AbstractField 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. Due to flow non-idealities, CFDC measurements are subject to systematic low biases. Here we investigate the effects of this undercounting bias on simulated cloud forcing in a global climate model. We assess the influence of measurement variability by constructing a stochastic parameterization framework to endogenize measurement uncertainty. We find that simulated anthropogenic long wave ice-bearing cloud forcing in a global climate model can vary up to 0.8 Wm−2 and 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.
Journal of the Atmospheric Sciences – American Meteorological Society
Published: Nov 1, 2017
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