National Academies Press: OpenBook
« Previous: Appendix B: June 24-25, 2015, Workshop Agenda
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×

Appendix C
June 24-25, 2015, Workshop Recap


National Academies Beckman Center
Irvine, CA

Disclaimer: This meeting recap was prepared by the Academies staff as an informal record of issues that were discussed during the public Academies workshop: Opportunities to Improve the Representation of Clouds and Aerosols in Climate Models with National Collection System held on June 24-25, 2015. This document was prepared for information purposes only. It has not been reviewed and should not be cited or quoted, as the views expressed do not necessarily reflect the views of the Academies or the Committee on Opportunities to Improve the Representation of Clouds and Aerosols in Climate Models with National Collection Systems.

Committee Members Present: Pamela Emch, Everette Joseph, Sonia Kreidenweis, Michael Prather, Jeffrey Reid, Robert Wood

Committee Members Absent: Steven Ghan

Academies Staff Present: Rita Gaskins, Michael Hudson, Kristina Pistone, Amanda Staudt, Katie Thomas

This recap is not a comprehensive summary of all the issues discussed at the workshop. Rather it summarizes the specific workshop discussions on challenges and gaps related to observing clouds and aerosols in the context of the classified assets potentially addressing those gaps. The purpose of this recap is to help inform the sponsor about which types of data to present at the follow-on classified workshop (September 28-30, 2015) and inform the committee in the planning of the workshop. Presentation slides are available upon request.

An often asked question during the workshop was whether the data, if identified as useful, will be declassified. Some participants asked if it would be possible for scientists to be “declassified” to speak to people without security clearances about the data or metadata. Other participants noted that even if the data are not published, perhaps they could inform (either positively or negatively) the directions and specifications for future missions.

Several participants said it is critical to know the specifications (e.g., wavelengths, tolerances, uncertainties, precision, viewing geometry, pushbroom versus whiskbroom) and calibration/validation details in order to use the data for scientific research. For example, information on wavelength spectral band location and width is important in cloud identification.

Furthermore, many participants said that, for any data that is declassified, it will be critical to also declassify the metadata associated with the observations. Others were more optimistic and noted that the current state of knowledge of some science questions (e.g., ice crystals, ice clouds) is in its infancy, and therefore any increase in statistics, even without spatial and temporal metadata, would advance the science. For other questions (e.g., process studies in warm clouds), spatial and temporal location needs to be known precisely so that scientists could geolocate the data with other observations.

Many participants said that the value of the classified data lies in its potential integration with other unclassified data and measurements. For example, it would be helpful to know coincidence (or offset) from other platforms, wavelengths, and time shifts.

Finally, numerous participants raised concerns related to human resources. Some noted that extracting data from A-train or A-train-like sensors (e.g., using CloudSat as a passive sensor to better understand precipitation processes) requires deeper analysis. Many participants highlighted the additional challenges of data calibration, intercalibration, storage, and quality control. Georectification of data from

Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×

multiple platforms (with different instruments, view volumes, times, etc.) may also be intensive and difficult, and require significant staff time.

To inform the sponsor about specific types of data to present at the September workshop, individual workshop participants discussed a number of ways that understanding of clouds, aerosols, and their interactions could be advanced with additional data from classified assets. These concepts and issues should not be seen as conclusions of the workshop or as consensus statements of the workshop participants or organizing committee.

  • Improve understanding of aerosol-cloud interactions.
    • These interactions could be elucidated with better observations of changes in cloud brightness (sometimes), droplet size, cloud height, and cloud fraction.
    • A significant challenge is that the greatest microphysical effects are in the cleanest environments, while the greatest radiative effects are in environments with the heaviest aerosol loading.
    • Aerosol-cloud interactions span many scales.
      • The spatial scales range 13 orders of magnitude from the different classes of aerosols and droplets (fine mode [100 nm]/coarse mode/cloud drop/drizzle drop/rain drop, with a factor of 10 between each); to the macroscales of turbulence (10 m) updrafts, cumulus, thunderstorms, convective systems (also factor of 10 between each). In some cases the spatial resolution of the instrument will determine the values calculated for the fields, especially in the case of cloud fraction.
      • The timescale ranges from seconds to days.
    • Some participants said periodic snapshots are sufficient, even though observations of how processes evolve over time provide more information. Some participants said available satellite observations provide enough constraints to retrieve process rates, while others said vertically-resolved measurements would be needed.
  • Improve understanding of cloud physical processes and their representation in models.
    • Important variables to measure include:
      • Liquid cloud: water content or liquid water path (LWP), optical depth, particle size, and number concentration.
      • Ice variables: water content, particle size, extinction or optical depth, number concentration and crystal shape.
  • Improve discrimination between liquid and ice phases.
    • Important variables to measure include:
      • A combination of particle size and backscatter (lidar and radar).
  • Improve understanding of long-term processes.
    • Targeted observations of processes that are stable year-to-year might advance understanding.
  • Improve observations of aerosols located over snow or ice.
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
  • Aerosol retrievals from satellites located over snow and ice are difficult because their surface optical properties are uncertain.

Throughout the workshop, individual workshop participants identified numerous types of data and instruments that would potentially be useful to advance understanding of cloud, aerosols, and their interactions. These concepts and issues should not be seen as conclusions of the workshop or as consensus statements of the workshop participants or the organizing committee.

  • Measurements (e.g., air quality or meteorological monitoring) from under-observed areas (e.g., embassies or military bases) to estimate emissions.
  • Aerosol sources (e.g., injection site, frequency of emissions, plume height, etc.) would be useful to initialize models.
  • Aerosol type (e.g., chemical composition of aerosols, measurements of local visibility) to validate remote sensing retrievals.
  • Vertical velocity measurements (e.g., vertical profiles of temperature, humidity) as many cloud and aerosol processes have strong dependencies on the meteorology.
  • Observations from a radiative forcing perspective over the “pristine” Southern Hemisphere ocean.
  • Measurements of microphysical properties rather than bulk or column properties (aerosol optical depth, cloud condensation nuclei, aerosol index).
    • Aerosol optical depth, for example, is taken to be a proxy for number of particles, which limits process-level understanding. Cloud effective radius does not represent droplet size distribution, which varies throughout the cloud. These measurements are important for comparing model outputs, which give different parameters.
  • High-resolution ground- or aircraft-based radar or lidar data (e.g., radar with more or different wavelengths or high sensitivity, even if in one location, to either co-locate or get a general climatology).
    • HSRL (High Spectral Resolution Lidar) can measure aerosol optical properties and derive measures of size, composition, aerosol backscatter, extinction, and optical depth. HSRL can also measure cloud properties including: fraction, optical depth, extinction, and cloud droplet number concentration, particularly in difficult retrieval conditions (high latitudes, nighttime, above clouds).
    • HSRL measurements of aerosol extensive and intensive parameters provide additional constraints for developing and assessing models. The aerosol profiles are used to assess and improve aerosol data assimilation systems.
  • Hyperspectral infrared (IR) provides good horizontal measurements to tell us something about cloud height and depth. IR can also be utilized over ice and snow.
  • High-resolution (~1 km) microwave observations.
  • Computed tomography to leverage full information content of various sensors.
  • Observations from satellites with unique orbits (e.g., highly elliptical) that could “dwell” on specific areas. One example is the proposed Canadian PCW (Polar Communications and Weather) system focused on Arctic.
  • Very high spatial resolution (e.g., Landsat) combined with time information. A system that could stare and be pointed (versus the typical nadir-view of satellites), as it moves over the variable of interest. It could look at the variable from different angles: multi-angle, stereo, visible and infrared (IR)/thermal IR.
  • In situ data sources and observations (e.g., in situ atmospheric ice crystals, buoys that make atmospheric measurements above the ocean).
  • A sensor similar to VIIRS (Visible/Infrared Imager/Radiometer Suite) that could be used for nighttime aerosol retrievals.
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
  • Instruments which employ the A-Train model: multiple simultaneous measurements from active and passive sensors.
  • Observations that have overlap with existing observation systems to improve utility and to avoid to the extent possible difficult geo-rectification efforts. Ideally, the observations would be completely coincident or slightly offset in time to observe the development of systems (e.g., between the A-train satellites or Terra MISR/Landsat offset).
  • Pre-A-Train observations, in similar orbit, to help extend the record.
  • Miniaturization of current technologies to place on commercial or military aircraft, ships, satellites to improve coverage.
  • Multi-angle, stereoscopic observations and high capability geosynchronous observations.
  • Observations from broadband radiometers such as CERES (Clouds and the Earth's Radiant Energy System) to better constrain the global radiation budget.
  • Polarimeter and polarized data could offer insights on droplet size distributions, for example, and in understanding the full 3D character of the atmosphere.
    • Retrievals of fine and coarse mode aerosol: optical depth, size distribution, complex refractive indices for each mode, cloud phase, droplet size distribution, shape, roughness, and asymmetry parameter retrievals for ice clouds, aerosols retrievals under cirrus. These instruments are not sensitive to absorption.
      • Ideally has wide spectral range from blue to NIR; perhaps also UV. Wide angular range to capture a wide range of scattering angles. High accuracy: 0.5% or smaller, 0.1-0.2% would be ideal. Dense angular sampling to resolve structures in rainbow peak to calculate droplet size distribution while not being sensitive to 3D cloud effects or cloud shadows. This allows for aerosol retrievals over oceans, over land, and under (thin) clouds.
    • Retrievals of water and ice clouds: dense angular sampling (~50 viewing angles; aerosol could be done with ~5-6 viewing angles).
  • Observing systems that could track the evolution of cloud systems to compare with cloud models.
    • This could be achieved by a few satellites flying in unison in low orbit, providing views of a cloud system with temporal resolution on the order of 5 minutes and recording at least 15-30 minutes of evolution.
    • It would be difficult to record a cloud system from birth to senescence except via geo-orbit. The definition of a single cloud becomes vague as cloud systems evolve. However, a series of snapshots of the system over a brief period of time (e.g., on the order of 30 minutes) would catch a range of systems at different stages of their evolution and thus it would be useful to test the dynamics of aerosol-cloud models if the swath of observations covered a range of aerosol-cloud systems.
    • To the extent that some of these cloud systems exhibit self-similar properties, much could be deduced about system evolution from composites of a series of snapshots.
    • Commercial satellite imagery such as the Quickbird or Digital Globe systems would provide a zoomed in view of cloud structure for detailed process inference. These instruments have a resolution of 0.6 m panchromatic, and 2.4 m in 4 bands (RGB [red, green, blue] + NIR) over a 16 km footprint. With 30-sec retargeting, it would be possible to get multi-angle views (-45°, -30°, nadir, +30°, +45°) but this only covers 2 minutes.
  • Suborbital sampling (even if scientists do not know the location) to provide verification, albeit at poorer spatial sampling.
    • Examples include ER-2 and Global Hawk.
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
  • The Google Loon balloon project is designed to hover over an area to provide Wi-Fi connectivity in remote areas. Balloons currently have small payload (10 kg) but can stay aloft for up to 6 months at approximately 18 km altitude. Such technology might be very useful over heavily instrumented surface sites such as DOE/ARM (Department of Energy’s Atmospheric Radiation Measurement Program) sites.
  • Other sub-orbital assets, such as military flights, could be tasked with providing regular in situ sampling.
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×

This page intentionally left blank.

Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
Page 23
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
Page 24
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
Page 25
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
Page 26
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
Page 27
Suggested Citation:"Appendix C: June 24-25, 2015, Workshop Recap." National Academies of Sciences, Engineering, and Medicine. 2016. Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version. Washington, DC: The National Academies Press. doi: 10.17226/23527.
×
Page 28
Next: Appendix D: September 28-30, 2015, Classified Workshop Agenda »
Opportunities to Improve Representation of Clouds and Aerosols in Climate Models with Classified Observing Systems: Proceedings of a Workshop: Abbreviated Version Get This Book
×
Buy Ebook | $14.99
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

One of the most significant and uncertain aspects of climate change projections is the impact of aerosols on the climate system. Aerosols influence the climate indirectly by interacting with nearby clouds leading to small changes in cloud cover, thickness, and altitude, which significantly affect Earth’s radiative balance. Advancements have been made in recent years on understanding the complex processes and atmospheric interactions involved when aerosols interact with surrounding clouds, but further progress has been hindered by limited observations.

The National Academies of Sciences, Engineering, and Medicine organized a workshop to discuss the usefulness of the classified observing systems in advancing understanding of cloud and aerosol interactions. Because these systems were not developed with weather and climate modeling as a primary mission objective, many participants said it is necessary for scientists to find creative ways to utilize the data. The data from these systems have the potential to be useful in advancing understanding of cloud and aerosol interactions. This publication summarizes the presentations and discussions from the workshop.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    Switch between the Original Pages, where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

    « Back Next »
  6. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  7. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  8. ×

    View our suggested citation for this chapter.

    « Back Next »
  9. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!