August 1, 2014


The parasol effect is a well-known phenomenon, and is much appreciated on a hot day. It is often called albedo effect due to the clouds’ reflective power (called albedo), but this name highlights the reflection of solar radiation by the clouds while dismissing diffusion and absorption. The greenhouse effect of clouds during the night also has an influence on morning temperatures.

Lower clouds have a strong parasol effect and a weak greenhouse effect. The opposite is true for high, thin clouds such as cirri. How will rising global temperatures affect clouds, their type and their distribution? How will this affect the planet’s net radiation? These questions are essential in designing accurate climate models which account for cloud cover.

Low clouds: strong albedo effect, weak infrared effectHigh clouds (cirri): weak albedo effect, strong infrared effectDeep clouds (cumulonimbi): strong albedo and infrared effects

In order to better understand interactions between clouds and radiation or the effect climate change can have on clouds, it is important to have a firm grasp their properties. These include microphysical properties, distribution, phase, predominant shape, crystal orientation (in the case of ice clouds), and morphology (spatial distribution of liquid water within the cloud). Distinctions must be made between two microphysically different types of clouds: high clouds, such as cirri, and heterogeneous clouds, such as nimbi.

Optical and microphysical properties

According to the latest sensors, at any given time, cirri and other types of high clouds cover about 30% of our planet. Their impact on our climate is widely accepted (Liou, 1986), but our understanding of their microphysical properties is still insufficient to integrate them in climate models and predict their effects on Earth’s net radiation. It has been shown (Brogniez, 1988; Takano and Liou, 1989a; Brogniez et al., 1992; Macke, 1996; Takano and Liou, 1998; Doutriaux et al., 1999) that the shape, size and orientation of ice crystals determine their radiative properties; thus one would have to be able to determine these characteristics on a global scale to accurately describe their impact on climate. However these properties depend on where the clouds form, their temperature, their water content, and the physical conditions in which they appear. Cirri’s properties are difficult to observe from space; thin and heterogeneous cirri are harder to detect and their phase and altitude cannot be measured using traditional sensors. Combining data obtained from sensors on board Parasol, Aqua, Calipso and CloudSat should help dissipate ambiguity and tackle the multi-layer cloud cover (Doutriaux-Boucher et Al., 1998) and mixed ice-liquid water phases.

When it comes to heterogeneous clouds, microphysical properties are better understood. However, scientists don’t yet have access to comprehensive measurements linking clouds’ microphysical properties, geometric shape, and vertical structure to independent radiative field measurements to validate the model and establish parameters. One obvious obstacle when it comes to modelling cloud radiative properties stems from the fact that they are often treated as flat parallel layers, horizontally constant, with a set microphysical composition. This is a problem both when estimating clouds’ effect on radiation in general circulation models and when trying to determine cloud properties (albedo, optical thickness, water and/or ice content) using luminance observations made from space. Even in the case of relatively horizontally constant clouds such as stratocumuli, it has been shown (Cahalan et al., 1994) that liquid water distribution inside the cloud can have a significant influence when trying to determine the cloud’s optical thickness from space. For other types of clouds, the effect is even greater. Several studies have shown the undeniable effect the cloud’s shape can have on flow (Welch and Wielicki, 1985; Parot et al., 1994) as well as on bidirectional reflectance (Davis, 1984; Bréon, 1992, Kobayashi, 1993; Loeb et al., 1999). Since then, significant progress has been made; however scientists have yet to rank the major sources of error: (i) underestimating the importance of the cloud’s microphysical properties, (ii) uneven horizontal distribution of the (vertically integrated) liquid water content, and (iii) impact of the cloud’s shape (or the cloud’s highest point’s altitude).

A few cloud properties derived from Polder

The liquid water phase is an essential aspect of clouds and their effects on climate. It is also the first thing scientists need to know before they can carry out more detailed analyses of the cloud cover on microphysical (particle size, shape, orientation) and macrophysical (optical thickness, altitude) levels.

Measuring polarisation clearly distinguishes different phases as liquid droplets have a very specific, rainbow-like signature in a particular observation direction. 
Polarised reflectance above liquid water clouds (red) and ice clouds (blue)

Rendition of an IHM crystal’s polarisation diagram
Combined observations in normal and polarised light have been analysed. The IHM crystal model (Inhomogeneous Hexagonal Monocrystal) provides a relatively accurate rendition of angular and polarised variations of light reflected by ice clouds. This model will be able to accurately simulate ice clouds’ optical and radiative properties. 

When it comes to liquid water clouds, a new original method analyses polarised images’ polarisation curves for wavelengths of 443, 670, and 870 nm. When the cloud cover is homogenous, this method is very accurate in determining the size of water droplets at the top of the cloud. 
Polder image showing polarisation curves on a cloud field off the coast of Africa

Cloud droplet radius (in µm) measured by Polder (fig. 1)
(fig. 2)

Statistical relationship between aerosol content and cloud droplet size (in blue over ocean; in red over land surfaces).

Statistical analysis shows a significant contrast in average water droplet size between land and sea; 6 to 10 µm over land and 12 to 14 µm over sea. As shown in figure 2 above, analysis also shows a strong correlation between droplet size and aerosol concentration as calculated by Polder. Droplets are smaller over polluted areas when compared to “clean” areas. Smaller droplets can be found around continental edges. This is a symptom of aerosols’ effects on cloud physics which have significant consequences on Earth’s energy balance.

Polder can determine the atmospheric integrated water content (column) based on absorption contrasts in its 910 and 865 nm wavebands, as accurately as radiosondes. This method can be used in cloudless areas and offers an interesting coverage area, especially over land masses.

Total Column Water Vapor from Polder 2 on ADEOS II
15 June 2003 - monthly synthesis