Atmospheric Sciences & Global Change
Nailing Down Ice in a Cloud Model
Comparing ice cloud properties calculations puts cloud model house in order
The Ka-band ARM zenith radar (KAZR) probes the sky for information about the composition of clouds. Using information from ground-based observations, like those obtained with the KAZR, help scientists verify the accuracy of computed cloud properties. Enlarge Image. Photo courtesy of the ARM Climate Research Facility.
Results: A research team led by scientists at Pacific Northwest National Laboratory identified specific strengths and weaknesses of four different ice cloud retrieval algorithms. Their comparisons tested the ability of the algorithms to obtain cloud properties from radar and lidar observational measurements. The team noted the sometimes large variances in heating/cooling measurements compared to the observed data. Identifying specific weaknesses will help scientists improve our understanding of cloud properties in the atmosphere, which can be used for climate model development and evaluation.
"Measuring the effective size and mass of ice crystals impacts our understanding of clouds' reflective nature," said Dr. Jennifer Comstock, atmospheric scientist and lead author of the study. "Describing how these clouds contribute to the heating and cooling of the atmosphere gives climate modelers important information to predict future climate change."
Why It Matters: Building a house involves detailed instruction sets for separate systems such as plumbing, heating, and carpentry, to name a few. Climate models need similar instruction sets called algorithms to represent different climate properties and systems. For the best results scientists need to find the best algorithms. To compare how well a model represents clouds and their effects on the warming and cooling of the climate, scientists rely on direct observations to evaluate the skill of those representations in climate models. These comparisons also help them assess the models' ability to simulate clouds. The research described in this paper evaluates several ice cloud property algorithms to zero in on how well each can measure particular cloud properties, and whether that level of skill impacts our overall understanding of clouds' effects on the energy balance of the planet.
The inside window of the Micropulse Lidar (MPL), through which pulses of energy are transmitted into the atmosphere. The MPL receives scattered energy back to the transceiver for collecting data used to retrieve cloud properties. With accurate retrieval algorithms, scientists can better translate the raw instrument signals into rich datasets. Photo courtesy of the ARM Climate Research Facility.
Methods: The team, led by PNNL's Dr. Jennifer Comstock, used four different algorithms to uncover ice crystal size and ice mass cloud properties using an identical input observational dataset. They computed the radiative fluxes at the surface and top-of-atmosphere for these cloud properties using a radiative transfer model. Then, the team compared the computed fluxes with 3 years of ground-based lidar and radar observations from the Atmospheric Radiation Measurement (ARM) Climate Research Facility Tropical Western Pacific site in Darwin, Australia to help determine the algorithm's proficiency (uncertainty).
The bias and variance of the difference between computed and observed fluxes showed major differences between individual algorithms. The study identified individual algorithm weaknesses and assumptions that need improvement. They found that assumptions concerning ice crystal shape and the representation of ice crystal size are the primary drivers of the uncertainty in the models.
What's Next? The next step is to look closely at algorithm assumptions related to the definition and retrieval of ice crystal size and particle shape.
Sponsor: This research was funded by the U.S. Department of Energy's (DOE's) Biological and Environmental Research (BER) Atmospheric System Research (ASR) program and used observations from DOE's Atmospheric Radiation Measurement (ARM) Climate Research Facility site located in Darwin, Australia.
Research Team: Jennifer Comstock and Sally McFarlane, PNNL; Alain Protat, the Centre for Australian Weather and Climate Research in Australia; Julien Delanoe, the University of Versailles St. Quentin, France; and Min Deng, the University of Wyoming.
User Facility: ARM Climate Research Facility
Research Area: Climate & Earth Systems Science
Reference: Comstock J, A Protat, SA McFarlane, J Delanoe and Min Deng. "Assessment of Uncertainty in Cloud Radiative Effects and Heating Rates through Retrieval Algorithm Differences: Analysis Using 3 Years of ARM data at Darwin, Australia." Journal of Geophysical Research-Atmospheres 118(10): 4549-4571. DOI:10.1002/jgrd.50404.