Author Correction: Space lidar observations constrain longwave cloud feedback.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Space lidar observations constrain longwave cloud feedback.

Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. On centennial scales, the response of clouds could either weaken or enhance the warming due to greenhouse gas emissions. Here we use space lidar observations to quantify changes in cloud altitude, cover, and opacity over the oceans between 2008 and 2014, together with a climate model with a lidar simulator to also simulate these changes in the present-day climate and in a future, warmer climate. We find that the longwave cloud altitude feedback, found to be robustly positive in simulations since the early climate models and backed up by physical explanations, is not the dominant longwave feedback term in the observations, although it is in the model we have used. These results suggest that the enhanced longwave warming due to clouds might be overestimated in climate models. These results highlight the importance of developing a long-term active sensor satellite record to reduce uncertainties in cloud feedbacks and prediction of future climate.

Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations.

Aerosol-cloud interactions remain a major uncertainty in climate research. Studies have indicated that model estimates of cloud susceptibility to aerosols frequently exceed satellite estimates, motivating model reformulations to increase agreement. Here we show that conventional ways of using satellite information to estimate susceptibility can serve as only a weak constraint on models because the estimation is sensitive to errors in the retrieval procedures. Using instrument simulators to investigate differences between model and satellite estimates of susceptibilities, we find that low aerosol loading conditions are not well characterized by satellites, but model clouds are sensitive to aerosol perturbations in these conditions. We quantify the observational requirements needed to constrain models, and find that the nighttime lidar measurements of aerosols provide a better characterization of tenuous aerosols. We conclude that observational uncertainties and limitations need to be accounted for when assessing the role of aerosols in the climate system.

Quantifying climate feedbacks in polar regions.

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.

Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors.

Cloud profiling from active lidar and radar in the A-train satellite constellation has significantly advanced our understanding of clouds and their role in the climate system. Nevertheless, the response of clouds to a warming climate remains one of the largest uncertainties in predicting climate change and for the development of adaptions to change. Both observation of long-term changes and observational constraints on the processes responsible for those changes are necessary. We review recent progress in our understanding of the cloud feedback problem. Capabilities and advantages of active sensors for observing clouds are discussed, along with the importance of active sensors for deriving constraints on cloud feedbacks as an essential component of a global climate observing system.

Classification of particle effective shape ratios in cirrus clouds based on the lidar depolarization ratio.

A shape classification technique for cirrus clouds that could be applied to future spaceborne lidars is presented. A ray-tracing code has been developed to simulate backscattered and depolarized lidar signals from cirrus clouds made of hexagonal-based crystals with various compositions and optical depth, taking into account multiple scattering. This code was used first to study the sensitivity of the linear depolarization rate to cloud optical and microphysical properties, then to classify particle shapes in cirrus clouds based on depolarization ratio measurements. As an example this technique has been applied to lidar measurements from 15 mid-latitude cirrus cloud cases taken in Palaiseau, France. Results show a majority of near-unity shape ratios as well as a strong correlation between shape ratios and temperature: The lowest temperatures lead to high shape ratios. The application of this technique to space-borne measurements would allow a large-scale classification of shape ratios in cirrus clouds, leading to better knowledge of the vertical variability of shapes, their dependence on temperature, and the formation processes of clouds.