JRA3: A framework for cloud-aerosol interaction studies

WP Leader:  Herman Russchenberg


To develop an observational framework for cloud-aerosol interaction studies in Europe. More specifically:

  • To develop optimized sensor-synergetic algorithms for the physical characterization of clouds and aerosols in the context of cloud formation and its impact on climate change.
  • To develop and test observation strategies for the study of cloud-aerosol interaction studies through combined use of remote sensing, in situ observations and atmospheric models.

Description of work

This work package aims at developing the experimental means to quantify one of the least Understood aspects of the climate system: the indirect effects of aerosols. Although these effects are conceptually understood, their quantification has proven to be difficult as the impact of cloud-aerosol interaction is often cluttered by concurrent atmospheric processes and the technological means to unravel those do not exist yet.
The need for unambiguous measurements of the indirect aerosol effects challenges the state-of-the-art remote sensing technologies of today and calls for the development of new observation methods.
In order to quantify the indirect aerosol effect, the following atmospheric parameters have to be observed and put into context:
− Cloud properties: height, thickness, microphysics, spatial structures
− Boundary layer: wind fields below and inside the cloud, and water vapour distribution.
− Aerosols: size distributions and number concentrations, chemical composition, spatial distribution.
In addition, output of numerical weather prediction models is needed as descriptive information of the general weather conditions and for back trajectory analyses to identify sources of aerosols.
Most of the current global data on clouds come from passive satellite measurements, but these are unable to measure the detailed vertical profile crucial for both radiative transfer and microphysical evolution. Profiling satellites, like the CloudSat-CALIPSO combination, or the future EarthCARE mission, can give vertical profiles, but lack sufficient temporal and spatial coverage. Active instruments, in particular radar and lidar, are uniquely able to provide essential information on the vertical profile, while passive radiometers can provide an integral constraint that improves the accuracy of the retrieved profiles. The best results are obtained by using the available instruments in synergy, either combining active sensors at different wavelengths or by augmenting the active sensors with passive radiometric measurements. These combined retrieval algorithms can deliver physically consistent profiles of cloud parameters, such as water content, particle size, and optical extinction.
However, the current techniques were developed for the improvement of weather forecast models, and are not optimized for cloud-aerosol interaction studies. Continuous cloud-observing sites operate in both Europe and elsewhere as part of the US. The standard fit includes a cloud radar, lidars at 905 and 532 nm, a total sky imager and various radiometers. The cloud-profiling ACTRIS sites (Cabauw, Chilbolton, Lindenberg, Mace Head and Palaiseau) each have a similar extensive suite of active and passive instruments, with Chilbolton and Cabauw also being equipped with radars at different radar frequencies, enabling dual-frequency algorithms to be applied routinely. In the Framework 5 programme Cloudnet, the groundwork has been carried out to harmonise the data produced by each of the European sites, enabling algorithms to be applied to all sites with equal ease. The Cloudnet processing system has been extended to the ARM sites. This work has facilitated the application of identical algorithms to the long-term datasets from all the ARM and Cloudnet sites. Also within Framework 5, the CLIWA-NET [Crewell et al, 2004] project, with Cabauw as a central facility, established a prototype European cloud observation network during three extensive operation periods, providing integrated water vapour and cloud liquid water for validation of regional climate models. This work package will therefore be able to take advantage of what has already been achieved and extend it significantly by application of more sophisticated algorithms to data from a wider array of instruments than ever before available in Europe.
Task description (coordinating partner is underlined)

 Task 22.1

 Advanced sensor-synergetic algorithms for the physical characterization of clouds and aerosols. (TUD, UREAD, CNRS, CNR, MPG)

The study of cloud-aerosol interaction requires data accuracies that can not be delivered with the current generation of routine algorithms. Crucial here are profiles of water vapour, aerosol extinction, vertical wind and the vertical (microphysical) structure of clouds. Of particular importance is an accurate determination of the cloud base and top. In this task we will enhance existing and develop new algorithms, such that they deliver data accurate enough for the physical characterization of clouds and aerosols with sufficient temporal and spatial resolution. Based on a requirement study, we will:
- optimize, test and implement Raman lidar techniques for the retrieval of water vapour profiles;
- develop, test and implement joint radar - lidar - microwave radiometer techniques to derive accurately the cloud base and top, the cloud droplet number concentration, and the average particle size.
- test the Earlinet aerosol products (WP2) and, if needed, advise on additional requirements for cloudaerosol interaction observations in the boundary layer.
- use CloudNet data and in situ data of previous aircraft campaigns to improve the physical cloud models inherent in the sensor synergetic algorithms.
Starting point of this task is a set of earlier developed retrieval algorithms (e.g., Baedi et al, 2000; Boers et al, 2000; Krasnov and Russchenberg, 2002; Loehnert et al, 2008; Gaussiat et al, 2004; O'Connor et al, 2004). This task will be performed in close collaboration with the network activities WP2, WP5 and WP6, and involve the observatories of Chilbolton, Palaiseau, Cabauw, Lindenberg, Potenza and Mace Head. The product of this task will be a set of retrieval algorithms.



 Task 22.2

New observation methods of cloud formation. (TUD, CNRS, UREAD, MPG, NUIG)

The results of task 1, advanced retrieval algorithms, will be used to develop observation methods for cloud formation against a variable aerosol background. The method should enable the study of cloud-aerosol interaction: do variations in the aerosol background result in variations of the cloud structure? Cabauw will be used as the central observatory for the development, because of the large variety of ancillary measurements performed there. The resulting method will be tested at the other cloud profiling ACTRIS sites.
Task 22.2.1 Categorization. The effect of a change in aerosol background on the cloud Microstructure can only be observed when other influential factors are kept constant. Therefore, the clouds need to be conditionally sampled in specific categories according to the following criteria: equal liquid water path, equal vertical extent, absence/presence of drizzle, equal ‘level of adiabicity’ (Boers et al, 2000).
Task 22.2.2 Descriptor of the aerosol-cloud relationship. Within each cloud class the relationship between the lidar-derived aerosol extinction and the cloud parameters will be derived, quantified by the indirect aerosol effect index (Feingold et al, 2003), which is basically the sensitivity of the effective cloud droplet radius to the aerosol optical thickness- the effective radius is relevant in this case as it is the dominant descriptor in the radiative transfer theories. However, we will extend this concept to the sensitivity of the cloud droplet number concentration to the aerosol optical thickness, as this is closer to the physics of the indirect aerosol effect..


 Task 22.3

Classification of concurrent atmospheric processes: aerosol sources (TUD, CNRS, CNR, DWD, NUIG)

The result of Tasks 22.1 and 22.2 will be a multi-parameter data set of long time series of cloud-aerosol-radiation parameters, categorized in different classes of clouds and descriptors of boundary layer dynamics. Multi-variate statistical techniques will be developed to classify this data set into the different sources and sinks of the observed correlations and thus separate indirect aerosol effects from concurrent processes. Finally, the question 'What is the origin of cloud-producing aerosol?' needs to be addressed. To this end, a back trajectory analysis will be implemented to identify the sources of aerosols, using the output of numerical weather prediction models and aerosol transport models (e.g. the ECMWF, the KNMI RACMO model, LOTOS-EUROS) data in combination with satellite data (MODIS, MSG). This task will be performed in close collaboration with WP6 and involve the observatories of Chilbolton, Palaiseau, Cabauw, Lindenberg, Potenza and Mace Head. The product of this task will be set of tools to identify aerosol sources relevant to cloud-aerosol interaction studies.


 Task 22.4

 Optimal observations to expand the method to all cloud profiling stations (TUD, CNRS, UREAD, CNR, DWD, NUIG)

While the complete observation strategy will be based on the equipment at CESAR Observatory in Cabauw, scaled-down versions, and hence a selection of optimum observables and retrieved parameters, need to be developed for use at observatories that only have part of the facilities. This will be achieved through sensitivity analyses of the methodology to a reduction of the number of parameters, the development of optimum estimators and appropriate parameterizations of relationships between physical properties, which can be used to replace part of the observations. This task will be performed in close collaboration with WP5 and involve the observatories of Chilbolton, Palaiseau, Cabauw, Lindenberg, Potenza and Mace Head. The product of this task will be set of scaled-down methods for observational cloud-aerosol interaction studies for implementation in all cloud profiling sites of the ACTRIS network.

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