Satellites offer a perspective on the world’s oceans impossible to achieve with localized observations from ships or buoys. This practice, known as satellite oceanography, uses remote sensing instruments to gather global, continuous data about the sea surface. Satellites provide a synoptic view, enabling the monitoring of large-scale processes like global current systems and long-term climate trends. By collecting information across the electromagnetic spectrum, satellites measure diverse ocean variables, providing a comprehensive understanding of the marine environment.
Mapping Ocean Surface Topography
Satellites precisely measure the height of the sea surface using radar altimetry. This involves the satellite transmitting microwave pulses toward the ocean and timing the signal return. By knowing the satellite’s exact position and the precise travel time, scientists calculate the sea surface height (SSH) beneath the spacecraft with centimeter-level accuracy.
These height measurements reveal the “hills and valleys” of the ocean surface, caused by factors like currents and temperature variations. The varying height allows for the calculation of surface currents, known as geostrophic currents, since water flows down the slope of these sea surface hills. Monitoring these variations in SSH over decades provides a continuous record of long-term global sea level rise, which has been accelerating.
Radar altimetry data is also used to track ocean tides and large-scale circulation patterns like ocean eddies. These measurements are collected frequently along the satellite’s ground track, providing a uniform global observation of the ocean’s dynamic topography. This dataset is far more extensive than traditional tide gauges located only along coastlines.
Monitoring Sea Surface Temperature
Satellites monitor the thermal energy radiating from the ocean’s surface to determine Sea Surface Temperature (SST). This measurement uses two primary instruments: infrared radiometers and passive microwave radiometers. Infrared sensors provide accurate, high-resolution SST data, but clouds completely block their measurements.
Microwave radiometers, in contrast, penetrate through non-precipitating clouds, offering better spatial coverage regardless of atmospheric conditions. However, microwave data often have a coarser resolution and can be affected by wind-induced surface roughness. Scientists blend data from both infrared and microwave sensors to produce all-weather, high-resolution SST products.
SST is a fundamental measurement for understanding the air-sea interface and its influence on weather and climate. Large-scale temperature anomalies, such as El Niño and La Niña, are easily tracked globally. Tracking SST is also important for predicting the intensity of tropical storms and hurricanes, as warm ocean water provides the energy needed for their development.
Assessing Ocean Color and Biological Activity
The color of the ocean, observed from space, measures the substances suspended in the water. Visible light sensors, or spectrometers, measure the spectrum of light reflected from the surface. Clear ocean water appears deep blue because it absorbs red light and scatters blue light.
Microscopic marine plants called phytoplankton change the ocean’s color because they contain chlorophyll, a pigment that absorbs blue and red light while reflecting green light. By measuring the intensity of light in these bands, scientists calculate the concentration of chlorophyll-a, a proxy for phytoplankton abundance. Phytoplankton form the base of the marine food web and play a role in the global carbon cycle by absorbing carbon dioxide through photosynthesis.
Satellite ocean color data also tracks concentrations of suspended sediments and dissolved organic matter, indicating coastal runoff or river plumes. Monitoring chlorophyll patterns is important for managing fisheries, identifying areas of high biological productivity, and tracking harmful algal blooms. The long-term record helps researchers understand how primary productivity responds to changes in ocean conditions.
Tracking Surface Winds and Sea Ice
Surface winds over the ocean are measured using active microwave instruments called scatterometers. A scatterometer transmits a microwave pulse and measures the “backscatter,” which is the energy reflected from the sea surface. Wind creates small waves and ripples, increasing surface roughness.
Rougher surfaces produce a stronger backscatter signal, allowing scientists to calculate the wind speed and direction near the surface. This data is vital for weather forecasting, predicting storm tracks, and improving marine safety. Scatterometers provide a near-global map of ocean winds multiple times per day.
For monitoring polar regions, satellites use active radar and passive microwave sensors to track sea ice. These sensors determine the extent, concentration, and movement of sea ice. Since sea ice is less than five meters thick, its presence acts as a thermal insulator, influencing heat exchange between the ocean and the atmosphere. Long-term records of sea ice cover are essential for cryosphere monitoring and understanding polar climate changes.
Integrating Satellite Data for Climate Modeling
The individual measurements of height, temperature, color, wind, and ice are systematically combined to create a comprehensive view of the Earth system. This vast dataset is continuously fed into sophisticated computer programs known as data assimilation systems. These systems merge real-time satellite observations with complex numerical models that simulate physical processes.
Integrating these data streams allows scientists to track global energy transfer and the movement of heat. Combining sea surface height (currents) with sea surface temperature helps model how heat is transported by ocean circulation. This process refines the models, correcting for errors that accumulate and providing a more accurate starting point for future predictions.
The long-term record provided by satellite oceanography is crucial for predicting climate trends. By maintaining a continuous observation record of all these variables, scientists can better isolate the effects of natural variability from human-induced changes. This combined dataset improves both short-term hazard prediction, like storm tracking, and long-term climate projections.