How uncertainty in field measurements of ice nucleating particles influences modeled cloud forcing

How uncertainty in field measurements of ice nucleating particles influences modeled cloud forcing 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Atmospheric Sciences American Meteorological Society

How uncertainty in field measurements of ice nucleating particles influences modeled cloud forcing

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0469
D.O.I.
10.1175/JAS-D-17-0089.1
Publisher site
See Article on Publisher Site

Abstract

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

Journal of the Atmospheric SciencesAmerican Meteorological Society

Published: Nov 1, 2017

References

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