How Uncertainty in Field Measurements of Ice Nucleating Particles Influences Modeled Cloud Forcing

• Main Authors: Cziczo, Daniel James (Contributor), Garimella, Sarvesh (Contributor), Rothenberg, Daniel Abram (Contributor), Wolf, Martin Johann (Contributor), Wang, C. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor), Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences (Contributor)
• Format: Article
• Language: English
• Published: American Meteorological Society, 2018-10-04T15:58:03Z.
• Subjects: Article
• Online Access: Get fulltext

 Field and laboratory measurements using continuous flow diffusion chambers (CFDCs) have been used to construct parameterizations of the number of ice nucleating particles (INPs) in mixed-phase and completely glaciated clouds in weather and climate models. Because of flow nonidealities, CFDC measurements are subject to systematic low biases. Here, the authors investigate the effects of this undercounting bias on simulated cloud forcing in a global climate model. The authors assess the influence of measurement variability by constructing a stochastic parameterization framework to endogenize measurement uncertainty. The authors find that simulated anthropogenic longwave ice-bearing cloud forcing in a global climate model can vary up to 0.8 W m-2and can change sign from positive to negative within the experimentally constrained bias range. Considering the variability in the undercounting bias, in a range consistent with recent experiments, leads to a larger negative cloud forcing than that when the variability is ignored and only a constant bias is assumed. Keywords: Clouds; Aerosols; Climate change; Cloud microphysics; Ice crystals; Ice particles