Exposing biases in retrieved low cloud properties from CloudSat: A guide for evaluating observations and climate data

Abstract

[1] This study provides an assessment of low cloud properties retrieved from CloudSat, MODIS (Moderate Resolution Imaging Spectroradiometer), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation with the goal of exposing biases that hinder meaningful comparisons with the simulated cloud properties in global climate models (GCMs). Being pertinent to GCM comparisons, CloudSat is the only satellite that can provide the vertical structure of cloud water and ice content from space. Biases in CloudSat low cloud properties are found to be tied to problems involving cloud detection and algorithm retrieval failures related to precipitation and strict cloud screening procedures. We show that MODIS and CloudSat cloud liquid water path (LWP) data agree when carefully screened for lack of precipitation but significantly depart in precipitating clouds due to rain water contamination of LWP in the CloudSat retrieval algorithm. The presence of drizzle and rain (occurring about 20% of the time) is associated with different mean LWP, mean particle sizes, and optical depths of all low clouds and therefore the radiative properties of the oceanic low clouds. Another more significant source of the LWP bias stems from the apparent lack of cloud detection. On average, the Cloud Profiling Radar misses clouds with adequate liquid and ice water retrievals as detected by MODIS in approximately 45% of warm clouds with the bulk of the bias occurring in clouds below 1 km in the so-called "ground clutter zone." By incorporating additional sensors such as MODIS, the following results suggest that this LWP bias can be greatly reduced.

Aerosol indirect effects on the nighttime Arctic Ocean surface from thin, predominantly liquid clouds

“…Instead, the CALIPSO product was used to measure CODs, as it offers substantially higher data availability in the optical thickness range of interest (COD < 3) than CloudSat (Christensen et al, 2013). Only non-qualityflagged (i.e., the highest quality) CALIPSO COD data were used.…”

Joint retrievals of cloud and drizzle in marine boundary layer clouds using ground-based radar, lidar and zenith radiances

“…Cloud base height can be determined using sophisticated existing algorithms that rely on the magnitude or gradient of lidar attenuated backscatter (e.g. Platt et al, 1994;Clothiaux et al, 1998). For simplicity, we determine cloud base using a threshold in attenuated lidar backscatter of 0.0001 m −1 sr −1 (similar to O'Connor et al, 2004) in both cloud types.…”

“…Lee et al, 2010) and the presence of drizzle from space (e.g. Leon et al, 2008;Lebsock et al, 2013), but often fails to observe the drizzle that occurs in the lowest 1 km of the atmosphere due to contamination from the strong surface return (Christensen et al, 2013). In addition, surface-based observations tend to have better resolution and sensitivity due to their proximity to their targets.…”

The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors

“…Compared to the Advanced Very High Resolution Radiometer (AVHRR), MODIS has several additional spectral channels at shortwave infrared wavelengths that provide cloud microphysical information (Platnick et al, 2017), such that MODIS data provide more information for retrieving cloud products than AVHRR. Still, the MODIS C6 cloud top retrieval loses sensitivity for optically thinner clouds (COT < 2; Menzel et al, 2015;Christensen et al, 2013). This complicates validation against independent measurements such as those derived from lidar, which explicitly observe the cloud top.…”