Link between opaque cloud properties and atmospheric dynamics in observations and simulations of current climate in the Tropics, and impact on future predictions

International audienceClimate models predict a weakening of the tropical atmospheric circulation, more specifically a slowdown of Hadley and Walker circulations. Many climate models predict that global warming will have a major impact on cloud properties, including their geographic and vertical distribution. Climate feedbacks from clouds, which amplify warming when positive, are today the main source of uncertainty in climate forecasts. Tropical clouds play a key role in the redistribution of solar energy and their evolution will likely affect climate. Therefore, it is crucial to better understand how tropical clouds will evolve in a changing climate. Among cloud properties, the vertical distribution is sensitive to climate change. Active sensors integrated into satellites, such as CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization), make it possible to obtain a detailed vertical distribution of clouds. CALIOP measurements and calibration are more stable over time and more precise than passive remote sensing satellite detectors. CALIOP observations can be simulated in the atmospheric conditions predicted by climate models using lidar simulators such as COSP (</span><span>CFMIP Observation Simulator Package). Moreover, </span><span>cloud properties directly drive the Cloud Radiative Effect (CRE). Understanding how models predict cloud vertical distribution will evolve in the future has implications for how models predict the Cloud Radiative Effect (CRE) at the Top of the Atmosphere (TOA) will evolve in the future. </span></p><p><span>The purpose of our study is to compare, firstly, based on satellite observations (GOCCP) and reanalyzes (ERA5), we will establish the relationship between atmospheric dynamic circulation, opaque cloud properties and TOA CRE. Then, we will compare this observed relationship with the one found in climate model simulations of current climate conditions (CESM1 and IPSL-CM6). Finally, we will identify how model biases in present climate conditions influence the cloud feedback spread between models in a warmer climate.</span></p&gt