AAAR 35th Annual Conference October 17 - October 21, 2016 Oregon Convention Center Portland, Oregon, USA
Abstract View
How Uncertainty in Field Measurements of Ice Nucleating Particles Influences Modeled Cloud Forcing
SARVESH GARIMELLA, Daniel Rothenberg, Chien Wang, Daniel Cziczo, MIT
Abstract Number: 668 Working Group: Aerosols, Clouds, and Climate
Abstract This study investigates the systematic low bias in field measurements of ice nucleating particles using continuous flow diffusion chambers. Such instruments have been deployed in the field for decades to measure the formation of ice crystals using ambient aerosol populations. These measurements have, in turn, been used to construct parameterizations for use in global climate models by relating the formation of ice crystals to temperature and aerosol particle number. Non-ideal instrument behavior, which exposes particles to lower humidities than reported, has resulted in a systematic underestimation of the number of ice nucleating particles. Variability in this bias affects climate model response to these parameterizations. We show that a machine learning approach can be used to minimize this uncertainty. In addition, we find that the simulated long wave cloud forcing in a global climate model simulation can vary up to 0.8 W/m^2 and can change sign from positive to negative depending on the treatment of this uncertainty. Based on these results, more careful treatment is required at both the experimental and modeling stages of parameterization development in order to account for such biases.