skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?

Abstract

To date, climate and regional models have generally proven unsuccessful at simulating Arctic cloudiness, particularly during the colder months. Models tend to underpredict the amount of liquid water in mixed-phase clouds, which are ubiquitous in this region. This is problematic because cloud coverage and phase can greatly impact the Arctic radiative budget. Using recent measurements of ice nucleating aerosol, we show that incorrect, or nonexistent, parameterizations of aerosol-cloud interactions are at least partially responsible for the poor model predictions. Moreover, we show that this can lead to errors in the modeled surface radiative energy budget of 10-100 W m-2.

Authors:
; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
985063
Report Number(s):
PNNL-SA-47093
Journal ID: ISSN 0003-0007; ISSN 1520-0477; KP1704010; TRN: US201016%%1750
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Bulletin of the American Meteorological Society, 88(4):541-550; Journal Volume: 88; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; AEROSOLS; CLIMATES; CLOUDS; ECOSYSTEMS; ENERGY BALANCE; WATER; aerosols; Arctic; cloudiness

Citation Formats

Prenni, Anthony J., Harrington, Jerry Y., Tjernstrom, Michael, DeMott, Paul J., Avramov, Alexander, Long, Charles N., Kreidenweis, Sonia M., Olsson, Peter Q., and Verlinde, J.. Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?. United States: N. p., 2007. Web. doi:10.1175/BAMS-88-4-541.
Prenni, Anthony J., Harrington, Jerry Y., Tjernstrom, Michael, DeMott, Paul J., Avramov, Alexander, Long, Charles N., Kreidenweis, Sonia M., Olsson, Peter Q., & Verlinde, J.. Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?. United States. doi:10.1175/BAMS-88-4-541.
Prenni, Anthony J., Harrington, Jerry Y., Tjernstrom, Michael, DeMott, Paul J., Avramov, Alexander, Long, Charles N., Kreidenweis, Sonia M., Olsson, Peter Q., and Verlinde, J.. Sun . "Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?". United States. doi:10.1175/BAMS-88-4-541.
@article{osti_985063,
title = {Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?},
author = {Prenni, Anthony J. and Harrington, Jerry Y. and Tjernstrom, Michael and DeMott, Paul J. and Avramov, Alexander and Long, Charles N. and Kreidenweis, Sonia M. and Olsson, Peter Q. and Verlinde, J.},
abstractNote = {To date, climate and regional models have generally proven unsuccessful at simulating Arctic cloudiness, particularly during the colder months. Models tend to underpredict the amount of liquid water in mixed-phase clouds, which are ubiquitous in this region. This is problematic because cloud coverage and phase can greatly impact the Arctic radiative budget. Using recent measurements of ice nucleating aerosol, we show that incorrect, or nonexistent, parameterizations of aerosol-cloud interactions are at least partially responsible for the poor model predictions. Moreover, we show that this can lead to errors in the modeled surface radiative energy budget of 10-100 W m-2.},
doi = {10.1175/BAMS-88-4-541},
journal = {Bulletin of the American Meteorological Society, 88(4):541-550},
number = 4,
volume = 88,
place = {United States},
year = {Sun Apr 01 00:00:00 EDT 2007},
month = {Sun Apr 01 00:00:00 EDT 2007}
}
  • Ice-nucleating particles (INPs) are known to affect the amount of ice in mixed-phase clouds, thereby influencing many of their properties. The atmospheric INP concentration changes by orders of magnitude from terrestrial to marine environments, which typically contain much lower concentrations. Many modelling studies use parameterizations for heterogeneous ice nucleation and cloud ice processes that do not account for this difference because they were developed based on INP measurements made predominantly in terrestrial environments without considering the aerosol composition. Errors in the assumed INP concentration will influence the simulated amount of ice in mixed-phase clouds, leading to errors in top-of-atmosphere radiativemore » flux and ultimately the climate sensitivity of the model. Here we develop a global model of INP concentrations relevant for mixed-phase clouds based on laboratory and field measurements of ice nucleation by K-feldspar (an ice-active component of desert dust) and marine organic aerosols (from sea spray). The simulated global distribution of INP concentrations based on these two species agrees much better with currently available ambient measurements than when INP concentrations are assumed to depend only on temperature or particle size. Underestimation of INP concentrations in some terrestrial locations may be due to the neglect of INPs from other terrestrial sources. Our model indicates that, on a monthly average basis, desert dusts dominate the contribution to the INP population over much of the world, but marine organics become increasingly important over remote oceans and they dominate over the Southern Ocean. However, day-to-day variability is important. Because desert dust aerosol tends to be sporadic, marine organic aerosols dominate the INP population on many days per month over much of the mid- and high-latitude Northern Hemisphere. This study advances our understanding of which aerosol species need to be included in order to adequately describe the global and regional distribution of INPs in models, which will guide ice nucleation researchers on where to focus future laboratory and field work.« less
  • A technique for deriving ice temperature in the Arctic seasonal sea ice zone from passive microwave radiances has been developed. The algorithm operates on brightness temperatures derived from the Special Sensor Microwave/Imager (SSM/I) and uses ice concentration and type from a previously developed thin ice algorithm to estimate the surface emissivity. Comparisons of the microwave derived temperatures with estimates derived from infrared imagery of the Bering Strait yield a correlation coefficient of 0.93 and an RMS difference of 2.1 K when coastal and cloud contaminated pixels are removed. SSM/I temperatures were also compared with a time series of air temperaturemore » observations from Gambell on St. Lawrence Island and from Point Barrow, AK weather stations. These comparisons indicate that the relationship between the air temperature and the ice temperature depends on ice type.« less
  • The authors are interested in the general question of how low and high latitude regions interact on a climatic scale. Here they present results for modeled climatic influences in Asia and Africa, due to boundary condition changes in surrounding regions. The factors varied were the amount of solar insolation, the extent of glacial ice cover in high latitude areas, the north Atlantic sea surface temperatures, and the height of the Asian orography elevations. Results of using the GISS general circulation model, with these different imposed boundary conditions are then observed for their impact on the seasonal climate of the Asianmore » and African monsoons. These results are then looked at in light of paleoclimatic evidence to see if these influences might be a major factor in driving the climate changes in Asia and Africa.« less
  • Significant advances are being made in our understanding of the Arctic sea ice-climate system. The mean circulation of the Arctic sea ice cover is now well defined through analysis of data from drifting stations and buoys. Analysis of nearly 20 years of daily satellite data from optical, infrared, and passive microwave sensors has documented the regional variability in monthly ice extent, concentrations, and surface albedo. Advances in modeling include better treatments of sea ice dynamics and thermodynamics, improved atmosphere-ice-ocean coupling, and the development of high resolution regional models. Diagnostic studies of monthly and interannual sea ice variability have benefited frommore » better sea ice data and geostrophic wind analyses that incorporate drifting buoy data. Some evidence exists for a small retreat of Arctic sea ice over the last 2 decades, but there are large decadal fluctuations in regional ice extent. Antiphase relationships between ice anomalies in different sectors can be related to changes in atmospheric circulation. Evidence suggests that episodes of significant salinity reduction in the North Atlantic, associated with extensive sea ice in the Greenland Sea, may be a manifestation of a decadal oscillation in the Arctic climate system. Aspects of the Arctic system in need of further attention include the surface energy budget and its variability, particularly with respect to the roles of cloud cover and surface types in summer. Sea ice thickness distribution data remain meager, and there are many unknowns regarding the circulation and hydrologic cycle of the Arctic Ocean and its links to the world ocean. Planned measurements from a new generation of satellites, supported by field programs, will provide much needed data to address these issues. 195 refs., 20 figs., 2 tabs.« less
  • The low-frequency natural variability of the arctic climate system is modeled using a single-column, energy balance model of the atmosphere, sea ice, and upper-ocean system. Variability in the system is induced by forcing with realistic, random perturbations in the atmospheric energy transport and cloudiness. The model predicts that the volume of perennial sea ice varies predominantly on decadal timescales, while other arctic climate variables vary mostly on intraannual and interannual timescales. The variance of the simulated sea ice volume is most sensitive to perturbations of the atmospheric forcing in late spring, at the onset of melt. The variance of themore » simulated sea ice volume is most sensitive to perturbations of the atmospheric forcing in the late spring, at the onset of melt. The variance of sea ice volume increases with the mean sea ice thickness and with the number of layers resolved in the sea ice model. This suggests that much of the simulated variance develops when the surface temperature decouples from the sea ice interior during the late spring, when melting snow abruptly exposes the sea ice surface and decreases the surface albedo. The minimum model requirements to simulate the natural variability in the arctic climate are identified. The implications of the low-frequency, natural variability in sea ice volume for detecting a climate change are discussed. Finally, calculations suggest that the variability in the thermodynamic forcing of the polar cap could lead to a freshening in North Atlantic that is comparable to the freshening associated with the Great Salinity Anomaly. 28 refs., 14 figs., 5 tabs.« less