Indirect and Semi-Direct Aerosol Campaign (ISDAC)
1 April 2008 - 30 April 2008
Lead Scientist: Steven Ghan
An intensive cloud and aerosol observing system was deployed to the ARM Climate Research Facility’s (ACRF) North Slope of Alaska (NSA) locale for three weeks in April 2008. This period was chosen because it was during the International Polar Year when many ancillary observing systems were collecting data that would be synergistic for interpreting the Indirect and Semi-Direct Aerosol Campaign (ISDAC) data. It also provided an important contrast with the October 2004 Mixed-Phase Arctic Cloud Experiment (M-PACE). Thirty to 45 hours of flight time were required with aircraft capable of measuring temperature, humidity, total particle number, aerosol size distribution, aerosol hygroscopicity, cloud condensation nuclei concentration, ice nuclei concentration, optical scattering and absorption, updraft velocity, cloud liquid water and ice contents, cloud droplet and crystal size distributions, cloud particle shape, and cloud extinction. In addition to these aircraft measurements, there was a surface deployment of a spectroradiometer for retrieving cloud optical depth and effective radius.
These measurements will be used by members of the ARM Science Team to answer the following key questions:
How do properties of the arctic aerosol during April differ from those measured during the MPACE in October?
To what extent do the different properties of the arctic aerosol during April produce differences in the microphysical and macrophysical properties of clouds and the surface energy balance?
To what extent can cloud models and the cloud parameterizations used in climate models simulate the sensitivity of arctic clouds and the surface energy budget to the differences in aerosol between April and October?
How well can long-term surface-based measurements at the ACRF NSA locale provide retrievals of aerosol, cloud, precipitation, and radiative heating in the Arctic?
By using many of the same instruments used during M-PACE, we were able to contrast the arctic aerosol and cloud properties during October and April. The aerosol measurements can be used in cloud models driven by objectively analyzed boundary conditions to test whether the cloud models can simulate the aerosol influence on the clouds. The influence of aerosol and boundary conditions on the simulated clouds can be separated by running the cloud models with all four combinations of M-PACE and ISDAC aerosol and boundary conditions: M-PACE aerosol and boundary conditions, M-PACE aerosol and ISDAC boundary conditions, ISDAC aerosol and M-PACE boundary conditions, and ISDAC aerosol and boundary conditions. ISDAC and M-PACE boundary conditions were likely to be very different because of the much more extensive ocean water during M-PACE. The uniformity of the surface conditions during ISDAC greatly simplifies the objective analysis (surface fluxes and precipitation are very weak), so that it can largely rely on the European Centre for Medium-Range Weather Forecasts analysis. The ISDAC cloud measurements can be used to evaluate the cloud simulations and to evaluate cloud retrievals. The aerosol measurements can also be used to evaluate the aerosol retrievals. By running the cloud models with and without solar absorption by the aerosols, we can determine the semi-direct effect of the aerosol on the clouds.
The U.S. Department of Energy, primary sponsor for ISDAC, is collaborating with the following agencies, universities, and companies.
- Canadian National Research Council
- Environment Canada
- University of Alaska, Fairbanks
- Pacific Northwest National Laboratory
- University of Illinois
- Stratton Park Engineering Company
- Droplet Measurement Technologies
- Texas A&M University
- The Pennsylvania State University
- Scripps Institution of Oceanography
- NOAA Earth System Research Laboratory
- CIRES, University Colorado
- Los Alamos National Laboratory
- Sandia National Laboratory
Shi Y and X Liu. 2019. "Dust Radiative Effects on Climate by Glaciating Mixed‐Phase Clouds." Geophysical Research Letters, , doi:10.1029/2019GL082504.
Ghate V, P Kollias, S Crewell, A Fridlind, T Heus, U Löehnert, M Maahn, G McFarquhar, D Moisseev, M Oue, M Wendisch, and C Williams. 2019. "The Second ARM Training and Science Application Event: Training the Next Generation of Atmospheric Scientists." Bulletin of the American Meteorological Society, 100(1), 10.1175/BAMS-D-18-0242.1.
Solomon A, G de Boer, J Creamean, A McComiskey, M Shupe, M Maahn, and C Cox. 2018. "The relative impact of cloud condensation nuclei and ice nucleating particle concentrations on phase partitioning in Arctic mixed-phase stratocumulus clouds." Atmospheric Chemistry and Physics, 18(23), 10.5194/acp-18-17047-2018.
Chen Y, J Verlinde, E Clothiaux, A Ackerman, A Fridlind, M Chamecki, P Kollias, M Kirkpatrick, B Chen, G Yu, and A Avramov. 2018. "On the Forward Modeling of Radar Doppler Spectrum Width From LES: Implications for Model Evaluation." Journal of Geophysical Research: Atmospheres, 123(14), 10.1029/2017JD028104.
Knopf D, P Alpert, and B Wang. 2018. "The Role of Organic Aerosol in Atmospheric Ice Nucleation: A Review." ACS Earth and Space Chemistry, 2(3), doi:10.1021/acsearthspacechem.7b00120.
Fridlind AM and AS Ackerman. 2018. Simulations of Arctic Mixed-Phase Boundary Layer Clouds: Advances in Understanding and Outstanding Questions. In Mixed-Phase Clouds Observations and Modeling, pp. 153-183. Ed. by Constantin Andronache, Elsevier.
Stofferahn E and Z Boybeyi. 2017. "Investigation of aerosol effects on the Arctic surface temperature during the diurnal cycle: Part 1 - Total aerosol effect." International Journal of Climatology, 37(S1), 10.1002/joc.5036.
Roesler E, D Posselt, and R Rood. 2017. "Using large eddy simulations to reveal the size, strength, and phase of updraft and downdraft cores of an Arctic mixed-phase stratocumulus cloud." Journal of Geophysical Research: Atmospheres, 122(8), 10.1002/2016JD026055.
Fridlind AM, R Atlas, B van Diedenhoven, J Um, GM McFarquhar, AS Ackerman, EJ Moyer, and RP Lawson. 2016. "Derivation of physical and optical properties of mid-latitude cirrus ice crystals for a size-resolved cloud microphysics model." Atmospheric Chemistry and Physics, 16(11), 10.5194/acp-16-7251-2016.
Keita SA and E Girard. 2016. "Importance of Chemical Composition of Ice Nuclei on the Formation of Arctic Ice Clouds." Pure and Applied Geophysics, 173(9), 10.1007/s00024-016-1294-z.
View All Related Publications
Campaign Data Sets
|IOP Participant||Data Source Name||Final Data|
|Sarah Brooks||Continuous Flow Thermal Diffusion Chamber||Order Data|
|Don Collins||Tandem Differential Mobility Analyzer||Order Data|
|Manvendra Dubey||Photoacoustic Soot Spectrometer||Order Data|
|Richard Ferrare||HSR Lidar||Order Data|
|Ismail Gultepe||Campbell Scientific Data Logger||Order Data|
|Ismail Gultepe||Climatronics Aerosol Profiler||Order Data|
|Ismail Gultepe||Fog Monitoring Device||Order Data|
|Ismail Gultepe||Ott Laser Optical Disdrometer||Order Data|
|Ismail Gultepe||Road Surface State Sensor||Order Data|
|Ismail Gultepe||Sentry Visibility Sensor||Order Data|
|Ismail Gultepe||Snow Depth||Order Data|
|Ismail Gultepe||Sunshine Pyranometer||Order Data|
|Ismail Gultepe||Total Precipitation Sensor||Order Data|
|Ismail Gultepe||Vaisala Precipitation Gauge||Order Data|
|Ismail Gultepe||Vaisala Weather Sensor||Order Data|
|Ismail Gultepe||Young Ultrasonic Anemometer||Order Data|
|Alexei Korolev||Korolev Cloud Extinction Probe||Order Data|
|Alexander Laskin||Cloud Condensation Nuclei Counter||Order Data|
|Paul Lawson||2D-S Particle Size||Order Data|
|Paul Lawson||Cloud Particle Imager (CPI)||Order Data|
|Dan Lubin||ASD Spectroradiometer||Order Data|
|Ann Marie Macdonald||Counterflow Virtual Impactor (CVI)||Order Data|
|Greg McFarquhar||Cloud Aerosol Precip Spectrometer||Order Data|
|Greg McFarquhar||Cloud Droplet Probe||Order Data|
|Greg McFarquhar||Microphysical Cloud Properties||Order Data|
|John Ogren||Nephelometer||Order Data|
|John Ogren||Particle Soot Absorption Photometer||Order Data|
|J. Walter Strapp||2D Probes||Order Data|
|J. Walter Strapp||Convair 580 State Parameters||Order Data|
|J. Walter Strapp||DOE Cloud Imaging Probe||Order Data|
|J. Walter Strapp||EC Cloud Imaging Probe||Order Data|
|J. Walter Strapp||FSSP 100||Order Data|
|J. Walter Strapp||FSSP 300||Order Data|
|J. Walter Strapp||Passive Cavity Aerosol Spectrometer||Order Data|
|J. Walter Strapp||Radiometer||Order Data|
|J. Walter Strapp||Ultra-High Sensitivity Aerosol Spectrometer and Condensation Particle Counter||Order Data|
|Mengistu Wolde||CONVAIR Cabin||Order Data|
|Mengistu Wolde||NAWX Radar||Order Data|
|Shaocheng Xie||Constrained Variational Objective Analysis Data||Order Data|
|Shaocheng Xie||European Centre for Medium Range Weather Forecasting||Order Data|
NSA Data Sources
|Name||Full Name||Browse Data|
||Aircraft Communications Addressing and Reporting System||Browse Data|
||Atmospheric Emitted Radiance Interferometer||Browse Data|
||AERI Noise Filtered||Browse Data|
||Aerosol Intensive Properties||Browse Data|
||Aerosol Optical Depth, derived from atmospheric extinction of solar irradiance||Browse Data|
||Aerosol Observing System||Browse Data|
||ARM Best Estimate Data Products||Browse Data|
||Active Remotely-Sensed Cloud Locations||Browse Data|
||Advanced Very High Resolution Radiometer||Browse Data|
||Cloud Condensation Nuclei Particle Counter||Browse Data|
||Cimel Sunphotometer||Browse Data|
||European Centre for Medium Range Weather Forecasts Diagnostic Analyses||Browse Data|
||Eddy Correlation Flux Measurement System||Browse Data|
||Ground Radiometers on Stand for Upwelling Radiation||Browse Data|
||G-band (183 GHz) Vapor Radiometer Profiler||Browse Data|
||G-Band Vapor Radiometer Precipitable Water Vapor||Browse Data|
||Interpolated Sonde||Browse Data|
||Infrared Thermometer||Browse Data|
||Merged Sounding||Browse Data|
||Surface Meteorological Instrumentation||Browse Data|
||Multifilter Radiometer||Browse Data|
||Multifilter Rotating Shadowband Radiometer||Browse Data|
||Cloud Optical Properties from MFRSR Using Min Algorithm||Browse Data|
||Continuous Baseline Microphysical Retrieval||Browse Data|
||Millimeter Wavelength Cloud Radar||Browse Data|
||MMCR mode moments, derived by ARSCL process||Browse Data|
||Model Output Location Time Series||Browse Data|
||Micropulse Lidar||Browse Data|
||MPL: data averaged to fixed 30 second interval, e.g. for polarized data||Browse Data|
||Cloud mask from Micropulse Lidar||Browse Data|
||Microwave Radiometer||Browse Data|
||Microwave Radiometer - High Frequency||Browse Data|
||Microwave Radiometer Profiler||Browse Data|
||MWR Retrievals||Browse Data|
||National Centers for Environment Prediction Global Forecast System||Browse Data|
||Normal Incidence Multifilter Radiometer||Browse Data|
||NOAA Climate Reference Network||Browse Data|
||NOAA/ESRL/GMD Radiometers||Browse Data|
||National Weather Service Upper Air Measurements||Browse Data|
||Optical Rain Gauge||Browse Data|
||Planetary Boundary Layer Height||Browse Data|
||Data Quality Assessment for ARM Radiation Data||Browse Data|
||Radiative Flux Analysis||Browse Data|
||Radar Wind Profiler||Browse Data|
||Sky Radiometers on Stand for Downwelling Radiation||Browse Data|
||Balloon-Borne Sounding System||Browse Data|
||Sonde Adjust||Browse Data|
||ultrasonic wind sensor||Browse Data|
||Surface Spectral Albedo||Browse Data|
||Shortwave Flux Analysis||Browse Data|
||Total Precipitation Sensor||Browse Data|
||Total Sky Imager||Browse Data|
||Facility-specific multi-level Meteorological Instrumentation||Browse Data|
||Tower Camera||Browse Data|
||Minnis Cloud Products Using Visst Algorithm||Browse Data|