Observational Data of the Atmosphere and Oceans

Two important areas of concern for Earth scientists are the oceans and atmosphere. Technologies have improved significantly in this arena, with sensors and other equipment integrated onboard satellites, aircraft, and ships and on buoys placed in key areas. These systems provide extensive observational data on such issues as air quality, pressure, ocean currents, salinity, and temperature changes. Although areas of the oceans and atmosphere remain difficult to gauge, the continued evolution of technologies and research approaches may soon enable effective analyses of these regions.

Basic Principles

Earth’s oceans and atmosphere are two key areas to which scientists are increasingly paying attention as they examine climate change. For most of the twentieth century, however, scientific technology used in these arenas was limited in terms of the amount of data it could collect.

Many sensor systems, for example, could detect conditions at the ocean’s surface but not in areas well beneath the surface. Similarly, meteorological sensors were for many years hampered by an inability to penetrate cloud cover when studying atmospheric phenomena. Since the latter half of the twentieth century, however, technologies have improved steadily, enabling researchers to examine a wide range of aspects related to trends in the oceans and atmosphere.

In addition to improvements to data-gathering systems found in ground observation facilities; aboard planes, ships, and buoys; and attached to weather balloons, satellite-based remote sensors have generated an even greater amount of data on much larger target areas. This technological evolution means that a greater amount of observational data can be collected, collated, and utilized to generate comprehensive models of the environment and of climate change. Such data also can help scientists to understand and even predict atmospheric and oceanic phenomena with greater precision than ever before.

Collecting and Analyzing Data

In the pursuit of observational data, scientists utilize a wide range of technologies. Some of these systems (such as weather balloons) are sent directly into a layer of the atmosphere or are placed on a research ship. Others are placed on board satellites, enabling the observation of a much wider target range.

In the oceans, a growing network of surface and subsurface buoys using sophisticated sensors is generating data. One such data area is sea-level tracking. Here, buoys can be used to track tidal changes, currents, and even high-water events such as tsunamis (a series of long, high sea waves that are caused by seismic activity or other disturbances). Buoys designed by the Science Applications International Corporation are among the types of state-of-the-art buoy systems commonly used, many of which can be configured for multiple tasks, such as detecting tsunamis or monitoring temperature or chemical changes associated with climate change.

The National Oceanic and Atmospheric Administration (NOAA) in the United States utilizes an extensive network of such buoy technologies. NOAA operates nearly one hundred weather buoys along the Atlantic and Pacific coastlines and in the Gulf of Mexico and the Great Lakes. This network gathers observational data on sea-level changes brought on by the tides and other phenomena, and it detects changes in water temperatures, currents, and salt content.

Meanwhile, meteorologists utilize airborne and satellite-based sensors to collect observational data on atmospheric conditions and related phenomena. Using such technologies as passive and active radars, scientists can detect high concentrations of gases and particles in a given target area. For example, international scientific organizations (such as the World Meteorological Organization) are using data compiled from these technologies to study the emission and distribution of greenhouse gases from their sources.

The growing ability of research technologies to compile larger and more comprehensive observational data sets in turn fosters the need for programs and systems that can compile the data and help generate models. In some cases, models of oceanic and atmospheric trends and events are generated using complex mathematical algorithms (sets of instructions used to perform a task), assigning values to certain data sets. Algorithms also are utilized to analyze irregularities within data sets; by isolating these inconsistencies (and, potentially, errors), researchers can develop observational data sets with greater simplicity and reliability.

In addition to using mathematical models, researchers examining oceanic and atmospheric observational data can call upon computer modeling systems. This software can compile large amounts of observational data, generating frameworks on local, regional, and global scales as they occur through time. These models can be created using multiple grids, allowing the user to analyze data collected within a specific region and to compare conditions in one grid with conditions in another.

Studying Climate Change

For decades, scientists from a wide range of fields (including oceanography and meteorology) have looked critically at the effects of climate change. Remote sensors and other technologies have been used to analyze changes in atmospheric and surface temperatures, precipitation patterns, and other environmental changes associated with global warming and climate shift. These technological systems can gather large volumes of observational data and use it to generate models that analyze climate changes.

Scientists, for example, are monitoring the rate of temperature changes and salinity (the concentration of salt in a given area of the ocean) to monitor the rate of climate change occurring in the oceans. Combining the observational data acquired from sensor networks from all over the world, researchers can create a composite of the changes in temperature and salinity associated with global climate change.

Observational data on the relationship between salinity and temperature also are proving useful in predicting the length and potential effects of El Niño and La Niña (cyclical increases and decreases, respectively, in water temperature that foster shifts in weather patterns worldwide). As scientists argue that El Niño and La Niña patterns may become prolonged because of climate change, observational data on such phenomena are becoming an important component of this aspect of climatological research.

The use of observational data on different types of severe weather events also can assist researchers who are attempting to track and predict future climate shifts. For example, one study analyzed strong winter storms in the higher latitudes of the oceans. Such storms are powerful and frequently cause damage to offshore oil rigs and disrupt shipping lanes. However, these polar lows, as they are called, are relatively small in size and have seen little scientific study as distinct phenomena. Still, scientists believe that, as the climate continues to shift, polar lows may increase both in terms of size and volume. Such a trend could mean further disruptions to international trade routes and the energy industry and could mean harm to populated areas.

Meteorologists are studying observational data from polar lows to model their development and behavior. It is hoped that such data can help them predict future polar lows and assist interested parties in adapting to the likely increase in polar low occurrences.

In addition to the copious amounts of data gathered by individual studies of oceanic and atmospheric phenomena are the combined research efforts of regional and global networks and organizations. For example, the World Climate Research Programme, an international network of governmental and nongovernmental organizations, operates the Climate Variability and Predictability program. This network enables participants to collaborate, share observational data, and use this information to generate models on a broad spectrum of research areas, such as sea temperatures, atmospheric circulation, and ocean currents.

Predicting Severe Phenomena

In addition to providing localized data on climate change, observational data of the oceans and atmosphere help in the prediction of severe events and phenomena. In the oceans, for example, the presence of buoy nets in areas susceptible to volcanic and seismic activity (and hurricanes) has helped scientists understand the conditions that can create tsunamis and other high-water events.

NOAA, in collaboration with other organizations around the world, has formed an extensive network of observational data-collecting buoys. The National Data Buoy Center, which provides real-time data on conditions at a single buoy, provides useful data on sea-level changes and on underwater pressure (a key indicator of a tsunami). These data can help scientists track so-called killer waves and dangerous surf. In turn, emergency authorities can notify coastal residents of potential danger.

Similarly, observational data gathered from ground-based, airborne, and satellite-borne sensors are being used to more accurately track the development and movement of severe atmospheric storms. In some cases, observational data can help scientists generate models that can cast a light on the nature of previously unpredictable atmospheric phenomena.

For example, meteorologists are learning more and more about tornadoes but have been somewhat hampered in the study of downbursts. Unlike a tornado, in which winds spin violently, a downburst is a convective windstorm, in which cool air may rush from the storm cloud downward toward the surface. Once the downburst reaches the ground it quickly spreads outward, sometimes at gusts of more than 160 kilometers (100 miles) per hour.

Predicting and gauging downburst activity has long been a challenge for researchers. In 2010, however, scientists developed a three-dimensional model of downbursts based on a tremendous amount of observational data gathered at storm sites. This computer model, which assigns numerical values to observational data, can help researchers simulate a wide range of storm events that may produce downbursts. While such research cannot prevent these dangerous storm phenomena, it can help residents prepare for such storms, potentially reducing casualties and property damage.

Artificial intelligence (AI) has helped researchers not only monitor but also predict weather phenomena. In 2023, scientists from the National Institute of Standards and Technology developed a groundbreaking, digital tool that examined 1,500 storms from the National Hurricane Center's database using algorithms in order to study and potentially predict the trajectory and wind speed of future hurricanes. The artificial intelligence (AI) tool was able to accurately predict the path and impact of storms and weather phenomena it had not previously studied. Its accuracy in areas that typically experience impactful weather was such that scientists and researchers found it a viable means of tracking future patterns. However, in areas of the world where weather is changing rapidly and unpredictably, the tool was not always accurate. This was due to a lack of historical data. The technology, though, has proven helpful in potentially gauging the impact of future weather patterns.

Principal Terms

algorithm: a set of instructions used to perform a task

downburst: a convective windstorm associated with strong thunderstorm systems

El Niño/La Niña: cyclical increases and decreases, respectively, in Pacific Ocean water temperature that foster shifts in weather patterns worldwide

polar low: a severe, mesocyclonic winter storm that occurs in higher ocean latitudes

salinity: concentration of salt in a given area of the ocean

tsunami: a series of long, high sea waves caused by seismic activity or other disturbances

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