Particle diversity metric for atmospheric particle population helps quantify cloud droplet errors in climate model predictions assumptions.
While particle cloud droplet nucleation efficiency can in principle be calculated for particles of known size and composition, models break down for atmospheric aerosols which typically consist of mixtures of varying composition. DOE funded scientists have defined a new mixing state index to quantify the degree of internal (all particles have the same composition) and external mixing (subpopulations with distinct but differing compositions) of aerosol populations. The current study investigates the errors in predicted cloud condensation nuclei (CCN) concentrations when the atmospheric particle (aerosol) populations vary between external and internal mixtures.
The results show that state-of-the-science single particle measurements need to be deployed and analyzed more frequently to determine when and where externally mixed conditions exist in the atmosphere. They found that establishing a better relationship between the mixing state and CCN will help improve the representation of cloud-aerosol interactions in climate models.
Researchers used the new diversity metric X to assess errors in calculating CCN that result from the treatment of aerosol populations in models, which typically assume total internal mixing. If the aerosol populations are simple (nearly all particles have the same relative composition), these errors are small. However, the errors become large for complex aerosol populations that contain a range of compositions per particle. Scientists combined the new metric with particle-resolved model simulations to quantify errors in CCN predictions when mixing state information is neglected. Researchers explored a range of scenarios that cover different conditions of aerosol aging. The study showed that mixing state information is unimportant for more internally mixed populations; that is, for populations with mixing state index X larger than 60 percent. For more externally mixed populations (X below 20 percent), the relationship between X and the error in CCN predictions is not unique. The error ranges from lower than -40 percent to about 150 percent, depending on the underlying aerosol population and the environmental supersaturation.
Contacts (BER PM)
Atmospheric System Research
Atmospheric System Research
Jerome D. Fast
Pacific Northwest National Laboratory
The Department of Energy Office of Science Office of Biological and Environmental Research supported Joseph Ching and Jerome D. Fast as part of the Atmospheric System Research (ASR) program. Additional support for others was provided by the National Science Foundation and the Environmental Protection Agency.
J. Ching, J. Fast, M. West, N. Riemer, “Metrics to Quantify the Importance of Mixing State for CCN Activity.” Atmospheric Chemistry and Physics, 17, 7445-7458 (2017). [DOI: 10.5194/acp-17-7445-2017] (Reference link)
SC-23.1 Climate and Environmental Sciences Division, BER
New Approach for Studying How Microbes Influence Their Environment
A diverse group of scientists suggests a common framework and targeting of known microbial processes [more...]
Nutrient-Hungry Peatland Microbes Reduce Carbon Loss Under Warmer Conditions
Enzyme production in peatlands reduces carbon lost to respiration under future high temperatures. [more...]
Amazon Forest Response to CO2 Fertilization Dependent on Plant Phosphorus Acquisition
AmazonFACE Model Intercomparison. The Science Plant growth is dependent on the availabi [more...]
A Slippery Slope: Soil Carbon Destabilization
Carbon gain or loss depends on the balance between competing biological, chemical, and physical reac [more...]
Field Evaluation of Gas Analyzers for Measuring Ecosystem Fluxes
How gas analyzer type and correction method impact measured fluxes. The Science A side- [more...]
List all highlights (possible long download time)