Contributions of Clouds, Surface Albedos, and Mixed-Phase Ice Nucleation Schemes to Arctic Radiation Biases in CAM5

• Published in: Journal of Climate
• Main Authors: English, Jason M., Kay, Jennifer E., Gettelman, Andrew, Liu, Xiaohong, Wang, Yong, Zhang, Yuying, Chepfer, Helene
• Format: Other Non-Article Part of Journal/Newspaper
• Language: English
• Published: University of Wyoming. Libraries 2014
• Subjects: Engineering; Arctic; albedo
• Online Access: https://hdl.handle.net/20.500.11919/692; https://doi.org/10.1175/JCLI-D-13-00608.1

The Arctic radiation balance is strongly affected by clouds and surface albedo. Prior work has identified Arctic cloud liquid water path (LWP) and surface radiative flux biases in the Community Atmosphere Model, version 5 (CAMS), and reductions to these biases with improved mixed-phase ice nucleation schemes. Here, CAMS net top-of-atmosphere (TOA) Arctic radiative flux biases are quantified along with the contributions of clouds, surface albedos, and new mixed-phase ice nucleation schemes to these biases. CAMS net TOA all-sky shortwave (SW) and outgoing longwave radiation (OLR) fluxes are generally within 10W m-2 of Clouds and the Earth's Radiant Energy System Energy Balanced and Filled (CERES-EBAF) observations. However, CAMS has compensating SW errors: Surface albedos over snow are too high while cloud amount and LWP are too low. Use of a new CAMS Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar simulator that corrects an error in the treatment of snow crystal size confirms insufficient cloud amount in CAMS year-round. CAMS OLR is too low because of low surface temperature in winter, excessive atmospheric water vapor in summer, and excessive cloud heights year-round. Simulations with two new mixed-phase ice nucleation schemes-one based on an empirical fit to ice nuclei observations and one based on classical nucleation theory with prognostic ice nuclei improve surface climate in winter by increasing cloud amount and LWP. However, net TOA and surface radiation biases remain because of increases in midlevel clouds and a persistent deficit in cloud LWP. These findings highlight challenges with evaluating and modeling Arctic cloud, radiation, and climate processes.