Forecasting Climate Adaptation Through Cirrus Cloud Radiative Forcing Analysis Using 20 Years of MPLNET Lidar Measurements

Cirrus clouds play a critical role in Earth's radiation budget and are key to understanding and forecasting climate adaptation in response to global warming. Leveraging 20 years of high-resolution lidar data from NASA's MPLNET network, we analyze and forecast cirrus cloud radiative forcing with the aim of projecting how the climate system will adapt to changing atmospheric conditions. Using ensemble learning methods, we simulate the monthly radiative impacts of cirrus clouds, emphasizing their variability and feedback mechanisms. The study also integrates future climate scenarios under shared socio-economic pathways ( CMIP6SSP2-4.5 and SSP5-8.5) to explore potential shifts in regional climate patterns driven by cirrus cloud interactions. Results highlight how increased temperatures and altered precipitation regimes may influence the climate's adaptive processes, particularly in regions currently sensitive to radiative forcing fluctuations. This research underscores the importance of long-term lidar data for advancing climate adaptation modeling and identifying critical atmospheric feedbacks.

[1] Lolli, S., 2023. Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey. Remote Sensing, 15(17), p.4318.