Comparing remotely sensed observations of clouds and aerosols in the Southern Ocean with climate model simulations.

• Main Author: Kuma, Peter
• Format: Other/Unknown Material
• Language: English
• Published: University of Canterbury 2020
• Subjects: Austral; Merra; New Zealand; Southern Ocean
• Online Access: https://hdl.handle.net/10092/101337; https://doi.org/10.26021/10400

Southern Ocean (SO) shortwave (SW) radiation biases are a common problem in contemporary general circulation models (GCMs), with most models exhibiting a tendency to absorb too much incoming SW radiation. These biases have been attributed to deficiencies in the representation of clouds during the austral summer months, either due to cloud cover or cloud albedo being too low. They affect simulation of New Zealand (NZ) and global climate in GCMs due to excessive heating of the sea surface and the effect on large-scale circulation. Therefore, improvement of GCMs is necessary for accurate prediction of future NZ and global climate. We performed ship-based lidar, radar, radiosonde and weather observations on two SO voyages and processed data from multiple past SO voyages. We used the observations and satellite measurements for evaluation of the Hadley Centre Global Environmental Model version 3 (HadGEM3) and contrasting with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) to better understand the source of the problem. Due to the nature of lidar observations (the laser signal is quickly attenuated by clouds) they cannot be used for 1:1 comparison with a model without using a lidar simulator, which performs atmospheric radiative transfer calculations of the laser signal. We modify an existing satellite lidar simulator present in the Cloud Feedback Model Intercomparison Project (CFMIP) Observational Simulator Package (COSP) for use with the ground-based lidars used in our observations by modifying the geometry of the radiative transfer calculations, Mie and Rayleigh scattering of the laser signal. We document and make the modified lidar simulator available to the scientific community as part of a newly-developed lidar processing tool called the Automatic Lidar and Ceilometer Framework (ALCF), which enables unbiased comparison between lidar observations and models by performing calibration of lidar backscatter, noise removal and consistent cloud detection. We apply the lidar ...