The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m−2 K−1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.
Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. In addition, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.
Snow albedo feedback (SAF) behaves similarly in the current and future climate contexts; thus, constraining the large intermodel variance in SAF will likely reduce uncertainty in climate projections. To better understand this intermodel spread, structural and parametric biases contributing to SAF variability are investigated. We find that structurally varying snowpack, vegetation, and albedo parameterizations drive most of the spread, while differences arising from model parameters are generally smaller. Models with the largest SAF biases exhibit clear structural or parametric errors. Additionally, despite widespread intermodel similarities, model interdependency has little impact on the strength of the relationship between SAF in the current and future climate contexts. Furthermore, many models now feature a more realistic SAF than in the prior generation, but shortcomings from two models limit the reduction in ensemble spread. Lastly, preliminary signs from ongoing model development are positive and suggest a likely reduction in SAF spread among upcoming models.
Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climates with observations. Under Gaussian assumptions, the mean and variance of the future state are shown analytically to be a function of the signal‐to‐noise ratio between current climate uncertainty and observation error and the correlation between future and current climate states. We apply the HEC to the climate change, snow‐albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow‐albedo feedback prediction interval of (−1.25,−0.58)%/K. The critical dependence on signal‐to‐noise ratio and correlation shows that neglecting these terms can lead to bias and underestimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth system is discussed.
This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).