Clouds have a crucial role in the Earth’s water cycle and the energy balance of the climate system; understanding them and their composition is vital in comprehending the Earth-atmosphere system.
However, accurately observing clouds is challenging because of their high variability and complexity, plus their unpredictability in occurrence. Sensors, both ground-based and airborne, can help scientists decipher their macrophysical properties, such as cloud cover, boundaries and thickness, as well as their microphysical features, such as cloud particle size.
Clouds have a strong effect on solar heating by reflecting part of the incident solar radiation back to space, but they also reduce the planet’s self-cooling ability by intercepting thermal infrared radiation emitted below the cloud’s surface and reemitting part of this back to the surface of the Earth. Global changes in surface temperature are highly sensitive to the amount and type of cloud. Therefore, it should be no surprise that the largest uncertainty in models of global warming arises due to clouds.
Global Climate Models (GCMs) are an important tool for predicting climate change and simulate physical processes taking place in the atmosphere, oceans and on land. While useful, they are limited by the fact that they nearly always assume that clouds are flat, homogenous, infinite slabs. This unrealistic representation of cloud cover and cloud composition hampers the accuracy of GCMs. Rather than being flat masses, clouds are dynamic aggregates of water droplets, ice crystals and other particles such as aerosols. They can be classified according to cloud height and thickness.
A recent study from China employed long-term cloud data collated during the ARM program at the Southern Great Plains central facility between 2001 and 2010 on six different cloud types: low, mid-low, high-mid-low, mid, high-mid and high. The data was collected four times a day using a radiosonde, a battery-powered instrument launched into the atmosphere by a weather balloon to measure various parameters, and compared to data from three ground-based remote sensing instruments: millimeter microwave cloud radar, micropulse LiDAR and ceilometers which measure the height of the cloud ceiling or cloud base. The scientists studied the differences and similarities in the clouds using the two different methods, concluding that both methods largely agree for cloud types.
Satellites in orbit around the Earth are particularly important for cloud research as they have the ability to give global coverage of the situation and provide insight into what is happening on top of a cloud. NASA employs many active sensors and sounder instruments (which measure the vertical distribution of precipitation and other atmospheric characteristics) to monitor clouds including:
- CALIPSO (Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation)
- CATS (Cloud-Aerosol Transport System onboard the International Space Station)
- ICE-SAT2 (Ice, Cloud and Land Elevation Satellite-2)
The most prominent satellite sensors used are MODIS (Moderate Resolution Imaging Spectroradiometer), a worldwide leader in computation on reflection, absorption and transmission of highly inhomogeneous clouds.
However, satellites have a coarser resolution than the human eye so detailed identification of clouds is impossible. It is therefore important to understand that cloud types differ from those identified on Earth, so the classification most similar to the structure of cloud form determined by surface observation is used. There are seven classifications for clouds observed from satellites: high, mid, stratus/fog, stratocumulus, cumulus, cumulus congestus and cumulonimbus. Stratus/fog, stratocumulus and cumulus make up a low-level cloud category.
There are a wealth of sensors, both ground-based and in the atmosphere and space, that can help scientists understand clouds and classify them based on properties such as cloud height, thickness and behavior. Identifying and grouping clouds enables scientists to develop more accurate global climate models that could help predict the path of global warming and how our planet might change over time.
References & Further Reading