Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?

Prenni, A. J.; Harrington, Jerry Y.; Tjernström, Michael; DeMott, Paul J.; Avramov, Alexander; Long, Charles; Kreidenweis, Sonia M.; Olsson, Peter Q.; Verlinde, Johannes

doi:10.1175/bams-88-4-541

Cited by 225 publications

(306 citation statements)

References 44 publications

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“…The droplet size distribution affects albedo and infrared opacity directly and also indirectly affects cloud cover because of changes in drizzle production and cellular structure in broken clouds (e.g., Rosenfeld et al, 2006). Similarly in mixed phase (water plus ice) clouds, changes in the fraction of ice affect the infrared opacity (Prenni et al, 2007).…”

Section: The Global Electric Circuit (Gec) and The Modulation Of J Z mentioning

confidence: 99%

Solar wind-atmospheric electricity-cloud microphysics connections to weather and climate

Lam

Tinsley

2016

Journal of Atmospheric and Solar-Terrestrial Physics

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ABSTRACT: We review recent research articles that present observations of the large-scale day-to-day dynamic tropospheric response to changes in the downward current density J z of the global atmospheric electric circuit (GEC). The evidence for the global circuit downward current density, J z , causing changes in atmospheric dynamics is now even stronger than as reviewed by Tinsley (Reports on Progress in Physics volume 71, 2008). We consider proposed mechanisms for these responses, and suggest future directions for research.

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Section: The Global Electric Circuit (Gec) and The Modulation Of J Z mentioning

confidence: 99%

Solar wind-atmospheric electricity-cloud microphysics connections to weather and climate

Lam

Tinsley

2016

Journal of Atmospheric and Solar-Terrestrial Physics

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“…Climate and weather models tend to have difficulty predicting the observed frequency and persistence of Arctic mixed-phase clouds, leading to biases in surface radiative fluxes Girard and Curry, 2001;Morrison et al, 2003Morrison et al, , 2005bInoue et al, 2006;Morrison and Pinto, 2006;Prenni et al, 2007;Sandvik et al, 2007). These models tend to have difficulty simulating midlatitude mixed-phase clouds as well (Illingworth et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Models with less sophisticated microphysics may prescribe a ratio of liquid to ice mass that is inconsistent with Arctic observations. However, models with separate prognostic variables for liquid and ice and detailed microphysics may also produce poor simulations (Morrison et al, 2003;Inoue et al, 2006;Prenni et al, 2007;Sandvik et al, 2007). In these models, a more realistic treatment of ice microphysics, and in particular the number concentration of both small ice and snow, may be needed to improve results.…”

Section: Introductionmentioning

confidence: 99%

“…In these models, a more realistic treatment of ice microphysics, and in particular the number concentration of both small ice and snow, may be needed to improve results. Numerous modelling studies have demonstrated a strong sensitivity of mixed-phase clouds to ice crystal number concentration (Pinto, 1998;Harrington et al, 1999;Jiang et al, 2000;Morrison and Pinto, 2006;Prenni et al, 2007). Prenni et al (2007) substantially improved their simulation of mixed-phase clouds by reducing ice nuclei number concentrations, which influence ice crystal number concentrations, from values typical of midlatitudes to the low values observed in the Arctic.…”

Section: Introductionmentioning

confidence: 99%

“…During M-PACE, a suite of in situ and remote sensors gathered measurements of mixed-phase cloud microphysics, dynamics, radiation and aerosol. Already, several studies have used M-PACE observations to assess single-column, cloudresolving, mesoscale, weather and climate model simulations of mixed-phase clouds (Xie et al, 2006(Xie et al, , 2008Fridlind et al, 2007;Liu et al, 2007b;Prenni et al, 2007;Luo et al, 2008aLuo et al, , 2008b.…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of model simulations of mixed‐phase clouds observed during the ARM Mixed‐Phase Arctic Cloud Experiment. I: single‐layer cloud

Klein

McCoy

Morrison

et al. 2009

Quart J Royal Meteoro Soc

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275

341

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ABSTRACT:Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) programme's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud-top temperature of −15 • C. The average liquid water path of around 160 g m −2 was about two-thirds of the adiabatic value and far greater than the average mass of ice which when integrated from the surface to cloud top was around 15 g m −2 .Simulations of 17 single-column models (SCMs) and 9 cloud-resolving models (CRMs) are compared. While the simulated ice water path is generally consistent with observed values, the median SCM and CRM liquid water path is a factor-of-three smaller than observed. Results from a sensitivity study in which models removed ice microphysics suggest that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path.Despite this underestimate, the simulated liquid and ice water paths of several models are consistent with observed values. Furthermore, models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter exists. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics.

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Technical note: Introduction to MIMICA, a large‐eddy simulation solver for cloudy planetary boundary layers

Savre

Ekman

Svensson

2014

J Adv Model Earth Syst

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In large-eddy simulation (LES), large-scale turbulent structures are explicitly resolved on the numerical grid while the dissipative turbulent eddies, typically smaller than the grid size, must be modeled. Because in the atmospheric boundary layer a large disparity of turbulent scales exists (about 9 orders of magnitude separate the largest and smallest scales), LES is considered as an essential modeling approach to capture the physics and dynamics of boundary layer clouds. A new LES solver developed at Stockholm University is presented here for the first time. The model solves for nonhydrostatic anelastic equations using high-order low-dissipative numerical schemes for the advection of scalars and momentum. A two-moment bulk microphysics scheme is implemented representing five types of hydrometeors including ice crystals and snow. The LES is evaluated based on simulations of two well-documented stratiform cloud events that were previously used for LES intercomparisons. In the first one, a marine drizzling stratocumulus observed during DYCOMS-II, the model is shown to predict bulk cloud microphysical and dynamical properties within the range of the intercomparison model results. In the second case, based on a monolayer Arctic mixedphase cloud observed during ISDAC, we found that when using fast-falling crystals, ice quickly precipitates out of the cloud without significant growth, resulting in very low ice water paths. The simulated clouds are also found to be very sensitive to the prescribed ice crystal number concentration: multiplying the ice concentration by a factor 2.5 results in rapid cloud dissipation in the most extreme case. Overall, these results are found to be consistent with former studies of Arctic mixed-phase clouds as well as in situ measurements. More specifically, when the ice number concentration and parameterized ice habit are constrained by measurements, simulated microphysical properties such as the ice water path and ice crystal size distribution are found to agree well with observations.

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Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?

Abstract: The inability of regional models and global climate models to reproduce Arctic clouds and the Arctic radiation budget may be due to inadequate parameterizations of ice nuclei.

Cited by 225 publications

References 44 publications

Solar wind-atmospheric electricity-cloud microphysics connections to weather and climate

Solar wind-atmospheric electricity-cloud microphysics connections to weather and climate

Intercomparison of model simulations of mixed‐phase clouds observed during the ARM Mixed‐Phase Arctic Cloud Experiment. I: single‐layer cloud

Technical note: Introduction to MIMICA, a large‐eddy simulation solver for cloudy planetary boundary layers

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