECS, and each is associated with a different spatial pattern of tropical shortwave cloud feedback in the SME. However, neither of those metrics is linked to the type of cloud response hypothesized by its authors. The resolved lower-tropospheric mixing (LTMID) is positively correlated to ECS for a subset of the SME having LTMID over a threshold value. LTMI and the RH for the dry, descending branch of the Hadley cell (RHD) narrow and shift upward the posterior estimates of ECS in the SME, but the SME bias in RHD and concerns over poorly understood physical mechanisms suggest the narrowing could be spurious for both constraints. While calibrated SME results may not generalize to multimodel ensembles, they can enhance the process understanding of emergent constraints and serve as out-of-sample tests of robustness.

The long-term climate variations of the Quaternary were primarily influenced by concurrent changes in Earth’s orbit, greenhouse gases, and ice sheets. However, because climate changes over the coming century will largely be driven by changes in greenhouse gases alone, it is important to better understand the separate contributions of each of these forcings in the past. To investigate this, idealized equilibrium simulations are conducted in which the climate is driven by separate changes in obliquity, precession, CO2, and ice sheets. To test the linearity of past climate change, anomalies from these single-forcing experiments are scaled and summed to compute linear reconstructions of past climate, which are then compared to mid-Holocene and last glacial maximum (LGM) snapshot simulations, where all forcings are applied together, as well as proxy climate records. This comparison shows that much of the climate response may be approximated as a linear response to forcings, while some features, such as modeled changes in sea ice and Atlantic meridional overturning circulation (AMOC), appear to be heavily influenced by nonlinearities. In regions where the linear reconstructions replicate the full-forcing experiments well, this analysis can help identify how each forcing contributes to the climate response. Monsoons at the mid-Holocene respond strongly to precession, while LGM monsoons are heavily influenced by the altered greenhouse gases and ice sheets. Contrary to previous studies, ice sheets produce pronounced tropical cooling at the LGM. Compared to proxy temperature records, the linear reconstructions replicate long-term changes well and also show which climate variations are not easily explained as direct responses to long-term forcings.

Although climate models have steadily improved their ability to reproduce the observed climate, over the years there has been little change to the wide range of sensitivities exhibited by different models to a doubling of atmospheric CO2 concentrations. Stochastic optimization is used to mimic how six independent climate model development efforts might use the same atmospheric general circulation model, set of observational constraints, and model skill criteria to choose different settings for parameters thought to be important sources of uncertainty related to clouds and convection. Each optimized model improved its skill with respect to observations selected as targets of model development. Of particular note were the improvements seen in reproducing observed extreme rainfall rates over the tropical Pacific, which was not specifically targeted during the optimization process. As compared to the default model sensitivity of 2.4°C, the ensemble of optimized model configurations had a larger and narrower range of sensitivities around 3°C but with different regional responses related to the uncertain choice in optimized parameter settings. These results suggest current generation models, if similarly optimized, may become more convergent in their measure of global sensitivity to greenhouse gas forcing. However, this exploration of the possible sources of modeling and observational uncertainty is not exhaustive. The optimization process illustrates an objective means for selecting an ensemble of plausible climate model configurations that quantify a portion of the uncertainty in the climate model development process.

Milankovitch proposed that variations in the earth’s orbit cause climate variability through a local thermodynamic response to changes in insolation. This hypothesis is tested by examining variability in an atmospheric general circulation model coupled to an ocean mixed layer model subjected to the orbital forcing of the past 165 000 yr. During Northern Hemisphere summer, the model’s response conforms to Milankovitch’s hypothesis, with high (low) insolation generating warm (cold) temperatures throughout the hemisphere. However, during Northern Hemisphere winter, the climate variations stemming from orbital forcing cannot be solely understood as a local thermodynamic response to radiation anomalies. Instead, orbital forcing perturbs the atmospheric circulation in a pattern bearing a striking resemblance to the northern annular mode, the primary mode of simulated and observed unforced atmospheric variability. The hypothesized reason for this similarity is that the circulation response to orbital forcing reflects the same dynamics generating unforced variability. These circulation anomalies are in turn responsible for significant fluctuations in other climate variables: Most of the simulated orbital signatures in wintertime surface air temperature over midlatitude continents are directly traceable not to local radiative forcing, but to orbital excitation of the northern annular mode. This has paleoclimate implications: during the point of the model integration corresponding to the last interglacial (Eemian) period, the orbital excitation of this mode generates a 1°–2°C warm surface air temperature anomaly over Europe, providing an explanation for the warm anomaly of comparable magnitude implied by the paleoclimate proxy record. The results imply that interpretations of the paleoclimate record must account for changes in surface temperature driven not only by changes in insolation, but also by perturbations in atmospheric dynamics.

One source of uncertainty for climate model predictions arises from the fact that climate models have been optimized to reproduce observational means. To quantify the uncertainty resulting from a realistic range of model configurations, it is necessary to estimate a multidimensional probability distribution that quantifies how likely different model parameter combinations are, given knowledge of the uncertainties in the observations. The computational cost of mapping a multidimensional probability distribution for a climate model using traditional means (e.g., Monte Carlo or Metropolis/Gibbs sampling) is impractical, requiring 104–106 model evaluations for problems involving less than 10 parameters. This paper examines whether such a calculation is more feasible using a particularly efficient but approximate algorithm called Bayesian stochastic inversion, based on multiple very fast simulated annealing (VFSA). Investigated here is how the number of model parameters, natural variability, and the d...