Abstract
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.
Highlights
Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms
The positive LW cloud altitude feedback is generally thought to be robust as it is persistently found in climate model simulations since the very first models[3,6,7,8,9,10,11,12,13] and is backed by a plausible physical explanation: high clouds should rise in a warming climate such that cloud temperatures remain nearly constant[13]
We propose to take advantage of seven years of space lidar observations to look for verification of the LW cloud altitude feedback mechanism in observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite[26]
Summary
Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. The positive LW cloud feedback is primarily due to an increase in cloud altitude[6,7] These results from climate model simulations do not directly provide physical explanations, and require validation against observations. Spaceborne passive instrument datasets are currently the longest records available[15,16] but have shown limited accuracy due to LW surface radiation influence through thin clouds[17,18,19,20] and limited calibration stability over decadal timescales mainly due to calibration drifts[21,22], which significantly increases the time required to detect climate trends Another approach is to derive constraints on the long-term cloud feedbacks from observations of cloud natural variability on interannual scales, assuming there is a link between changes driven by natural variability and transient changes on multidecade scales[23].
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